chore: release Python SDK v2.0.5 and TypeScript SDK v3.0.7 (#5470)

Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>

.claude-plugin/marketplace.json

@@ -12,7 +12,7 @@
       "name": "mem0",
       "source": "./mem0-plugin",
       "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
-      "version": "0.2.6"
+      "version": "0.2.9"
     }
   ]
 }

.codex-plugin/marketplace.json

@@ -0,0 +1,20 @@
+{
+  "name": "mem0-plugins",
+  "interface": {
+    "displayName": "Mem0 Plugins"
+  },
+  "plugins": [
+    {
+      "name": "mem0",
+      "source": {
+        "source": "local",
+        "path": "./mem0-plugin"
+      },
+      "policy": {
+        "installation": "AVAILABLE",
+        "authentication": "ON_INSTALL"
+      },
+      "category": "Productivity"
+    }
+  ]
+}

.cursor-plugin/marketplace.json

@@ -12,7 +12,7 @@
       "name": "mem0",
       "source": "./mem0-plugin",
       "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search.",
-      "version": "0.2.6"
+      "version": "0.2.9"
     }
   ]
 }

.github/workflows/cd.yml

@@ -1,18 +1,33 @@
 name: Publish Python 🐍 distributions 📦 to PyPI and TestPyPI
 
+# Dispatched by release.yml (Release Router) when a release tagged v* is
+# published. Can also be dispatched manually to re-publish a tag.
 on:
-  release:
-    types: [published]
+  workflow_dispatch:
+    inputs:
+      tag:
+        description: 'Release tag to build and publish (e.g. v1.2.3)'
+        required: true
+        type: string
+      prerelease:
+        description: 'Unused for PyPI (pre-releases are expressed in the version itself); accepted for router uniformity'
+        required: false
+        type: boolean
+        default: false
 
 jobs:
   build-n-publish:
     name: Build and publish Python 🐍 distributions 📦 to PyPI and TestPyPI
-    if: startsWith(github.event.release.tag_name, 'v')
+    # Pure SDK version tags only (v1.2.3) — excludes package-prefixed tags
+    # like vercel-ai-v* that also start with 'v'
+    if: startsWith(inputs.tag, 'v') && !contains(inputs.tag, '-v')
     runs-on: ubuntu-latest
     permissions:
       id-token: write
     steps:
       - uses: actions/checkout@v2
+        with:
+          ref: ${{ inputs.tag }}
 
       - name: Set up Python
         uses: actions/setup-python@v2
@@ -39,7 +54,6 @@ jobs:
       #     packages_dir: dist/
 
       - name: Publish distribution 📦 to PyPI
-        if: startsWith(github.ref, 'refs/tags/v')
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           packages_dir: dist/

.github/workflows/cli-node-cd.yml

@@ -1,13 +1,25 @@
 name: Publish @mem0/cli 📦 to npm
 
+# Dispatched by release.yml (Release Router) when a release tagged
+# cli-node-v* is published. Can also be dispatched manually to re-publish
+# a tag.
 on:
-  release:
-    types: [published]
+  workflow_dispatch:
+    inputs:
+      tag:
+        description: 'Release tag to build and publish (e.g. cli-node-v0.2.0)'
+        required: true
+        type: string
+      prerelease:
+        description: 'Publish under the version preid dist-tag instead of latest'
+        required: false
+        type: boolean
+        default: false
 
 jobs:
   build-n-publish:
     name: Build and publish @mem0/cli 📦 to npm
-    if: startsWith(github.event.release.tag_name, 'cli-node-v')
+    if: startsWith(inputs.tag, 'cli-node-v')
     runs-on: ubuntu-latest
     permissions:
       id-token: write
@@ -16,6 +28,8 @@ jobs:
         working-directory: cli/node
     steps:
       - uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.tag }}
 
       - name: Install pnpm
         uses: pnpm/action-setup@v4
@@ -38,7 +52,7 @@ jobs:
 
       - name: Publish to npm
         run: |
-          if [ "${{ github.event.release.prerelease }}" = "true" ]; then
+          if [ "${{ inputs.prerelease }}" = "true" ]; then
             PREID=$(node -p "require('./package.json').version.split('-')[1].split('.')[0]")
             npx npm@latest publish --provenance --access public --tag "$PREID"
           else

.github/workflows/cli-python-cd.yml

@@ -1,13 +1,24 @@
 name: Publish mem0-cli 🐍 distributions 📦 to PyPI
 
+# Dispatched by release.yml (Release Router) when a release tagged cli-v* is
+# published. Can also be dispatched manually to re-publish a tag.
 on:
-  release:
-    types: [published]
+  workflow_dispatch:
+    inputs:
+      tag:
+        description: 'Release tag to build and publish (e.g. cli-v0.2.0)'
+        required: true
+        type: string
+      prerelease:
+        description: 'Unused for PyPI (pre-releases are expressed in the version itself); accepted for router uniformity'
+        required: false
+        type: boolean
+        default: false
 
 jobs:
   build-n-publish:
     name: Build and publish mem0-cli 📦 to PyPI
-    if: startsWith(github.event.release.tag_name, 'cli-v')
+    if: startsWith(inputs.tag, 'cli-v')
     runs-on: ubuntu-latest
     permissions:
       id-token: write
@@ -16,6 +27,8 @@ jobs:
         working-directory: cli/python
     steps:
       - uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.tag }}
 
       - name: Set up Python
         uses: actions/setup-python@v5

.github/workflows/openclaw-cd.yml

@@ -1,13 +1,25 @@
 name: Publish @mem0/openclaw-mem0 📦 to npm
 
+# Dispatched by release.yml (Release Router) when a release tagged
+# openclaw-v* is published. Can also be dispatched manually to re-publish
+# a tag.
 on:
-  release:
-    types: [published]
+  workflow_dispatch:
+    inputs:
+      tag:
+        description: 'Release tag to build and publish (e.g. openclaw-v0.5.0)'
+        required: true
+        type: string
+      prerelease:
+        description: 'Publish under the version preid dist-tag instead of latest'
+        required: false
+        type: boolean
+        default: false
 
 jobs:
   build-n-publish:
     name: Build and publish @mem0/openclaw-mem0 📦 to npm
-    if: startsWith(github.event.release.tag_name, 'openclaw-v')
+    if: startsWith(inputs.tag, 'openclaw-v')
     runs-on: ubuntu-latest
     permissions:
       id-token: write
@@ -16,6 +28,8 @@ jobs:
         working-directory: openclaw
     steps:
       - uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.tag }}
 
       - name: Install pnpm
         uses: pnpm/action-setup@v4
@@ -38,7 +52,7 @@ jobs:
 
       - name: Publish to npm
         run: |
-          if [ "${{ github.event.release.prerelease }}" = "true" ]; then
+          if [ "${{ inputs.prerelease }}" = "true" ]; then
             PREID=$(node -p "require('./package.json').version.split('-')[1].split('.')[0]")
             npx npm@latest publish --provenance --access public --tag "$PREID"
           else

.github/workflows/opencode-plugin-cd.yml

@@ -0,0 +1,58 @@
+name: Publish @mem0/opencode-plugin 📦 to npm
+
+# Dispatched by release.yml (Release Router) when a release tagged
+# opencode-v* is published. Can also be dispatched manually to re-publish
+# a tag.
+on:
+  workflow_dispatch:
+    inputs:
+      tag:
+        description: 'Release tag to build and publish (e.g. opencode-v0.2.0)'
+        required: true
+        type: string
+      prerelease:
+        description: 'Publish under the version preid dist-tag instead of latest'
+        required: false
+        type: boolean
+        default: false
+
+jobs:
+  build-n-publish:
+    name: Build and publish @mem0/opencode-plugin 📦 to npm
+    if: startsWith(inputs.tag, 'opencode-v')
+    runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+    defaults:
+      run:
+        working-directory: mem0-plugin/.opencode-plugin
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.tag }}
+
+      - name: Install Bun
+        uses: oven-sh/setup-bun@v2
+        with:
+          bun-version: latest
+
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '22'
+          registry-url: 'https://registry.npmjs.org'
+
+      - name: Install dependencies
+        run: bun install --frozen-lockfile
+
+      - name: Build
+        run: bun run build
+
+      - name: Publish to npm
+        run: |
+          if [ "${{ inputs.prerelease }}" = "true" ]; then
+            PREID=$(node -p "require('./package.json').version.split('-')[1].split('.')[0]")
+            npx npm@latest publish --provenance --access public --tag "$PREID"
+          else
+            npx npm@latest publish --provenance --access public
+          fi

.github/workflows/opencode-plugin-checks.yml

@@ -0,0 +1,40 @@
+name: opencode-plugin checks
+
+on:
+  workflow_dispatch:
+  push:
+    branches: [main]
+    paths:
+      - 'mem0-plugin/.opencode-plugin/**'
+      - '.github/workflows/opencode-plugin-checks.yml'
+  pull_request:
+    paths:
+      - 'mem0-plugin/.opencode-plugin/**'
+      - '.github/workflows/opencode-plugin-checks.yml'
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: mem0-plugin/.opencode-plugin
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install Bun
+        uses: oven-sh/setup-bun@v2
+        with:
+          bun-version: latest
+
+      - name: Install dependencies
+        run: bun install --frozen-lockfile
+
+      - name: Type check
+        run: bun run type-check
+
+      - name: Build
+        run: bun run build
+
+      - name: Verify dist output exists
+        run: |
+          test -f dist/index.js || (echo "Build output missing: dist/index.js" && exit 1)

.github/workflows/pi-agent-plugin-cd.yml

@@ -0,0 +1,60 @@
+name: Publish @mem0/pi-agent-plugin 📦 to npm
+
+# Dispatched by release.yml (Release Router) when a release tagged
+# pi-agent-v* is published. Can also be dispatched manually to re-publish
+# a tag.
+on:
+  workflow_dispatch:
+    inputs:
+      tag:
+        description: 'Release tag to build and publish (e.g. pi-agent-v0.1.1)'
+        required: true
+        type: string
+      prerelease:
+        description: 'Publish under the version preid dist-tag instead of latest'
+        required: false
+        type: boolean
+        default: false
+
+jobs:
+  build-n-publish:
+    name: Build and publish @mem0/pi-agent-plugin 📦 to npm
+    if: startsWith(inputs.tag, 'pi-agent-v')
+    runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+    defaults:
+      run:
+        working-directory: pi-agent-plugin
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.tag }}
+
+      - name: Install pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: '22'
+          registry-url: 'https://registry.npmjs.org'
+          cache: 'pnpm'
+          cache-dependency-path: pi-agent-plugin/pnpm-lock.yaml
+
+      - name: Install dependencies
+        run: pnpm install --frozen-lockfile
+
+      - name: Build
+        run: pnpm build
+
+      - name: Publish to npm
+        run: |
+          if [ "${{ inputs.prerelease }}" = "true" ]; then
+            PREID=$(node -p "require('./package.json').version.split('-')[1].split('.')[0]")
+            npx npm@latest publish --provenance --access public --tag "$PREID"
+          else
+            npx npm@latest publish --provenance --access public
+          fi

.github/workflows/pi-agent-plugin-checks.yml

@@ -0,0 +1,93 @@
+name: pi-agent-plugin checks
+
+on:
+  workflow_dispatch:
+  push:
+    branches: [main]
+    paths:
+      - 'pi-agent-plugin/**'
+      - '.github/workflows/pi-agent-plugin-checks.yml'
+  pull_request:
+    paths:
+      - 'pi-agent-plugin/**'
+      - '.github/workflows/pi-agent-plugin-checks.yml'
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+          cache: 'pnpm'
+          cache-dependency-path: pi-agent-plugin/pnpm-lock.yaml
+
+      - name: Install dependencies
+        run: cd pi-agent-plugin && pnpm install --frozen-lockfile
+
+      - name: Type check
+        run: cd pi-agent-plugin && pnpm exec tsc --noEmit
+
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        node-version: [20, 22]
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
+      - name: Setup Node.js ${{ matrix.node-version }}
+        uses: actions/setup-node@v4
+        with:
+          node-version: ${{ matrix.node-version }}
+          cache: 'pnpm'
+          cache-dependency-path: pi-agent-plugin/pnpm-lock.yaml
+
+      - name: Install dependencies
+        run: cd pi-agent-plugin && pnpm install --frozen-lockfile
+
+      - name: Run tests
+        run: cd pi-agent-plugin && pnpm exec vitest run
+
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+          cache: 'pnpm'
+          cache-dependency-path: pi-agent-plugin/pnpm-lock.yaml
+
+      - name: Install dependencies
+        run: cd pi-agent-plugin && pnpm install --frozen-lockfile
+
+      - name: Build
+        run: cd pi-agent-plugin && pnpm build
+
+      - name: Verify dist output exists
+        run: |
+          test -f pi-agent-plugin/dist/index.js || (echo "Build output missing: dist/index.js" && exit 1)
+          test -f pi-agent-plugin/dist/index.d.ts || (echo "Build output missing: dist/index.d.ts" && exit 1)
+          test -f pi-agent-plugin/dist/entry.js || (echo "Build output missing: dist/entry.js" && exit 1)
+          test -f pi-agent-plugin/dist/entry.d.ts || (echo "Build output missing: dist/entry.d.ts" && exit 1)

.github/workflows/release.yml

@@ -0,0 +1,68 @@
+name: Release Router 🚦
+
+# Single entry point for all release publishing.
+#
+# Package CD workflows no longer listen to release events themselves — this
+# router inspects the release tag and dispatches only the matching pipeline,
+# so each release produces one routed run instead of one real run plus seven
+# skipped ones.
+#
+# Re-publishing a release (e.g. after fixing registry settings) does NOT
+# require deleting and recreating it anymore — manually dispatch the
+# package's CD workflow from the tag instead:
+#
+#   gh workflow run <package>-cd.yml --ref refs/tags/<tag> -f tag=<tag>
+#
+# Note: dispatching runs the workflow file as it exists at the given ref, so
+# this router can only dispatch tags created after the workflow_dispatch
+# conversion landed on main. For older tags, dispatch manually from main.
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  actions: write
+
+jobs:
+  route:
+    name: Route ${{ github.event.release.tag_name }} to its CD pipeline
+    runs-on: ubuntu-latest
+    steps:
+      - name: Match tag prefix to CD workflow
+        id: match
+        env:
+          TAG: ${{ github.event.release.tag_name }}
+        run: |
+          # Specific package prefixes first; the bare v* (Python SDK) arm
+          # must stay last so prefixed tags that also start with 'v'
+          # (vercel-ai-v*) can never be routed to the Python pipeline.
+          case "$TAG" in
+            ts-v*)        workflow="ts-sdk-cd.yml" ;;
+            cli-node-v*)  workflow="cli-node-cd.yml" ;;
+            cli-v*)       workflow="cli-python-cd.yml" ;;
+            vercel-ai-v*) workflow="vercel-ai-cd.yml" ;;
+            openclaw-v*)  workflow="openclaw-cd.yml" ;;
+            opencode-v*)  workflow="opencode-plugin-cd.yml" ;;
+            pi-agent-v*)  workflow="pi-agent-plugin-cd.yml" ;;
+            v*)           workflow="cd.yml" ;;
+            *)
+              echo "::error::Release tag '$TAG' does not match any known package prefix — nothing will be published. See the tag prefix table in AGENTS.md."
+              exit 1
+              ;;
+          esac
+          echo "workflow=$workflow" >> "$GITHUB_OUTPUT"
+          echo ":outbox_tray: Routed \`$TAG\` → \`$workflow\`" >> "$GITHUB_STEP_SUMMARY"
+
+      - name: Dispatch ${{ steps.match.outputs.workflow }}
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          TAG: ${{ github.event.release.tag_name }}
+        run: |
+          # --ref points at the tag so the dispatched run builds (and signs
+          # provenance for) the exact tagged commit.
+          gh workflow run "${{ steps.match.outputs.workflow }}" \
+            --repo "$GITHUB_REPOSITORY" \
+            --ref "refs/tags/$TAG" \
+            -f tag="$TAG" \
+            -f prerelease="${{ github.event.release.prerelease }}"

.github/workflows/ts-sdk-cd.yml

@@ -1,13 +1,24 @@
 name: Publish mem0ai 📦 to npm
 
+# Dispatched by release.yml (Release Router) when a release tagged ts-v* is
+# published. Can also be dispatched manually to re-publish a tag.
 on:
-  release:
-    types: [published]
+  workflow_dispatch:
+    inputs:
+      tag:
+        description: 'Release tag to build and publish (e.g. ts-v2.1.0)'
+        required: true
+        type: string
+      prerelease:
+        description: 'Publish under the version preid dist-tag instead of latest'
+        required: false
+        type: boolean
+        default: false
 
 jobs:
   build-n-publish:
     name: Build and publish mem0ai 📦 to npm
-    if: startsWith(github.event.release.tag_name, 'ts-v')
+    if: startsWith(inputs.tag, 'ts-v')
     runs-on: ubuntu-latest
     permissions:
       id-token: write
@@ -16,6 +27,8 @@ jobs:
         working-directory: mem0-ts
     steps:
       - uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.tag }}
 
       - name: Install pnpm
         uses: pnpm/action-setup@v4
@@ -38,7 +51,7 @@ jobs:
 
       - name: Publish to npm
         run: |
-          if [ "${{ github.event.release.prerelease }}" = "true" ]; then
+          if [ "${{ inputs.prerelease }}" = "true" ]; then
             PREID=$(node -p "require('./package.json').version.split('-')[1].split('.')[0]")
             npx npm@latest publish --provenance --access public --tag "$PREID"
           else

.github/workflows/vercel-ai-cd.yml

@@ -1,13 +1,25 @@
 name: Publish @mem0/vercel-ai-provider 📦 to npm
 
+# Dispatched by release.yml (Release Router) when a release tagged
+# vercel-ai-v* is published. Can also be dispatched manually to re-publish
+# a tag.
 on:
-  release:
-    types: [published]
+  workflow_dispatch:
+    inputs:
+      tag:
+        description: 'Release tag to build and publish (e.g. vercel-ai-v2.0.7)'
+        required: true
+        type: string
+      prerelease:
+        description: 'Publish under the version preid dist-tag instead of latest'
+        required: false
+        type: boolean
+        default: false
 
 jobs:
   build-n-publish:
     name: Build and publish @mem0/vercel-ai-provider 📦 to npm
-    if: startsWith(github.event.release.tag_name, 'vercel-ai-v')
+    if: startsWith(inputs.tag, 'vercel-ai-v')
     runs-on: ubuntu-latest
     permissions:
       id-token: write
@@ -16,6 +28,8 @@ jobs:
         working-directory: vercel-ai-sdk
     steps:
       - uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.tag }}
 
       - name: Install pnpm
         uses: pnpm/action-setup@v4
@@ -38,7 +52,7 @@ jobs:
 
       - name: Publish to npm
         run: |
-          if [ "${{ github.event.release.prerelease }}" = "true" ]; then
+          if [ "${{ inputs.prerelease }}" = "true" ]; then
             PREID=$(node -p "require('./package.json').version.split('-')[1].split('.')[0]")
             npx npm@latest publish --provenance --access public --tag "$PREID"
           else

AGENTS.md

@@ -413,20 +413,30 @@ To add a new LLM, embedding, vector store, or reranker provider:
 | Python CLI | `cli-python-ci.yml` | Push to `cli/python/`, PRs, manual | Ruff lint + pytest + hatch build on Python 3.10, 3.11, 3.12 |
 | Node CLI | `cli-node-ci.yml` | Push to `cli/node/`, PRs, manual | Biome lint + tsc + vitest + tsup build on Node 20, 22 |
 | OpenClaw | `openclaw-checks.yml` | Push to `openclaw/`, PRs, manual | tsc + vitest (with Codecov) + tsup build on Node 20, 22 |
+| OpenCode Plugin | `opencode-plugin-checks.yml` | Push to `mem0-plugin/.opencode-plugin/`, PRs, manual | Bun: tsc type-check + build + dist artifact check |
+| Pi Agent Plugin | `pi-agent-plugin-checks.yml` | Push to `pi-agent-plugin/`, PRs, manual | tsc + vitest + tsup build (dist artifact check) on Node 20, 22 |
 
 ### CD Workflows (automated publishing)
 
+Publishing is routed through a single entry point: **`release.yml` (Release Router)** is the only workflow that listens to `release: published` events. It matches the release tag prefix and dispatches the corresponding package workflow via `workflow_dispatch`, so each release produces exactly one routed run (no skipped runs from the other pipelines).
+
 | Workflow | File | Tag Prefix | Target |
 |----------|------|------------|--------|
+| Release Router | `release.yml` | (all releases) | dispatches the matching workflow below |
 | Python SDK | `cd.yml` | `v*` | PyPI (`mem0ai`) |
 | TypeScript SDK | `ts-sdk-cd.yml` | `ts-v*` | npm (`mem0ai`) |
 | Python CLI | `cli-python-cd.yml` | `cli-v*` | PyPI (`mem0-cli`) |
 | Node CLI | `cli-node-cd.yml` | `cli-node-v*` | npm (`@mem0/cli`) |
 | Vercel AI SDK | `vercel-ai-cd.yml` | `vercel-ai-v*` | npm (`@mem0/vercel-ai-provider`) |
 | OpenClaw | `openclaw-cd.yml` | `openclaw-v*` | npm (`@mem0/openclaw-mem0`) |
+| OpenCode Plugin | `opencode-plugin-cd.yml` | `opencode-v*` | npm (`@mem0/opencode-plugin`) |
+| Pi Agent Plugin | `pi-agent-plugin-cd.yml` | `pi-agent-v*` | npm (`@mem0/pi-agent-plugin`) |
 
+- Package CD workflows are `workflow_dispatch`-only (inputs: `tag`, `prerelease`); they check out and build the given tag. Registry trusted-publisher settings stay pinned to each package's own workflow filename.
 - All publishing uses **OIDC trusted publishing** — no tokens or secrets required.
 - First publish of a new npm package must be done manually; OIDC works for subsequent versions.
+- To re-publish a release (e.g. after a registry settings fix), do **not** delete/recreate the GitHub release — manually dispatch the package workflow instead: `gh workflow run <package>-cd.yml --ref refs/tags/<tag> -f tag=<tag>`.
+- When adding a new package: add its CD workflow (`workflow_dispatch` with `tag`/`prerelease` inputs), then register its tag prefix in the `case` block in `release.yml`. Keep the bare `v*` arm last.
 
 ### Utility Workflows
 

MIGRATION_GUIDE_v1.0.md

@@ -1,221 +0,0 @@
-# Migration Guide: Upgrading to mem0 1.0.0
-
-## TL;DR
-
-**What changed?** We simplified the API by removing confusing version parameters. Now everything returns a consistent format: `{"results": [...]}`.
-
-**What you need to do:**
-1. Upgrade: `pip install mem0ai==1.0.0`
-2. Remove `version` and `output_format` parameters from your code
-3. Update response handling to use `result["results"]` instead of treating responses as lists
-
-**Time needed:** ~5-10 minutes for most projects
-
----
-
-## Quick Migration Guide
-
-### 1. Install the Update
-
-```bash
-pip install mem0ai==1.0.0
-```
-
-### 2. Update Your Code
-
-**If you're using the Memory API:**
-
-```python
-# Before
-memory = Memory(config=MemoryConfig(version="v1.1"))
-result = memory.add("I like pizza")
-
-# After
-memory = Memory()  # That's it - version is automatic now
-result = memory.add("I like pizza")
-```
-
-**If you're using the Client API:**
-
-```python
-# Before
-client.add(messages, output_format="v1.1")
-client.search(query, version="v2", output_format="v1.1")
-
-# After
-client.add(messages)  # Just remove those extra parameters
-client.search(query)
-```
-
-### 3. Update How You Handle Responses
-
-All responses now use the same format: a dictionary with `"results"` key.
-
-```python
-# Before - you might have done this
-result = memory.add("I like pizza")
-for item in result:  # Treating it as a list
-    print(item)
-
-# After - do this instead
-result = memory.add("I like pizza")
-for item in result["results"]:  # Access the results key
-    print(item)
-
-# Graph relations (if you use them)
-if "relations" in result:
-    for relation in result["relations"]:
-        print(relation)
-```
-
----
-
-## Enhanced Message Handling
-
-The platform client (MemoryClient) now supports the same flexible message formats as the OSS version:
-
-```python
-from mem0 import MemoryClient
-
-client = MemoryClient(api_key="your-key")
-
-# All three formats now work:
-
-# 1. Single string (automatically converted to user message)
-client.add("I like pizza", user_id="alice")
-
-# 2. Single message dictionary
-client.add({"role": "user", "content": "I like pizza"}, user_id="alice")
-
-# 3. List of messages (conversation)
-client.add([
-    {"role": "user", "content": "I like pizza"},
-    {"role": "assistant", "content": "I'll remember that!"}
-], user_id="alice")
-```
-
-### Async Mode Configuration
-
-The `async_mode` parameter now defaults to `True` but can be configured:
-
-```python
-# Default behavior (async_mode=True)
-client.add(messages, user_id="alice")
-
-# Explicitly set async mode
-client.add(messages, user_id="alice", async_mode=True)
-
-# Disable async mode if needed
-client.add(messages, user_id="alice", async_mode=False)
-```
-
-**Note:** `async_mode=True` provides better performance for most use cases. Only set it to `False` if you have specific synchronous processing requirements.
-
----
-
-## That's It!
-
-For most users, that's all you need to know. The changes are:
-- ✅ No more `version` or `output_format` parameters
-- ✅ Consistent `{"results": [...]}` response format
-- ✅ Cleaner, simpler API
-
----
-
-## Common Issues
-
-**Getting `KeyError: 'results'`?**
-
-Your code is still treating the response as a list. Update it:
-```python
-# Change this:
-for memory in response:
-
-# To this:
-for memory in response["results"]:
-```
-
-**Getting `TypeError: unexpected keyword argument`?**
-
-You're still passing old parameters. Remove them:
-```python
-# Change this:
-client.add(messages, output_format="v1.1")
-
-# To this:
-client.add(messages)
-```
-
-**Seeing deprecation warnings?**
-
-Remove any explicit `version="v1.0"` from your config:
-```python
-# Change this:
-memory = Memory(config=MemoryConfig(version="v1.0"))
-
-# To this:
-memory = Memory()
-```
-
----
-
-## What's New in 1.0.0
-
-- **Better vector stores:** Fixed OpenSearch and improved reliability across all stores
-- **Cleaner API:** One way to do things, no more confusing options
-- **Enhanced GCP support:** Better Vertex AI configuration options
-- **Flexible message input:** Platform client now accepts strings, dicts, and lists (aligned with OSS)
-- **Configurable async_mode:** Now defaults to `True` but users can override if needed
-
----
-
-## Need Help?
-
-- Check [GitHub Issues](https://github.com/mem0ai/mem0/issues)
-- Read the [documentation](https://docs.mem0.ai/)
-- Open a new issue if you're stuck
-
----
-
-## Advanced: Configuration Changes
-
-**If you configured vector stores with version:**
-
-```python
-# Before
-config = MemoryConfig(
-    version="v1.1",
-    vector_store=VectorStoreConfig(...)
-)
-
-# After
-config = MemoryConfig(
-    vector_store=VectorStoreConfig(...)
-)
-```
-
----
-
-## Testing Your Migration
-
-Quick sanity check:
-
-```python
-from mem0 import Memory
-
-memory = Memory()
-
-# Add should return a dict with "results"
-result = memory.add("I like pizza", user_id="test")
-assert "results" in result
-
-# Search should return a dict with "results"
-search = memory.search("food", user_id="test")
-assert "results" in search
-
-# Get all should return a dict with "results"
-all_memories = memory.get_all(user_id="test")
-assert "results" in all_memories
-
-print("✅ Migration successful!")
-```

cli/node/CHANGELOG.md

@@ -5,6 +5,21 @@ All notable changes to `@mem0/cli` are documented here.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.2.8] — 2026-06-01
+
+### Security
+
+- Pinned transitive dependencies via pnpm overrides to remediate high-severity CVEs:
+  - `jws` → 4.0.1 (CVE-2025-65945)
+  - `langsmith` → ^0.6.0 (CVE-2026-45134)
+  - `tar-fs` → ^2.1.4 (CVE-2025-48387, CVE-2025-59343)
+  - `picomatch` → ^2.3.2 (CVE-2026-33671)
+  - `minimatch` → ^3.1.3 / ^5.1.8 / ^9.0.7 (CVE-2026-27903, CVE-2026-27904, CVE-2026-26996)
+  - `path-to-regexp` → ^8.4.0 (CVE-2026-4926)
+  - `rollup` → ^4.59.0 (CVE-2026-27606)
+  - `glob` → ^10.5.0 (CVE-2025-64756)
+  - `@modelcontextprotocol/sdk` → ^1.25.4 (CVE-2025-66414, CVE-2026-0621)
+
 ## [0.2.7] — 2026-05-20
 
 ### Added

cli/node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mem0/cli",
-  "version": "0.2.7",
+  "version": "0.2.8",
   "description": "The official CLI for mem0 — the memory layer for AI agents",
   "type": "module",
   "bin": {
@@ -40,7 +40,8 @@
     "typescript": "^5.4.0",
     "tsup": "^8.0.0",
     "tsx": "^4.7.0",
-    "vitest": "^1.5.0",
+    "vite": "^6.0.0",
+    "vitest": "^4.1.0",
     "@biomejs/biome": "^1.7.0",
     "@types/node": "^20.0.0"
   }

cli/node/pnpm-workspace.yaml

@@ -0,0 +1,12 @@
+packages:
+  - '.'
+
+onlyBuiltDependencies:
+  - "@biomejs/biome"
+  - esbuild
+
+overrides:
+  jws@4.0.0: 4.0.1
+  langsmith@<0.6.0: ^0.6.0
+  tar-fs@>=2.0.0 <2.1.4: ^2.1.4
+  picomatch@<2.3.2: ^2.3.2

cli/node/src/commands/memory.ts

@@ -46,7 +46,7 @@ export async function cmdAdd(
 		file?: string;
 		metadata?: string;
 		immutable: boolean;
-		noInfer: boolean;
+		infer?: boolean;
 		expires?: string;
 		categories?: string;
 		output: string;
@@ -136,7 +136,7 @@ export async function cmdAdd(
 				runId: opts.runId,
 				metadata: meta,
 				immutable: opts.immutable,
-				infer: !opts.noInfer,
+				infer: opts.infer !== false,
 				expires: opts.expires,
 				categories: cats,
 			});

cli/node/tests/commands.test.ts

@@ -3,6 +3,7 @@
  */
 
 import { describe, it, expect, vi, beforeEach } from "vitest";
+import { Command } from "commander";
 import { createMockBackend } from "./setup.js";
 import type { Backend } from "../src/backend/base.js";
 import { setAgentMode } from "../src/state.js";
@@ -41,8 +42,6 @@ describe("cmdAdd", () => {
     await cmdAdd(mockBackend, "I prefer dark mode", {
       userId: "alice",
       immutable: false,
-      noInfer: false,
-
       output: "text",
     });
     expect(mockBackend.add).toHaveBeenCalledOnce();
@@ -54,8 +53,6 @@ describe("cmdAdd", () => {
       userId: "alice",
       messages: JSON.stringify([{ role: "user", content: "I love Python" }]),
       immutable: false,
-      noInfer: false,
-
       output: "text",
     });
     expect(mockBackend.add).toHaveBeenCalledOnce();
@@ -66,8 +63,6 @@ describe("cmdAdd", () => {
     await cmdAdd(mockBackend, "test", {
       userId: "alice",
       immutable: false,
-      noInfer: false,
-
       output: "json",
     });
     expect(output).toContain("results");
@@ -78,14 +73,59 @@ describe("cmdAdd", () => {
     await cmdAdd(mockBackend, "test", {
       userId: "alice",
       immutable: false,
-      noInfer: false,
-
       output: "quiet",
     });
     expect(output).not.toContain("dark mode");
   });
 });
 
+describe("cmdAdd forwards --no-infer (regression for #5261)", () => {
+  it("forwards infer: false when --no-infer is set", async () => {
+    const { cmdAdd } = await import("../src/commands/memory.js");
+    // `infer: false` is the shape Commander produces for `--no-infer`.
+    await cmdAdd(mockBackend, "store me verbatim", {
+      userId: "alice",
+      immutable: false,
+      infer: false,
+      output: "text",
+    });
+    expect(mockBackend.add).toHaveBeenCalledWith(
+      "store me verbatim",
+      undefined,
+      expect.objectContaining({ infer: false }),
+    );
+  });
+
+  it("forwards infer: true by default (flag absent)", async () => {
+    const { cmdAdd } = await import("../src/commands/memory.js");
+    await cmdAdd(mockBackend, "infer me", {
+      userId: "alice",
+      immutable: false,
+      output: "text",
+    });
+    expect(mockBackend.add).toHaveBeenCalledWith(
+      "infer me",
+      undefined,
+      expect.objectContaining({ infer: true }),
+    );
+  });
+
+  it("Commander stores --no-infer as opts.infer, not opts.noInfer", () => {
+    // Pins the assumption the fix relies on: Commander's `--no-X` option
+    // populates the positive camelCase key (`infer`), never `noInfer`.
+    const withFlag = new Command();
+    withFlag.option("--no-infer", "Skip inference, store raw.").action(() => {});
+    withFlag.parse(["--no-infer"], { from: "user" });
+    expect(withFlag.opts().infer).toBe(false);
+    expect(withFlag.opts().noInfer).toBeUndefined();
+
+    const withoutFlag = new Command();
+    withoutFlag.option("--no-infer", "Skip inference, store raw.").action(() => {});
+    withoutFlag.parse([], { from: "user" });
+    expect(withoutFlag.opts().infer).toBe(true);
+  });
+});
+
 describe("cmdAdd deduplicates PENDING", () => {
   const DUPLICATE_PENDING = {
     results: [
@@ -100,8 +140,6 @@ describe("cmdAdd deduplicates PENDING", () => {
     await cmdAdd(mockBackend, "test", {
       userId: "alice",
       immutable: false,
-      noInfer: false,
-
       output: "text",
     });
     expect(output.match(/Queued/g)?.length).toBe(1);
@@ -113,8 +151,6 @@ describe("cmdAdd deduplicates PENDING", () => {
     await cmdAdd(mockBackend, "test", {
       userId: "alice",
       immutable: false,
-      noInfer: false,
-
       output: "json",
     });
     const data = JSON.parse(output);
@@ -129,8 +165,6 @@ describe("cmdAdd deduplicates PENDING", () => {
     await cmdAdd(mockBackend, "test", {
       userId: "alice",
       immutable: false,
-      noInfer: false,
-
       output: "agent",
     });
     const data = JSON.parse(output);
@@ -315,8 +349,6 @@ describe("agent mode", () => {
     await cmdAdd(mockBackend, "test preference", {
       userId: "alice",
       immutable: false,
-      noInfer: false,
-
       output: "agent",
     });
     const parsed = JSON.parse(output.trim());

docs/api-reference/organizations-projects.mdx

@@ -79,7 +79,7 @@ new_project = client.project.create(
 
 ### Update Project Settings
 
-Modify project configuration including custom instructions, categories, graph settings, and language preferences:
+Modify project configuration including custom instructions, categories, and language preferences:
 
 ```python
 # Update project with custom categories

docs/changelog/sdk.mdx

@@ -7,6 +7,33 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
+<Update label="2026-06-10" description="v2.0.5">
+
+**New Features:**
+- **Memory:** Warn at init time when hybrid/BM25 search silently degrades to semantic-only because the configured vector store does not implement `keyword_search`. Affected stores: Chroma, FAISS, Cassandra, LangChain, Neptune Analytics, S3 Vectors, Supabase, TurboPuffer, Valkey ([#5444](https://github.com/mem0ai/mem0/pull/5444))
+- **Memory:** Add opt-in `explain=True` parameter to `Memory.search()` and `AsyncMemory.search()`. When enabled, each result includes a `score_breakdown` dict with `semantic`, `keyword` (normalized BM25), `entity_boost`, and `temporal_boost` signals so callers can understand and tune retrieval ranking ([#5102](https://github.com/mem0ai/mem0/pull/5102))
+
+**Bug Fixes:**
+- **Vector Stores:** Normalize similarity scores to `[0, 1]` (higher = better) consistently across all backends. 11 adapters previously returned raw distance metrics (lower = better) — FAISS, Chroma, Milvus, Redis, Cassandra, PGVector, S3 Vectors, Supabase, Valkey, Azure MySQL, and Vertex AI Vector Search — causing incorrect ranking in multi-store setups ([#5391](https://github.com/mem0ai/mem0/pull/5391))
+- **Memory:** Parallelize entity boost searches in `Memory.search()` and `AsyncMemory.search()`. Previously up to 8 entities were embedded and queried sequentially (16 serial round-trips with remote embedders); all entity lookups now run concurrently, eliminating multi-second latency on entity-rich queries ([#5377](https://github.com/mem0ai/mem0/pull/5377))
+- **Memory:** Reject empty or whitespace-only queries in `Memory.search()`, `AsyncMemory.search()`, `MemoryClient.search()`, and `AsyncMemoryClient.search()` before any embedding or API call is made. Also strips leading/trailing whitespace from valid queries ([#5258](https://github.com/mem0ai/mem0/pull/5258))
+- **LLMs:** Add `is_reasoning_model: Optional[bool]` override to `BaseLlmConfig` (surfaced on `OpenAILlmConfig` and `AzureOpenAILlmConfig`). Fixes silent zero-extraction when using Azure deployments with versioned `gpt-5.x` names that the automatic name-based heuristic cannot recognize ([#5327](https://github.com/mem0ai/mem0/pull/5327))
+- **LLMs:** Fix xAI LLM provider: add `XAIConfig` with `xai_base_url`, forward `tools`/`tool_choice` in `generate_response()`, and parse `tool_calls` in the response. Previously the provider raised `AttributeError` at init and silently dropped tool results ([#5190](https://github.com/mem0ai/mem0/pull/5190))
+- **Vector Stores:** Fix PGVector `ConnectionPool` hang in Docker Compose environments where the app container starts before Postgres is DNS-resolvable — switched to `open=False` to avoid blocking constructor or silent zombie pool ([#5155](https://github.com/mem0ai/mem0/pull/5155))
+- **Vector Stores:** Fix PGVector `sslmode` handling for PostgreSQL URIs — the `sslmode` query parameter is now correctly extracted and forwarded when building the async connection pool ([#5308](https://github.com/mem0ai/mem0/pull/5308))
+- **Vector Stores:** Fix S3 Vectors `list()` not applying metadata filters — filtering is now done client-side after fetching, with pagination preserved and `top_k` applied after filtering to prevent pre-truncation of matching rows ([#5018](https://github.com/mem0ai/mem0/pull/5018))
+- **Vector Stores:** Fix Upstash Vector `search()` routing all queries to the default namespace — `namespace` is now passed as a top-level keyword argument to `query_many()` instead of inside the per-query dict where it was silently ignored ([#5202](https://github.com/mem0ai/mem0/pull/5202))
+- **Core:** Replace mutable default arguments with `None` sentinels in embedder configs and the proxy module, preventing cross-request state contamination ([#5302](https://github.com/mem0ai/mem0/pull/5302))
+
+</Update>
+
+<Update label="2026-05-27" description="v2.0.4">
+
+**New Features:**
+- **Client:** `delete()` and async `delete()` accept `delete_linked` (default `False`). When `True`, deleting a memory also removes the older memories it superseded (the v3 `linked_memory_ids` chain), transitively — the delete-side counterpart of `latest_only`, so a superseded memory does not resurface after the current one is deleted ([#5270](https://github.com/mem0ai/mem0/pull/5270))
+
+</Update>
+
 <Update label="2026-05-26" description="v2.0.3">
 
 **Bug Fixes:**
@@ -932,6 +959,34 @@ See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-
 </Tab>
 
 <Tab title="TypeScript">
+
+<Update label="2026-06-10" description="v3.0.7">
+
+**New Features:**
+- **Embeddings:** Add `LMStudioEmbedding` provider for local embeddings via the LM Studio server ([#5377](https://github.com/mem0ai/mem0/pull/5377))
+- **Memory:** Add opt-in `explain: true` option to `Memory.search()`. When enabled, each result includes a `scoreBreakdown` object with `semantic`, `keyword`, `entityBoost`, and `temporalBoost` fields so callers can inspect and tune retrieval ranking ([#5102](https://github.com/mem0ai/mem0/pull/5102))
+
+**Bug Fixes:**
+- **Memory:** Parallelize entity boost searches in `Memory.search()`. All entity embed + store lookups now run concurrently instead of sequentially, eliminating multi-second latency on entity-rich queries with remote embedding providers ([#5377](https://github.com/mem0ai/mem0/pull/5377))
+- **Vector Stores:** Normalize similarity scores to `[0, 1]` (higher = better) — fixed score inversion in the Redis vector store adapter ([#5391](https://github.com/mem0ai/mem0/pull/5391))
+- **Embeddings:** Request `encoding_format: "float"` from the OpenAI embedder in both `embed()` and `embedBatch()`. Fixes incorrect vector dimensions when using OpenAI-compatible proxies that default to base64 encoding ([#5170](https://github.com/mem0ai/mem0/pull/5170))
+
+</Update>
+
+<Update label="2026-06-01" description="v3.0.6">
+
+**Security:**
+- **Dependencies:** Bumped `axios` to `^1.16.0` to remediate high-severity prototype-pollution CVEs (credential theft, MITM, DoS). Pinned transitive dependencies via pnpm overrides: `jws` → 4.0.1 (CVE-2025-65945), `langsmith` → ^0.6.0 (CVE-2026-45134), `tar-fs` → ^2.1.4 (CVE-2025-48387, CVE-2025-59343), `picomatch` → ^2.3.2 (CVE-2026-33671), `minimatch` → ^3.1.3 / ^5.1.8 / ^9.0.7 (CVE-2026-27903, CVE-2026-27904, CVE-2026-26996), `path-to-regexp` → ^8.4.0 (CVE-2026-4926), `rollup` → ^4.59.0 (CVE-2026-27606), `glob` → ^10.5.0 (CVE-2025-64756), `@modelcontextprotocol/sdk` → ^1.25.4 (CVE-2025-66414, CVE-2026-0621)
+
+</Update>
+
+<Update label="2026-05-27" description="v3.0.5">
+
+**New Features:**
+- **Client:** `delete()` accepts an options object with `deleteLinked` (serialized as `delete_linked`, default `false`). When `true`, deleting a memory also removes the older memories it superseded (the v3 linked chain), transitively — the delete-side counterpart of `latestOnly`, so a superseded memory does not resurface after the current one is deleted ([#5270](https://github.com/mem0ai/mem0/pull/5270))
+
+</Update>
+
 <Update label="2026-05-26" description="v3.0.4">
 
 **Bug Fixes:**
@@ -1338,6 +1393,13 @@ See the [TypeScript SDK migration guide](https://docs.mem0.ai/migration/ts-v2-to
 
 <Tab title="CLI">
 
+<Update label="2026-06-01" description="Node v0.2.8">
+
+**Security:**
+- **Dependencies:** Pinned transitive dependencies via pnpm overrides to remediate high-severity CVEs: `jws` → 4.0.1 (CVE-2025-65945), `langsmith` → ^0.6.0 (CVE-2026-45134), `tar-fs` → ^2.1.4 (CVE-2025-48387, CVE-2025-59343), `picomatch` → ^2.3.2 (CVE-2026-33671), `minimatch` → ^3.1.3 / ^5.1.8 / ^9.0.7 (CVE-2026-27903, CVE-2026-27904, CVE-2026-26996), `path-to-regexp` → ^8.4.0 (CVE-2026-4926), `rollup` → ^4.59.0 (CVE-2026-27606), `glob` → ^10.5.0 (CVE-2025-64756), `@modelcontextprotocol/sdk` → ^1.25.4 (CVE-2025-66414, CVE-2026-0621)
+
+</Update>
+
 <Update label="2026-05-16" description="Python v0.2.6 / Node v0.2.6">
 
 **Bug Fixes:**
@@ -1454,6 +1516,43 @@ A full-featured command-line interface for Mem0, available in both Python and No
 
 <Tab title="Plugins">
 
+<Update label="2026-06-01" description="openclaw-mem0 v1.0.12">
+
+**Security:**
+- **Dependencies:** Pinned transitive dependencies via pnpm overrides to remediate high-severity CVEs: `protobufjs` → ^7.5.5, `vite` → ^8.0.5, `langsmith` → ^0.6.0 (CVE-2026-45134), `picomatch` → ^2.3.2 (CVE-2026-33671), `@qdrant/js-client-rest` → ^1.18.0
+
+</Update>
+
+<Update label="2026-06-10" description="Vercel AI SDK v3.0.0">
+
+**Major Release** — Migrated to Vercel AI SDK v6 (`LanguageModelV3` / `ProviderV3`) and Mem0 v3 API.
+
+**Breaking Changes:**
+- **AI SDK v6:** Upgraded from AI SDK v5 (`LanguageModelV2`) to v6 (`LanguageModelV3`). Users must upgrade `ai` to `^6.0.199` and all `@ai-sdk/*` provider packages to `^3.x` ([#4741](https://github.com/mem0ai/mem0/pull/4741))
+- **Mem0 v3 API:** Memory endpoints migrated from `/v1/memories/` and `/v2/memories/search/` to `/v3/memories/add/` and `/v3/memories/search/`. Entity IDs (`user_id`, `agent_id`, `run_id`) now go inside the `filters` object for search requests ([#4741](https://github.com/mem0ai/mem0/pull/4741))
+- **Graph memory removed:** All `enable_graph`, graph prompts, and relation-extraction code removed. Graph memory is now a project-level setting on the Platform ([#4741](https://github.com/mem0ai/mem0/pull/4741))
+- **Deprecated params removed:** `org_id`, `project_id`, `org_name`, `project_name`, `output_format`, `filter_memories`, `async_mode`, `enable_graph`, `version`, `api_version` removed from `Mem0ConfigSettings` ([#4741](https://github.com/mem0ai/mem0/pull/4741))
+
+**New Features:**
+- **V3 provider contract:** `specificationVersion: 'v3'`, `supportedUrls` property, V3 content array in `doGenerate`, V3 stream lifecycle events in `doStream` ([#4741](https://github.com/mem0ai/mem0/pull/4741))
+- **Mem0 source in responses:** Memories are attached as a `source` in `generateText`/`streamText` responses with `providerMetadata.mem0.memories` for programmatic access ([#4741](https://github.com/mem0ai/mem0/pull/4741))
+
+**Bug Fixes:**
+- **Async memory storage:** `addMemories` is now properly `await`ed — memories no longer silently fail to store ([#4741](https://github.com/mem0ai/mem0/pull/4741))
+- **Prompt mutation:** Prompt array is now cloned before injecting memory context, preventing side effects on the caller's array ([#4741](https://github.com/mem0ai/mem0/pull/4741))
+- **Null guard on content:** `doGenerate` guards against null `content` from upstream providers ([#4741](https://github.com/mem0ai/mem0/pull/4741))
+- **Stream response:** `doStream` now returns the full `LanguageModelV3StreamResult` object preserving all V3 fields ([#4741](https://github.com/mem0ai/mem0/pull/4741))
+- **Response normalization:** `getMemories` and `retrieveMemories` now handle both array and `{results: [...]}` envelope responses from the v3 API ([#4741](https://github.com/mem0ai/mem0/pull/4741))
+
+</Update>
+
+<Update label="2026-06-01" description="Vercel AI SDK v2.0.6">
+
+**Security:**
+- **Dependencies:** Pinned transitive dependencies via pnpm overrides to remediate high-severity CVEs: `glob` → ^10.5.0 (CVE-2025-64756), `minimatch` → ^3.1.3 / ^5.1.8 / ^9.0.7 (CVE-2026-27903, CVE-2026-27904, CVE-2026-26996), `picomatch` → ^2.3.2 (CVE-2026-33671), `rollup` → ^4.59.0 (CVE-2026-27606)
+
+</Update>
+
 <Update label="2026-04-02" description="mem0-plugin v1.0.0">
 
 **Mem0 Plugin for Claude Code, Cursor, and Codex**

docs/components/vectordbs/dbs/neon.mdx

@@ -0,0 +1,156 @@
+---
+title: "Neon"
+description: "Use Neon as a vector store in Mem0, powered by PostgreSQL and pgvector."
+---
+
+Use [Neon](https://neon.com/) as a vector store in Mem0, powered by PostgreSQL and the
+[pgvector extension](https://neon.com/docs/extensions/pgvector).
+
+Neon is a serverless Postgres platform. Since Mem0 supports Postgres through the
+`pgvector` provider, Neon can be used with a standard Postgres connection string.
+
+## Usage
+
+<CodeGroup>
+```python Python
+import os
+
+from dotenv import load_dotenv
+from mem0 import Memory
+
+load_dotenv()
+
+config = {
+    "vector_store": {
+        "provider": "pgvector",
+        "config": {
+            "connection_string": os.environ["DATABASE_URL"],
+            "collection_name": "memories",
+            "embedding_model_dims": 1536,
+            "hnsw": True,
+        },
+    },
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."},
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+
+results = m.search(
+    "What movies should I recommend?",
+    filters={"user_id": "alice"},
+)
+
+print(results)
+```
+
+```typescript TypeScript
+import "dotenv/config";
+import { Memory } from "mem0ai/oss";
+
+const databaseUrl = new URL(process.env.DATABASE_URL!);
+
+const m = new Memory({
+  vectorStore: {
+    provider: "pgvector",
+    config: {
+      user: decodeURIComponent(databaseUrl.username),
+      password: decodeURIComponent(databaseUrl.password),
+      host: databaseUrl.hostname,
+      port: Number(databaseUrl.port || 5432),
+      dbname: databaseUrl.pathname.slice(1) || "neondb",
+      collectionName: "memories",
+      dimension: 1536,
+      embeddingModelDims: 1536,
+      hnsw: true,
+    },
+  },
+});
+
+const messages = [
+  { role: "user" as const, content: "I'm planning to watch a movie tonight. Any recommendations?" },
+  { role: "assistant" as const, content: "How about thriller movies? They can be quite engaging." },
+  { role: "user" as const, content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
+  { role: "assistant" as const, content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
+];
+
+await m.add(messages, {
+  userId: "alice",
+  metadata: { category: "movies" },
+});
+
+const results = await m.search("What movies should I recommend?", {
+  filters: { user_id: "alice" },
+});
+
+console.log(results);
+```
+</CodeGroup>
+
+## SQL Migration
+
+You don't need to run any SQL migrations. Mem0 creates the collection table when it initializes the `pgvector` store.
+
+## Environment
+
+```env
+OPENAI_API_KEY=sk-xx...
+DATABASE_URL=postgresql://user:password@ep-example.us-east-2.aws.neon.tech/neondb?sslmode=require
+```
+
+## Config
+
+<Tabs>
+<Tab title="Python">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `connection_string` | Neon Postgres connection string. | Required |
+| `collection_name` | Name for the vector collection. | `mem0` |
+| `embedding_model_dims` | Embedding model dimensions. | `1536` |
+| `hnsw` | Enables HNSW indexing. | `False` |
+| `sslmode` | PostgreSQL SSL mode. Use `require` for Neon. | Driver default |
+</Tab>
+<Tab title="TypeScript">
+The current Mem0 TypeScript `pgvector` adapter takes individual Postgres fields,
+so parse `DATABASE_URL` before creating `Memory`.
+
+| Parameter | Description | Default |
+| --- | --- | --- |
+| `user` | Database user. | Required |
+| `password` | Database password. | Required |
+| `host` | Database host. | Required |
+| `port` | Database port. | `5432` |
+| `dbname` | Database name. | `vector_store` |
+| `collectionName` | Name for the vector collection. | `memories` |
+| `dimension` | Vector dimension for Mem0 config. | Auto-detected |
+| `embeddingModelDims` | Embedding model dimensions for table creation. | Required |
+| `hnsw` | Enables HNSW indexing. | `false` |
+</Tab>
+</Tabs>
+
+### Indexing
+
+The `pgvector` provider can create an HNSW index for faster vector search.
+
+- Set `hnsw` to `true` to enable a Hierarchical Navigable Small World index.
+- Leave `hnsw` as `false` if you want to create or manage indexes yourself.
+
+### Similarity Search
+
+The `pgvector` provider uses cosine similarity for vector search. Make sure your
+embedding dimensions match the configured `embedding_model_dims` value.
+
+### Best Practices
+
+1. **Index Selection**:
+   - Use `hnsw` for faster search performance when memory usage is not a constraint
+   - Manage indexes manually if you need a different pgvector index strategy
+
+2. **Connection String**:
+   - Always use environment variables or even better, a secret manager for sensitive information in the connection string
+   - Format: `postgresql://user:password@host:port/database`

docs/cookbooks/essentials/controlling-memory-ingestion.mdx

@@ -323,10 +323,6 @@ Metadata: {'verified': True, 'updated_date': '2025-04-02'}
 That “no duplicates” promise comes from the inference pipeline. Keep `infer=True` when you rely on automatic updates. Raw imports (`infer=False`) skip conflict checks, so mixing the two modes for the same fact will create duplicates.
 </Warning>
 
-**Maintains relationships:**
-
-- If using graph memory, connections to other entities persist
-
 ### Pick the right inference mode
 
 | Mode | What it does | Best for | Watch out for |

docs/cookbooks/frameworks/gemini-3-with-mem0-mcp.mdx

@@ -216,7 +216,6 @@ This information was retrieved from your memory history where you previously men
 
 - **Smart Memory Management** - Organizes memories into searchable information *without setting up vector databases*
 - **Fast Retrieval** - Instant lookups with *sub-millisecond ping*, handles large datasets
-- **Graph Capabilities** - Builds knowledge *automatically* as you push information
 - **Simple Integration** - Uses Mem0 API in the backend, works with *any MCP client* with just a few lines of code
 
 ### Gemini 3 + Mem0 Benefits

docs/cookbooks/overview.mdx

@@ -156,14 +156,7 @@ Here are some examples of how Mem0 can be integrated into various applications:
     icon="aws"
     href="/cookbooks/integrations/aws-bedrock"
   >
-    Mem0 with AWS Bedrock and Neptune.
-  </Card>
-  <Card
-    title="Graph Memory on Neptune"
-    icon="network-wired"
-    href="/cookbooks/integrations/neptune-analytics"
-  >
-    Graph memory with Neptune Analytics.
+    Mem0 with AWS Bedrock.
   </Card>
 </CardGroup>
 

docs/core-concepts/memory-evaluation.mdx

@@ -47,7 +47,7 @@ When a query arrives, the retrieval pipeline scores candidates across three sign
 
 1. **Semantic Search** — Vector similarity scoring against memory embeddings
 2. **Keyword Search** — Normalized term matching via BM25 with verb-form lemmatization
-3. **Entity Search** — Entity graph matching boosts memories linked to query entities
+3. **Entity Search** — Entity matching boosts memories linked to query entities
 
 Results are fused via rank scoring into a final top-K set. Different query types lean on different signals:
 

docs/core-concepts/memory-operations/add.mdx

@@ -27,15 +27,11 @@ Adding memory is how Mem0 captures useful details from a conversation so your ag
 
 Mem0 offers two flows:
 
-- **Mem0 Platform** – Fully managed API with dashboard, scaling, and graph features.
+- **Mem0 Platform** – Fully managed API with dashboard and scaling.
 - **Mem0 Open Source** – Local SDK that you run in your own environment.
 
 Both flows take the same payload and pass it through the same pipeline.
 
-<Frame caption="Architecture diagram illustrating the process of adding memories.">
-  <img src="../../images/add_architecture.png" />
-</Frame>
-
 <Steps>
 <Step title="Information extraction">
 Mem0 sends the messages through an LLM that pulls out key facts, decisions, or preferences to remember.
@@ -44,7 +40,7 @@ Mem0 sends the messages through an LLM that pulls out key facts, decisions, or p
 Existing memories are checked for duplicates or contradictions so the latest truth wins.
 </Step>
 <Step title="Storage">
-The resulting memories land in managed vector storage (and optional graph storage) so future searches return them quickly.
+The resulting memories land in managed vector storage so future searches return them quickly.
 </Step>
 </Steps>
 
@@ -177,7 +173,7 @@ For full list of supported fields, required formats, and advanced options, see t
 
 ## Put it into practice
 
-- Review the <Link href="/platform/advanced-memory-operations">Advanced Memory Operations</Link> guide to layer metadata, rerankers, and graph toggles.
+- Review the <Link href="/platform/advanced-memory-operations">Advanced Memory Operations</Link> guide to layer metadata and rerankers.
 - Explore the <Link href="/api-reference/memory/add-memories">Add Memories API reference</Link> for every request/response field.
 
 ## See it live

docs/core-concepts/memory-operations/search.mdx

@@ -25,10 +25,6 @@ Mem0's search operation lets agents ask natural-language questions and get back
 
 ## Architecture
 
-<Frame caption="Architecture diagram illustrating the memory search process.">
-  <img src="../../images/search_architecture.png" />
-</Frame>
-
 <Steps>
 <Step title="Query processing">
 Mem0 cleans and enriches your natural-language query so the downstream embedding search is accurate.
@@ -160,6 +156,33 @@ const memories = memory.search("food preferences", {
   On Mem0 Platform v3, time-aware queries use Temporal Reasoning internally while preserving the normal search response shape. See <Link href="/platform/features/temporal-reasoning">Temporal Reasoning</Link>.
 </Note>
 
+### Explain OSS search scores
+
+OSS search combines semantic similarity with optional keyword and entity signals. Pass `explain=True` when tuning retrieval quality or debugging why a memory ranked where it did:
+
+<CodeGroup>
+```python Python
+results = m.search(
+    "food preferences",
+    filters={"user_id": "alice"},
+    explain=True,
+)
+
+print(results["results"][0]["score_details"])
+```
+
+```javascript JavaScript
+const results = await memory.search("food preferences", {
+    filters: { user_id: "alice" },
+    explain: true,
+});
+
+console.log(results.results[0].score_details);
+```
+</CodeGroup>
+
+Each result includes `score_details` with the semantic score, normalized BM25 score, entity boost, raw combined score, maximum possible score, final score, and threshold used for filtering. The field is omitted unless `explain` is enabled, so existing response shapes stay unchanged.
+
 ## Filter patterns
 
 Filters help narrow down search results. Common use cases:

docs/core-concepts/memory-types.mdx

@@ -104,7 +104,7 @@ results = memory.search(
 ## Put it into practice
 
 - Use the <Link href="/core-concepts/memory-operations/add">Add Memory</Link> guide to persist user preferences.
-- Follow <Link href="/platform/advanced-memory-operations">Advanced Memory Operations</Link> to tune metadata and graph writes.
+- Follow <Link href="/platform/advanced-memory-operations">Advanced Memory Operations</Link> to tune metadata and retrieval.
 
 ## See it live
 

docs/docs.json

@@ -143,7 +143,8 @@
                     "icon": "robot",
                     "pages": [
                       "integrations/openclaw",
-                      "integrations/hermes"
+                      "integrations/hermes",
+                      "integrations/pi-agent"
                     ]
                   }
                 ]
@@ -245,6 +246,7 @@
                               "components/vectordbs/dbs/cassandra",
                               "components/vectordbs/dbs/s3_vectors",
                               "components/vectordbs/dbs/databricks",
+                              "components/vectordbs/dbs/neon",
                               "components/vectordbs/dbs/neptune_analytics",
                               "components/vectordbs/dbs/turbopuffer"
                             ]
@@ -302,7 +304,8 @@
                     "group": "Migration",
                     "icon": "arrow-right",
                     "pages": [
-                      "migration/oss-v2-to-v3"
+                      "migration/oss-v2-to-v3",
+                      "migration/server-pgvector-upgrade"
                     ]
                   },
                   {
@@ -452,7 +455,9 @@
                     "pages": [
                       "integrations/claude-code",
                       "integrations/cursor",
-                      "integrations/codex"
+                      "integrations/codex",
+                      "integrations/opencode",
+                      "integrations/antigravity"
                     ]
                   },
                   {
@@ -460,7 +465,8 @@
                     "icon": "robot",
                     "pages": [
                       "integrations/openclaw",
-                      "integrations/hermes"
+                      "integrations/hermes",
+                      "integrations/pi-agent"
                     ]
                   }
                 ]

docs/integrations/antigravity.mdx

@@ -0,0 +1,82 @@
+---
+title: Antigravity
+description: "Add persistent memory to Google Antigravity with the Mem0 plugin — MCP server, lifecycle hooks, and slash commands."
+---
+
+Add persistent memory to [**Google Antigravity**](https://antigravity.google) (`agy` CLI and Desktop IDE) with the Mem0 plugin. Your agent forgets everything between sessions — Mem0 fixes that by storing decisions, preferences, and learnings so they carry over automatically.
+
+## Prerequisites
+
+1. A Mem0 API key (starts with `m0-`):
+   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-antigravity" rel="nofollow">Get your API key</a> (free sign-up at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-antigravity" rel="nofollow">app.mem0.ai</a>)
+
+2. Add it to your shell profile so it persists across sessions:
+
+<CodeGroup>
+```bash zsh
+echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.zshrc && source ~/.zshrc
+```
+
+```bash bash
+echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.bashrc && source ~/.bashrc
+```
+</CodeGroup>
+
+## Installation
+
+**Option A — degit** (recommended):
+
+```bash
+# Install the plugin (MCP server, hooks, scripts)
+npx degit mem0ai/mem0/mem0-plugin ~/.gemini/config/plugins/mem0
+```
+
+This installs the MCP server, lifecycle hooks, and shared scripts.
+
+## What's Included
+
+| Component | Included |
+|-----------|:--------:|
+| MCP Server (9 memory tools) | Yes |
+| Lifecycle Hooks | Yes |
+| 16 Slash Commands | Yes |
+
+## Available MCP Tools
+
+| Tool | Description |
+|------|-------------|
+| `add_memory` | Save text or conversation history for a user/agent |
+| `search_memories` | Semantic search across memories with filters |
+| `get_memories` | List memories with filters and pagination |
+| `get_memory` | Retrieve a specific memory by ID |
+| `update_memory` | Overwrite a memory's text by ID |
+| `delete_memory` | Delete a single memory by ID |
+| `delete_all_memories` | Bulk delete all memories in scope |
+| `delete_entities` | Delete a user/agent/app/run entity and its memories |
+| `list_entities` | List users/agents/apps/runs stored in Mem0 |
+
+## Lifecycle Hooks
+
+The plugin uses the same shell scripts as Claude Code, Cursor, and Codex — hooks bridge environment variables using `${extensionPath}` (Antigravity's plugin-root token).
+
+| Hook | Event | What it does |
+|------|-------|-------------|
+| **Session start** | `SessionStart` | Loads prior memories and displays status banner |
+| **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message |
+| **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tools |
+| **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
+
+## Troubleshooting
+
+- **No tools appearing** — Restart your Antigravity session after installation
+- **"Connection failed"** — Verify your key is set: `echo $MEM0_API_KEY`
+- **MCP 401 Unauthorized** — If `${MEM0_API_KEY}` interpolation doesn't work in your `agy` version, replace with your literal key in `mcp_config.json`
+
+<CardGroup cols={2}>
+  <Card title="Mem0 MCP Setup" icon="puzzle-piece" href="/platform/mem0-mcp">
+    Detailed MCP configuration for all clients
+  </Card>
+  <Card title="OpenCode Integration" icon="code" href="/integrations/opencode">
+    Add Mem0 memory to OpenCode workflows
+  </Card>
+</CardGroup>

docs/integrations/claude-code.mdx

@@ -5,13 +5,6 @@ description: "Add persistent memory to Claude Code and Claude Cowork with the Me
 
 Add persistent memory to [**Claude Code**](https://docs.anthropic.com/en/docs/claude-code) (CLI) and **Claude Cowork** (desktop app) with the Mem0 plugin. Your agent forgets everything between sessions — this plugin fixes that by connecting to Mem0's cloud memory layer via MCP, automatically capturing learnings at key lifecycle points, and retrieving relevant context before every response.
 
-## Overview
-
-1. **MCP Server** — Connect to Mem0's remote MCP server for memory tools (add, search, update, delete)
-2. **Lifecycle Hooks** — Automatic memory capture at session start, context compaction, task completion, and session end
-3. **SDK Skill** — Teaches the agent how to integrate the Mem0 SDK into your applications
-4. **Zero local dependencies** — Cloud-hosted MCP server, no local setup required
-
 ## Prerequisites
 
 Before setting up Mem0 with Claude Code, ensure you have:
@@ -115,23 +108,6 @@ This runs the setup wizard which:
 
 The onboarding is idempotent — safe to re-run anytime. It auto-triggers on first session in a new project, but you can always invoke it manually.
 
-## Available Skills
-
-The plugin includes 17 skills accessible via `/mem0:` commands:
-
-| Command | Description |
-|---------|-------------|
-| `/mem0:remember` | Store a memory verbatim — decisions, preferences, conventions |
-| `/mem0:tour` | Browse all memories grouped by category |
-| `/mem0:peek` | Quick search with compact one-liner results |
-| `/mem0:stats` | Session and project memory statistics |
-| `/mem0:dream` | Consolidate memories — merge duplicates, resolve contradictions |
-| `/mem0:pin` | Protect critical memories from pruning |
-| `/mem0:forget` | Delete memories by search or ID |
-| `/mem0:health` | Diagnose connectivity, API key, and read/write |
-| `/mem0:export` | Export memories to portable Markdown |
-| `/mem0:import` | Import memories from export file or MEMORY.md |
-
 ## What's Included
 
 | Component | Plugin Install | MCP Only |
@@ -160,20 +136,13 @@ Once installed, the following tools are available in every Claude Code session:
 
 When installed via the plugin marketplace, Mem0 hooks into Claude Code's lifecycle to automatically manage memory:
 
-### Session Start
-On every new session, the plugin prompts Claude to call `search_memories` to load relevant context from prior sessions. On resumed or post-compaction sessions, it adjusts the prompt accordingly.
-
-### User Prompt
-Before processing each user message, the plugin searches Mem0 for memories relevant to the current prompt and injects them into context. Short prompts (< 20 characters) are skipped to minimize latency.
-
-### Pre-Compaction
-Before context compaction, the plugin prompts Claude to store a comprehensive session summary — including goals, accomplishments, decisions, modified files, and current state — so nothing is lost.
-
-### Task Completed
-After each task completion, the plugin prompts Claude to extract and store key learnings: successful strategies, failed approaches, architectural decisions, and new conventions.
-
-### Session End
-When Claude finishes responding, the plugin prompts for any unstored learnings and captures transcript state via the Mem0 REST API as a background safety net.
+| Hook | Event | What it does |
+|------|-------|-------------|
+| **Session start** | `SessionStart` | Loads prior memories and displays status banner |
+| **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message; skips short prompts |
+| **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
+| **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
+| **Pre-compact** | `PreCompact` | Stores a session summary before context compaction |
 
 ## Example Workflow
 

docs/integrations/codex.mdx

@@ -1,16 +1,9 @@
 ---
 title: Codex
-description: "Add persistent memory to OpenAI Codex with the Mem0 plugin — MCP server, memory protocol skill, and plugin marketplace support."
+description: "Add persistent memory to OpenAI Codex with the Mem0 plugin — MCP server, lifecycle hooks, and SDK skill."
 ---
 
-Add persistent memory to [**OpenAI Codex**](https://openai.com/index/codex/) with the Mem0 plugin. Codex forgets everything between tasks — this plugin fixes that by connecting to Mem0's cloud memory layer via MCP and using a skill-based memory protocol to automatically retrieve context and store learnings.
-
-## Overview
-
-1. **MCP Server** — Connect to Mem0's remote MCP server for memory tools (add, search, update, delete)
-2. **Memory Protocol Skill** — Instructs the agent to retrieve memories at task start, store learnings on completion, and capture session state before context loss
-3. **Plugin Marketplace** — Install via Codex's repo-level or personal plugin marketplace
-4. **Zero local dependencies** — Cloud-hosted MCP server, no local setup required
+Add persistent memory to [**OpenAI Codex**](https://openai.com/index/codex/) with the Mem0 plugin. Codex forgets everything between tasks — this plugin fixes that by connecting to Mem0's cloud memory layer via MCP, automatically capturing learnings at key lifecycle points, and retrieving relevant context before every response.
 
 ## Prerequisites
 
@@ -38,9 +31,25 @@ source ~/.bashrc
 
 ## Installation
 
-### Option A — Direct MCP (Recommended)
+### Option A — Plugin Marketplace (Recommended)
 
-The fastest way to connect Codex to Mem0 — no downloads, no marketplace. Codex reads MCP servers from `~/.codex/config.toml` as TOML. Add:
+Install the full plugin including MCP server, lifecycle hooks, and SDK skill.
+
+1. Add the Mem0 marketplace:
+
+   ```bash
+   codex plugin marketplace add mem0ai/mem0
+   ```
+
+2. Restart Codex, open the Plugin Directory, browse the **Mem0 Plugins** marketplace, and install **Mem0**.
+
+<Info>
+  Do not combine with Option B. The plugin manifest auto-registers the `mem0` MCP server, so adding both will create a duplicate registration.
+</Info>
+
+### Option B — Direct MCP
+
+The fastest way to connect Codex to Mem0 — no plugin, no marketplace. Add to `~/.codex/config.toml`:
 
 ```toml
 [mcp_servers.mem0]
@@ -54,71 +63,16 @@ Make sure `MEM0_API_KEY` is exported in the shell you launch Codex from, then re
   Codex's `codex mcp add` CLI only supports stdio MCP servers. Because Mem0's MCP is HTTP/streamable, you configure it by editing `config.toml` directly (or via the **Plugins → Connect to a custom MCP → Streamable HTTP** UI in the Codex app).
 </Info>
 
-### Option B — Sideload the Plugin (Advanced)
-
-For the full plugin experience — MCP server **plus** the Mem0 SDK skill, memory protocol skill, and opt-in lifecycle hooks — sideload the plugin from a local clone. The Mem0 repo already ships a marketplace manifest at [`.agents/plugins/marketplace.json`](https://github.com/mem0ai/mem0/blob/main/.agents/plugins/marketplace.json), so there's no JSON to author by hand. This follows the Codex [build-plugins](https://developers.openai.com/codex/plugins/build) local-testing workflow.
-
-<Info>
-  Don't combine Option B with Option A. The plugin manifest declares its MCP server via [`.codex-mcp.json`](https://github.com/mem0ai/mem0/blob/main/mem0-plugin/.codex-mcp.json), so Codex auto-registers the `mem0` MCP server when the plugin loads. Adding the same `[mcp_servers.mem0]` block to `~/.codex/config.toml` will create a duplicate registration.
-</Info>
-
-**Step 1.** Clone the Mem0 repository anywhere on disk:
-
-```bash
-git clone https://github.com/mem0ai/mem0.git ~/codex-plugins/mem0-source
-```
-
-**Step 2.** Register the bundled marketplace with Codex's CLI:
-
-```bash
-codex plugin marketplace add ~/codex-plugins/mem0-source
-```
-
-This points Codex at the repo's `.agents/plugins/marketplace.json`. The bundled file uses `path: "./mem0-plugin"`, which Codex resolves relative to the clone root.
-
-<Info>
-  **Why we recommend this over hand-authoring `~/.agents/plugins/marketplace.json`:** Codex requires `source.path` in any marketplace manifest to be **relative** (starting with `./`) and **inside the marketplace root**. The repo's bundled manifest already satisfies this — the marketplace root is the clone directory, and `mem0-plugin/` lives inside it. With a personal `~/.agents/plugins/marketplace.json`, the root is `~/` and the clone has to live under `~/` too. The CLI form sidesteps that constraint.
-</Info>
-
-**Step 3.** Restart Codex, run `/plugins`, browse the `Mem0 Plugins` marketplace, and install **Mem0**.
-
-**Step 4 (optional) — enable lifecycle hooks.** Codex doesn't auto-wire hooks from plugin manifests; it only reads them from `~/.codex/hooks.json` (or `<repo>/.codex/hooks.json`). Run the bundled installer once to merge the Mem0 entries into your global hooks file:
-
-```bash
-python3 ~/codex-plugins/mem0-source/mem0-plugin/scripts/install_codex_hooks.py
-```
-
-Then enable the hooks feature flag in `~/.codex/config.toml`:
-
-```toml
-[features]
-codex_hooks = true
-```
-
-Restart Codex. The installer registers three hooks pointing at scripts inside your clone:
-
-| Event | Behavior |
-|-------|----------|
-| `SessionStart` | Loads prior memories as bootstrap context |
-| `UserPromptSubmit` | Injects relevant memories before each prompt |
-| `Stop` | Reminds the agent to persist learnings at turn end |
-
-Re-running the installer is idempotent. To remove the hooks: `python3 ~/codex-plugins/mem0-source/mem0-plugin/scripts/install_codex_hooks.py --uninstall`.
-
-<Warning>
-  The hooks file stores absolute paths into your clone (e.g. `~/codex-plugins/mem0-source/mem0-plugin/scripts/...`). If you move or delete the clone, the hooks will break silently — re-run the installer from the new location, or run `--uninstall` first.
-</Warning>
+This gives you the MCP tools but not the lifecycle hooks or SDK skill.
 
 ### Managing the Plugin
 
-Codex provides CLI commands for managing marketplaces after install:
-
 ```bash
 codex plugin marketplace upgrade               # pull latest plugin versions
 codex plugin marketplace remove mem0-plugins   # unregister the marketplace
 ```
 
-To pull updates to the plugin source itself, `git pull` inside your clone (`~/codex-plugins/mem0-source`) and then run `codex plugin marketplace upgrade` to refresh Codex's plugin cache. Plugins are cached at `~/.codex/plugins/cache/<marketplace>/<plugin>/<version>/`.
+To update, run `codex plugin marketplace upgrade` to pull the latest from the Mem0 repo.
 
 <Info icon="check">
   After either option, start a new Codex task and ask: *"List my mem0 entities"* or *"Search my memories for hello"*. If the `mem0` tools appear and respond, you're all set.
@@ -126,12 +80,11 @@ To pull updates to the plugin source itself, `git pull` inside your clone (`~/co
 
 ## What's Included
 
-| Component | Sideloaded Plugin | Direct MCP |
-|-----------|:-----------------:|:----------:|
+| Component | Plugin Install | MCP Only |
+|-----------|:--------------:|:--------:|
 | MCP Server (9 memory tools) | Yes | Yes |
-| Memory Protocol Skill | Yes | No |
+| Lifecycle Hooks | Yes | No |
 | Mem0 SDK Skill | Yes | No |
-| Lifecycle Hooks (opt-in) | Yes | No |
 
 ## Available MCP Tools
 
@@ -149,49 +102,17 @@ Once installed, the following tools are available in every Codex session:
 | `delete_entities` | Delete a user/agent/app/run entity and its memories |
 | `list_entities` | List users/agents/apps/runs stored in Mem0 |
 
-## Memory Protocol Skill
+## Lifecycle Hooks
 
-When the plugin is sideloaded, the memory protocol skill instructs the agent to:
+When installed via the plugin marketplace, Mem0 hooks into Codex's lifecycle to automatically manage memory:
 
-### On Every New Task
-1. Call `search_memories` with a query related to the current task to load relevant context
-2. Review returned memories to understand what was learned in prior sessions
-3. Optionally call `get_memories` to browse all stored memories
-
-### After Completing Significant Work
-Store key learnings using `add_memory` with structured metadata:
-
-| What to store | Metadata type |
-|--------------|---------------|
-| Architectural decisions | `{"type": "decision"}` |
-| Strategies that worked | `{"type": "task_learning"}` |
-| Failed approaches | `{"type": "anti_pattern"}` |
-| User preferences observed | `{"type": "user_preference"}` |
-| Environment discoveries | `{"type": "environmental"}` |
-| Conventions established | `{"type": "convention"}` |
-
-### Before Losing Context
-Store a comprehensive session summary including goals, accomplishments, decisions, files modified, and current state with metadata `{"type": "session_state"}`.
-
-## Plugin Manifest
-
-The Codex plugin manifest (`.codex-plugin/plugin.json`) follows the Codex plugin specification:
-
-```json
-{
-  "name": "mem0",
-  "version": "0.2.5",
-  "description": "Mem0 memory layer for AI applications.",
-  "skills": "./skills/",
-  "mcpServers": "./.codex-mcp.json",
-  "interface": {
-    "displayName": "Mem0",
-    "shortDescription": "Persistent memory layer for AI coding workflows",
-    "category": "Productivity",
-    "capabilities": ["Read", "Write"]
-  }
-}
-```
+| Hook | Event | What it does |
+|------|-------|-------------|
+| **Session start** | `SessionStart` | Loads prior memories and displays status banner |
+| **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message |
+| **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
+| **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
+| **Pre-compact** | `PreCompact` | Stores a session summary before context compaction |
 
 ## Example Workflow
 
@@ -214,14 +135,10 @@ You: Add WebSocket support for real-time notification delivery.
 
 ## Troubleshooting
 
-- **"Connection failed"** — Verify `MEM0_API_KEY` is set in your shell: `echo $MEM0_API_KEY`
-- **No tools appearing** — Restart your Codex session after plugin installation
-- **Duplicate `mem0` MCP server / "tool collision" errors** — You combined Option A (Direct MCP) with Option B (sideload). The sideloaded plugin auto-registers `mem0` from `.codex-mcp.json`, so remove the `[mcp_servers.mem0]` block from `~/.codex/config.toml`.
-- **`plugin/read failed in TUI`** — Codex can't find the plugin directory the marketplace points at. If you used `codex plugin marketplace add <path>`, confirm the path is your clone root and that `<clone>/.agents/plugins/marketplace.json` exists. If you hand-authored `~/.agents/plugins/marketplace.json`, `source.path` must be relative (start with `./`), inside the marketplace root (`~/` for personal installs), and end in `mem0-plugin` — e.g. `"./codex-plugins/mem0-source/mem0-plugin"`.
-- **Plugin not found in `/plugins`** — Run `codex plugin marketplace add ~/path/to/clone` again, or confirm the marketplace was registered with `codex plugin marketplace remove mem0-plugins` then re-add.
-- **Skills not loading** — Verify the `skills` field in `plugin.json` points to a valid directory containing `SKILL.md` files.
-- **Hooks not firing** — Confirm `codex_hooks = true` is in `~/.codex/config.toml` under `[features]`, and that `~/.codex/hooks.json` contains the Mem0 entries (re-run the installer if not). Restart Codex after enabling the flag.
-- **Hooks broke after moving the clone** — The installer bakes absolute paths into `~/.codex/hooks.json` pointing at scripts inside your clone. If you moved or renamed the clone directory, run `python3 <new-clone>/mem0-plugin/scripts/install_codex_hooks.py` from the new location — the installer is idempotent and replaces the old entries.
+- **"Connection failed"** — Verify `MEM0_API_KEY` is set: `echo $MEM0_API_KEY`
+- **No tools appearing** — Restart your Codex session after installation
+- **Duplicate `mem0` MCP / "tool collision" errors** — You combined Option A with Option B. Remove the `[mcp_servers.mem0]` block from `~/.codex/config.toml`; the plugin registers it automatically
+- **Hooks not firing** — Ensure the plugin is installed via the marketplace (Option A). MCP-only installs do not include hooks
 
 <CardGroup cols={2}>
   <Card title="Mem0 MCP Setup" icon="puzzle-piece" href="/platform/mem0-mcp">

docs/integrations/cursor.mdx

@@ -5,13 +5,6 @@ description: "Add persistent memory to Cursor with the Mem0 plugin — MCP serve
 
 Add persistent memory to [**Cursor**](https://cursor.com) with the Mem0 plugin. Your AI assistant forgets everything between sessions — this plugin fixes that by connecting to Mem0's cloud memory layer via MCP, automatically capturing learnings at key lifecycle points, and retrieving relevant context before every response.
 
-## Overview
-
-1. **MCP Server** — Connect to Mem0's remote MCP server for memory tools (add, search, update, delete)
-2. **Lifecycle Hooks** — Automatic memory capture at session start, compaction, and user prompts (Marketplace install)
-3. **SDK Skill** — Teaches the agent how to integrate the Mem0 SDK into your applications
-4. **Zero local dependencies** — Cloud-hosted MCP server, no local setup required
-
 ## Prerequisites
 
 Before setting up Mem0 with Cursor, ensure you have:
@@ -111,14 +104,13 @@ Once installed, the following tools are available in every Cursor session:
 
 When installed via the Cursor Marketplace, Mem0 hooks into Cursor's lifecycle:
 
-### Session Start
-On every new session, the plugin prompts the agent to call `search_memories` to load relevant context from prior sessions.
-
-### User Prompt
-Before processing each user message, the plugin searches Mem0 for relevant memories and injects them into context. Short prompts are skipped to minimize latency.
-
-### Pre-Compaction
-Before context compaction, the plugin captures a comprehensive session summary so nothing is lost when the context window resets.
+| Hook | Event | What it does |
+|------|-------|-------------|
+| **Session start** | `sessionStart` | Loads prior memories and displays status banner |
+| **User prompt** | `beforeSubmitPrompt` | Searches relevant memories before each message; skips short prompts |
+| **Pre-tool (2 handlers)** | `preToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
+| **Post-tool (2 handlers)** | `postToolUse` | Tracks stats, scans bash errors for related memories |
+| **Pre-compact** | `preCompact` | Stores a session summary before context compaction |
 
 ## Example Workflow
 

docs/integrations/google-ai-adk.mdx

@@ -7,285 +7,338 @@ Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [Google ADK (Agent Dev
 
 ## Overview
 
-1. Store and retrieve memories from Mem0 within Google ADK agents
-2. Multi-agent workflows with shared memory across hierarchies
-3. Retrieve relevant memories from past conversations
-4. Personalized responses based on user history
+In this guide, we'll create a Google ADK agent that:
+1. Uses ADK's native `MemoryService` interface to connect Mem0
+2. Automatically injects relevant memories using ADK's built-in `load_memory` tool
+3. Persists session history to Mem0 after each turn via an after-agent callback
+4. Shares memory seamlessly across multi-agent hierarchies
 
-## Prerequisites
+## Setup and Configuration
 
-Before setting up Mem0 with Google ADK, ensure you have:
+Install the necessary libraries:
 
-1. Installed the required packages:
 ```bash
 pip install google-adk mem0ai python-dotenv
 ```
 
-2. Valid API keys:
+Set up your API keys:
    - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-google-ai-adk" rel="nofollow">Mem0 API Key</a>
    - Google AI Studio API Key
 
-## Basic Integration Example
-
-The following example demonstrates how to create a Google ADK agent with Mem0 memory integration:
+<Note>Remember to get your API key from <a href="https://app.mem0.ai" rel="nofollow">Mem0 Platform</a> and set up a [Google AI Studio API Key](https://aistudio.google.com/apikey).</Note>
 
 ```python
 import os
-import asyncio
-from google.adk.agents import Agent
-from google.adk.runners import Runner
-from google.adk.sessions import InMemorySessionService
-from google.genai import types
-from mem0 import MemoryClient
 from dotenv import load_dotenv
 
 load_dotenv()
 
-# Set up environment variables
 # os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
 # os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+```
 
-# Initialize Mem0 client
-mem0 = MemoryClient()
+## Implement Mem0MemoryService
 
-# Define memory function tools
-def search_memory(query: str, user_id: str) -> dict:
-    """Search through past conversations and memories"""
-    # For Platform API, user_id goes in filters
-    filters = {"user_id": user_id}
-    memories = mem0.search(query, filters=filters)
-    if memories.get('results', []):
-        memory_list = memories['results']
-        memory_context = "\n".join([f"- {mem['memory']}" for mem in memory_list])
-        return {"status": "success", "memories": memory_context}
-    return {"status": "no_memories", "message": "No relevant memories found"}
+Create a custom `MemoryService` by implementing ADK's `BaseMemoryService`. Save the following as **`mem0_memory_service.py`**:
 
-def save_memory(content: str, user_id: str) -> dict:
-    """Save important information to memory"""
+```python
+import asyncio
+import os
+from typing import Optional
+from typing_extensions import override
+
+from google.adk.memory.base_memory_service import BaseMemoryService, SearchMemoryResponse
+from google.adk.memory.memory_entry import MemoryEntry
+from google.adk.sessions import Session
+from google.genai.types import Content, Part
+from mem0 import MemoryClient
+
+
+class Mem0MemoryService(BaseMemoryService):
+    """MemoryService implementation backed by the Mem0 Platform."""
+
+    def __init__(self, api_key: Optional[str] = None):
+        super().__init__()
+        api_key = api_key or os.environ.get("MEM0_API_KEY")
+        self._client: Optional[MemoryClient] = MemoryClient(api_key=api_key) if api_key else None
+
+    @override
+    async def search_memory(
+        self, *, app_name: str, user_id: str, query: str
+    ) -> SearchMemoryResponse:
+        """Search for memories relevant to the current user and query."""
+        if not self._client:
+            return SearchMemoryResponse(memories=[])
+
+        try:
+            results = await asyncio.to_thread(
+                self._client.search,
+                query,
+                filters={"AND": [{"user_id": user_id}, {"app_id": app_name}]},
+                top_k=5,
+            )
+
+            entries = []
+            for mem in results.get("results", []):
+                text = mem.get("memory", "")
+                if not text:
+                    continue
+
+                raw_ts = mem.get("created_at") or mem.get("updated_at")
+                entries.append(
+                    MemoryEntry(
+                        content=Content(parts=[Part(text=text)]),
+                        author=mem.get("metadata", {}).get("author", "user"),
+                        timestamp=str(raw_ts) if raw_ts else None,
+                    )
+                )
+
+            return SearchMemoryResponse(memories=entries)
+
+        except Exception as e:
+            print(f"[Mem0MemoryService] search_memory error: {e}")
+            return SearchMemoryResponse(memories=[])
+
+    @override
+    async def add_session_to_memory(self, session: Session) -> None:
+        """Persist a completed ADK session into Mem0."""
+        if not self._client:
+            return
+
+        user_id = session.user_id
+        if not user_id:
+            return
+
+        app_name = getattr(session, "app_name", None)
+
+        try:
+            messages = []
+            for event in session.events:
+                if not (event.content and event.content.parts):
+                    continue
+                role = getattr(event.content, "role", None) or "user"
+                if role == "model":
+                    role = "assistant"
+                elif role not in ("user", "assistant"):
+                    continue
+                text_parts = [
+                    p.text for p in event.content.parts if hasattr(p, "text") and p.text
+                ]
+                if text_parts:
+                    messages.append({"role": role, "content": " ".join(text_parts)})
+
+            if messages:
+                metadata = {"app_id": app_name} if app_name else {}
+                await asyncio.to_thread(
+                    self._client.add, messages, user_id=user_id, metadata=metadata
+                )
+
+        except Exception as e:
+            print(f"[Mem0MemoryService] add_session_to_memory error: {e}")
+```
+
+## Add Auto-Save Callback
+
+This after-agent callback fires at the end of every turn and saves the session to Mem0. Save as **`memory_callbacks.py`**:
+
+```python
+async def save_session_to_memory(callback_context) -> None:
+    """Persist the completed session to Mem0 after each agent turn."""
     try:
-        result = mem0.add([{"role": "user", "content": content}], user_id=user_id)
-        return {"status": "success", "message": "Information saved to memory", "result": result}
+        await callback_context.add_session_to_memory()
+    except ValueError:
+        pass
     except Exception as e:
-        return {"status": "error", "message": f"Failed to save memory: {str(e)}"}
+        print(f"[save_session_to_memory] error: {e}")
+```
 
-# Create agent with memory capabilities
-personal_assistant = Agent(
+## Basic Integration Example
+
+The following example demonstrates creating an ADK agent with automatic Mem0 memory:
+
+```python
+import asyncio
+from google.adk.agents import LlmAgent
+from google.adk.runners import Runner
+from google.adk.sessions import InMemorySessionService
+from google.adk.tools import load_memory
+from google.genai.types import Content, Part
+
+from mem0_memory_service import Mem0MemoryService
+from memory_callbacks import save_session_to_memory
+
+memory_service = Mem0MemoryService()
+session_service = InMemorySessionService()
+
+agent = LlmAgent(
     name="personal_assistant",
     model="gemini-2.0-flash",
-    instruction="""You are a helpful personal assistant with memory capabilities.
-    Use the search_memory function to recall past conversations and user preferences.
-    Use the save_memory function to store important information about the user.
-    Always personalize your responses based on available memory.""",
+    instruction="""You are a helpful personal assistant.
+    Relevant memories from past conversations are provided to you automatically.
+    Use them to personalize your responses.""",
     description="A personal assistant that remembers user preferences and past interactions",
-    tools=[search_memory, save_memory]
+    tools=[load_memory],
+    after_agent_callback=save_session_to_memory,
 )
 
-async def chat_with_agent(user_input: str, user_id: str) -> str:
-    """
-    Handle user input with automatic memory integration.
+runner = Runner(
+    agent=agent,
+    session_service=session_service,
+    memory_service=memory_service,
+    app_name="memory_assistant",
+)
 
-    Args:
-        user_input: The user's message
-        user_id: Unique identifier for the user
 
-    Returns:
-        The agent's response
-    """
-    # Set up session and runner
-    session_service = InMemorySessionService()
+async def chat(user_input: str, user_id: str) -> str:
     session = await session_service.create_session(
         app_name="memory_assistant",
         user_id=user_id,
-        session_id=f"session_{user_id}"
     )
-    runner = Runner(agent=personal_assistant, app_name="memory_assistant", session_service=session_service)
-
-    # Create content and run agent
-    content = types.Content(role='user', parts=[types.Part(text=user_input)])
-    events = runner.run(user_id=user_id, session_id=session.id, new_message=content)
-
-    # Extract final response
-    for event in events:
-        if event.is_final_response():
-            response = event.content.parts[0].text
-
-            return response
-
+    content = Content(role="user", parts=[Part(text=user_input)])
+    async for event in runner.run_async(user_id=user_id, session_id=session.id, new_message=content):
+        if event.is_final_response() and event.content and event.content.parts:
+            return event.content.parts[0].text
     return "No response generated"
 
-# Example usage
+
 if __name__ == "__main__":
-    response = asyncio.run(chat_with_agent(
+    print(asyncio.run(chat(
         "I love Italian food and I'm planning a trip to Rome next month",
-        user_id="alice"
-    ))
-    print(response)
+        user_id="alice",
+    )))
+
+    print(asyncio.run(chat(
+        "Any food recommendations for my trip?",
+        user_id="alice",
+    )))
 ```
 
 ## Multi-Agent Hierarchy with Shared Memory
 
-Create specialized agents in a hierarchy that share memory:
+Because `memory_service` is passed to the `Runner`, every agent in the hierarchy shares the same memory automatically. Only the root coordinator needs the auto-save callback — ADK fires it once when the full turn completes:
 
 ```python
+import asyncio
+from google.adk.agents import LlmAgent
+from google.adk.runners import Runner
+from google.adk.sessions import InMemorySessionService
 from google.adk.tools.agent_tool import AgentTool
+from google.adk.tools import load_memory
+from google.genai.types import Content, Part
 
-# Travel specialist agent
-travel_agent = Agent(
+from mem0_memory_service import Mem0MemoryService
+from memory_callbacks import save_session_to_memory
+
+memory_service = Mem0MemoryService()
+session_service = InMemorySessionService()
+
+travel_agent = LlmAgent(
     name="travel_specialist",
     model="gemini-2.0-flash",
-    instruction="""You are a travel planning specialist. Use search_memory to
-    understand the user's travel preferences and history before making recommendations.
-    After providing advice, use save_memory to save travel-related information.""",
+    instruction="""You are a travel planning specialist.
+    Relevant memories about the user's travel preferences are provided automatically.
+    Use them to make personalized recommendations.""",
     description="Specialist in travel planning and recommendations",
-    tools=[search_memory, save_memory]
+    tools=[load_memory],
 )
 
-# Health advisor agent
-health_agent = Agent(
+health_agent = LlmAgent(
     name="health_advisor",
     model="gemini-2.0-flash",
-    instruction="""You are a health and wellness advisor. Use search_memory to
-    understand the user's health goals and dietary preferences.
-    After providing advice, use save_memory to save health-related information.""",
+    instruction="""You are a health and wellness advisor.
+    Relevant memories about the user's health goals are provided automatically.
+    Use them to give personalized advice.""",
     description="Specialist in health and wellness advice",
-    tools=[search_memory, save_memory]
+    tools=[load_memory],
 )
 
-# Coordinator agent that delegates to specialists
-coordinator_agent = Agent(
+coordinator = LlmAgent(
     name="coordinator",
     model="gemini-2.0-flash",
     instruction="""You are a coordinator that delegates requests to specialist agents.
-    For travel-related questions (trips, hotels, flights, destinations), delegate to the travel specialist.
-    For health-related questions (fitness, diet, wellness, exercise), delegate to the health advisor.
-    Use search_memory to understand the user before delegation.""",
+    For travel-related questions, delegate to the travel specialist.
+    For health-related questions, delegate to the health advisor.
+    Relevant memories about the user are provided automatically.""",
     description="Coordinates requests between specialist agents",
     tools=[
+        load_memory,
         AgentTool(agent=travel_agent, skip_summarization=False),
-        AgentTool(agent=health_agent, skip_summarization=False)
-    ]
+        AgentTool(agent=health_agent, skip_summarization=False),
+    ],
+    after_agent_callback=save_session_to_memory,
 )
 
-def chat_with_specialists(user_input: str, user_id: str) -> str:
-    """
-    Handle user input with specialist agent delegation and memory.
+runner = Runner(
+    agent=coordinator,
+    session_service=session_service,
+    memory_service=memory_service,
+    app_name="specialist_system",
+)
 
-    Args:
-        user_input: The user's message
-        user_id: Unique identifier for the user
 
-    Returns:
-        The specialist agent's response
-    """
-    session_service = InMemorySessionService()
-    session = session_service.create_session(
+async def chat_with_specialists(user_input: str, user_id: str) -> str:
+    session = await session_service.create_session(
         app_name="specialist_system",
         user_id=user_id,
-        session_id=f"session_{user_id}"
     )
-    runner = Runner(agent=coordinator_agent, app_name="specialist_system", session_service=session_service)
-
-    content = types.Content(role='user', parts=[types.Part(text=user_input)])
-    events = runner.run(user_id=user_id, session_id=session.id, new_message=content)
-
-    for event in events:
-        if event.is_final_response():
-            response = event.content.parts[0].text
-
-            # Store the conversation in shared memory
-            conversation = [
-                {"role": "user", "content": user_input},
-                {"role": "assistant", "content": response}
-            ]
-            mem0.add(conversation, user_id=user_id)
-
-            return response
-
+    content = Content(role="user", parts=[Part(text=user_input)])
+    async for event in runner.run_async(user_id=user_id, session_id=session.id, new_message=content):
+        if event.is_final_response() and event.content and event.content.parts:
+            return event.content.parts[0].text
     return "No response generated"
 
-# Example usage
-response = chat_with_specialists("Plan a healthy meal for my Italy trip", user_id="alice")
-print(response)
-```
-
-
-
-## Quick Start Chat Interface
-
-Simple interactive chat with memory and Google ADK:
-
-```python
-def interactive_chat():
-    """Interactive chat interface with memory and ADK"""
-    user_id = input("Enter your user ID: ") or "demo_user"
-    print(f"Chat started for user: {user_id}")
-    print("Type 'quit' to exit")
-    print("=" * 50)
-
-    while True:
-        user_input = input("\nYou: ")
-
-        if user_input.lower() == 'quit':
-            print("Goodbye! Your conversation has been saved to memory.")
-            break
-        else:
-            response = chat_with_specialists(user_input, user_id)
-            print(f"Assistant: {response}")
 
 if __name__ == "__main__":
-    interactive_chat()
+    response = asyncio.run(chat_with_specialists("Plan a healthy meal for my Italy trip", user_id="alice"))
+    print(response)
 ```
 
 ## Key Features
 
-### 1. Memory-Enhanced Function Tools
-- **Function Tools**: Standard Python functions that can search and save memories
-- **Tool Context**: Access to session state and memory through function parameters
-- **Structured Returns**: Dictionary-based returns with status indicators for better LLM understanding
-
-### 2. Multi-Agent Memory Sharing
-- **Agent-as-a-Tool**: Specialists can be called as tools while maintaining shared memory
-- **Hierarchical Delegation**: Coordinator agents route to specialists based on context
-- **Memory Categories**: Store interactions with metadata for better organization
-
-### 3. Flexible Memory Operations
-- **Search Capabilities**: Retrieve relevant memories through conversation history
-- **User Segmentation**: Organize memories by user ID
-- **Memory Management**: Built-in tools for saving and retrieving information
+1. **Automatic Memory Injection**: ADK's built-in `load_memory` tool searches Mem0 at the start of each turn and injects relevant memories directly into the agent context — no prompt instructions needed.
+2. **Automatic Session Saving**: The `save_session_to_memory` callback persists every completed turn to Mem0 without any manual calls.
+3. **Native ADK Integration**: `Mem0MemoryService` implements ADK's `BaseMemoryService` and integrates via the `Runner` — works natively across the entire agent hierarchy.
+4. **User Scoping**: `user_id` is passed automatically from the ADK session context, ensuring memories are always scoped to the correct user.
+5. **Multi-Agent Support**: A single `Mem0MemoryService` instance shared through the `Runner` gives all agents — coordinators and specialists — access to the same user memory.
 
 ## Configuration Options
 
-Customize memory behavior and agent setup:
+### Using Vertex AI
+
+To use Google Cloud Vertex AI instead of AI Studio, set the following environment variables before creating agents:
 
 ```python
-# Configure memory search with filters
-# For Platform API, all filters including user_id go in filters object
-memories = mem0.search(
-    query="travel preferences",
-    filters={
-        "AND": [
-            {"user_id": "alice"},
-            {"categories": {"contains": "travel"}}
-        ]
-    },
-    top_k=5
-)
-
-# Configure agent with custom model settings
-agent = Agent(
-    name="custom_agent",
-    model="gemini-2.0-flash",  # or use LiteLLM for other models
-    instruction="Custom agent behavior",
-    tools=[memory_tools],
-    # Additional ADK configurations
-)
-
-# Use Google Cloud Vertex AI instead of AI Studio
+import os
 os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "True"
 os.environ["GOOGLE_CLOUD_PROJECT"] = "your-project-id"
 os.environ["GOOGLE_CLOUD_LOCATION"] = "us-central1"
 ```
 
+### Advanced Memory Filtering
+
+You can customize how memories are searched by modifying `Mem0MemoryService.search_memory`. For example, to filter by category:
+
+```python
+results = await asyncio.to_thread(
+    self._client.search,
+    query,
+    filters={
+        "AND": [
+            {"user_id": user_id},
+            {"app_id": app_name},
+            {"categories": {"contains": "travel"}}
+        ]
+    },
+    top_k=10,
+)
+```
+
+<Note>`InMemorySessionService` stores sessions in memory and is intended for prototyping. For production, use a persistent session service and clean up sessions when they are no longer needed.</Note>
+
+## Conclusion
+
+By implementing `Mem0MemoryService` as an ADK `BaseMemoryService`, you get persistent, user-scoped memory across single agents and complex multi-agent hierarchies with minimal code. Memory injection and session saving happen automatically, keeping your agent prompts clean and your token usage efficient.
+
 <CardGroup cols={2}>
   <Card title="Healthcare Agent Cookbook" icon="heart-pulse" href="/cookbooks/integrations/healthcare-google-adk">
     Build HIPAA-compliant healthcare agents with Google ADK
@@ -294,4 +347,3 @@ os.environ["GOOGLE_CLOUD_LOCATION"] = "us-central1"
     Compare with OpenAI's agent framework
   </Card>
 </CardGroup>
-

docs/integrations/opencode.mdx

@@ -0,0 +1,119 @@
+---
+title: OpenCode
+description: "Add persistent memory to OpenCode with the Mem0 plugin — MCP server, lifecycle hooks, and slash commands."
+---
+
+Add persistent memory to [**OpenCode**](https://opencode.ai) with the Mem0 plugin. Your agent forgets everything between sessions — Mem0 fixes that by storing decisions, preferences, and learnings so they carry over automatically.
+
+## Prerequisites
+
+1. A Mem0 API key (starts with `m0-`):
+   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-opencode" rel="nofollow">Get your API key</a> (free sign-up at <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-opencode" rel="nofollow">app.mem0.ai</a>)
+
+2. Add it to your shell profile so it persists across sessions:
+
+<CodeGroup>
+```bash zsh
+echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.zshrc && source ~/.zshrc
+```
+
+```bash bash
+echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.bashrc && source ~/.bashrc
+```
+</CodeGroup>
+
+## Installation
+
+### Option A — Plugin Install (Recommended)
+
+```bash
+opencode plugin @mem0/opencode-plugin
+```
+
+
+Or using this command which does the same thing:
+
+
+```bash
+bunx @mem0/opencode-plugin@latest install
+```
+
+
+
+**Or let your agent do it** — paste this into OpenCode:
+
+```
+Install @mem0/opencode-plugin by following https://raw.githubusercontent.com/mem0ai/mem0/main/mem0-plugin/.opencode-plugin/README.md
+```
+
+All commands auto-add the plugin and MCP server to your `~/.config/opencode/opencode.json`. Restart OpenCode — you get the MCP server, lifecycle hooks, and all `/mem0:` slash commands.
+
+### Option B — MCP Only
+
+If you only need the memory tools without hooks or skills, add this to your `opencode.json` (project-level or global at `~/.config/opencode/opencode.json`):
+
+```json
+{
+  "mcp": {
+    "mem0": {
+      "type": "remote",
+      "url": "https://mcp.mem0.ai/mcp/",
+      "headers": {
+        "Authorization": "Token {env:MEM0_API_KEY}"
+      },
+      "oauth": false
+    }
+  }
+}
+```
+
+## What's Included
+
+| Component | Plugin (A) | MCP Only (B) |
+|-----------|:----------:|:------------:|
+| MCP Server (9 memory tools) | Yes | Yes |
+| Lifecycle Hooks | Yes | No |
+| 16 Slash Commands | Yes | No |
+
+## Available MCP Tools
+
+| Tool | Description |
+|------|-------------|
+| `add_memory` | Save text or conversation history for a user/agent |
+| `search_memories` | Semantic search across memories with filters |
+| `get_memories` | List memories with filters and pagination |
+| `get_memory` | Retrieve a specific memory by ID |
+| `update_memory` | Overwrite a memory's text by ID |
+| `delete_memory` | Delete a single memory by ID |
+| `delete_all_memories` | Bulk delete all memories in scope |
+| `delete_entities` | Delete a user/agent/app/run entity and its memories |
+| `list_entities` | List users/agents/apps/runs stored in Mem0 |
+
+## Lifecycle Hooks
+
+The plugin uses the [mem0ai](https://www.npmjs.com/package/mem0ai) TypeScript SDK directly — pure TypeScript, no Python, no shell scripts.
+
+| OpenCode Event | Hook | What happens |
+|----------------|------|-------------|
+| `chat.message` | **Chat message** | Searches prior memories on session start, searches relevant memories before each prompt, auto-captures learnings periodically |
+| `tool.execute.before` | **Pre-tool** | Blocks MEMORY.md writes, injects `user_id`/`app_id` on mem0 tool calls |
+| `tool.execute.after` | **Post-tool** | Tracks stats, scans Bash errors and pre-fetches related error memories |
+| `experimental.chat.system.transform` | **System transform** | Injects memory context (session memories, search results, error lookups) into the system prompt |
+| `experimental.session.compacting` | **Compaction** | Stores session state memory, then injects prior memories into compaction context so nothing is lost |
+| `shell.env` | **Shell env** | Exports `MEM0_USER_ID`, `MEM0_APP_ID`, `MEM0_SESSION_ID`, and `MEM0_BRANCH` to all shell executions |
+
+## Troubleshooting
+
+- **No tools appearing** — Restart OpenCode after installing
+- **"Connection failed"** — Verify your key is set: `echo $MEM0_API_KEY`
+- **Plugin not loading** — Run `opencode plugin @mem0/opencode-plugin` again, then restart
+- **Hooks not firing** — Hooks require the plugin install (Option A). MCP-only installs don't include hooks.
+
+<CardGroup cols={2}>
+  <Card title="Mem0 MCP Setup" icon="puzzle-piece" href="/platform/mem0-mcp">
+    Detailed MCP configuration for all clients
+  </Card>
+  <Card title="Antigravity Integration" icon="google" href="/integrations/antigravity">
+    Add Mem0 memory to Google Antigravity
+  </Card>
+</CardGroup>

docs/integrations/pi-agent.mdx

@@ -0,0 +1,181 @@
+---
+title: Pi Agent
+description: "Add persistent memory to Pi Agent with the Mem0 plugin semantic search, auto-capture, and dream consolidation."
+---
+
+Add persistent memory to [**Pi Agent**](https://pi.dev) with `@mem0/pi-agent-plugin`. Your agent forgets everything between sessions — this plugin fixes that by automatically capturing knowledge from conversations, storing it in Mem0's cloud memory layer, and retrieving relevant context before every response.
+
+## Overview
+
+The plugin provides:
+1. **Auto-capture** — Extracts durable facts from both user and assistant messages automatically
+2. **Semantic recall** — Retrieves relevant memories via the `mem0_memory` tool before each response
+3. **Dream consolidation** — Periodic maintenance: merges duplicates, resolves contradictions, prunes stale entries
+4. **Monorepo-aware scoping** — Uses git root for project detection, consistent across subdirectories
+5. **Confirmation dialogs** — Destructive commands ask before acting via Pi's built-in UI
+6. **8 skills + 8 commands** — Essential memory management from slash commands and agent-guided workflows
+
+## Prerequisites
+
+1. A Mem0 Platform account and API key:
+   - <a href="https://app.mem0.ai?utm_source=oss&utm_medium=integration-pi-agent" rel="nofollow">Sign up at app.mem0.ai</a>
+   - <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-pi-agent" rel="nofollow">Get your API key</a> (starts with `m0-`)
+
+2. Pi Agent installed ([pi.dev](https://pi.dev))
+
+3. Your API key added to your shell profile:
+
+<CodeGroup>
+```bash zsh
+echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.zshrc
+source ~/.zshrc
+```
+
+```bash bash
+echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.bashrc
+source ~/.bashrc
+```
+</CodeGroup>
+
+## Installation
+
+```bash
+pi install npm:@mem0/pi-agent-plugin
+```
+
+That's it. The extension loads automatically on every Pi session. No config files needed — `MEM0_API_KEY` from your environment is picked up automatically.
+
+<Info>
+  Start a new Pi session and run `/mem0-status` to verify the connection. You should see your user ID, detected project, and memory count.
+</Info>
+
+### Optional Configuration
+
+For advanced settings, create `~/.pi/agent/mem0-config.json`:
+
+```json
+{
+  "apiKey": "m0-your-key-here",
+  "userId": "your-username",
+  "autoCapture": true,
+  "defaultScope": "project",
+  "dream": {
+    "enabled": true,
+    "auto": true,
+    "minHours": 24,
+    "minSessions": 5,
+    "minMemories": 20
+  }
+}
+```
+
+| Key | Type | Default | Description |
+|-----|------|---------|-------------|
+| `apiKey` | `string` | `$MEM0_API_KEY` | Mem0 API key. Environment variable takes precedence. |
+| `userId` | `string` | `$MEM0_USER_ID` or `"default"` | User identity for memory scoping |
+| `autoCapture` | `boolean` | `true` | Store facts from conversations automatically |
+| `defaultScope` | `string` | `"project"` | Default memory scope: `project`, `session`, or `global` |
+| `dream.enabled` | `boolean` | `true` | Enable dream consolidation |
+| `dream.auto` | `boolean` | `true` | Auto-trigger dreams when thresholds are met |
+| `dream.minHours` | `number` | `24` | Minimum hours between auto-dreams |
+| `dream.minSessions` | `number` | `5` | Minimum sessions before first auto-dream |
+| `dream.minMemories` | `number` | `20` | Minimum memories before auto-dream triggers |
+
+## What's Included
+
+| Component | Description |
+|-----------|-------------|
+| `mem0_memory` tool | Agent-callable tool for search, add, get_all, delete, delete_all |
+| 8 slash commands | Essential memory management from the command line |
+| 8 skills | Guide the agent on how to use each capability |
+| Auto-capture | Extracts and stores facts on every `agent_end` event |
+| System prompt | Appends memory policy to every agent turn |
+| Dream consolidation | Automated memory maintenance with session/time/count gates |
+
+## Agent Tool
+
+The `mem0_memory` tool is registered with Pi and callable by the agent during conversations:
+
+| Action | Required Params | Description |
+|--------|----------------|-------------|
+| `search` | `query` | Semantic search across memories |
+| `add` | `content` | Store a new memory |
+| `get_all` | — | List all memories in scope |
+| `delete` | `memory_id` | Delete a specific memory |
+| `delete_all` | — | Delete all memories in scope |
+
+All actions accept an optional `scope` parameter: `project` (default), `session`, or `global`.
+
+Tool output is truncated to 200 lines / 50KB to prevent context overflow.
+
+## Commands
+
+| Command | Description |
+|---------|-------------|
+| `/mem0-remember <text>` | Store a memory verbatim (no inference) |
+| `/mem0-forget <query>` | Search and delete memories (with confirmation dialog) |
+| `/mem0-search <query>` | Semantic search across memories |
+| `/mem0-tour [scope]` | Browse all memories grouped by category |
+| `/mem0-dream` | Consolidate — merge duplicates, prune stale, resolve contradictions |
+| `/mem0-pin <query>` | Pin a memory to protect from dream pruning (preserves memory ID) |
+| `/mem0-scope <scope>` | Change default scope for this session (project, session, global) |
+| `/mem0-status` | Connection health, identity, and memory count |
+
+## Memory Scopes
+
+Memories are scoped using Mem0's `user_id`, `app_id`, and `run_id` parameters:
+
+| Scope | Filters | Use Case |
+|-------|---------|----------|
+| `project` | user_id + app_id (git root) | **Default.** Project-specific knowledge — decisions, architecture, config |
+| `session` | user_id + app_id + run_id | Ephemeral context for the current session only |
+| `global` | user_id only | All memories across all your projects |
+
+The `app_id` is auto-detected from the git repository root (`git rev-parse --show-toplevel`), so all subdirectories within a monorepo share the same memory pool. Falls back to the working directory name for non-git directories. The `run_id` is derived from Pi's session file path.
+
+## Dream Consolidation
+
+### Confirmation Dialogs
+
+Destructive and mutating commands use Pi's built-in `ctx.ui.confirm()` dialog before acting:
+
+- `/mem0-forget` asks "Delete this memory?" before deleting a single match
+- `/mem0-pin` asks "Pin this memory?" before modifying it
+- Cancelling either operation is always safe — no changes are made
+
+### Pin
+
+`/mem0-pin` uses Mem0's `update()` API to prepend `[PINNED]` to the memory text. This preserves the original memory ID — no add+delete cycle that would lose history or change the UUID.
+
+### Dream Consolidation
+
+The plugin includes automated memory maintenance ("dream") that merges duplicates, resolves contradictions, and prunes stale entries. When enabled, dreams auto-trigger after enough sessions, time, and memories accumulate (configurable via `dream.*` settings). Run `/mem0-dream` to trigger consolidation manually at any time. Pinned memories (via `/mem0-pin`) are protected from pruning.
+
+## Example Workflow
+
+```text
+# Session 1
+You: I prefer dark mode and concise answers.
+# Mem0 auto-captures preferences
+
+# Session 2 (days later)
+You: What do you know about my preferences?
+# Pi retrieves stored memories — no re-explaining needed
+```
+
+## Troubleshooting
+
+- **"No API key found"** — Verify `MEM0_API_KEY` is set: `echo $MEM0_API_KEY`. If empty, add it to your shell profile (see Prerequisites)
+- **Extension not loading** — Check Pi startup output for errors. Run `pi -e ./src/entry.ts` from the plugin directory for verbose output
+- **Memories not capturing** — Verify `autoCapture` is `true` (default). Check `/mem0-status` for connection health
+- **Wrong project detected** — The plugin uses the git repository root as `app_id`. If not in a git repo, it falls back to the working directory name. Run `/mem0-status` to see the detected project
+- **Dream not triggering** — All three gates must pass (time, sessions, memories). Use `/mem0-dream` to force it manually
+
+<CardGroup cols={2}>
+  <Card title="Claude Code Integration" icon="terminal" href="/integrations/claude-code">
+    Add Mem0 memory to Claude Code
+  </Card>
+  <Card title="OpenClaw Integration" icon="plug" href="/integrations/openclaw">
+    Add Mem0 memory to OpenClaw agents
+  </Card>
+</CardGroup>

docs/integrations/vercel-ai-sdk.mdx

@@ -6,25 +6,33 @@ description: "Use the Mem0 AI SDK Provider with Vercel AI SDK for persistent mem
 The [**Mem0 AI SDK Provider**](https://www.npmjs.com/package/@mem0/vercel-ai-provider) is a library developed by **Mem0** to integrate with the Vercel AI SDK. This library brings enhanced AI interaction capabilities to your applications by introducing persistent memory functionality.
 
 <Note type="info">
-  Mem0 AI SDK now supports <strong>Vercel AI SDK V5</strong>.
+  Mem0 AI SDK Provider v3.0.0 supports <strong>Vercel AI SDK v6</strong> (<code>LanguageModelV3</code> / <code>ProviderV3</code>). If you are upgrading from v2.x, see the <a href="https://ai-sdk.dev/docs/migration-guides/migration-guide-6-0">AI SDK v6 migration guide</a>.
 </Note>
 
 ## Overview
 
 1. Offers persistent memory storage for conversational AI
-2. Enables smooth integration with the Vercel AI SDK
-3. Ensures compatibility with multiple LLM providers
+2. Enables smooth integration with the Vercel AI SDK v6
+3. Ensures compatibility with multiple LLM providers (OpenAI, Anthropic, Google, Groq, Cohere)
 4. Supports structured message formats for clarity
 5. Facilitates streaming response capabilities
+6. Attaches Mem0 memories as sources in responses for programmatic access
 
 ## Setup and Configuration
 
-Install the SDK provider using npm:
+Install the SDK provider and AI SDK:
 
 ```bash
-npm install @mem0/vercel-ai-provider
+npm install @mem0/vercel-ai-provider ai@^6
 ```
 
+### Peer Dependencies
+
+`@mem0/vercel-ai-provider` v3.0.0 requires:
+- `ai` v6+ (`^6.0.199`)
+- `@ai-sdk/provider` v3+ (`^3.0.10`)
+- Provider packages at v3+: `@ai-sdk/openai@^3`, `@ai-sdk/anthropic@^3`, `@ai-sdk/google@^3`, `@ai-sdk/groq@^3`, `@ai-sdk/cohere@^3`
+
 ## Getting Started
 
 ### Setting Up Mem0
@@ -41,7 +49,7 @@ npm install @mem0/vercel-ai-provider
       mem0ApiKey: "m0-xxx",
       apiKey: "provider-api-key",
       config: {
-        // Options for LLM Provider
+        // Options for the upstream LLM provider (e.g. baseURL)
       },
       // Optional Mem0 Global Config
       mem0Config: {
@@ -57,154 +65,153 @@ npm install @mem0/vercel-ai-provider
 3. Add Memories to Enhance Context:
 
     ```typescript
-    import { LanguageModelV2Prompt } from "@ai-sdk/provider";
     import { addMemories } from "@mem0/vercel-ai-provider";
 
-    const messages: LanguageModelV2Prompt = [
+    const messages = [
       { role: "user", content: [{ type: "text", text: "I love red cars." }] },
     ];
 
     await addMemories(messages, { user_id: "borat" });
     ```
 
-### Standalone Features:
+### Standalone Features
 
-    ```typescript
-    await addMemories(messages, { user_id: "borat", mem0ApiKey: "m0-xxx" });
-    await retrieveMemories(prompt, { user_id: "borat", mem0ApiKey: "m0-xxx" });
-    await getMemories(prompt, { user_id: "borat", mem0ApiKey: "m0-xxx" });
-    ```
-     > For standalone features, such as `addMemories`, `retrieveMemories`, and `getMemories`, you must either set `MEM0_API_KEY` as an environment variable or pass it directly in the function call.
+```typescript
+await addMemories(messages, { user_id: "borat", mem0ApiKey: "m0-xxx" });
+await retrieveMemories(prompt, { user_id: "borat", mem0ApiKey: "m0-xxx" });
+await getMemories(prompt, { user_id: "borat", mem0ApiKey: "m0-xxx" });
+```
 
-     > `getMemories` will return raw memories in the form of an array of objects, while `retrieveMemories` will return a response in string format with a system prompt ingested with the retrieved memories.
+> For standalone features, such as `addMemories`, `retrieveMemories`, and `getMemories`, you must either set `MEM0_API_KEY` as an environment variable or pass it directly in the function call.
 
-     > `getMemories` returns an array of memory objects.
+> `getMemories` will return raw memories in the form of an array of objects, while `retrieveMemories` will return a response in string format with a system prompt ingested with the retrieved memories.
 
 ### 1. Basic Text Generation with Memory Context
 
-    ```typescript
-    import { generateText } from "ai";
-    import { createMem0 } from "@mem0/vercel-ai-provider";
+```typescript
+import { generateText } from "ai";
+import { createMem0 } from "@mem0/vercel-ai-provider";
 
-    const mem0 = createMem0();
+const mem0 = createMem0();
 
-    const { text } = await generateText({
-      model: mem0("gpt-4-turbo", { user_id: "borat" }),
-      prompt: "Suggest me a good car to buy!",
-    });
-    ```
+const { text } = await generateText({
+  model: mem0("gpt-5-mini", { user_id: "borat" }),
+  prompt: "Suggest me a good car to buy!",
+});
+```
 
 ### 2. Combining OpenAI Provider with Memory Utils
 
-    ```typescript
-    import { generateText } from "ai";
-    import { openai } from "@ai-sdk/openai";
-    import { retrieveMemories } from "@mem0/vercel-ai-provider";
+```typescript
+import { generateText } from "ai";
+import { openai } from "@ai-sdk/openai";
+import { retrieveMemories } from "@mem0/vercel-ai-provider";
 
-    const prompt = "Suggest me a good car to buy.";
-    const memories = await retrieveMemories(prompt, { user_id: "borat" });
+const prompt = "Suggest me a good car to buy.";
+const memories = await retrieveMemories(prompt, { user_id: "borat" });
 
-    const { text } = await generateText({
-      model: openai("gpt-4-turbo"),
-      prompt: prompt,
-      system: memories,
-    });
-    ```
+const { text } = await generateText({
+  model: openai("gpt-5-mini"),
+  prompt: prompt,
+  system: memories,
+});
+```
 
 ### 3. Structured Message Format with Memory
 
-    ```typescript
-    import { generateText } from "ai";
-    import { createMem0 } from "@mem0/vercel-ai-provider";
+```typescript
+import { generateText } from "ai";
+import { createMem0 } from "@mem0/vercel-ai-provider";
 
-    const mem0 = createMem0();
+const mem0 = createMem0();
 
-    const { text } = await generateText({
-      model: mem0("gpt-4-turbo", { user_id: "borat" }),
-      messages: [
-        {
-          role: "user",
-          content: [
-            { type: "text", text: "Suggest me a good car to buy." },
-            { type: "text", text: "Why is it better than the other cars for me?" },
-          ],
-        },
+const { text } = await generateText({
+  model: mem0("gpt-5-mini", { user_id: "borat" }),
+  messages: [
+    {
+      role: "user",
+      content: [
+        { type: "text", text: "Suggest me a good car to buy." },
+        { type: "text", text: "Why is it better than the other cars for me?" },
       ],
-    });
-    ```
+    },
+  ],
+});
+```
 
-### 3. Streaming Responses with Memory Context
+### 4. Streaming Responses with Memory Context
 
-    ```typescript
-    import { streamText } from "ai";
-    import { createMem0 } from "@mem0/vercel-ai-provider";
+```typescript
+import { streamText } from "ai";
+import { createMem0 } from "@mem0/vercel-ai-provider";
 
-    const mem0 = createMem0();
+const mem0 = createMem0();
 
-    const { textStream } = streamText({
-        model: mem0("gpt-4-turbo", {
-            user_id: "borat",
-        }),
-        prompt: "Suggest me a good car to buy! Why is it better than the other cars for me? Give options for every price range.",
-    });
+const { textStream } = streamText({
+  model: mem0("gpt-5-mini", {
+    user_id: "borat",
+  }),
+  prompt: "Suggest me a good car to buy! Why is it better than the other cars for me? Give options for every price range.",
+});
 
-    for await (const textPart of textStream) {
-        process.stdout.write(textPart);
-    }
-    ```
+for await (const textPart of textStream) {
+  process.stdout.write(textPart);
+}
+```
 
-### 4. Generate Responses with Tools Call
+### 5. Generate Responses with Tools Call
 
-    ```typescript
-    import { generateText } from "ai";
-    import { createMem0 } from "@mem0/vercel-ai-provider";
-    import { z } from "zod";
+```typescript
+import { generateText, tool } from "ai";
+import { createMem0 } from "@mem0/vercel-ai-provider";
+import { z } from "zod";
 
-    const mem0 = createMem0({
-      provider: "anthropic",
-      apiKey: "anthropic-api-key",
-      mem0Config: {
-        // Global User ID
-        user_id: "borat"
-      }
-    });
+const mem0 = createMem0({
+  provider: "anthropic",
+  apiKey: "anthropic-api-key",
+  mem0Config: {
+    user_id: "borat"
+  }
+});
 
-    const prompt = "What the temperature in the city that I live in?"
+const result = await generateText({
+  model: mem0('claude-sonnet-4-20250514'),
+  tools: {
+    weather: tool({
+      description: 'Get the weather in a location',
+      parameters: z.object({
+        location: z.string().describe('The location to get the weather for'),
+      }),
+      execute: async ({ location }) => ({
+        location,
+        temperature: 72 + Math.floor(Math.random() * 21) - 10,
+      }),
+    }),
+  },
+  prompt: "What the temperature in the city that I live in?",
+});
 
-    const result = await generateText({
-      model: mem0('claude-3-5-sonnet-20240620'),
-      tools: {
-        weather: tool({
-          description: 'Get the weather in a location',
-          parameters: z.object({
-            location: z.string().describe('The location to get the weather for'),
-          }),
-          execute: async ({ location }) => ({
-            location,
-            temperature: 72 + Math.floor(Math.random() * 21) - 10,
-          }),
-        }),
-      },
-      prompt: prompt,
-    });
+console.log(result);
+```
 
-    console.log(result);
-    ```
+### 6. Get Sources from Memory
 
-### 5. Get sources from memory
+`generateText` and `streamText` responses include Mem0 memories as a source, giving you programmatic access to the memories that influenced the response:
 
 ```typescript
 const { text, sources } = await generateText({
-    model: mem0("gpt-4-turbo"),
-    prompt: "Suggest me a good car to buy!",
+  model: mem0("gpt-5-mini", { user_id: "borat" }),
+  prompt: "Suggest me a good car to buy!",
 });
 
+// sources[0].title === "Mem0 Memories"
+// sources[0].providerMetadata.mem0.memories — array of memory objects
 console.log(sources);
 ```
 
 The same can be done for `streamText` as well.
 
-### 6. File Support with Memory Context
+### 7. File Support with Memory Context
 
 Mem0 AI SDK supports file processing with memory context. Here's an example of analyzing a PDF file:
 
@@ -226,15 +233,11 @@ const mem0 = createMem0({
 });
 
 async function main() {
-  // Read the PDF file
   const filePath = join(process.cwd(), 'my_pdf.pdf');
   const fileBuffer = readFileSync(filePath);
 
-  // Convert the file's arrayBuffer to a Base64 data URL
   const arrayBuffer = fileBuffer.buffer.slice(fileBuffer.byteOffset, fileBuffer.byteOffset + fileBuffer.byteLength);
   const uint8Array = new Uint8Array(arrayBuffer);
-
-  // Convert Uint8Array to an array of characters
   const charArray = Array.from(uint8Array, byte => String.fromCharCode(byte));
   const binaryString = charArray.join('');
   const base64Data = Buffer.from(binaryString, 'binary').toString('base64');
@@ -274,24 +277,56 @@ main();
 
 | Provider | Configuration Value |
 |----------|-------------------|
-| OpenAI | openai |
-| Anthropic | anthropic |
-| Google | google |
-| Groq | groq |
+| OpenAI | `openai` |
+| Anthropic | `anthropic` |
+| Google / Gemini | `google` or `gemini` |
+| Groq | `groq` |
+| Cohere | `cohere` |
 
-> **Note**: You can use `google` as provider for Gemini (Google) models. They are same and internally they use `@ai-sdk/google` package.
+> **Note**: You can use either `google` or `gemini` as the provider value for Google Gemini models. Both map to the `@ai-sdk/google` package internally.
+
+## Configuration Options
+
+### Mem0ConfigSettings
+
+These options can be passed per-request when creating a model instance:
+
+| Option | Type | Description |
+|--------|------|-------------|
+| `user_id` | `string` | User identifier for memory scoping |
+| `agent_id` | `string` | Agent identifier |
+| `app_id` | `string` | Application identifier |
+| `run_id` | `string` | Run/session identifier |
+| `metadata` | `object` | Custom metadata for memories |
+| `filters` | `object` | Filters for memory search |
+| `infer` | `boolean` | Enable inference-based retrieval |
+| `top_k` | `number` | Number of memories to retrieve (default: 10) |
+| `threshold` | `number` | Relevance threshold for search |
+| `rerank` | `boolean` | Enable reranking of results |
+| `page` | `number` | Page number for pagination |
+| `page_size` | `number` | Results per page |
 
 ## Key Features
 
-- `createMem0()`: Initializes a new Mem0 provider instance.
-- `retrieveMemories()`: Retrieves memory context for prompts.
+- `createMem0()`: Initializes a new Mem0 provider instance implementing `ProviderV3`.
+- `retrieveMemories()`: Retrieves memory context for prompts as a formatted system prompt string.
 - `getMemories()`: Get memories from your profile in array format.
 - `addMemories()`: Adds user memories to enhance contextual responses.
 
+## Migrating from v2.x
+
+If you're upgrading from `@mem0/vercel-ai-provider` v2.x:
+
+1. **Upgrade AI SDK**: `npm install ai@^6` and update all `@ai-sdk/*` provider packages to `^3.x`
+2. **Remove deprecated params**: Remove `org_id`, `project_id`, `output_format`, `filter_memories`, `async_mode`, `enable_graph` from your config
+3. **Remove graph memory**: All graph-related options (`enable_graph`, graph prompts) have been removed. Graph memory is now a project-level setting on the Mem0 Platform
+4. **Update imports**: `LanguageModelV2Prompt` is now `LanguageModelV3Prompt` if you import types directly
+
 ## Best Practices
 
 1. **User Identification**: Use a unique `user_id` for consistent memory retrieval.
 2. **Memory Cleanup**: Regularly clean up unused memory data.
+3. **Sources**: Access `result.sources` to inspect which memories influenced the response.
 
     > **Note**: We also have support for `agent_id`, `app_id`, and `run_id`. Refer [Docs](/api-reference/memory/add-memories).
 

docs/llms.txt

@@ -228,6 +228,7 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 - [OSS v2 to v3 Migration](https://docs.mem0.ai/migration/oss-v2-to-v3) [OSS]: Use when upgrading a self-hosted deployment across major versions.
 - [Platform v2 to v3 Migration](https://docs.mem0.ai/migration/platform-v2-to-v3) [Platform]: Use when upgrading a Platform integration across major versions.
 - [API Changes](https://docs.mem0.ai/migration/api-changes) [Both]: Use when the upgrade involves API surface changes.
+- [Server pgvector Image Upgrade](https://docs.mem0.ai/migration/server-pgvector-upgrade) [OSS]: Use when upgrading the self-hosted server Docker image from ankane/pgvector to pgvector/pgvector.
 - [Changelog](https://docs.mem0.ai/changelog/highlights) [Both]: Use when the user asks what shipped recently.
 
 ## Open Source
@@ -258,6 +259,7 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 - [Camel AI](https://docs.mem0.ai/integrations/camel-ai) [Both]: Use when the user is on Camel AI.
 - [ChatDev](https://docs.mem0.ai/integrations/chatdev) [Both]: Use when the user is on ChatDev.
 - [Hermes](https://docs.mem0.ai/integrations/hermes) [Both]: Use when the user is on Hermes.
+- [Pi Agent](https://docs.mem0.ai/integrations/pi-agent) [Platform]: Use when adding persistent memory to Pi Agent with the Mem0 plugin.
 - [OpenAI Agents SDK](https://docs.mem0.ai/integrations/openai-agents-sdk) [Both]: Use when the user is on the OpenAI Agents SDK.
 - [Google AI ADK](https://docs.mem0.ai/integrations/google-ai-adk) [Both]: Use when the user is on Google's Agent Development Kit.
 - [Mastra](https://docs.mem0.ai/integrations/mastra) [Both]: Use when the user is on Mastra (TypeScript).
@@ -268,6 +270,8 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 - [Claude Code](https://docs.mem0.ai/integrations/claude-code) [Both]: Use when wiring memory into Claude Code.
 - [Cursor](https://docs.mem0.ai/integrations/cursor) [Both]: Use when wiring memory into Cursor.
 - [Codex](https://docs.mem0.ai/integrations/codex) [Both]: Use when wiring memory into Codex / other editor assistants.
+- [OpenCode](https://docs.mem0.ai/integrations/opencode) [Both]: Use when wiring memory into OpenCode.
+- [Antigravity](https://docs.mem0.ai/integrations/antigravity) [Both]: Use when wiring memory into Google Antigravity.
 
 ### Voice & Real-time
 - [LiveKit](https://docs.mem0.ai/integrations/livekit) [Both]: Use when building real-time voice/video with memory.
@@ -396,13 +400,15 @@ Each subdirectory is a Claude Code Skill (`SKILL.md` + supporting assets). Load
 
 Source: https://github.com/mem0ai/mem0/tree/main/mem0-plugin
 
-The `mem0-plugin/` directory provides MCP server connection, lifecycle hooks, and skill bundling for Claude Code, Cursor, and Codex. It exposes 9 MCP tools: `add_memory`, `search_memories`, `get_memories`, `get_memory`, `update_memory`, `delete_memory`, `delete_all_memories`, `delete_entities`, `list_entities`.
+The `mem0-plugin/` directory provides MCP server connection, lifecycle hooks, and skill bundling for Claude Code, Cursor, Codex, OpenCode, and Antigravity. It exposes 9 MCP tools: `add_memory`, `search_memories`, `get_memories`, `get_memory`, `update_memory`, `delete_memory`, `delete_all_memories`, `delete_entities`, `list_entities`.
 
 Editor-specific setup docs (already listed above under `## Integrations > AI Coding Tools`):
 
 - `integrations/claude-code` [Both]
 - `integrations/cursor` [Both]
 - `integrations/codex` [Both]
+- `integrations/opencode` [Both]
+- `integrations/antigravity` [Both]
 - `integrations/openclaw` [Both]
 
 ### MCP Endpoints
@@ -470,6 +476,7 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
 - [Elasticsearch](https://docs.mem0.ai/components/vectordbs/dbs/elasticsearch) [OSS]: Use when Elasticsearch is the backing store.
 - [OpenSearch](https://docs.mem0.ai/components/vectordbs/dbs/opensearch) [OSS]: Use when OpenSearch is the backing store.
 - [Supabase](https://docs.mem0.ai/components/vectordbs/dbs/supabase) [OSS]: Use when Supabase with pgvector is the backing store.
+- [Neon](https://docs.mem0.ai/components/vectordbs/dbs/neon) [OSS]: Use when Neon Postgres with pgvector is the backing store.
 - [Upstash Vector](https://docs.mem0.ai/components/vectordbs/dbs/upstash-vector) [OSS]: Use for serverless Upstash Vector.
 - [Vectorize](https://docs.mem0.ai/components/vectordbs/dbs/vectorize) [OSS]: Use when the store is Cloudflare Vectorize.
 - [Vertex AI Vector Search](https://docs.mem0.ai/components/vectordbs/dbs/vertex_ai) [OSS]: Use when the store is Google Cloud Vertex Vector Search.

docs/migration/oss-to-platform.mdx

@@ -14,9 +14,9 @@ Move your Mem0 implementation to managed infrastructure with enterprise features
 | --------------------- | -------------- | ---------------------------- |
 | Infrastructure & Code | Low (~30 mins) | None (Parallel run possible) |
 
-<Info>
+<Note>
   Using Mem0 Open Source with **hosted Qdrant**? You can migrate your existing memories to Mem0 Platform with a one-line script below.
-</Info>
+</Note>
 
 <Info>
   **Why migrate to Platform?**

docs/migration/server-pgvector-upgrade.mdx

@@ -0,0 +1,158 @@
+---
+title: "Server: Upgrading the pgvector Docker Image"
+description: "Migrate your self-hosted Mem0 server from the archived ankane/pgvector image to the official pgvector/pgvector image."
+icon: "arrow-right"
+iconType: "solid"
+---
+
+## Overview
+
+The self-hosted Mem0 server has upgraded its PostgreSQL Docker image:
+
+| | Before | After |
+| --- | --- | --- |
+| Docker image | `ankane/pgvector:v0.5.1` | `pgvector/pgvector:pg17` |
+| PostgreSQL | 15 | 17 |
+| pgvector | 0.5.1 | 0.8.0 |
+| Credentials | Hardcoded `postgres` / `postgres` | Set via `POSTGRES_USER` / `POSTGRES_PASSWORD` env vars |
+
+<Warning>
+The `ankane/pgvector` image is **archived and no longer maintained**. The new `pgvector/pgvector` image is the official distribution maintained by the pgvector project.
+</Warning>
+
+<Info>
+**Should you migrate?**
+- You are running the Mem0 server via `docker-compose.yaml` in the `server/` directory.
+- You want to stay on a maintained, actively-patched PostgreSQL + pgvector image.
+- You want pgvector 0.8.0 features (improved HNSW performance, parallel index builds).
+</Info>
+
+## Fresh Installs
+
+No migration is needed. Copy the example env file, set your password, and start the stack:
+
+```bash
+cd server
+cp .env.example .env
+# Edit .env — set POSTGRES_PASSWORD (required) and OPENAI_API_KEY at minimum
+make up
+```
+
+## Migrating an Existing Install
+
+PostgreSQL 17 cannot read data files created by PostgreSQL 15 directly. You need to export your data from the old container and import it into the new one.
+
+### 1. Back Up Your Data
+
+With the **old** stack still running:
+
+```bash
+cd server
+docker compose exec -T postgres pg_dumpall -U postgres > mem0_backup.sql
+```
+
+Verify the dump is non-empty:
+
+```bash
+ls -lh mem0_backup.sql
+```
+
+<Warning>
+Do not skip this step. The next step permanently deletes your Postgres data volume.
+</Warning>
+
+### 2. Stop the Old Stack and Remove the Volume
+
+```bash
+docker compose down
+docker compose down -v
+```
+
+### 3. Update Your `.env`
+
+Postgres credentials are no longer hardcoded in `docker-compose.yaml`. Add them to your `.env`:
+
+```bash
+POSTGRES_HOST=postgres
+POSTGRES_PORT=5432
+POSTGRES_DB=postgres
+POSTGRES_USER=postgres
+POSTGRES_PASSWORD=<your-password>    # required — compose will refuse to start without it
+POSTGRES_COLLECTION_NAME=memories
+```
+
+<Info>
+`POSTGRES_PASSWORD` is **required** — `docker compose up` will refuse to start without it. If you previously relied on the hardcoded default, set `POSTGRES_PASSWORD=postgres`.
+</Info>
+
+### 4. Start Only Postgres
+
+Start **only** the Postgres container first — do **not** start the mem0 API yet.
+The API runs `alembic upgrade head` on startup, which creates empty tables that
+would conflict with the restore.
+
+```bash
+docker compose up -d postgres
+```
+
+Wait for Postgres to become healthy:
+
+```bash
+docker compose exec -T postgres pg_isready -q && echo "ready" || echo "not ready"
+```
+
+### 5. Restore Your Data
+
+```bash
+docker compose exec -T postgres psql -U postgres < mem0_backup.sql
+```
+
+You may see notices like `role "postgres" already exists` — these are safe to ignore.
+
+<Warning>
+You must restore **before** starting the mem0 API container. The API runs
+database migrations on startup which create empty tables — restoring after
+that would fail with duplicate-key errors and lose your API keys and settings.
+</Warning>
+
+### 6. Start the API
+
+Now start the mem0 API container. Alembic will detect the existing tables and
+only apply any new migrations:
+
+```bash
+docker compose up -d mem0
+```
+
+### 7. Verify
+
+```bash
+# Check service health
+cd server && make health
+
+# Confirm memories are accessible
+curl -s http://localhost:8888/memories?user_id=<your-user-id> \
+  -H "X-API-Key: <your-api-key>"
+```
+
+## Rollback
+
+If something goes wrong, revert the image tag in `docker-compose.yaml`:
+
+```yaml
+postgres:
+    image: ankane/pgvector:v0.5.1
+```
+
+Then destroy the new volume, start the old image, and restore from your backup:
+
+```bash
+docker compose down -v
+docker compose up -d --build
+docker compose exec -T postgres psql -U postgres < mem0_backup.sql
+```
+
+## Need Help?
+
+- Join our [Discord community](https://mem0.ai/discord) for real-time support
+- Open an issue on [GitHub](https://github.com/mem0ai/mem0/issues)

docs/open-source/features/metadata-filtering.mdx

@@ -4,22 +4,7 @@ description: Fine-grained metadata queries for precise OSS memory retrieval.
 icon: "filter"
 ---
 
-Enhanced metadata filtering in Mem0 1.0.0 lets you run complex queries across memory metadata. Combine comparisons, logical operators, and wildcard matches to zero in on the exact memories your agent needs.
-
-<Info>
-  **You’ll use this when…**
-  - Retrieval must respect multiple metadata conditions before returning context.
-  - You need to mix numeric, boolean, and string filters in a single query.
-  - Agents rely on deterministic filtering instead of broad semantic search alone.
-</Info>
-
-<Warning>
-  Enhanced filtering requires Mem0 1.0.0 or later and a vector store that supports the operators you enable. Unsupported operators fall back to simple equality filters.
-</Warning>
-
-<Note>
-  The TypeScript SDK accepts the same filter shape shown here—transpose the dictionaries to objects and reuse the keys unchanged.
-</Note>
+Enhanced metadata filtering in Mem0 lets you run complex queries across memory metadata. Combine comparisons, logical operators, and wildcard matches to zero in on the exact memories your agent needs.
 
 ---
 
@@ -148,8 +133,8 @@ Combine filters with `AND`, `OR`, and `NOT` to express complex decision trees. N
 results = m.search(
     "complex query",
     filters={
+        "user_id": "alice",
         "AND": [
-            {"user_id": "alice"},
             {"category": "work"},
             {"priority": {"gte": 7}},
             {"status": {"ne": "completed"}}
@@ -161,15 +146,11 @@ results = m.search(
 results = m.search(
     "flexible query",
     filters={
-        "AND": [
-            {"user_id": "alice"},
-            {
-                "OR": [
-                    {"category": "urgent"},
-                    {"priority": {"gte": 9}},
-                    {"deadline": {"contains": "today"}}
-                ]
-            }
+        "user_id": "alice",
+        "OR": [
+            {"category": "urgent"},
+            {"priority": {"gte": 9}},
+            {"deadline": {"contains": "today"}}
         ]
     }
 )
@@ -178,14 +159,10 @@ results = m.search(
 results = m.search(
     "exclusion query",
     filters={
-        "AND": [
-            {"user_id": "alice"},
-            {
-                "NOT": [
-                    {"category": "archived"},
-                    {"status": "deleted"}
-                ]
-            }
+        "user_id": "alice",
+        "NOT": [
+            {"category": "archived"},
+            {"status": "deleted"}
         ]
     }
 )
@@ -194,8 +171,8 @@ results = m.search(
 results = m.search(
     "advanced query",
     filters={
+        "user_id": "alice",
         "AND": [
-            {"user_id": "alice"},
             {
                 "OR": [
                     {"category": "work"},
@@ -248,8 +225,8 @@ config = {
 ```python
 # More efficient: Filter on indexed fields first
 good_filters = {
+    "user_id": "alice",
     "AND": [
-        {"user_id": "alice"},
         {"category": "work"},
         {"content": {"contains": "meeting"}}
     ]
@@ -257,9 +234,9 @@ good_filters = {
 
 # Less efficient: Complex operations first
 avoid_filters = {
+    "user_id": "alice",
     "AND": [
-        {"description": {"icontains": "complex text search"}},
-        {"user_id": "alice"}
+        {"description": {"icontains": "complex text search"}}
     ]
 }
 ```
@@ -302,8 +279,8 @@ results = m.search(
 results = m.search(
     "query",
     filters={
+        "user_id": "alice",
         "AND": [
-            {"user_id": "alice"},
             {"category": "work"},
             {"status": {"ne": "archived"}},
             {"priority": {"gte": 5}}
@@ -327,8 +304,8 @@ results = m.search(
 results = m.search(
     "What tasks need attention?",
     filters={
+        "user_id": "project_manager",
         "AND": [
-            {"user_id": "project_manager"},
             {"project": {"in": ["alpha", ""]}},
             {"priority": {"gte": 8}},
             {"status": {"ne": "completed"}},
@@ -354,8 +331,8 @@ results = m.search(
 results = m.search(
     "pending support issues",
     filters={
+        "agent_id": "support_bot",
         "AND": [
-            {"agent_id": "support_bot"},
             {"ticket_status": {"ne": "resolved"}},
             {"priority": {"in": ["high", "critical"]}},
             {"created_date": {"gte": "2024-01-01"}},
@@ -380,8 +357,8 @@ results = m.search(
 results = m.search(
     "recommend content",
     filters={
+        "user_id": "reader123",
         "AND": [
-            {"user_id": "reader123"},
             {
                 "OR": [
                     {"genre": {"in": ["sci-fi", "fantasy"]}},

docs/open-source/features/overview.mdx

@@ -6,7 +6,7 @@ icon: "list"
 
 # Self-Hosting Features Overview
 
-Mem0 Open Source ships with capabilities that adapt memory behavior for production workloads—async operations, graph relationships, multimodal inputs, and fine-tuned retrieval. Configure these features with code or YAML to match your application's needs.
+Mem0 Open Source ships with capabilities that adapt memory behavior for production workloads—async operations, multimodal inputs, and fine-tuned retrieval. Configure these features with code or YAML to match your application's needs.
 
 <Info>
   Start with the <Link href="/open-source/python-quickstart">Python quickstart</Link> to validate basic memory operations, then enable the features below when you need them.

docs/open-source/features/rest-api.mdx

@@ -119,7 +119,7 @@ docker build -t mem0-api-server .
 </Tip>
 
 <Note>
-  The REST server reads the same configuration you use locally, so you can point it at your preferred LLM, vector store, graph backend, and reranker without changing code.
+  The REST server reads the same configuration you use locally, so you can point it at your preferred LLM, vector store, and reranker without changing code.
 </Note>
 
 ---
@@ -226,6 +226,20 @@ curl -X POST http://localhost:8888/search \
   }'
 ```
 
+Set `explain` to inspect the scoring signals used by OSS hybrid search:
+
+```bash
+curl -X POST http://localhost:8888/search \
+  -H "Content-Type: application/json" \
+  -d '{
+    "query": "vegetable pizza",
+    "user_id": "alice",
+    "explain": true
+  }'
+```
+
+Each returned memory includes `score_details` only when explanation mode is enabled.
+
 ### Explore with OpenAPI docs
 
 1. Navigate to `http://localhost:8888/docs` (Compose) or `http://localhost:8000/docs` (raw Docker / uvicorn).
@@ -319,7 +333,7 @@ The `/auth/*`, `/api-keys`, `/requests`, and `/entities` routes are new to the s
 
 <CardGroup cols={2}>
   <Card title="Configure OSS Components" icon="sliders" href="/open-source/configuration">
-    Fine-tune LLMs, vector stores, and graph backends that power the REST server.
+    Fine-tune LLMs, vector stores, and rerankers that power the REST server.
   </Card>
   <Card title="Automate Agent Integrations" icon="plug" href="/cookbooks/integrations/agents-sdk-tool">
     See how services call the REST endpoints as part of an automation pipeline.

docs/platform/advanced-memory-operations.mdx

@@ -64,7 +64,7 @@ const memory = new Memory({ apiKey: process.env.MEM0_API_KEY!, async: true });
   </Tab>
 </Tabs>
 
-## Add memories with metadata and graph context
+## Add memories with metadata
 
 <Tabs>
   <Tab title="Python">
@@ -109,7 +109,7 @@ const result = await memory.add(conversation, {
 </Tabs>
 
 <Info icon="check">
-  Successful calls return memories tagged with the metadata you passed. In the dashboard, confirm a graph edge between “Morgan” and “Tokyo” and verify the `trip=japan-2025` tag exists.
+  Successful calls return memories tagged with the metadata you passed. In the dashboard, verify the `trip=japan-2025` tag exists on the new memory.
 </Info>
 
 ## Retrieve and refine
@@ -201,7 +201,7 @@ await memory.deleteAll({ userId: "traveler-42", runId: "planning-call-1" });
   />
   <Card
     title="Explore Reranker Search"
-    description="See how rerankers boost accuracy after vector + graph retrieval."
+    description="See how rerankers boost accuracy after advanced retrieval."
     icon="sparkles"
     href="/open-source/features/reranker-search"
   />

docs/platform/cli.mdx

@@ -121,7 +121,6 @@ echo "Loves hiking on weekends" | mem0 add --user-id alice
 | `-f, --file` | Read messages from a JSON file |
 | `-m, --metadata` | Custom metadata as JSON |
 | `--categories` | Categories (JSON array or comma-separated) |
-| `--graph / --no-graph` | Enable or disable graph memory extraction |
 | `-o, --output` | Output format: `text`, `json`, `quiet` |
 
 ### `mem0 search`
@@ -141,7 +140,6 @@ mem0 search "preferred tools" --user-id alice --output json --top-k 5
 | `--rerank` | Enable reranking |
 | `--keyword` | Use keyword search instead of semantic |
 | `--filter` | Advanced filter expression (JSON) |
-| `--graph / --no-graph` | Enable or disable graph in search |
 | `-o, --output` | Output format: `text`, `json`, `table` |
 
 ### `mem0 list`
@@ -436,7 +434,6 @@ For non-interactive environments (CI, agent runtimes), set credentials via `mem0
 | `MEM0_AGENT_ID` | Default agent ID |
 | `MEM0_APP_ID` | Default app ID |
 | `MEM0_RUN_ID` | Default run ID |
-| `MEM0_ENABLE_GRAPH` | Enable graph memory (`true` / `false`) |
 
 Environment variables take precedence over values in the config file, which take precedence over defaults.
 

docs/platform/faqs.mdx

@@ -9,7 +9,7 @@ iconType: "solid"
     <Accordion title="How does Mem0 work?">
         Mem0 utilizes a sophisticated hybrid database system to efficiently manage and retrieve memories for AI agents and assistants. Each memory is linked to a unique identifier, such as a user ID or agent ID, enabling Mem0 to organize and access memories tailored to specific individuals or contexts.
 
-        When a message is added to Mem0 via the `add` method, the system extracts pertinent facts and preferences, distributing them across various data stores: a vector database and a graph database. This hybrid strategy ensures that diverse types of information are stored optimally, facilitating swift and effective searches.
+        When a message is added to Mem0 via the `add` method, the system extracts pertinent facts and preferences, distributing them in a managed vector store. This strategy ensures that diverse types of information are stored optimally, facilitating swift and effective searches.
 
         When an AI agent or LLM needs to access memories, it employs the `search` method. Mem0 conducts a comprehensive search across these data stores, retrieving relevant information from each.
 

docs/platform/features/mcp-integration.mdx

@@ -130,7 +130,6 @@ The Mem0 MCP server enables powerful memory capabilities for your AI application
 
 ## Performance tips
 
-- Enable graph memories for relationship-aware recall
 - Use specific filters when searching large memory sets
 - Batch operations when adding multiple memories
 - Monitor memory usage in the Mem0 dashboard

docs/platform/features/platform-overview.mdx

@@ -1,10 +1,10 @@
 ---
 title: Overview
-description: "See how Mem0 Platform features evolve from baseline filters to graph-powered retrieval."
+description: "See how Mem0 Platform features evolve from baseline filters to advanced retrieval."
 icon: "list"
 ---
 
-Mem0 Platform features help managed deployments scale from basic filtering to graph-powered retrieval and data governance. Use this page to pick the right feature lane for your team.
+Mem0 Platform features help managed deployments scale from basic filtering to advanced retrieval and data governance. Use this page to pick the right feature lane for your team.
 
 <Info>
   New to the platform? Start with the <Link href="/platform/quickstart">Platform quickstart</Link>,

docs/platform/overview.mdx

@@ -11,7 +11,7 @@ Mem0 is the memory engine that keeps conversations contextual so users never rep
 ## Why it matters
 
 - **Personalized replies**: Memories persist across users and agents, cutting prompt bloat and repeat questions.
-- **Hosted stack**: Mem0 runs the vector store, graph services, and rerankers—no provisioning, tuning, or maintenance.
+- **Hosted stack**: Mem0 runs the vector store and rerankers—no provisioning, tuning, or maintenance.
 - **Enterprise controls**: Audit logs and workspace governance ship by default for production readiness.
 
 <AccordionGroup>
@@ -21,7 +21,7 @@ Mem0 is the memory engine that keeps conversations contextual so users never rep
     | --- | --- |
     | Fast setup | Add a few lines of code and you’re production-ready—no vector database or LLM configuration required. |
     | Production scale | Automatic scaling, high availability, and managed infrastructure so you focus on product work. |
-    | Advanced features | Graph memory, webhooks, multimodal support, and custom categories are ready to enable. |
+    | Advanced features | webhooks, multimodal support, and custom categories are ready to enable. |
     | Enterprise ready | Audit logs, workspace governance, and dedicated support keep security and governance covered. |
   </Accordion>
 </AccordionGroup>
@@ -49,7 +49,7 @@ Mem0 is the memory engine that keeps conversations contextual so users never rep
     Add, search, update, and delete workflows.
   </Card>
   <Card title="Explore Platform Features" icon="sparkles" href="/platform/features/platform-overview">
-    Graph memory, async clients, and rerankers.
+    async clients and rerankers.
   </Card>
   <Card title="Configure Advanced Operations" icon="bolt" href="/platform/advanced-memory-operations">
     Metadata filters and per-request toggles.

docs/platform/platform-vs-oss.mdx

@@ -57,7 +57,6 @@ Mem0 offers two powerful ways to add memory to your AI applications. Choose base
   <Accordion title="Advanced Capabilities" icon="sparkles">
     | Feature | Platform | Open Source |
     |---------|----------|-------------|
-    | **Graph Memory** | ✅ (Managed) | ✅ (Self-configured) |
     | **Multimodal support** | ✅ | ✅ |
     | **Custom categories** | ✅ | Limited |
     | **Advanced retrieval** | ✅ | ✅ |

docs/platform/quickstart.mdx

@@ -155,7 +155,7 @@ Learn how to search, update, and delete memories with complete CRUD operations
 </Card>
 
 <Card title="Platform Features" icon="star" href="/platform/features/platform-overview">
-  Explore advanced features like metadata filtering, graph memory, and webhooks
+  Explore advanced features like metadata filtering and webhooks
 </Card>
 
 <Card title="API Reference" icon="code" href="/api-reference/memory/add-memories">

docs/vibecoding.mdx

@@ -96,7 +96,7 @@ applications that gives agents persistent context across sessions.
 Mem0 is a memory layer for AI apps — managed (Mem0 Platform) or self-hosted
 (Open Source). It stores, retrieves, and manages user memories so agents
 remember preferences, learn from interactions, and personalize over time.
-Sub-50ms retrieval. Dual storage: vector embeddings + graph databases.
+Sub-50ms retrieval. Storage: vector embeddings.
 
 **Architecture Overview:**
 - Memory is scoped by user_id, agent_id, or run_id

examples/mem0-demo/package.json

@@ -26,7 +26,7 @@
     "ai": "^4.1.46",
     "class-variance-authority": "^0.7.1",
     "clsx": "^2.1.1",
-    "js-cookie": "^3.0.5",
+    "js-cookie": "^3.0.6",
     "lucide-react": "^0.477.0",
     "next": "15.5.18",
     "react": "^19.0.0",

marketplace.json

@@ -0,0 +1,20 @@
+{
+  "name": "mem0-plugins",
+  "interface": {
+    "displayName": "Mem0 Plugins"
+  },
+  "plugins": [
+    {
+      "name": "mem0",
+      "source": {
+        "source": "local",
+        "path": "./mem0-plugin"
+      },
+      "policy": {
+        "installation": "AVAILABLE",
+        "authentication": "ON_INSTALL"
+      },
+      "category": "Productivity"
+    }
+  ]
+}

mem0-plugin/.claude-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0",
-  "version": "0.2.6",
+  "version": "0.2.9",
   "description": "Persistent memory for Claude Code. Remembers decisions, patterns, and preferences across sessions.",
   "author": {
     "name": "Mem0",
@@ -14,7 +14,7 @@
     "api_key": {
       "type": "string",
       "title": "Mem0 API Key",
-      "description": "Your Mem0 Platform API key. Get one at https://app.mem0.ai/dashboard/api-keys. Alternative: export MEM0_API_KEY in your shell profile.",
+      "description": "Your Mem0 Platform API key (starts with m0-). Get one at https://app.mem0.ai/dashboard/api-keys",
       "sensitive": true,
       "required": true
     }

mem0-plugin/.codex-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0",
-  "version": "0.2.6",
+  "version": "0.2.9",
   "description": "Persistent memory for Codex. Remembers decisions, patterns, and preferences across sessions.",
   "author": {
     "name": "Mem0",

mem0-plugin/.cursor-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0",
-  "version": "0.2.6",
+  "version": "0.2.9",
   "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search using the Mem0 Platform MCP server.",
   "author": {
     "name": "Mem0",

mem0-plugin/.mcp.json

@@ -5,8 +5,7 @@
       "url": "https://mcp.mem0.ai/mcp/",
       "headers": {
         "Authorization": "Token ${MEM0_API_KEY}"
-      },
-      "authorizationUrl": "https://mcp.mem0.ai/authorize"
+      }
     }
   }
 }

mem0-plugin/.opencode-plugin/CHANGELOG.md

@@ -0,0 +1,47 @@
+# Changelog
+
+All notable changes to the `@mem0/opencode-plugin` will be documented in this file.
+
+## 0.1.3 — File-context injection, session summaries & activity timeline
+
+### Added
+
+- **File-context injection (`tool.execute.before` / Read):** Before the agent reads a file, the plugin searches mem0 for memories referencing that file path and injects prior work as system context. Gates on file size (>= 1,500 bytes). Gives the agent "I've worked on this file before" awareness automatically.
+- **Stop hook session summary (`experimental.session.compacting`):** Enhanced session compaction to store a structured `session_summary` memory with `infer=True`, letting the mem0 backend AI extract key facts (request, decisions, learnings, next steps). Previously only stored a raw stats string.
+- **SessionStart activity timeline:** The initial memory loading now formats recent memories with type icons (⚖️ decision, 🔴 bug_fix, 🔵 task_learning, etc.) and relative age indicators (2h ago, 1d ago) instead of bare text. Provides a visual "Recent Activity" timeline on first message.
+
+### Changed
+
+- **`experimental.session.compacting` handler:** Now stores `metadata.type=session_summary` with `metadata.source=opencode-stop` instead of `metadata.type=session_state` with `metadata.source=pre-compaction`. Includes a structured prompt that instructs mem0's AI to extract request, decisions, learnings, and next steps.
+- **Initial context formatting:** Memories shown on first message now include type icons and age labels for quick scanning.
+
+## 0.1.2 — Automatic coding categories & global search
+
+### Added
+
+- **Auto-configured coding categories:** The plugin now automatically sets up 17 coding categories (e.g. `architecture_decisions`, `api_design`, `security`, `debugging_notes`) on the Mem0 project at startup. Runs in the background on every session start via `autoSetupCategories()`, is fully idempotent, and never blocks initialization. Uses SHA-256 fingerprints of the category list and API key — stored in `~/.mem0/categories_setup.json` — to skip redundant API calls on subsequent sessions.
+- **Global search mode (`global_search` setting):** New `global_search` toggle in `~/.mem0/settings.json` (default: `false`). When enabled, all `search_memories` and `get_memories` calls use `{"OR": [{"user_id": "*"}]}` instead of the per-user per-project `AND` filter — returning all memories across all users and all `app_id` scopes. Writes (`add_memory`) still tag with the current `user_id` and `app_id`. Applies to all plugin search paths: initial load, per-message recall, resume detection, error-pattern lookup, and compaction context.
+- **`/mem0:switch-project --global` / `--no-global`:** Enables or disables global search via the switch-project skill. Persists to `~/.mem0/settings.json`. No manual config editing needed.
+- **`MEM0_GLOBAL_SEARCH` environment variable:** Exported to child shells via the `shell.env` hook (`"true"` or `"false"`).
+
+### Changed
+
+- **Search filters are now dynamic:** All search paths throughout the plugin construct filters based on the `global_search` setting instead of always using `AND [user_id, app_id]`.
+- **Resume-context searches broadened:** Resume and error-pattern searches no longer include `metadata.type` sub-filters (`session_state`, `decision`, `anti_pattern`, `bug_fix`), broadening recall.
+- **System context message updated:** Informs the model when global search is active (`"Global search is ON — searches return all memories across all users and projects. Writes still use user_id=..., app_id=..."`).
+- **`/mem0:onboard` Step 5 is no longer interactive:** Removed the manual category installation prompt. Categories now configure automatically in the background; the onboarding step only verifies status and stores a fallback `project_profile` memory if the background run hasn't finished yet.
+- **`/mem0:switch-project` skill expanded:** Description and execution updated to document the `--global` and `--no-global` flags alongside the existing project-name argument.
+
+## 0.1.1
+
+- CI/CD publish flow test (`#5288`).
+- Fixed tsconfig, added `publishConfig` and bun lockfile (`#5273`).
+- Renamed package to `@mem0/opencode-plugin` (`#5272`).
+- Added plugin array to bundled `opencode.json` (`#5271`).
+
+## 0.1.0 — Initial release
+
+- **OpenCode plugin** (`@mem0/opencode-plugin` on npm): Pure TypeScript plugin using the `mem0ai` TS SDK — no Python, no shell scripts. Hooks into all 6 OpenCode events (`chat.message`, `tool.execute.before`, `tool.execute.after`, `experimental.chat.system.transform`, `experimental.session.compacting`, `shell.env`). Features: session start memory loading, per-prompt semantic search, error pattern detection with memory lookup, resume/remember intent detection, auto-capture every 3rd message, periodic save nudges, full metadata defaults injection (confidence, source, type, session_id, files, branch), identity injection for search/get/delete filters, type-filtered error pre-fetch (anti_pattern + bug_fix), pre-compaction memory capture, MEMORY.md write blocking, and secret redaction.
+- **16 OpenCode-native skills** bundled in `opencode-skills/`: `context-loader`, `dream`, `export`, `forget`, `health`, `import`, `list-projects`, `mem0` (SDK reference), `memory-reviewer`, `onboard`, `peek`, `pin`, `remember`, `stats`, `switch-project`, `tour`. All skills are pure MCP-tool-based — no Python scripts, no shell scripts, no Claude Code dependencies.
+- **Auto-install skills and commands (`installSkills()`):** On plugin load, copies all 16 skills to `.opencode/skills/` and creates command wrapper files in `.opencode/commands/` so they appear in the OpenCode `/` palette.
+- **CLI installer (`cli.ts`):** `bunx @mem0/opencode-plugin install` auto-configures plugin and MCP server in `~/.config/opencode/opencode.json`.

mem0-plugin/.opencode-plugin/LICENSE

@@ -0,0 +1,201 @@
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mem0-plugin/.opencode-plugin/README.md

@@ -0,0 +1,84 @@
+# @mem0/opencode-plugin
+
+Persistent memory for [OpenCode](https://opencode.ai). Your agent remembers decisions, preferences, and learnings across sessions automatically.
+
+## Install
+
+```bash
+bunx @mem0/opencode-plugin@latest install
+```
+
+Or using OpenCode's built-in CLI:
+
+```bash
+opencode plugin @mem0/opencode-plugin
+```
+
+**Or let your agent do it** — paste this into OpenCode:
+
+```
+Install @mem0/opencode-plugin by following https://raw.githubusercontent.com/mem0ai/mem0/main/mem0-plugin/.opencode-plugin/README.md
+```
+
+All commands auto-add the plugin and MCP server to your `~/.config/opencode/opencode.json`. No manual config needed.
+
+Get your API key (free): [app.mem0.ai/dashboard/api-keys](https://app.mem0.ai/dashboard/api-keys)
+
+```bash
+echo 'export MEM0_API_KEY="m0-your-key"' >> ~/.zshrc && source ~/.zshrc
+```
+
+Restart OpenCode.
+
+## What's included
+
+| Component | Description |
+|-----------|-------------|
+| **MCP Server** | 9 memory tools — add, search, get, update, delete memories |
+| **Lifecycle Hooks** | Auto-search on session start and every prompt, metadata enforcement, error memory lookup, compaction context |
+| **16 Slash Commands** | `/mem0:remember`, `/mem0:tour`, `/mem0:stats`, `/mem0:health`, `/mem0:dream`, and more |
+
+## Hooks
+
+Pure TypeScript — no Python, no shell scripts. Uses the [mem0ai](https://www.npmjs.com/package/mem0ai) SDK directly.
+
+| Hook | Event | What it does |
+|------|-------|-------------|
+| **Chat message** | `chat.message` | Loads prior memories on session start, searches relevant memories before each prompt, auto-captures learnings periodically |
+| **Pre-tool** | `tool.execute.before` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tools |
+| **Post-tool** | `tool.execute.after` | Tracks stats, scans bash errors for related memories |
+| **System transform** | `experimental.chat.system.transform` | Injects memory context (session memories, search results, error lookups) into system prompt |
+| **Compaction** | `experimental.session.compacting` | Stores session state memory, then injects prior memories into compaction context so nothing is lost |
+| **Shell env** | `shell.env` | Exports `MEM0_USER_ID`, `MEM0_APP_ID`, `MEM0_SESSION_ID`, and `MEM0_BRANCH` to shell |
+
+## MCP Tools
+
+| Tool | Description |
+|------|-------------|
+| `add_memory` | Save text or conversation history |
+| `search_memories` | Semantic search across memories |
+| `get_memories` | List memories with filters and pagination |
+| `get_memory` | Retrieve a specific memory by ID |
+| `update_memory` | Overwrite a memory's text by ID |
+| `delete_memory` | Delete a single memory by ID |
+| `delete_all_memories` | Bulk delete all memories in scope |
+| `delete_entities` | Delete an entity and its memories |
+| `list_entities` | List users/agents/apps stored in Mem0 |
+
+## Verify
+
+Start OpenCode and ask: *"Search my memories for recent decisions"*
+
+If the `mem0` tools respond, you're all set.
+
+## Troubleshooting
+
+| Problem | Fix |
+|---------|-----|
+| No tools appearing | Restart OpenCode after installing |
+| 401 Unauthorized | `echo $MEM0_API_KEY` must print your `m0-` key |
+| Plugin not loading | Run `opencode plugin @mem0/opencode-plugin` again |
+
+## License
+
+Apache-2.0

mem0-plugin/.opencode-plugin/bun.lock

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+    "underscore": ["underscore@1.13.8", "", {}, "sha512-DXtD3ZtEQzc7M8m4cXotyHR+FAS18C64asBYY5vqZexfYryNNnDc02W4hKg3rdQuqOYas1jkseX0+nZXjTXnvQ=="],
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+    "undici": ["undici@5.28.5", "", { "dependencies": { "@fastify/busboy": "^2.0.0" } }, "sha512-zICwjrDrcrUE0pyyJc1I2QzBkLM8FINsgOrt6WjA+BgajVq9Nxu2PbFFXUrAggLfDXlZGZBVZYw7WNV5KiBiBA=="],
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+    "undici-types": ["undici-types@7.24.6", "", {}, "sha512-WRNW+sJgj5OBN4/0JpHFqtqzhpbnV0GuB+OozA9gCL7a993SmU+1JBZCzLNxYsbMfIeDL+lTsphD5jN5N+n0zg=="],
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+    "util-deprecate": ["util-deprecate@1.0.2", "", {}, "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw=="],
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+    "uuid": ["uuid@9.0.1", "", { "bin": { "uuid": "dist/bin/uuid" } }, "sha512-b+1eJOlsR9K8HJpow9Ok3fiWOWSIcIzXodvv0rQjVoOVNpWMpxf1wZNpt4y9h10odCNrqnYp1OBzRktckBe3sA=="],
+
+    "web-streams-polyfill": ["web-streams-polyfill@3.3.3", "", {}, "sha512-d2JWLCivmZYTSIoge9MsgFCZrt571BikcWGYkjC1khllbTeDlGqZ2D8vD8E/lJa8WGWbb7Plm8/XJYV7IJHZZw=="],
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+    "webidl-conversions": ["webidl-conversions@3.0.1", "", {}, "sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ=="],
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+    "whatwg-url": ["whatwg-url@5.0.0", "", { "dependencies": { "tr46": "~0.0.3", "webidl-conversions": "^3.0.0" } }, "sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw=="],
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+    "wrappy": ["wrappy@1.0.2", "", {}, "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ=="],
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+    "ws": ["ws@8.21.0", "", { "peerDependencies": { "bufferutil": "^4.0.1", "utf-8-validate": ">=5.0.2" }, "optionalPeers": ["bufferutil", "utf-8-validate"] }, "sha512-Vsp28b7DRcimFQvrqu2Wek3z1iYxDCWqHYB8Qsnk/S4RfaCQzPGPyBNuVjJV3cd6UiKtUtp6sNM77gWvzcCH+g=="],
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+    "xtend": ["xtend@4.0.2", "", {}, "sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ=="],
+
+    "yallist": ["yallist@4.0.0", "", {}, "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A=="],
+
+    "yaml": ["yaml@2.9.0", "", { "bin": { "yaml": "bin.mjs" } }, "sha512-2AvhNX3mb8zd6Zy7INTtSpl1F15HW6Wnqj0srWlkKLcpYl/gMIMJiyuGq2KeI2YFxUPjdlB+3Lc10seMLtL4cA=="],
+
+    "zod": ["zod@4.1.8", "", {}, "sha512-5R1P+WwQqmmMIEACyzSvo4JXHY5WiAFHRMg+zBZKgKS+Q1viRa0C1hmUKtHltoIFKtIdki3pRxkmpP74jnNYHQ=="],
+
+    "zod-to-json-schema": ["zod-to-json-schema@3.25.2", "", { "peerDependencies": { "zod": "^3.25.28 || ^4" } }, "sha512-O/PgfnpT1xKSDeQYSCfRI5Gy3hPf91mKVDuYLUHZJMiDFptvP41MSnWofm8dnCm0256ZNfZIM7DSzuSMAFnjHA=="],
+
+    "@anthropic-ai/sdk/@types/node": ["@types/node@18.19.130", "", { "dependencies": { "undici-types": "~5.26.4" } }, "sha512-GRaXQx6jGfL8sKfaIDD6OupbIHBr9jv7Jnaml9tB7l4v068PAOXqfcujMMo5PhbIs6ggR1XODELqahT2R8v0fg=="],
+
+    "@typespec/ts-http-runtime/https-proxy-agent": ["https-proxy-agent@7.0.6", "", { "dependencies": { "agent-base": "^7.1.2", "debug": "4" } }, "sha512-vK9P5/iUfdl95AI+JVyUuIcVtd4ofvtrOr3HNtM2yxC9bnMbEdp3x01OhQNnjb8IJYi38VlTE3mBXwcfvywuSw=="],
+
+    "chalk/ansi-styles": ["ansi-styles@4.3.0", "", { "dependencies": { "color-convert": "^2.0.1" } }, "sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg=="],
+
+    "cloudflare/@types/node": ["@types/node@18.19.130", "", { "dependencies": { "undici-types": "~5.26.4" } }, "sha512-GRaXQx6jGfL8sKfaIDD6OupbIHBr9jv7Jnaml9tB7l4v068PAOXqfcujMMo5PhbIs6ggR1XODELqahT2R8v0fg=="],
+
+    "effect/uuid": ["uuid@13.0.2", "", { "bin": { "uuid": "dist-node/bin/uuid" } }, "sha512-vzi9uRZ926x4XV73S/4qQaTwPXM2JBj6/6lI/byHH1jOpCzb0zDbfytgA9LcN/hzb2l7WQSQnxITOVx5un/wGw=="],
+
+    "formdata-node/web-streams-polyfill": ["web-streams-polyfill@4.0.0-beta.3", "", {}, "sha512-QW95TCTaHmsYfHDybGMwO5IJIM93I/6vTRk+daHTWFPhwh+C8Cg7j7XyKrwrj8Ib6vYXe0ocYNrmzY4xAAN6ug=="],
+
+    "gaxios/https-proxy-agent": ["https-proxy-agent@7.0.6", "", { "dependencies": { "agent-base": "^7.1.2", "debug": "4" } }, "sha512-vK9P5/iUfdl95AI+JVyUuIcVtd4ofvtrOr3HNtM2yxC9bnMbEdp3x01OhQNnjb8IJYi38VlTE3mBXwcfvywuSw=="],
+
+    "gaxios/node-fetch": ["node-fetch@3.3.2", "", { "dependencies": { "data-uri-to-buffer": "^4.0.0", "fetch-blob": "^3.1.4", "formdata-polyfill": "^4.0.10" } }, "sha512-dRB78srN/l6gqWulah9SrxeYnxeddIG30+GOqK/9OlLVyLg3HPnr6SqOWTWOXKRwC2eGYCkZ59NNuSgvSrpgOA=="],
+
+    "groq-sdk/@types/node": ["@types/node@18.19.130", "", { "dependencies": { "undici-types": "~5.26.4" } }, "sha512-GRaXQx6jGfL8sKfaIDD6OupbIHBr9jv7Jnaml9tB7l4v068PAOXqfcujMMo5PhbIs6ggR1XODELqahT2R8v0fg=="],
+
+    "http-proxy-agent/agent-base": ["agent-base@7.1.4", "", {}, "sha512-MnA+YT8fwfJPgBx3m60MNqakm30XOkyIoH1y6huTQvC0PwZG7ki8NacLBcrPbNoo8vEZy7Jpuk7+jMO+CUovTQ=="],
+
+    "mem0ai/zod": ["zod@3.25.76", "", {}, "sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ=="],
+
+    "mongodb-connection-string-url/whatwg-url": ["whatwg-url@14.2.0", "", { "dependencies": { "tr46": "^5.1.0", "webidl-conversions": "^7.0.0" } }, "sha512-De72GdQZzNTUBBChsXueQUnPKDkg/5A5zp7pFDuQAj5UFoENpiACU0wlCvzpAGnTkj++ihpKwKyYewn/XNUbKw=="],
+
+    "natural/pg": ["pg@8.21.0", "", { "dependencies": { "pg-connection-string": "^2.13.0", "pg-pool": "^3.14.0", "pg-protocol": "^1.14.0", "pg-types": "2.2.0", "pgpass": "1.0.5" }, "optionalDependencies": { "pg-cloudflare": "^1.4.0" }, "peerDependencies": { "pg-native": ">=3.0.1" }, "optionalPeers": ["pg-native"] }, "sha512-AUP1EYJuHraQGsVoCQVIcM7TEJVGtDzxWtGFZd8rds9d+CCXlU5Js1rYgfLNvxy9iJrpHjGrRjoi/3BT9fRyiA=="],
+
+    "natural/redis": ["redis@5.12.1", "", { "dependencies": { "@redis/bloom": "5.12.1", "@redis/client": "5.12.1", "@redis/json": "5.12.1", "@redis/search": "5.12.1", "@redis/time-series": "5.12.1" } }, "sha512-LDsoVvb/CpoV9EN3FXvgvSHNJWuCIzl9MiO3ppOevuGLpSGJhwfQjpEwfFJcQvNSddHADDdZaWx0HnmMxRXG7g=="],
+
+    "natural/uuid": ["uuid@13.0.2", "", { "bin": { "uuid": "dist-node/bin/uuid" } }, "sha512-vzi9uRZ926x4XV73S/4qQaTwPXM2JBj6/6lI/byHH1jOpCzb0zDbfytgA9LcN/hzb2l7WQSQnxITOVx5un/wGw=="],
+
+    "openai/@types/node": ["@types/node@18.19.130", "", { "dependencies": { "undici-types": "~5.26.4" } }, "sha512-GRaXQx6jGfL8sKfaIDD6OupbIHBr9jv7Jnaml9tB7l4v068PAOXqfcujMMo5PhbIs6ggR1XODELqahT2R8v0fg=="],
+
+    "pg/pg-types": ["pg-types@2.2.0", "", { "dependencies": { "pg-int8": "1.0.1", "postgres-array": "~2.0.0", "postgres-bytea": "~1.0.0", "postgres-date": "~1.0.4", "postgres-interval": "^1.1.0" } }, "sha512-qTAAlrEsl8s4OiEQY69wDvcMIdQN6wdz5ojQiOy6YRMuynxenON0O5oCpJI6lshc6scgAY8qvJ2On/p+CXY0GA=="],
+
+    "rc/ini": ["ini@1.3.8", "", {}, "sha512-JV/yugV2uzW5iMRSiZAyDtQd+nxtUnjeLt0acNdw98kKLrvuRVyB80tsREOE7yvGVgalhZ6RNXCmEHkUKBKxew=="],
+
+    "@anthropic-ai/sdk/@types/node/undici-types": ["undici-types@5.26.5", "", {}, "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA=="],
+
+    "@typespec/ts-http-runtime/https-proxy-agent/agent-base": ["agent-base@7.1.4", "", {}, "sha512-MnA+YT8fwfJPgBx3m60MNqakm30XOkyIoH1y6huTQvC0PwZG7ki8NacLBcrPbNoo8vEZy7Jpuk7+jMO+CUovTQ=="],
+
+    "cloudflare/@types/node/undici-types": ["undici-types@5.26.5", "", {}, "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA=="],
+
+    "gaxios/https-proxy-agent/agent-base": ["agent-base@7.1.4", "", {}, "sha512-MnA+YT8fwfJPgBx3m60MNqakm30XOkyIoH1y6huTQvC0PwZG7ki8NacLBcrPbNoo8vEZy7Jpuk7+jMO+CUovTQ=="],
+
+    "groq-sdk/@types/node/undici-types": ["undici-types@5.26.5", "", {}, "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA=="],
+
+    "mongodb-connection-string-url/whatwg-url/tr46": ["tr46@5.1.1", "", { "dependencies": { "punycode": "^2.3.1" } }, "sha512-hdF5ZgjTqgAntKkklYw0R03MG2x/bSzTtkxmIRw/sTNV8YXsCJ1tfLAX23lhxhHJlEf3CRCOCGGWw3vI3GaSPw=="],
+
+    "mongodb-connection-string-url/whatwg-url/webidl-conversions": ["webidl-conversions@7.0.0", "", {}, "sha512-VwddBukDzu71offAQR975unBIGqfKZpM+8ZX6ySk8nYhVoo5CYaZyzt3YBvYtRtO+aoGlqxPg/B87NGVZ/fu6g=="],
+
+    "natural/pg/pg-types": ["pg-types@2.2.0", "", { "dependencies": { "pg-int8": "1.0.1", "postgres-array": "~2.0.0", "postgres-bytea": "~1.0.0", "postgres-date": "~1.0.4", "postgres-interval": "^1.1.0" } }, "sha512-qTAAlrEsl8s4OiEQY69wDvcMIdQN6wdz5ojQiOy6YRMuynxenON0O5oCpJI6lshc6scgAY8qvJ2On/p+CXY0GA=="],
+
+    "natural/redis/@redis/bloom": ["@redis/bloom@5.12.1", "", { "peerDependencies": { "@redis/client": "^5.12.1" } }, "sha512-PUUfv+ms7jgPSBVoo/DN4AkPHj4D5TZSd6SbJX7egzBplkYUcKmHRE8RKia7UtZ8bSQbLguLvxVO+asKtQfZWA=="],
+
+    "natural/redis/@redis/client": ["@redis/client@5.12.1", "", { "dependencies": { "cluster-key-slot": "1.1.2" }, "peerDependencies": { "@node-rs/xxhash": "^1.1.0", "@opentelemetry/api": ">=1 <2" }, "optionalPeers": ["@node-rs/xxhash", "@opentelemetry/api"] }, "sha512-7aPGWeqA3uFm43o19umzdl16CEjK/JQGtSXVPevplTaOU3VJA/rseBC1QvYUz9lLDIMBimc4SW/zrW4S89BaCA=="],
+
+    "natural/redis/@redis/json": ["@redis/json@5.12.1", "", { "peerDependencies": { "@redis/client": "^5.12.1" } }, "sha512-eOze75esLve4vfqDel7aMX08CNaiLLQS2fV8mpRN9NxPe1rVR4vQyYiW/OgtGUysF6QOr9ANhfxABKNOJfXdKg=="],
+
+    "natural/redis/@redis/search": ["@redis/search@5.12.1", "", { "peerDependencies": { "@redis/client": "^5.12.1" } }, "sha512-ItlxbxC9cKI6IU1TLWoczwJCRb6TdmkEpWv05UrPawqaAnWGRu3rcIqsc5vN483T2fSociuyV1UkWIL5I4//2w=="],
+
+    "natural/redis/@redis/time-series": ["@redis/time-series@5.12.1", "", { "peerDependencies": { "@redis/client": "^5.12.1" } }, "sha512-c6JL6E3EcZJuNqKFz+KM+l9l5mpcQiKvTwgA3blt5glWJ8hjDk0yeHN3beE/MpqYIQ8UEX44ItQzgkE/gCBELQ=="],
+
+    "openai/@types/node/undici-types": ["undici-types@5.26.5", "", {}, "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA=="],
+
+    "pg/pg-types/postgres-array": ["postgres-array@2.0.0", "", {}, "sha512-VpZrUqU5A69eQyW2c5CA1jtLecCsN2U/bD6VilrFDWq5+5UIEVO7nazS3TEcHf1zuPYO/sqGvUvW62g86RXZuA=="],
+
+    "pg/pg-types/postgres-bytea": ["postgres-bytea@1.0.1", "", {}, "sha512-5+5HqXnsZPE65IJZSMkZtURARZelel2oXUEO8rH83VS/hxH5vv1uHquPg5wZs8yMAfdv971IU+kcPUczi7NVBQ=="],
+
+    "pg/pg-types/postgres-date": ["postgres-date@1.0.7", "", {}, "sha512-suDmjLVQg78nMK2UZ454hAG+OAW+HQPZ6n++TNDUX+L0+uUlLywnoxJKDou51Zm+zTCjrCl0Nq6J9C5hP9vK/Q=="],
+
+    "pg/pg-types/postgres-interval": ["postgres-interval@1.2.0", "", { "dependencies": { "xtend": "^4.0.0" } }, "sha512-9ZhXKM/rw350N1ovuWHbGxnGh/SNJ4cnxHiM0rxE4VN41wsg8P8zWn9hv/buK00RP4WvlOyr/RBDiptyxVbkZQ=="],
+
+    "natural/pg/pg-types/postgres-array": ["postgres-array@2.0.0", "", {}, "sha512-VpZrUqU5A69eQyW2c5CA1jtLecCsN2U/bD6VilrFDWq5+5UIEVO7nazS3TEcHf1zuPYO/sqGvUvW62g86RXZuA=="],
+
+    "natural/pg/pg-types/postgres-bytea": ["postgres-bytea@1.0.1", "", {}, "sha512-5+5HqXnsZPE65IJZSMkZtURARZelel2oXUEO8rH83VS/hxH5vv1uHquPg5wZs8yMAfdv971IU+kcPUczi7NVBQ=="],
+
+    "natural/pg/pg-types/postgres-date": ["postgres-date@1.0.7", "", {}, "sha512-suDmjLVQg78nMK2UZ454hAG+OAW+HQPZ6n++TNDUX+L0+uUlLywnoxJKDou51Zm+zTCjrCl0Nq6J9C5hP9vK/Q=="],
+
+    "natural/pg/pg-types/postgres-interval": ["postgres-interval@1.2.0", "", { "dependencies": { "xtend": "^4.0.0" } }, "sha512-9ZhXKM/rw350N1ovuWHbGxnGh/SNJ4cnxHiM0rxE4VN41wsg8P8zWn9hv/buK00RP4WvlOyr/RBDiptyxVbkZQ=="],
+  }
+}

mem0-plugin/.opencode-plugin/cli.ts

@@ -0,0 +1,254 @@
+#!/usr/bin/env bun
+import {
+  readFileSync,
+  writeFileSync,
+  existsSync,
+  mkdirSync,
+  copyFileSync,
+  readdirSync,
+  rmSync,
+  statSync,
+} from "fs";
+import { join, dirname } from "path";
+import { homedir } from "os";
+
+const PLUGIN_NAME = "@mem0/opencode-plugin";
+const MCP_CONFIG = {
+  mem0: {
+    type: "remote",
+    url: "https://mcp.mem0.ai/mcp/",
+    headers: {
+      Authorization: "Token {env:MEM0_API_KEY}",
+    },
+    oauth: false,
+  },
+};
+
+const SKILLS_NAMESPACE = "mem0";
+
+function getConfigDir(): string {
+  const dir = join(homedir(), ".config", "opencode");
+  if (!existsSync(dir)) mkdirSync(dir, { recursive: true });
+  return dir;
+}
+
+function getConfigPath(): string {
+  const configDir = getConfigDir();
+  const jsonc = join(configDir, "opencode.jsonc");
+  if (existsSync(jsonc)) return jsonc;
+  return join(configDir, "opencode.json");
+}
+
+function stripJsonComments(text: string): string {
+  let result = "";
+  let i = 0;
+  let inString = false;
+  let escape = false;
+  while (i < text.length) {
+    const ch = text[i];
+    if (escape) {
+      result += ch;
+      escape = false;
+      i++;
+      continue;
+    }
+    if (inString) {
+      if (ch === "\\") escape = true;
+      else if (ch === '"') inString = false;
+      result += ch;
+      i++;
+      continue;
+    }
+    if (ch === '"') {
+      inString = true;
+      result += ch;
+      i++;
+      continue;
+    }
+    if (ch === "/" && text[i + 1] === "/") {
+      while (i < text.length && text[i] !== "\n") i++;
+      continue;
+    }
+    if (ch === "/" && text[i + 1] === "*") {
+      i += 2;
+      while (i < text.length && !(text[i] === "*" && text[i + 1] === "/")) i++;
+      i += 2;
+      continue;
+    }
+    result += ch;
+    i++;
+  }
+  return result;
+}
+
+function resolvePluginDir(): string {
+  try {
+    return dirname(new URL(import.meta.url).pathname);
+  } catch {}
+  return __dirname ?? process.cwd();
+}
+
+function findSkillsDir(): string {
+  const base = resolvePluginDir();
+  const candidates = [
+    join(base, "opencode-skills"),
+    join(dirname(base), "opencode-skills"),
+    join(base, "..", "opencode-skills"),
+  ];
+  for (const c of candidates) {
+    try {
+      if (existsSync(c) && statSync(c).isDirectory()) return c;
+    } catch {}
+  }
+  return "";
+}
+
+function installSkills(): number {
+  const skillsSource = findSkillsDir();
+  if (!skillsSource) {
+    console.log("  ! Skills directory not found — skipping slash command install");
+    return 0;
+  }
+
+  const skillsTarget = join(getConfigDir(), "skills");
+  if (!existsSync(skillsTarget)) mkdirSync(skillsTarget, { recursive: true });
+
+  let count = 0;
+  const entries = readdirSync(skillsSource);
+
+  for (const name of entries) {
+    const skillDir = join(skillsSource, name);
+    try {
+      if (!statSync(skillDir).isDirectory()) continue;
+    } catch {
+      continue;
+    }
+
+    const skillFile = join(skillDir, "SKILL.md");
+    if (!existsSync(skillFile)) continue;
+
+    const targetDir = join(skillsTarget, `${SKILLS_NAMESPACE}-${name}`);
+    if (!existsSync(targetDir)) mkdirSync(targetDir, { recursive: true });
+
+    copyFileSync(skillFile, join(targetDir, "SKILL.md"));
+    count++;
+  }
+
+  return count;
+}
+
+function uninstallSkills(): number {
+  const skillsTarget = join(getConfigDir(), "skills");
+  if (!existsSync(skillsTarget)) return 0;
+
+  let count = 0;
+  const entries = readdirSync(skillsTarget);
+
+  for (const name of entries) {
+    if (!name.startsWith(`${SKILLS_NAMESPACE}-`)) continue;
+    const fullPath = join(skillsTarget, name);
+    try {
+      if (!statSync(fullPath).isDirectory()) continue;
+      rmSync(fullPath, { recursive: true });
+      count++;
+    } catch {}
+  }
+
+  return count;
+}
+
+function install() {
+  console.log("Installing Mem0 plugin for OpenCode...\n");
+
+  const configPath = getConfigPath();
+  let config: any = {};
+
+  if (existsSync(configPath)) {
+    try {
+      const raw = readFileSync(configPath, "utf-8");
+      config = JSON.parse(stripJsonComments(raw));
+    } catch {
+      console.log(`  ! Could not parse ${configPath}, creating fresh config`);
+      config = {};
+    }
+  }
+
+  if (!Array.isArray(config.plugin)) config.plugin = [];
+  if (!config.plugin.includes(PLUGIN_NAME)) {
+    config.plugin.push(PLUGIN_NAME);
+    console.log(`  + Added "${PLUGIN_NAME}" to plugin array`);
+  } else {
+    console.log(`  ~ "${PLUGIN_NAME}" already in plugin array`);
+  }
+
+  if (!config.mcp) config.mcp = {};
+  if (!config.mcp.mem0) {
+    config.mcp.mem0 = MCP_CONFIG.mem0;
+    console.log("  + Added mem0 MCP server config");
+  } else {
+    console.log("  ~ mem0 MCP server already configured");
+  }
+
+  writeFileSync(configPath, JSON.stringify(config, null, 2) + "\n");
+  console.log(`\n  Wrote ${configPath}`);
+
+  const skillCount = installSkills();
+  if (skillCount > 0) {
+    console.log(`  + Installed ${skillCount} slash commands to ~/.config/opencode/skills/`);
+  }
+
+  console.log("");
+
+  if (!process.env.MEM0_API_KEY) {
+    console.log("  ! MEM0_API_KEY is not set in your environment.");
+    console.log(
+      '  Run: echo \'export MEM0_API_KEY="m0-your-key"\' >> ~/.zshrc && source ~/.zshrc',
+    );
+    console.log(
+      "  Get a free key at: https://app.mem0.ai/dashboard/api-keys\n",
+    );
+  } else {
+    console.log("  MEM0_API_KEY detected\n");
+  }
+
+  console.log("Done! Restart OpenCode to activate Mem0.");
+  console.log("  Then run /mem0-onboard in the TUI to complete setup.\n");
+}
+
+function uninstall() {
+  const configPath = getConfigPath();
+  if (!existsSync(configPath)) {
+    console.log("No OpenCode config found. Nothing to remove.");
+    return;
+  }
+
+  const raw = readFileSync(configPath, "utf-8");
+  const config = JSON.parse(stripJsonComments(raw));
+
+  if (Array.isArray(config.plugin)) {
+    config.plugin = config.plugin.filter((p: string) => p !== PLUGIN_NAME);
+    if (config.plugin.length === 0) delete config.plugin;
+  }
+
+  if (config.mcp?.mem0) {
+    delete config.mcp.mem0;
+    if (Object.keys(config.mcp).length === 0) delete config.mcp;
+  }
+
+  writeFileSync(configPath, JSON.stringify(config, null, 2) + "\n");
+  console.log(`  Removed plugin + MCP from ${configPath}`);
+
+  const skillCount = uninstallSkills();
+  if (skillCount > 0) {
+    console.log(`  Removed ${skillCount} slash commands from ~/.config/opencode/skills/`);
+  }
+
+  console.log("Restart OpenCode to complete removal.");
+}
+
+const cmd = process.argv[2];
+if (cmd === "uninstall" || cmd === "remove") {
+  uninstall();
+} else {
+  install();
+}

mem0-plugin/.opencode-plugin/opencode-mem0.ts

@@ -0,0 +1,744 @@
+// Mem0 memory plugin for OpenCode: captures and recalls memories across sessions
+// (add / search / manage) via the Mem0 platform, wired through OpenCode plugin hooks.
+import type { Plugin } from "@opencode-ai/plugin";
+import { MemoryClient } from "mem0ai";
+import { userInfo } from "os";
+import { basename, resolve, dirname } from "path";
+import { randomBytes } from "crypto";
+import { existsSync, readdirSync, cpSync, mkdirSync, readFileSync, writeFileSync } from "fs";
+import { homedir } from "os";
+import { join } from "path";
+import { createHash } from "crypto";
+
+async function getUserId(): Promise<string> {
+  if (process.env.MEM0_USER_ID) return process.env.MEM0_USER_ID;
+  try {
+    return userInfo().username;
+  } catch {}
+  return process.env.USER || process.env.USERNAME || "unknown";
+}
+
+async function getProjectId($: any): Promise<string> {
+  if (process.env.MEM0_APP_ID) return process.env.MEM0_APP_ID;
+  try {
+    const r = await $`git remote get-url origin`.quiet();
+    const remote = r.stdout.toString().trim();
+    const m = remote.match(/[:/]([^/]+\/[^/]+?)(?:\.git)?$/);
+    if (m) return m[1].replace("/", "-");
+  } catch {}
+  return basename(process.cwd());
+}
+
+async function getBranch($: any): Promise<string> {
+  try {
+    const r = await $`git branch --show-current`.quiet();
+    return r.stdout.toString().trim() || "main";
+  } catch {}
+  return "main";
+}
+
+function extractMemories(res: any): Array<{ memory: string; id: string }> {
+  const arr = res?.results ?? res;
+  if (!Array.isArray(arr)) return [];
+  return arr.map((m: any) => ({ memory: m.memory ?? "", id: m.id ?? "" }));
+}
+
+function generateSessionId(): string {
+  const ts = Math.floor(Date.now() / 1000);
+  const rnd = randomBytes(3).toString("hex");
+  return `ses_${ts}_${rnd}`;
+}
+
+const SECRET_PATTERNS = [
+  /sk-[A-Za-z0-9]{20,}/g,
+  /m0-[A-Za-z0-9]{20,}/g,
+  /AKIA[0-9A-Z]{16}/g,
+  /xox[baprs]-[A-Za-z0-9-]{20,}/g,
+  /ghp_[A-Za-z0-9]{36,}/g,
+  /gho_[A-Za-z0-9]{36,}/g,
+];
+
+function redact(text: string): string {
+  let out = text;
+  for (const re of SECRET_PATTERNS) {
+    out = out.replace(re, "[REDACTED]");
+  }
+  return out;
+}
+
+function formatAge(createdAt: string): string {
+  try {
+    const dt = new Date(createdAt);
+    const now = Date.now();
+    const seconds = Math.floor((now - dt.getTime()) / 1000);
+    if (seconds < 3600) return `${Math.floor(seconds / 60)}m ago`;
+    if (seconds < 86400) return `${Math.floor(seconds / 3600)}h ago`;
+    const days = Math.floor(seconds / 86400);
+    if (days === 1) return "1d ago";
+    if (days < 30) return `${days}d ago`;
+    return `${Math.floor(days / 30)}mo ago`;
+  } catch {
+    return "";
+  }
+}
+
+const TYPE_ICONS: Record<string, string> = {
+  decision: "⚖️",
+  anti_pattern: "🔴",
+  bug_fix: "🔴",
+  convention: "🔄",
+  task_learning: "🔵",
+  user_preference: "🟣",
+  session_summary: "📋",
+  session_state: "📋",
+  project_profile: "📖",
+  compact_summary: "📋",
+  auto_capture: "✅",
+};
+
+const FILE_READ_GATE_MIN_BYTES = 1500;
+
+function loadGlobalSearch(): boolean {
+  try {
+    const settingsPath = join(homedir(), ".mem0", "settings.json");
+    if (!existsSync(settingsPath)) return false;
+    const settings = JSON.parse(readFileSync(settingsPath, "utf8"));
+    return settings.global_search === true;
+  } catch {}
+  return false;
+}
+
+const CODING_CATEGORIES = [
+  "architecture_decisions", "api_design", "data_models", "algorithms",
+  "dependencies", "environment_setup", "testing_strategy", "debugging_notes",
+  "performance", "security", "deployment", "code_conventions",
+  "error_handling", "refactoring_history", "integrations", "onboarding",
+  "project_meta",
+];
+
+function categoriesFingerprint(): string {
+  const sorted = [...CODING_CATEGORIES].sort();
+  return createHash("sha256").update(sorted.join("\n")).digest("hex").slice(0, 16);
+}
+
+function apiKeyFingerprint(apiKey: string): string {
+  return createHash("sha256").update(apiKey).digest("hex").slice(0, 16);
+}
+
+async function autoSetupCategories(mem0: MemoryClient, apiKey: string): Promise<void> {
+  const stateDir = join(homedir(), ".mem0");
+  const stateFile = join(stateDir, "categories_setup.json");
+  const keyFp = apiKeyFingerprint(apiKey);
+  const catFp = categoriesFingerprint();
+
+  let state: Record<string, string> = {};
+  try {
+    if (existsSync(stateFile)) {
+      state = JSON.parse(readFileSync(stateFile, "utf8"));
+    }
+  } catch {}
+
+  if (state[keyFp] === catFp) return;
+
+  try {
+    const project = await mem0.getProject({ fields: ["customCategories"] });
+    const existing: string[] = (project as any)?.custom_categories ?? (project as any)?.customCategories ?? [];
+    const sortedExisting = [...existing].sort();
+    const sortedTarget = [...CODING_CATEGORIES].sort();
+    if (JSON.stringify(sortedExisting) === JSON.stringify(sortedTarget)) {
+      state[keyFp] = catFp;
+      mkdirSync(stateDir, { recursive: true });
+      writeFileSync(stateFile, JSON.stringify(state, null, 2) + "\n");
+      return;
+    }
+
+    await mem0.updateProject({ customCategories: CODING_CATEGORIES as any });
+
+    state[keyFp] = catFp;
+    mkdirSync(stateDir, { recursive: true });
+    writeFileSync(stateFile, JSON.stringify(state, null, 2) + "\n");
+  } catch {}
+}
+
+const NUDGE_RE =
+  /\b(remember\s+(this|that)|memorize|save\s+this|note\s+(this|that)|don'?t\s+forget|always\s+remember|never\s+forget|keep\s+(this|that)\s+in\s+(mind|memory)|store\s+(this|that))\b/i;
+
+const RESUME_RE =
+  /where\s+(did\s+)?(we|I)\s+(leave|left)\s+off|continue\s+(from\s+)?(where|last)|what\s+were\s+we\s+(working|doing)|pick\s+up\s+where|resume\s+(from\s+|where\s+)|what.s\s+the\s+(current|latest)\s+(state|status)|catch\s+me\s+up|where\s+are\s+we/i;
+
+const ERROR_STRONG_RE =
+  /Traceback \(most recent call last\)|panic: |FATAL:|error\[E\d+\]/;
+const ERROR_MULTI_RE = /(Error:|Exception:)/g;
+
+const MEM0_MCP_RE = /mem0.*(?:add_memory|search_memories|get_memor|delete_memor|update_memory|delete_entities|list_entities)/i;
+const WRITE_TOOLS = new Set(["Write", "Edit", "MultiEdit", "write", "edit", "multiEdit"]);
+
+function isMem0Tool(name: string): boolean {
+  return MEM0_MCP_RE.test(name);
+}
+
+function isMem0AddMemory(name: string): boolean {
+  return /mem0.*add_memory/i.test(name);
+}
+
+function isMem0SearchOrGet(name: string): boolean {
+  return /mem0.*(search_memories|get_memories)/i.test(name);
+}
+
+function isMem0DeleteAll(name: string): boolean {
+  return /mem0.*delete_all_memories/i.test(name);
+}
+
+function extractUserText(input: any, output: any): string {
+  const parts: any[] = output?.parts;
+  if (Array.isArray(parts)) {
+    return parts
+      .filter((p: any) => p.type === "text" && !p.synthetic)
+      .map((p: any) => p.text ?? "")
+      .join("\n");
+  }
+  const msg = output?.message ?? input?.message;
+  if (typeof msg?.content === "string") return msg.content;
+  if (typeof msg?.text === "string") return msg.text;
+  return "";
+}
+
+function installSkills(projectDir: string): void {
+  const pluginDir = dirname(dirname(import.meta.filename));
+  const srcSkills = resolve(pluginDir, "opencode-skills");
+  if (!existsSync(srcSkills)) return;
+
+  const destSkills = resolve(projectDir, ".opencode", "skills");
+  const destCommands = resolve(projectDir, ".opencode", "commands");
+  try {
+    const skills = readdirSync(srcSkills, { withFileTypes: true });
+    for (const entry of skills) {
+      if (!entry.isDirectory()) continue;
+      const dest = resolve(destSkills, `mem0-${entry.name}`);
+      if (existsSync(dest)) continue;
+      cpSync(resolve(srcSkills, entry.name), dest, { recursive: true });
+    }
+
+    for (const entry of skills) {
+      if (!entry.isDirectory()) continue;
+      const cmdFile = resolve(destCommands, `mem0-${entry.name}.md`);
+      if (existsSync(cmdFile)) continue;
+      const skillMd = resolve(srcSkills, entry.name, "SKILL.md");
+      if (!existsSync(skillMd)) continue;
+      let desc = "Mem0 " + entry.name + " skill";
+      try {
+        const content = readFileSync(skillMd, "utf8");
+        const m = content.match(/^description:\s*(.+)$/m);
+        if (m) desc = m[1].trim();
+      } catch {}
+      const cmdContent = `---\ndescription: ${desc}\n---\nLoad and follow the skill at .opencode/skills/mem0-${entry.name}/SKILL.md\n\nUse the mem0 MCP tools (search_memories, get_memories, add_memory, delete_memory, update_memory, list_entities, delete_entities, get_event_status) to execute the skill instructions.\n\nIdentity context (from environment):\n- user_id: Use MEM0_USER_ID env var, or fall back to $USER\n- app_id: Use MEM0_APP_ID env var\n- session_id: Use MEM0_SESSION_ID env var\n- branch: Use MEM0_BRANCH env var\n`;
+      try {
+        mkdirSync(destCommands, { recursive: true });
+        writeFileSync(cmdFile, cmdContent, "utf8");
+      } catch {}
+    }
+  } catch {}
+}
+
+const Mem0Plugin: Plugin = async (ctx) => {
+  const { $, client } = ctx;
+
+  try {
+    const projectDir = (ctx as any).directory ?? process.cwd();
+    installSkills(projectDir);
+  } catch {}
+
+  const apiKey = process.env.MEM0_API_KEY;
+
+  if (!apiKey) {
+    try {
+      await client.app.log({
+        body: {
+          service: "mem0",
+          level: "warn",
+          message:
+            "MEM0_API_KEY not set. Get one at https://app.mem0.ai/dashboard/api-keys",
+        },
+      });
+    } catch {}
+    return {};
+  }
+
+  const mem0 = new MemoryClient({ apiKey });
+  const userId = await getUserId();
+  const appId = await getProjectId($);
+  const branch = await getBranch($);
+  const stats = { adds: 0, searches: 0, messages: 0 };
+  const sessionId = generateSessionId();
+  const globalSearch = loadGlobalSearch();
+
+  let initialized = false;
+  let memoryCount = 0;
+  let msgCount = 0;
+
+  const systemContext: string[] = [];
+
+  // Auto-configure coding categories in background (idempotent, never blocks)
+  Promise.resolve().then(() => autoSetupCategories(mem0, apiKey)).catch(() => {});
+
+  return {
+    "chat.message": async (input: any, output: any) => {
+      const userText = extractUserText(input, output);
+      if (!userText || userText.length < 10) return;
+
+      const safeText = redact(userText);
+      msgCount++;
+      stats.messages++;
+
+      if (!initialized) {
+        initialized = true;
+
+        const searchFilters = globalSearch
+          ? { OR: [{ user_id: "*" }] }
+          : { AND: [{ user_id: userId }, { app_id: appId }] };
+
+        try {
+          const all = await mem0.getAll({
+            filters: searchFilters,
+            page: 1,
+            pageSize: 1,
+          });
+          memoryCount =
+            (all as any)?.count ??
+            (all as any)?.results?.length ??
+            0;
+
+          if (globalSearch) {
+            systemContext.push(
+              `Global search is ON — searches return all memories across all users and projects. Writes still use user_id="${userId}", app_id="${appId}".`,
+            );
+          } else {
+            systemContext.push(
+              `Always include user_id="${userId}" and app_id="${appId}" in every search_memories filter and add_memory call.`,
+            );
+          }
+
+          if (memoryCount === 0) {
+            systemContext.push(
+              "New project with 0 memories. Suggest running /mem0:onboard to import project files and install coding categories.",
+            );
+          }
+
+          if (memoryCount > 0) {
+            systemContext.push(
+              "Search mem0 for recent decisions and task learnings before responding. Run 2 parallel searches: one for decision type, one for task_learning type.",
+            );
+            try {
+              const res = await mem0.search(
+                "recent session state decisions and learnings",
+                {
+                  filters: searchFilters,
+                  topK: 5,
+                },
+              );
+              stats.searches++;
+              const memories = extractMemories(res);
+              if (memories.length > 0) {
+                const memLines = memories
+                  .map((m) => {
+                    const meta = (m as any).metadata ?? {};
+                    const cat = meta.type ?? "unknown";
+                    const icon = TYPE_ICONS[cat] ?? "❓";
+                    const age = (m as any).created_at ? formatAge((m as any).created_at) : "";
+                    const ageStr = age ? ` (${age})` : "";
+                    return `- ${icon} [${cat}]${ageStr} ${m.memory.slice(0, 120)}`;
+                  })
+                  .join("\n");
+                systemContext.push(`### Recent Activity\n\n${memLines}`);
+              }
+            } catch {}
+          }
+
+          systemContext.push(
+            "Mem0 searches apply when user references past work, decision questions, errors, or non-trivial tasks. Queries use noun-phrases, 2-4 parallel calls with different metadata.type filters, and include user_id + app_id.",
+          );
+        } catch (err: any) {
+          try {
+            await client.app.log({
+              body: {
+                service: "mem0",
+                level: "error",
+                message: `Session init error: ${err?.message}`,
+              },
+            });
+          } catch {}
+        }
+      }
+
+      if (NUDGE_RE.test(safeText)) {
+        systemContext.push(
+          "[MEMORY TRIGGER] User asked to remember something. Call add_memory with the user's statement, confidence=1.0, infer=false.",
+        );
+      }
+
+      const hasResume = RESUME_RE.test(safeText);
+      if (hasResume) {
+        try {
+          const resumeFilters = globalSearch
+            ? { OR: [{ user_id: "*" }] }
+            : {
+                AND: [
+                  { user_id: userId },
+                  { app_id: appId },
+                ],
+              };
+          const [stateRes, decisionsRes] = await Promise.all([
+            mem0.search("session state current task", {
+              filters: resumeFilters,
+              topK: 3,
+            }),
+            mem0.search("recent decisions and learnings", {
+              filters: resumeFilters,
+              topK: 3,
+            }),
+          ]);
+          stats.searches += 2;
+          const all = [
+            ...extractMemories(stateRes),
+            ...extractMemories(decisionsRes),
+          ];
+          const seen = new Set<string>();
+          const unique = all.filter((m) => {
+            if (seen.has(m.id)) return false;
+            seen.add(m.id);
+            return true;
+          });
+          if (unique.length > 0) {
+            const memLines = unique.map((m) => `- ${m.memory}`).join("\n");
+            systemContext.push(
+              `Session resume context:\n${memLines}\n\nThese memories provide context for resuming work.`,
+            );
+          }
+        } catch {}
+      }
+
+      if (!hasResume && memoryCount > 0) {
+        try {
+          const msgFilters = globalSearch
+            ? { OR: [{ user_id: "*" }] }
+            : { AND: [{ user_id: userId }, { app_id: appId }] };
+          const res = await mem0.search(safeText, {
+            filters: msgFilters,
+            topK: 5,
+          });
+          stats.searches++;
+          const memories = extractMemories(res);
+          if (memories.length > 0) {
+            const memLines = memories.map((m) => `- ${m.memory}`).join("\n");
+            systemContext.push(`Relevant memories:\n${memLines}`);
+          }
+        } catch {}
+      }
+
+      if (msgCount % 3 === 0) {
+        Promise.resolve().then(async () => {
+          try {
+            await mem0.add([{ role: "user", content: safeText }], {
+              user_id: userId,
+              app_id: appId,
+              metadata: {
+                type: "auto_capture",
+                source: "opencode",
+                confidence: 0.7,
+                session_id: sessionId,
+                branch,
+              },
+              infer: true,
+            } as any);
+            stats.adds++;
+          } catch {}
+        });
+      }
+
+      if (msgCount % 5 === 0 && stats.adds < Math.floor(msgCount / 3)) {
+        systemContext.push(
+          "After responding, store any new decisions, learnings, or preferences from this exchange via add_memory. Keep it to 1 sentence per memory.",
+        );
+      }
+    },
+
+    "experimental.chat.messages.transform": async (
+      _input: any,
+      output: { messages: { info: any; parts: any[] }[] },
+    ) => {
+      if (systemContext.length === 0 || !output?.messages?.length) return;
+
+      const firstUser = output.messages.find(
+        (m) => m.info.role === "user",
+      );
+      if (!firstUser || !firstUser.parts.length) return;
+
+      const marker = "## Mem0 Memory Context";
+      if (firstUser.parts.some((p: any) => p.type === "text" && p.text?.includes(marker))) return;
+
+      const block = `${marker}\n\n${systemContext.join("\n\n")}`;
+      const ref = firstUser.parts[0];
+      firstUser.parts.unshift({ ...ref, type: "text", text: block });
+    },
+
+    "tool.execute.before": async (input: any, output: any) => {
+      const toolName: string = input?.tool ?? "";
+
+      // File-context injection: before reading a file, search mem0 for prior work on it
+      if (toolName === "read" || toolName === "Read") {
+        const filePath = String(output?.args?.file_path ?? output?.args?.filePath ?? "");
+        if (filePath && filePath.length > 0) {
+          try {
+            const absPath = filePath.startsWith("/") ? filePath : resolve(process.cwd(), filePath);
+            const { statSync } = await import("fs");
+            const stat = statSync(absPath);
+            if (stat.isFile() && stat.size >= FILE_READ_GATE_MIN_BYTES) {
+              const searchFilters = globalSearch
+                ? { OR: [{ user_id: "*" }] }
+                : { AND: [{ user_id: userId }, { app_id: appId }] };
+              const relPath = filePath.startsWith("/")
+                ? filePath.replace(process.cwd() + "/", "")
+                : filePath;
+              const res = await mem0.search(relPath, {
+                filters: searchFilters,
+                topK: 5,
+              });
+              stats.searches++;
+              const memories = extractMemories(res);
+              if (memories.length > 0) {
+                const lines = memories.map((m) => {
+                  const text = m.memory.slice(0, 150).replace(/\n/g, " ");
+                  return `- ${text} [mem0:${m.id.slice(0, 8)}]`;
+                });
+                systemContext.push(
+                  `Prior work on \`${relPath}\`:\n${lines.join("\n")}`,
+                );
+              }
+            }
+          } catch {}
+        }
+      }
+
+      if (WRITE_TOOLS.has(toolName)) {
+        const fp = String(
+          output?.args?.file_path ?? output?.args?.filePath ?? "",
+        );
+        if (/MEMORY\.md|\.claude\/memory/i.test(fp)) {
+          throw new Error(
+            "Use the add_memory MCP tool instead of writing to MEMORY.md",
+          );
+        }
+      }
+
+      if (isMem0Tool(toolName) && output?.args) {
+        if (!output.args.user_id) output.args.user_id = userId;
+        if (!output.args.app_id) output.args.app_id = appId;
+
+        if (isMem0AddMemory(toolName)) {
+          if (!output.args.metadata) output.args.metadata = {};
+          const meta = output.args.metadata;
+          if (meta.confidence === undefined) meta.confidence = 0.7;
+          if (!meta.source) meta.source = "opencode";
+          if (!meta.type) meta.type = "task_learning";
+          if (!meta.session_id) meta.session_id = sessionId;
+          if (!meta.files) meta.files = ["*"];
+          if (!meta.branch) meta.branch = branch;
+          if (meta.confidence >= 1.0 && output.args.infer === undefined) {
+            output.args.infer = false;
+          }
+        }
+
+        if (isMem0SearchOrGet(toolName)) {
+          if (globalSearch) {
+            output.args.filters = { OR: [{ user_id: "*" }] };
+          } else {
+            const existingFilters = output.args.filters;
+            if (existingFilters === undefined || existingFilters === null) {
+              output.args.filters = {
+                AND: [{ user_id: userId }, { app_id: appId }],
+              };
+            } else if (typeof existingFilters === "object") {
+              const andClauses: any[] = existingFilters.AND;
+              if (Array.isArray(andClauses)) {
+                const hasUid = andClauses.some(
+                  (c: any) => c && typeof c === "object" && "user_id" in c,
+                );
+                const hasAid = andClauses.some(
+                  (c: any) => c && typeof c === "object" && "app_id" in c,
+                );
+                if (!hasUid) andClauses.push({ user_id: userId });
+                if (!hasAid) andClauses.push({ app_id: appId });
+              } else if (andClauses === undefined) {
+                const hasUid = "user_id" in existingFilters;
+                const hasAid = "app_id" in existingFilters;
+                if (!hasUid || !hasAid) {
+                  const existing = Object.entries(existingFilters).map(
+                    ([k, v]) => ({ [k]: v }),
+                  );
+                  if (!hasUid) existing.push({ user_id: userId });
+                  if (!hasAid) existing.push({ app_id: appId });
+                  output.args.filters = { AND: existing };
+                }
+              }
+            }
+          }
+        }
+
+        if (isMem0DeleteAll(toolName)) {
+          if (!output.args.user_id) output.args.user_id = userId;
+          if (!output.args.app_id) output.args.app_id = appId;
+        }
+
+        if (!isMem0AddMemory(toolName) && !output.args.metadata) {
+          output.args.metadata = { source: "opencode", branch };
+        }
+      }
+    },
+
+    "tool.execute.after": async (input: any, _output: any) => {
+      const toolName: string = input?.tool ?? "";
+      const toolOutput: string = input?.output ?? _output?.output ?? "";
+
+      if (MEM0_MCP_RE.test(toolName)) {
+        if (toolName.includes("add_memory")) stats.adds++;
+        if (toolName.includes("search")) stats.searches++;
+      }
+
+      if (toolName === "bash" && toolOutput.length >= 50) {
+        const command: string = input?.args?.command ?? "";
+        if (/git\s+(commit|merge|rebase)/.test(command)) return;
+
+        const hasStrongError = ERROR_STRONG_RE.test(toolOutput);
+        const multiErrors = (toolOutput.match(ERROR_MULTI_RE) ?? []).length;
+        if (!hasStrongError && multiErrors < 2) return;
+
+        try {
+          const errorLine =
+            toolOutput
+              .split("\n")
+              .find((l: string) =>
+                /Error:|Exception:|panic:|FAIL:|fatal:/i.test(l),
+              )
+              ?.replace(/^\s+/, "")
+              .slice(0, 120) ?? "";
+
+          const traceFiles = [
+            ...new Set(
+              toolOutput.match(
+                /[a-zA-Z0-9_./-]+\.(py|ts|tsx|js|jsx|rs|go|rb|java|sh)(:\d+)?/g,
+              ) ?? [],
+            ),
+          ].slice(0, 5);
+
+          const errorQuery = errorLine.slice(0, 80);
+          if (errorQuery.length < 10) return;
+
+          const errorFilters = globalSearch
+            ? { OR: [{ user_id: "*" }] }
+            : {
+                AND: [
+                  { user_id: userId },
+                  { app_id: appId },
+                ],
+              };
+          const [antiPatternRes, bugFixRes] = await Promise.all([
+            mem0.search(`error: ${errorQuery}`, {
+              filters: errorFilters,
+              topK: 3,
+            }),
+            mem0.search(`error: ${errorQuery}`, {
+              filters: errorFilters,
+              topK: 3,
+            }),
+          ]);
+          stats.searches += 2;
+
+          const allResults = [
+            ...extractMemories(antiPatternRes),
+            ...extractMemories(bugFixRes),
+          ];
+          const seen = new Set<string>();
+          const unique = allResults.filter((m) => {
+            if (seen.has(m.id)) return false;
+            seen.add(m.id);
+            return true;
+          });
+
+          let ctx = `Error detected: \`${command.slice(0, 100)}\` produced:\n> ${errorLine}`;
+          if (traceFiles.length > 0) {
+            ctx += `\nFiles in stack trace: ${traceFiles.join(", ")}`;
+          }
+          if (unique.length > 0) {
+            const lines = unique.map((m) => `- ${m.memory}`).join("\n");
+            ctx += `\nPrior error memories:\n${lines}`;
+          }
+          ctx +=
+            "\nStore resolved errors as anti_pattern or bug_fix memories for future reference.";
+          systemContext.push(ctx);
+        } catch {}
+      }
+    },
+
+    "experimental.session.compacting": async (
+      input: { sessionID?: string },
+      output: { context: string[]; prompt?: string },
+    ) => {
+      try {
+        const compactSessionId = input?.sessionID ?? sessionId;
+
+        // Session summary capture: store a structured summary of the session
+        const summaryPrompt = [
+          `Session summary for project ${appId} (branch: ${branch}).`,
+          `Session: ${compactSessionId}.`,
+          `Stats: ${stats.adds} memories stored, ${stats.searches} searches, ${stats.messages} messages.`,
+          `Extract and remember: what was requested, what was investigated, key decisions made, what was completed, and what needs to happen next.`,
+        ].join(" ");
+        Promise.resolve().then(async () => {
+          try {
+            await mem0.add([{ role: "user", content: summaryPrompt }], {
+              user_id: userId,
+              app_id: appId,
+              metadata: {
+                type: "session_summary",
+                source: "opencode-stop",
+                session_id: compactSessionId,
+                branch,
+              },
+              infer: true,
+            } as any);
+          } catch {}
+        });
+
+        const compactFilters = globalSearch
+          ? { OR: [{ user_id: "*" }] }
+          : { AND: [{ user_id: userId }, { app_id: appId }] };
+        const res = await mem0.search("session state decisions learnings", {
+          filters: compactFilters,
+          topK: 10,
+        });
+        const memories = extractMemories(res);
+        if (memories.length > 0 && output?.context) {
+          const lines = memories.map((m) => `- ${m.memory}`).join("\n");
+          output.context.push(
+            `## Mem0 Memories (preserve across compaction)\n\n${lines}\n\nIMPORTANT: After compaction, store any key decisions or learnings using the add_memory MCP tool.`,
+          );
+        }
+      } catch {}
+    },
+
+    "shell.env": async (
+      _input: { cwd: string; sessionID?: string },
+      output: { env: Record<string, string> },
+    ) => {
+      if (output?.env) {
+        output.env.MEM0_USER_ID = userId;
+        output.env.MEM0_APP_ID = appId;
+        output.env.MEM0_SESSION_ID = sessionId;
+        output.env.MEM0_BRANCH = branch;
+        output.env.MEM0_GLOBAL_SEARCH = globalSearch ? "true" : "false";
+      }
+    },
+  };
+};
+
+export default Mem0Plugin;

mem0-plugin/.opencode-plugin/opencode-skills/context-loader/SKILL.md

@@ -0,0 +1,52 @@
+---
+name: context-loader
+description: Searches and injects relevant memories into context before starting work on a task. Use when beginning a new task, switching context, or when project history, past decisions, or coding conventions need to be loaded.
+---
+
+# Context Loader
+
+Pre-fetches relevant memories to prime context before working on a task.
+
+## When to use
+
+- Session start (invoke manually or auto-triggered by skill description matching)
+- User starts work on a specific feature or file set
+- Complex multi-step task begins
+- User says "what do we know about X" or "context for X"
+
+## Steps
+
+1. **Extract topics** from current message/task. Identify: file paths, module names, feature areas, error patterns.
+
+2. **Run 2-4 parallel `search_memories` calls** with different angles:
+
+   | Query angle | Filter | Purpose |
+   |---|---|---|
+   | Feature/module name | `{"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"metadata": {"type": "decision"}}]}` | Architecture decisions |
+   | File paths mentioned | `{"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"metadata": {"type": "convention"}}]}` | Coding patterns |
+   | Error keywords (if any) | `{"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"metadata": {"type": "anti_pattern"}}]}` | Known pitfalls |
+   | Broad project context | `{"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]}` | Catch-all |
+
+3. **Deduplicate** results by memory ID across all search responses.
+
+4. **Output compact context block** (max 10 memories):
+
+```
+context-loader: loaded <N> memories for "<task summary>"
+  - [decision] <content> [mem0:<short_id>]
+  - [convention] <content> [mem0:<short_id>]
+  - [anti_pattern] <content> [mem0:<short_id>]
+```
+
+5. If **zero results**: output nothing. Don't announce empty context.
+
+## Constraints
+
+- **Read-only** — never modify or delete memories
+- **Max 10 memories** returned (most relevant only)
+- **Silent on empty** — only surfaces findings if relevant context exists
+- Skip memories already visible in current session context
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/dream/SKILL.md

@@ -0,0 +1,237 @@
+---
+name: dream
+description: Consolidates stored memories by merging duplicates, resolving contradictions, and pruning stale entries. Use when memory count is high, search results feel noisy or repetitive, or periodic cleanup is needed to maintain memory quality.
+---
+
+# Mem0 Dream — Memory Consolidation
+
+This skill performs a memory consolidation pass: it fetches all project memories,
+identifies near-duplicates, flags contradictions, and prunes stale entries based on
+configured retention policies. All proposed changes are shown as a diff for user
+approval before anything is modified.
+
+**IMPORTANT: Execute steps strictly in order (1 → 2 → 3 → 4 → 5 → 6). Each step depends on the previous one. Do NOT run steps in parallel or skip ahead.**
+
+## Step 1: Load Retention Policies
+
+Check for a project config file in the project root (current working directory):
+
+1. Look for `.mem0.json` first. If it exists, parse it as JSON and read the
+   `retention` field (a dict of `category → days | null`).
+2. If `.mem0.json` is not present, look for `.mem0.md`. If it exists, scan it
+   for a `retention:` section or YAML front matter with retention settings and
+   parse what you find.
+3. If neither file exists, skip config loading entirely.
+
+If no config is found or the config contains no retention settings, fall back to
+these built-in defaults:
+
+| `metadata.type` | Default retention |
+|---|---|
+| `session_state` | 90 days |
+| `compact_summary` | 90 days |
+| all others | no pruning |
+
+Store the resolved policies for use in Step 3.
+
+---
+
+## Step 2: Fetch ALL Project Memories
+
+Call `get_memories` to retrieve every memory for the active project:
+
+```python
+get_memories(
+    filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]},
+    page_size=200,
+)
+```
+
+If the response indicates more pages exist, paginate until all memories are fetched.
+Collect the full list before proceeding. If zero memories are found, print:
+
+```
+No memories found for project <project_id>. Nothing to consolidate.
+```
+
+…and stop.
+
+---
+
+## Step 3: Analyze — Find Issues
+
+Work entirely in-memory; do not modify anything yet.
+
+Group memories by `metadata.type` (use `"unknown"` when the field is absent).
+For each group, identify the following:
+
+### 3a. Near-duplicate pairs (merge candidates)
+
+Two memories are near-duplicates when they express the same fact or decision but
+phrased differently (e.g., "Use PostgreSQL for auth" and "Auth DB is PostgreSQL").
+
+Heuristics — two memories are near-duplicates if **all** of these hold:
+- Similarity threshold: estimated cosine similarity > 0.9 (use noun/keyword overlap as proxy — if >60% of significant nouns overlap, treat as >0.9 similarity).
+- Same `metadata.type`.
+- Neither memory is pinned (`metadata.pinned != true`).
+
+For each qualifying pair, draft a merged version that is more complete and specific
+than either original.
+
+### 3b. Contradictions
+
+Two memories contradict when they assert opposing facts about the same topic
+(e.g., "Deploy to ECS" vs. "Deploy to Vercel").
+
+Identify the likely winner: the more recent memory with higher confidence wins.
+Store both IDs and their content for user review.
+
+### 3c. Prune candidates
+
+A memory is a prune candidate when **any** of the following is true:
+
+1. Its `metadata.type` has a retention policy and the memory is older than the
+   configured number of days (compare `created_at` to today).
+2. Its confidence score is below 0.3 AND it contains no information unique to
+   this project (no file paths, identifiers, or domain-specific nouns).
+
+**Always skip memories where `metadata.pinned == true`**, regardless of age or
+confidence.
+
+---
+
+## Step 4: Print Diff Report
+
+Print a structured diff to the terminal before making any changes. Use exactly
+this format:
+
+```
+## dream — consolidation report
+
+Merges (<N>):
+  [mem0:<id1>] + [mem0:<id2>] → "<merged content, 100 chars>"
+
+Conflicts (<N>):
+  [mem0:<idA>] vs [mem0:<idB>] — "<topic>" [A/B/skip]
+
+Prune (<N>):
+  [mem0:<id>] — <type>, <age>d old
+
+Proposed: <N> merges, <N> prunes, <N> conflicts. Apply? [Y/n]
+```
+
+If there are zero items in any category, omit that section entirely.
+
+If there are zero total proposals (no merges, no prunes, no conflicts), print:
+
+```
+Dream complete. No duplicate, contradictory, or stale memories found.
+```
+
+…and stop.
+
+---
+
+## Step 5: Wait for User Input and Apply
+
+### 5a. Contradictions
+
+For each `CONFLICT` pair in the report, wait for the user to type `A`, `B`, or
+`skip` (case-insensitive). If they enter nothing (empty), treat as `skip`.
+
+Record the winner for each pair before proceeding to the final apply confirmation.
+
+### 5b. Final confirmation
+
+After all conflict resolutions are collected, prompt:
+
+```
+Apply? [Y/n]
+```
+
+If the user types `n` or `no` (case-insensitive), print `Cancelled. No changes made.`
+and stop.
+
+If the user confirms (`Y`, `yes`, or empty / Enter), apply all changes in this order:
+
+#### Merges
+
+For each approved merge pair:
+1. `delete_memory(<id1>)`
+2. `delete_memory(<id2>)`
+3. `add_memory` with:
+   - `text="<merged content>"`
+   - `user_id=<active_user_id>`
+   - `app_id=<active_project_id>` (top-level, not in metadata)
+   - `metadata={"type": "<original type>", "branch": "<active_branch>", "confidence": <higher of the two original scores>, "source": "mem0-dream"}`
+   - `infer=False`
+
+#### Contradictions (resolved)
+
+For each resolved conflict where the user chose A or B:
+- Delete the loser (the non-chosen memory): `delete_memory(memory_id=<loser_id>)`
+
+Contradictions where the user chose `skip` are left untouched.
+
+#### Prunes
+
+For each prune candidate:
+- `delete_memory(<memory_id>)`
+
+---
+
+## Step 6: Print Summary
+
+After all changes are applied, print:
+
+```
+Dream complete — merged: <N>, pruned: <N>, conflicts resolved: <N>, skipped: <N>
+```
+
+---
+
+## Auto mode
+
+When invoked with `--auto` (e.g., `/mem0:dream --auto`), run non-interactively:
+
+- **Merges**: applied automatically (no contradiction, both are compatible).
+- **Prunes**: applied automatically (age/confidence-based, no ambiguity).
+- **Contradictions**: skipped — they require human judgment.
+
+### Concurrency guard
+
+Before doing any work, check for a lock file at `/tmp/mem0_dream_auto.lock`:
+- If the lock file exists and is less than 10 minutes old, print `[mem0-dream --auto] Another run in progress — skipping.` and stop.
+- Otherwise, create the lock file (write the current timestamp). Delete it when done (in all exit paths).
+
+### Execution
+
+In auto mode:
+1. Load policies and fetch memories (Steps 1–3) as normal.
+2. Apply merges and prunes silently without printing the diff or prompting.
+3. Print a compact summary:
+   ```
+   [mem0-dream --auto] project=<id>  merged=<N>  pruned=<N>  conflicts_skipped=<N>
+   ```
+4. If contradictions were detected but skipped, check if a `mem0-dream-auto` reminder already exists before storing one:
+   - Search for existing reminders: `search_memories(query="mem0-dream contradictions manual review", filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}, {"metadata": {"source": "mem0-dream-auto"}}]}, top_k=1)`
+   - If a result exists with similarity > 0.9, skip storing the reminder (one already exists).
+   - If no match, store the reminder:
+   ```python
+   add_memory(
+       text="mem0-dream detected <N> contradiction(s) requiring manual review. Run /mem0:dream to resolve them interactively.",
+       user_id="<active_user_id>",
+       app_id="<active_project_id>",
+       metadata={"type": "task_learning", "source": "mem0-dream-auto", "branch": "<active_branch>"},
+       infer=False,
+   )
+   ```
+
+## See also
+
+- `/mem0:forget` — targeted deletion of specific memories (search + confirm + delete)
+- `/mem0:health --deep` — quick quality scan without applying changes
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/export/SKILL.md

@@ -0,0 +1,80 @@
+---
+name: export
+description: Exports all project memories to a portable Markdown file for backup or migration. Use when backing up memories, migrating to another project, sharing memory state with teammates, or archiving before cleanup.
+---
+
+# Mem0 Export
+
+Export all memories for the current project to a portable Markdown file.
+
+## Execution
+
+### Step 1: Resolve identity
+
+Determine the active identity:
+- `user_id` from `MEM0_USER_ID` env var, else `$USER`, else `"default"`
+- `project_id` (used as `app_id`) from `MEM0_PROJECT_ID` env var, or via the project resolver
+
+### Step 2: Fetch all memories
+
+Call `get_memories` with:
+- `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}`
+- `page_size=200`
+
+If the response is paginated (i.e. the result contains a `next` cursor or the count equals `page_size`), continue fetching pages until all memories are retrieved.
+
+### Step 3: Format each memory as a YAML-frontmatter block
+
+For each memory record, produce a block in this exact format:
+
+```
+---
+id: <memory.id>
+created_at: <memory.created_at>
+type: <memory.metadata.type or "">
+confidence: <memory.metadata.confidence or "">
+branch: <memory.metadata.branch or "">
+files: <memory.metadata.files joined with ", " or "">
+categories: <memory.categories joined with ", " or "">
+---
+<memory.memory or memory content string>
+
+```
+
+Notes:
+- The `---` delimiters must be on their own lines with no extra whitespace.
+- `files` and `categories` are written as comma-separated values on a single line.
+- Leave a blank line after the content before the next `---` (for readability).
+- If a field is missing or null, write an empty string (not "null").
+
+### Step 4: Write the export file
+
+Determine the output filename:
+
+```
+mem0-export-<project_id>-<YYYY-MM-DD>.md
+```
+
+Where `<YYYY-MM-DD>` is today's date in UTC.
+
+Write all formatted blocks to this file using the Write tool (or equivalent). The file is written to the current working directory.
+
+### Step 5: Print summary
+
+```
+Exported <N> memories to <filename>
+```
+
+Where `<N>` is the total number of memory blocks written.
+
+## Error Handling
+
+- If `get_memories` returns an error or zero memories, print:
+  ```
+  No memories found for project <project_id>. Nothing exported.
+  ```
+- If the write fails, report the error to the user.
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/forget/SKILL.md

@@ -0,0 +1,74 @@
+---
+name: forget
+description: Deletes memories by search query or memory ID with confirmation before removal. Use when removing outdated decisions, incorrect memories, sensitive data, or cleaning up after experiments. Also handles undo of recent additions.
+---
+
+# Mem0 Forget
+
+Delete specific memories from mem0.
+
+## Execution
+
+### Step 1: Parse input
+
+The user provides either:
+- A search query: `/mem0:forget auth module decisions`
+- A memory ID: `/mem0:forget <memory_id>`
+
+If no argument, ask: "What should I forget? Provide a search query or memory ID."
+
+### Step 2: Find memories
+
+**If memory ID provided** (looks like a UUID or hex string):
+- Call `get_memory` with the ID to verify it exists.
+- Show: `Found: "<memory content first 120 chars>" (created <date>)`
+
+**If search query provided:**
+- Call `search_memories` with:
+  - `query=<user's query>`
+  - `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<project_id>"}]}`
+  - `top_k=10`
+- Show numbered list:
+  ```
+  Found <N> memories matching "<query>":
+  1. <content, 120 chars> (type: <type>, created: <date>) [ID: <short_id>]
+  2. ...
+  ```
+
+### Step 3: Confirm
+
+Ask: "Delete which memories? Enter numbers (e.g., 1,3,5), 'all', or 'cancel'."
+
+For a single memory ID, ask: "Delete this memory? [y/N]"
+
+**Never delete without confirmation.** This is destructive.
+
+### Step 4: Delete
+
+For each confirmed memory, call `delete_memory` with the memory ID.
+
+### Step 5: Report
+
+```
+Deleted <N> memories.
+```
+
+If any deletions failed, report which ones and why.
+
+## Undo recent writes
+
+If the user says "undo last N memories" or "undo last write":
+
+1. Check the `MEM0_SESSION_ID` environment variable (set by the plugin's shell.env hook).
+2. If `MEM0_SESSION_ID` is set, call `search_memories` with:
+   - `query="recently added"`
+   - `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<project_id>"}, {"metadata": {"session_id": "<MEM0_SESSION_ID>"}}]}`
+   - `top_k=20`
+3. Sort results by creation time descending and show the last N entries (default 1). Ask for confirmation.
+4. Delete confirmed entries via `delete_memory`.
+
+If `MEM0_SESSION_ID` is not set or the search returns no results, tell the user: "No recent memory IDs tracked this session. Try `/mem0:tour` to browse recent memories, or `/mem0:forget <search query>` to find specific ones."
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/health/SKILL.md

@@ -0,0 +1,159 @@
+---
+name: health
+description: Diagnoses mem0 connectivity, API key validity, and memory read/write functionality. Use when memory operations fail, searches return empty, add_memory errors occur, MCP connection drops, or to verify the plugin is working correctly.
+---
+
+# Mem0 Health Check
+
+Run a diagnostic check on the mem0 plugin. Useful for troubleshooting.
+
+## Execution
+
+Run ALL checks, then display a single summary. Do not stop on the first failure.
+
+### Check 1: API key
+
+```bash
+_KEY="${MEM0_API_KEY:-}"
+[ -n "$_KEY" ] && echo "${_KEY:0:6}..." || echo "NOT_SET"
+```
+
+- If `NOT_SET`: FAIL — "No API key configured"
+- If set: PASS — the command already prints only the first 6 chars
+
+### Check 2: Identity resolution
+
+Resolve identity from environment variables set by the plugin's `shell.env` hook:
+
+```bash
+echo "user_id=${MEM0_USER_ID:-${USER:-}}"
+echo "project_id=${MEM0_APP_ID:-}"
+echo "branch=$(git branch --show-current 2>/dev/null || echo '')"
+```
+
+- `user_id`: from `MEM0_USER_ID`, falling back to `$USER`
+- `project_id`: from `MEM0_APP_ID`
+- `branch`: from `git branch --show-current`
+
+PASS if all three are non-empty. WARN if any falls back to defaults.
+
+### Check 3: MCP server connectivity
+
+Call `search_memories` with:
+- `query="health check"`
+- `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}`
+- `top_k=1`
+
+- If returns successfully (even empty): PASS
+- If errors: FAIL — show the error message
+
+### Check 4: Memory write capability
+
+Call `add_memory` with:
+- `text="Health check probe — safe to delete."`
+- `user_id=<active_user_id>`
+- `app_id=<active_project_id>`
+- `metadata={"type": "health_check", "probe": true}`
+- `infer=False`
+
+The response returns `event_id` (v3 writes are async). Call `get_event_status(event_id=<event_id>)` to check processing.
+
+- If status is `SUCCEEDED`: PASS — extract the memory ID from the event result, then call `delete_memory` with that ID to clean up.
+- If status is `PENDING` after 5 seconds: PASS (write accepted, processing delayed)
+- If errors: FAIL — show the error.
+
+### Check 5: Session context
+
+Check that the plugin's `shell.env` hook has injected session context into the environment:
+
+```bash
+echo "session_id=${MEM0_SESSION_ID:-}"
+echo "app_id=${MEM0_APP_ID:-}"
+echo "branch=${MEM0_BRANCH:-}"
+```
+
+- If all three are non-empty: PASS — "Session active"
+- If any are missing: WARN — "Plugin env vars not set; shell.env hook may not have fired"
+
+### Display
+
+```
+## mem0 health
+
+PASS  API Key         m0-dVe...
+PASS  Identity        user=kartik, project=mem0, branch=main
+PASS  MCP Connection  142ms
+PASS  Write/Read      write + delete OK
+PASS  Session         session_id=abc123, app_id=mem0, branch=main
+
+All checks passed.
+```
+
+If any check fails, add a `## Troubleshooting` section with specific fix steps for each failure.
+
+## Extended mode: Memory Quality Analysis
+
+When invoked with `--deep` (e.g., `/mem0:health --deep`), run the standard 5 checks above **plus** a memory quality scan.
+
+### Quality Check 1: Duplicates
+
+Call `get_memories` with `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}`, `page_size=200`. Compare all pairs within the same `metadata.type` group for high textual overlap (shared nouns/keywords > 60%). Report:
+
+```
+Potential duplicates: <N> pairs
+  [mem0:<id1>] ≈ [mem0:<id2>] — both about "<shared topic>"
+```
+
+### Quality Check 2: Stale memories
+
+Flag memories where:
+- `metadata.type` is `session_state` or `compact_summary` AND older than 90 days
+- `metadata.confidence` < 0.3 AND older than 30 days
+
+```
+Stale candidates: <N>
+  [mem0:<id>] — session_state, 142d old
+```
+
+### Quality Check 2b: Low-confidence memories
+
+Flag memories where `metadata.confidence` < 0.5 (regardless of age). Report separately from stale:
+
+```
+Low-confidence memories: <N>
+  [mem0:<id>] — confidence=0.3, "<content preview>"
+```
+
+### Quality Check 3: Contradictions
+
+Within each `metadata.type` group, flag pairs that assert opposing facts about the same topic. Use semantic judgment — look for negation patterns, conflicting tool/framework choices, or reversed decisions.
+
+```
+Possible contradictions: <N>
+  [mem0:<idA>] vs [mem0:<idB>] — conflicting on "<topic>"
+```
+
+### Quality Check 4: Orphan memories
+
+Memories with no `metadata.type` set, or with `metadata.type` not in the 17 known coding categories. These were likely written without proper tagging.
+
+```
+Untagged/orphan memories: <N>
+```
+
+### Quality summary
+
+```
+## Memory Quality
+
+Duplicates: <N> · Stale: <N> · Contradictions: <N> · Orphans: <N>
+```
+
+If all counts are 0: `Memory quality: clean.`
+If any non-zero: append `Run /mem0:dream to fix.`
+
+To fix issues found by `--deep`, run `/mem0:dream` for automated consolidation (merges, prunes, conflict resolution).
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/import/SKILL.md

@@ -0,0 +1,185 @@
+---
+name: import
+description: Imports memories from an exported Markdown file or MEMORY.md into the current project. Use when migrating from another project, restoring from backup, importing Claude Code native MEMORY.md content, or setting up a new project with existing knowledge.
+---
+
+# Mem0 Import
+
+Import memories from a mem0 export file into the current project.
+
+## Execution
+
+### Step 1: Determine the export file to import
+
+If the user provided a filename as an argument to `/mem0:import <filename>`, use that file.
+
+Otherwise, list `.md` files in the current directory whose names contain `mem0-export`:
+
+```bash
+ls -1 *.md 2>/dev/null | grep mem0-export || echo "No export files found"
+```
+
+If multiple files are found, ask the user which one to import. If none are found, print:
+```
+No mem0-export files found in the current directory.
+Run /mem0:export first, or provide the filename: /mem0:import <path-to-file>
+```
+
+### Step 2: Parse the export file
+
+Read the export file directly. It is a JSON file containing a top-level `memories` array. Each element has:
+- `id` — original memory ID (for reference only; a new ID will be assigned on import)
+- `type` — metadata type
+- `confidence` — metadata confidence value
+- `branch` — metadata branch
+- `files` — list of associated files
+- `categories` — list of categories
+- `content` — the memory text
+
+Parse the JSON in-memory (do not run any external script). If the file cannot be read or parsed, or if the `memories` array is missing or empty, print:
+```
+Failed to parse <filename> or file contains no valid memory blocks.
+```
+and stop.
+
+### Step 3: Resolve identity
+
+Determine the active identity:
+- `user_id` from `MEM0_USER_ID` env var, else `$USER`, else `"default"`
+- `project_id` (used as `app_id`) from `MEM0_PROJECT_ID` env var, or via the project resolver
+
+### Step 4: Import each memory
+
+For each record in the parsed JSON array, call `add_memory` (MCP tool) with:
+
+- `text="<record.content>"`
+- `user_id=<active_user_id>`
+- `app_id=<active_project_id>`
+- `metadata={`
+  - `"type": "<record.type>"` (if non-empty)
+  - `"confidence": "<record.confidence>"` (if non-empty)
+  - `"branch": "<record.branch>"` (if non-empty)
+  - `"files": <record.files>` (the list, if non-empty)
+  - `"source": "import"`
+  - `}`
+- `infer=False`
+
+Notes:
+- Do NOT pass the original `id` — the platform assigns a new ID.
+- Skip records where `content` is empty.
+- Continue importing even if individual records fail; track the count of successes.
+
+### Step 5: Print results
+
+```
+Imported <N> memories into project <project_id>
+```
+
+Where `<N>` is the number of successfully imported memories.
+
+If any failed:
+```
+Imported <N>/<total> memories into project <project_id> (<failed> failed)
+```
+
+## Importing from competing AI tools (`--tools`)
+
+When invoked with `--tools` (e.g., `/mem0:import --tools`), detect and import
+from competing AI tool configuration files:
+
+### Supported tools
+
+| Tool | File/directory |
+|------|---------------|
+| Cursor | `.cursorrules` |
+| GitHub Copilot | `.github/copilot-instructions.md` |
+| Cline | `memory-bank/` (directory of `.md` files) |
+| Continue | `.continue/rules.md` |
+
+### T1: Detect
+
+```bash
+test -f .cursorrules && echo "cursor: .cursorrules"
+test -f .github/copilot-instructions.md && echo "copilot: .github/copilot-instructions.md"
+test -d memory-bank/ && echo "cline: memory-bank/"
+test -f .continue/rules.md && echo "continue: .continue/rules.md"
+```
+
+### T2: Ask user
+
+List found files, ask which to import (numbers, comma-separated, or "all").
+If none found:
+```
+No competing tool configuration files found.
+Checked: .cursorrules, .github/copilot-instructions.md, memory-bank/, .continue/rules.md
+```
+
+### T3: Import
+
+For each selected tool, read the file(s) directly and split the content into logical
+chunks to import as individual memories. No external scripts are used — all parsing
+and importing is done via MCP tools.
+
+Chunking rules per tool:
+
+- **cursorrules / copilot / continue**: Read the file as plain text. Split on blank
+  lines or section headers (`#`, `##`). Each non-empty chunk becomes one memory.
+  Skip chunks shorter than 50 characters.
+
+- **cline (memory-bank/)**: List all `.md` files in the directory. Read each file.
+  Split each file on blank lines or headers. Each non-empty chunk becomes one memory.
+  Skip chunks shorter than 50 characters.
+
+For each chunk, call `add_memory` (MCP tool) with:
+- `text="<chunk text>"`
+- `user_id=<active_user_id>`
+- `app_id=<active_project_id>`
+- `metadata={"type": "task_learning", "source": "<tool>-import", "confidence": 0.8}`
+- `infer=False`
+
+Where `<tool>` is `cursorrules`, `copilot`, `cline`, or `continue`.
+
+Notes: chunks longer than 10,000 characters are truncated before import. Safe to
+re-run — deduplication handles repeated entries.
+
+### T4: Report
+
+```
+Imported <N> memories into <project_id> (cursor: <N>, copilot: <N>)
+```
+
+---
+
+## Importing Claude Code's native MEMORY.md
+
+When invoked with a path to Claude Code's native `MEMORY.md` file (typically
+`~/.claude/projects/<proj-key>/memory/MEMORY.md`), or when `on_session_start.sh`
+detects native auto-memory and the user chooses to import:
+
+1. Read the file directly. It contains newline-separated memory entries (one fact per
+   line, sometimes with `- ` bullet prefix).
+2. Split by non-empty lines. Each line becomes one memory.
+3. Skip lines shorter than 20 characters or lines that are just headers (`#`).
+4. For each line, call `add_memory` (MCP tool) with:
+   - `text="<line>"`
+   - `user_id=<active_user_id>`
+   - `app_id=<active_project_id>`
+   - `metadata={"type": "task_learning", "source": "memory-md-import", "confidence": 0.8}`
+   - `infer=False`
+5. Report: `Imported <N> memories from MEMORY.md into project <project_id>`
+6. Suggest disabling native auto-memory:
+   ```
+   To avoid duplicate memory systems, add to ~/.claude/settings.json:
+     "autoMemoryEnabled": false
+   ```
+
+This handles the cold-start gap when a user has been using Claude Code's native
+memory and switches to mem0.
+
+## Error Handling
+
+- If `add_memory` calls fail consistently (e.g. auth error), report the issue and stop early.
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/list-projects/SKILL.md

@@ -0,0 +1,64 @@
+---
+name: list-projects
+description: Lists all projects with stored memories for the current user, showing memory counts and last activity dates. Use when checking which projects have memories, comparing memory distribution across repos, or finding a specific project scope.
+---
+
+# Mem0 List Projects
+
+Show all known project scopes for the current user.
+
+## Execution
+
+### Step 1: Fetch memories to discover app_ids
+
+There is no dedicated "list projects" API endpoint. Discover projects by fetching
+the user's memories across all scopes.
+
+**Important:** A filter with only `user_id` triggers implicit null scoping — it
+excludes memories that have a non-null `app_id`. Run two queries and merge:
+
+1. **Null-scoped:** `get_memories` with `filters={"AND": [{"user_id": "<active_user_id>"}]}`, `page_size=200`
+   — catches memories without `app_id`
+2. **App-scoped:** `get_memories` with `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": {"exists": true}}]}`, `page_size=200`
+   — catches memories with any `app_id`
+
+Run both calls in parallel. Merge results, deduplicate by memory `id`.
+
+If either response indicates more pages, paginate (up to 1000 total).
+
+### Step 2: Extract distinct projects
+
+For each memory, determine project by:
+1. Top-level `app_id` field (preferred)
+2. `metadata.project_id` (legacy memories)
+3. `metadata.project` (oldest format)
+4. `"(unscoped)"` if none found
+
+Group by resolved project name. For each project, count:
+- Total memories
+- Most recent `created_at` date
+- Top 3 `metadata.type` values by frequency
+
+### Step 3: Display
+
+```
+## mem0 projects
+
+  <app_id_1>  <count> memories  (last: <date>) ← current
+  <app_id_2>  <count> memories  (last: <date>)
+
+<N> projects, <M> total memories
+```
+
+Mark current project with `← current`. Sort by memory count descending.
+
+### Step 4: Empty state
+
+If zero memories found:
+```
+No projects found. Run /mem0:onboard to get started.
+```
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/mem0/LICENSE

@@ -0,0 +1,189 @@
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+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
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+
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+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

mem0-plugin/.opencode-plugin/opencode-skills/mem0/README.md

@@ -0,0 +1,73 @@
+# Mem0 Skill for Claude
+
+Add persistent memory to any AI application in minutes using [Mem0 Platform](https://app.mem0.ai?utm_source=oss&utm_medium=mem0-plugin-skill-readme).
+
+## What This Skill Does
+
+When installed, Claude can:
+
+- **Set up Mem0** in your Python or TypeScript project
+- **Integrate memory** into your existing AI app (LangChain, CrewAI, Vercel AI, OpenAI Agents, LangGraph, LlamaIndex, etc.)
+- **Generate working code** using real API references and tested patterns
+- **Search live docs** on demand for the latest Mem0 documentation
+
+## Installation
+
+This skill is included automatically when you install the Mem0 plugin:
+
+```
+/plugin marketplace add mem0ai/mem0
+/plugin install mem0@mem0-plugins
+```
+
+See the [plugin README](../../README.md) for full setup instructions.
+
+### Prerequisites
+
+- A Mem0 Platform API key ([Get one here](https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=mem0-plugin-skill-readme))
+- Python 3.10+ or Node.js 18+
+- Set the environment variable:
+
+  ```bash
+  export MEM0_API_KEY="m0-your-api-key"
+  ```
+
+## Quick Start
+
+After installing, just ask Claude:
+
+- "Set up mem0 in my project"
+- "Add memory to my chatbot"
+- "Help me search user memories with filters"
+- "Integrate mem0 with my LangChain app"
+- "Add graph memory to track entity relationships"
+
+## What's Inside
+
+```text
+skills/mem0/
+├── SKILL.md                    # Skill definition and instructions
+├── README.md                   # This file
+├── LICENSE                     # Apache-2.0
+├── scripts/
+│   └── mem0_doc_search.py      # Search live Mem0 docs on demand
+└── references/                 # Documentation (loaded on demand)
+    ├── quickstart.md           # Full quickstart (Python, TS, cURL)
+    ├── sdk-guide.md            # All SDK methods (Python + TypeScript)
+    ├── api-reference.md        # REST endpoints, filters, memory object
+    ├── architecture.md         # Processing pipeline, lifecycle, scoping, performance
+    ├── features.md             # Retrieval, graph, categories, MCP, webhooks, multimodal
+    ├── integration-patterns.md # LangChain, CrewAI, Vercel AI, LangGraph, LlamaIndex, etc.
+    └── use-cases.md            # 7 real-world patterns with Python + TypeScript code
+```
+
+## Links
+
+- [Mem0 Platform Dashboard](https://app.mem0.ai?utm_source=oss&utm_medium=mem0-plugin-skill-readme)
+- [Mem0 Documentation](https://docs.mem0.ai)
+- [Mem0 GitHub](https://github.com/mem0ai/mem0)
+- [API Reference](https://docs.mem0.ai/api-reference)
+
+## License
+
+Apache-2.0

mem0-plugin/.opencode-plugin/opencode-skills/mem0/SKILL.md

@@ -0,0 +1,191 @@
+---
+name: mem0
+description: Mem0 SDK reference covering Python and TypeScript APIs, memory client methods, configuration, and framework integrations. Use when writing code that calls mem0 APIs, configuring memory providers, or integrating mem0 into an application.
+license: Apache-2.0
+metadata:
+  author: mem0ai
+  version: "0.1.1"
+  category: ai-memory
+  tags: "memory, personalization, ai, python, typescript, vector-search"
+compatibility: Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var (Platform), and internet access to api.mem0.ai. Uses Mem0 v3 API.
+---
+
+# Mem0 Platform Integration
+
+> **Skill Graph:** This skill is part of the Mem0 skill graph:
+> - **mem0** (this skill) -- Platform Client SDK + OSS (Python + TypeScript)
+> - **[mem0-vercel-ai-sdk](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk)** -- Vercel AI SDK provider
+
+Mem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below.
+
+## Step 1: Install and authenticate
+
+**Python:**
+```bash
+pip install mem0ai
+export MEM0_API_KEY="m0-your-api-key"
+```
+
+**TypeScript/JavaScript:**
+```bash
+npm install mem0ai
+export MEM0_API_KEY="m0-your-api-key"
+```
+
+Get an API key at: https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=mem0-plugin-skill
+
+> **Don't have a `MEM0_API_KEY`?** Sign up at https://app.mem0.ai and create one from the dashboard. Keys start with `m0-`.
+
+## Step 2: Initialize the client
+
+**Python:**
+```python
+from mem0 import MemoryClient
+client = MemoryClient(api_key="m0-xxx")
+```
+
+**TypeScript:**
+```typescript
+import MemoryClient from 'mem0ai';
+const client = new MemoryClient({ apiKey: 'm0-xxx' });
+```
+
+For async Python, use `AsyncMemoryClient`.
+
+## Step 3: Core operations
+
+Every Mem0 integration follows the same pattern: **retrieve → generate → store**.
+
+### Add memories
+```python
+messages = [
+    {"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
+    {"role": "assistant", "content": "Got it! I'll remember that."}
+]
+client.add(messages, user_id="alice")
+```
+
+### Search memories
+```python
+results = client.search("dietary preferences", filters={"user_id": "alice"})
+for mem in results.get("results", []):
+    print(mem["memory"])
+```
+
+### Get all memories
+```python
+all_memories = client.get_all(filters={"user_id": "alice"})
+```
+
+### Update a memory
+```python
+client.update("memory-uuid", text="Updated: vegetarian, nut allergy, prefers organic")
+```
+
+### Delete a memory
+```python
+client.delete("memory-uuid")
+client.delete_all(user_id="alice")  # delete all for a user
+```
+
+## Common integration pattern
+
+```python
+from mem0 import MemoryClient
+from openai import OpenAI
+
+mem0 = MemoryClient()
+openai = OpenAI()
+
+def chat(user_input: str, user_id: str) -> str:
+    # 1. Retrieve relevant memories
+    memories = mem0.search(user_input, filters={"user_id": user_id})
+    context = "\n".join([m["memory"] for m in memories.get("results", [])])
+
+    # 2. Generate response with memory context
+    response = openai.chat.completions.create(
+        model="gpt-5-mini",
+        messages=[
+            {"role": "system", "content": f"User context:\n{context}"},
+            {"role": "user", "content": user_input},
+        ]
+    )
+    reply = response.choices[0].message.content
+
+    # 3. Store interaction for future context
+    mem0.add(
+        [{"role": "user", "content": user_input}, {"role": "assistant", "content": reply}],
+        user_id=user_id
+    )
+    return reply
+```
+
+## Common edge cases
+
+- **Search returns empty:** v3 processes `add()` asynchronously — returns an event ID immediately. Wait 2-3s before searching. Also verify `user_id` matches exactly (case-sensitive) and use `filters={"user_id": "..."}` syntax.
+- **AND filter with user_id + agent_id returns empty:** Entities are stored separately. `{"AND": [{"user_id": "alice"}, {"agent_id": "bot"}]}` returns nothing. Use `OR` instead, or query each separately.
+- **Duplicate memories:** Don't mix `infer=True` (default) and `infer=False` for the same data. `infer=True` extracts facts via LLM with dedup. `infer=False` stores raw — same text can be stored twice.
+- **Implicit null scoping:** `filters={"user_id": "alice"}` only returns memories where `agent_id`, `app_id`, `run_id` are ALL null. Wrap in `{"OR": [...]}` to include memories with non-null scoping fields.
+- **Platform vs OSS imports:** Platform: `from mem0 import MemoryClient`. OSS: `from mem0 import Memory`. Don't mix them — `MemoryClient` talks to `api.mem0.ai`, `Memory` runs locally.
+- **v3 defaults:** `top_k=20`, `threshold=0.1`, `rerank=False`. Adjust as needed.
+
+## v3 API (Current)
+
+Mem0 v3 uses single-pass extraction, entity linking, and multi-signal retrieval.
+
+**Key v3 changes from v2:**
+- **Endpoints:** `POST /v3/memories/add/`, `POST /v3/memories/search/`, `POST /v3/memories/` (paginated list)
+- **Extraction:** Single ADD-only pass — no more UPDATE/DELETE operations during extraction. Memories accumulate rather than consolidate.
+- **Entity linking:** Replaces graph memory. Auto-extracted during `add()`, no config needed. Remove `enable_graph` and `graph_store` from any old config.
+- **Defaults:** `top_k=20`, `threshold=0.1`, `rerank=False`
+- **Removed params:** `org_id`, `project_id`, `enable_graph` — all removed from SDK
+- **TypeScript:** Exclusively camelCase (`userId`, `agentId`, `appId`, `topK`)
+- **Add response:** Async — returns event ID immediately, poll via `GET /v1/event/{event_id}/`
+
+See the [migration guide](https://docs.mem0.ai/migration/platform-v2-to-v3) for details.
+
+## Live documentation search
+
+For the latest docs beyond what's in the references, use the doc search tool:
+
+```bash
+bun ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.ts --query "topic"
+bun ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.ts --page "/platform/features/graph-memory"
+bun ${CLAUDE_SKILL_DIR}/scripts/mem0_doc_search.ts --index
+```
+
+No API key needed — searches docs.mem0.ai directly.
+
+## Client SDK References
+
+Language-specific deep references (Platform + OSS):
+
+| Language | File |
+|----------|------|
+| Python (MemoryClient + AsyncMemoryClient + Memory OSS) | [client/python.md](client/python.md) |
+| TypeScript/Node.js (MemoryClient + Memory OSS) | [client/node.md](client/node.md) |
+| Python vs TypeScript differences | [client/differences.md](client/differences.md) |
+
+## Platform References
+
+Load these on demand for deeper detail:
+
+| Topic | File |
+|-------|------|
+| Quickstart (Python, TS, cURL) | [references/quickstart.md](references/quickstart.md) |
+| SDK guide (all methods, both languages) | [references/sdk-guide.md](references/sdk-guide.md) |
+| API reference (endpoints, filters, object schema) | [references/api-reference.md](references/api-reference.md) |
+| Architecture (pipeline, lifecycle, scoping, performance) | [references/architecture.md](references/architecture.md) |
+| Platform features (retrieval, graph, categories, MCP, etc.) | [references/features.md](references/features.md) |
+| Framework integrations (LangChain, CrewAI, OpenAI Agents, etc.) | [references/integration-patterns.md](references/integration-patterns.md) |
+| Use cases & examples (real-world patterns with code) | [references/use-cases.md](references/use-cases.md) |
+
+## Related Mem0 Skills
+
+| Skill | When to use | Link |
+|-------|-------------|------|
+| mem0-vercel-ai-sdk | Vercel AI SDK provider with automatic memory | [GitHub](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk) |
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/mem0/client/differences.md

@@ -0,0 +1,129 @@
+# Python vs TypeScript SDK Differences
+
+Quick-reference cheatsheet for developers working across both Mem0 SDKs.
+
+## Constructor
+
+| Aspect | Python | TypeScript |
+|--------|--------|------------|
+| Import (Platform) | `from mem0 import MemoryClient` | `import MemoryClient from 'mem0ai'` |
+| Import (OSS) | `from mem0 import Memory` | `import { Memory } from 'mem0ai/oss'` |
+| Constructor | `MemoryClient(api_key="m0-xxx")` | `new MemoryClient({ apiKey: 'm0-xxx' })` |
+| Required param | `api_key` (positional or kwarg) | `apiKey` (in options object) |
+
+Both read from `MEM0_API_KEY` env var if no key provided.
+
+## Method Naming
+
+| Operation | Python | TypeScript |
+|-----------|--------|------------|
+| Add | `add()` | `add()` |
+| Search | `search()` | `search()` |
+| Get | `get()` | `get()` |
+| Get all | `get_all()` | `getAll()` |
+| Update | `update()` | `update()` |
+| Delete | `delete()` | `delete()` |
+| Delete all | `delete_all()` | `deleteAll()` |
+| History | `history()` | `history()` |
+| Batch update | `batch_update()` | `batchUpdate()` |
+| Batch delete | `batch_delete()` | `batchDelete()` |
+| List users | `users()` | `users()` |
+| Delete users | `delete_users()` | `deleteUsers()` |
+| Get project | `project.get()` | `getProject()` |
+| Update project | `project.update()` | `updateProject()` |
+| Create webhook | `create_webhook()` | `createWebhook()` |
+| Get webhooks | `get_webhooks()` | `getWebhooks()` |
+| Update webhook | `update_webhook()` | `updateWebhook()` |
+| Delete webhook | `delete_webhook()` | `deleteWebhook()` |
+| Create export | `create_memory_export()` | `createMemoryExport()` |
+| Get export | `get_memory_export()` | `getMemoryExport()` |
+| Feedback | `feedback()` | `feedback()` |
+
+**Rule:** Python uses `snake_case`, TypeScript uses `camelCase` for method names.
+
+## Parameter Passing
+
+```python
+# Python: kwargs
+client.add(messages, user_id="alice", metadata={"source": "chat"})
+client.search("query", filters={"user_id": "alice"}, top_k=5, rerank=True)
+```
+
+```typescript
+// TypeScript: options object with camelCase for top-level params, snake_case for filter keys
+await client.add(messages, { userId: 'alice', metadata: { source: 'chat' } });
+await client.search('query', { filters: { user_id: 'alice' }, topK: 5, rerank: true });
+```
+
+**v3:** Python uses `snake_case` everywhere. TypeScript uses `camelCase` for top-level params (`userId`, `topK`) but `snake_case` for filter keys (`user_id`, `agent_id`).
+
+## Architectural Differences
+
+| Aspect | Python | TypeScript |
+|--------|--------|------------|
+| HTTP library | httpx | axios |
+| Default timeout | 300s | 60s |
+| Sync support | Yes (`MemoryClient`) | No (all async) |
+| Async support | Yes (`AsyncMemoryClient`) | All methods are async |
+| Project management | `client.project.*` (separate class) | `client.getProject()` / `client.updateProject()` |
+| Context manager | `async with AsyncMemoryClient()` | Not supported |
+
+## Platform Features: Python-only
+
+These methods exist in Python but not TypeScript:
+
+| Method | Description |
+|--------|-------------|
+| `get_summary(filters)` | Get summary of memories |
+| `reset()` | Delete ALL data (users + memories) |
+| `project.create(name)` | Create a new project |
+| `project.delete()` | Delete current project |
+| `project.get_members()` | List project members |
+| `project.add_member(email, role)` | Add member to project |
+| `project.update_member(email, role)` | Change member role |
+| `project.remove_member(email)` | Remove member |
+
+## Platform Features: TypeScript-only
+
+| Method | Description |
+|--------|-------------|
+| `deleteUser(data)` | Convenience method for single entity deletion |
+| `ping()` | Health check endpoint |
+
+## OSS Config Naming
+
+| Python config key | TypeScript config key |
+|-------------------|----------------------|
+| `vector_store` | `vectorStore` |
+| `history_db_path` | `historyDbPath` |
+| `custom_instructions` | `customInstructions` |
+
+## OSS Scope Parameter Naming
+
+| Python | TypeScript |
+|--------|------------|
+| `user_id="alice"` | `userId: 'alice'` |
+| `agent_id="bot"` | `agentId: 'bot'` |
+| `run_id="session"` | `runId: 'session'` |
+
+## Entity ID Passing (v3)
+
+| Method | Python | TypeScript |
+|--------|--------|------------|
+| add() | Top-level: `user_id="alice"` | Top-level: `{ userId: 'alice' }` |
+| search() | In filters: `filters={"user_id": "alice"}` | In filters: `{ filters: { user_id: 'alice' } }` |
+| get_all() | In filters: `filters={"user_id": "alice"}` | In filters: `{ filters: { user_id: 'alice' } }` |
+
+## Common Gotcha
+
+When searching/filtering, both Python and TypeScript use `snake_case` for filter keys. TypeScript only uses `camelCase` for top-level method parameters:
+
+```python
+# Python - snake_case in filters
+results = client.search("query", filters={"user_id": "alice"})
+```
+
+```typescript
+// TypeScript - snake_case in filters, camelCase for top-level params
+const results = await client.search('query', { filters: { user_id: 'alice' }, topK: 20 });
+```

mem0-plugin/.opencode-plugin/opencode-skills/mem0/client/node.md

@@ -0,0 +1,418 @@
+# Mem0 Node.js / TypeScript SDK Reference
+
+Complete reference for the `mem0ai` npm package. Covers both the Platform client (managed API) and the Open Source self-hosted variant.
+
+---
+
+## Platform Client
+
+### Installation
+
+```bash
+npm install mem0ai
+export MEM0_API_KEY="m0-your-api-key"
+```
+
+### MemoryClient
+
+```typescript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: 'm0-xxx' });
+```
+
+**Constructor:** `new MemoryClient({ apiKey })`. If `apiKey` is not provided, reads from `MEM0_API_KEY` environment variable.
+
+- HTTP library: `axios`
+- Timeout: 60 seconds
+- Base URL: `https://api.mem0.ai`
+- All methods are async (return `Promise`)
+
+---
+
+### Memory Methods
+
+#### add(messages, options?)
+
+Store new memories from messages.
+
+```typescript
+const messages = [
+    { role: 'user', content: "I'm a vegetarian and allergic to nuts." },
+    { role: 'assistant', content: "Got it! I'll remember that." },
+];
+await client.add(messages, { userId: 'alice' });
+```
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `messages` | `Message[]` | Array of `{role, content}` objects |
+| `options.userId` | string | User identifier |
+| `options.agentId` | string | Agent identifier |
+| `options.appId` | string | Application identifier |
+| `options.runId` | string | Session identifier |
+| `options.metadata` | object | Custom key-value pairs |
+| `options.infer` | boolean | If false, store raw text (default: true) |
+
+**Returns:** `Promise<any>` -- list of events
+
+#### search(query, options?)
+
+Search memories by semantic similarity.
+
+```typescript
+const results = await client.search('dietary preferences', { filters: { user_id: 'alice' }, topK: 20 });
+for (const mem of results.results) {
+    console.log(mem.memory, mem.score);
+}
+```
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `query` | string | Natural language search query |
+| `options.filters` | object | Filter object with entity IDs (`user_id`, `agent_id`, etc.) and/or `AND`/`OR`/`NOT` conditions |
+| `options.topK` | number | Number of results (default: 20) |
+| `options.rerank` | boolean | Enable semantic reranking (default: false) |
+| `options.threshold` | number | Minimum similarity (default: 0.1) |
+
+**Returns:** `Promise<SearchResult>` -- `{results: [{id, memory, score, ...}]}`
+
+#### get(memoryId)
+
+```typescript
+const memory = await client.get('ea925981-...');
+```
+
+#### getAll(options?)
+
+Retrieve all memories. Requires at least one entity identifier in filters.
+
+```typescript
+const memories = await client.getAll({ filters: { user_id: 'alice' } });
+// With filters
+const filtered = await client.getAll({
+    filters: { AND: [{ user_id: 'alice' }, { categories: { contains: 'health' } }] },
+});
+```
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `options.filters` | object | Filter object with entity IDs (`user_id`, `agent_id`, etc.) and/or `AND`/`OR`/`NOT` conditions |
+| `options.page` | number | Page number |
+| `options.pageSize` | number | Results per page |
+
+#### update(memoryId, data)
+
+```typescript
+await client.update('ea925981-...', { text: 'Updated: vegan since 2024' });
+await client.update('ea925981-...', { text: 'Updated', metadata: { verified: true } });
+```
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `memoryId` | string | Memory ID |
+| `data.text` | string | New content |
+| `data.metadata` | object | New metadata |
+| `data.timestamp` | string | New timestamp |
+
+#### delete(memoryId)
+
+```typescript
+await client.delete('ea925981-...');
+```
+
+#### deleteAll(options?)
+
+```typescript
+await client.deleteAll({ userId: 'alice' });
+```
+
+#### history(memoryId)
+
+```typescript
+const history = await client.history('ea925981-...');
+// Returns: [{previousValue, newValue, action, timestamps}]
+```
+
+---
+
+### Batch Methods
+
+#### batchUpdate(memories)
+
+```typescript
+await client.batchUpdate([
+    { memoryId: 'uuid-1', text: 'Updated text' },
+    { memoryId: 'uuid-2', text: 'Another update' },
+]);
+```
+
+#### batchDelete(memories)
+
+```typescript
+await client.batchDelete(['uuid-1', 'uuid-2', 'uuid-3']);
+```
+
+---
+
+### User/Entity Management
+
+#### users()
+
+```typescript
+const users = await client.users();
+// Returns: {results: [{type: "user", name: "alice"}, ...]}
+```
+
+#### deleteUser(data) / deleteUsers(data)
+
+```typescript
+await client.deleteUser({ userId: 'alice' });  // Single entity
+await client.deleteUsers({ agentId: 'bot-1' }); // Flexible
+```
+
+---
+
+### Project Management
+
+```typescript
+// Get project config
+const config = await client.getProject({ fields: ['customCategories'] });
+
+// Update project settings
+await client.updateProject({
+    customInstructions: 'Extract dietary preferences and health info',
+    customCategories: [{ health: 'Medical and dietary info' }],
+});
+```
+
+---
+
+### Webhooks
+
+```typescript
+// List
+const webhooks = await client.getWebhooks({ projectId: 'proj_123' });
+
+// Create
+const webhook = await client.createWebhook({
+    url: 'https://your-app.com/webhook',
+    name: 'Memory Logger',
+    projectId: 'proj_123',
+    eventTypes: ['memory_add', 'memory_update'],
+});
+
+// Update
+await client.updateWebhook({
+    webhookId: 'wh_123',
+    name: 'Updated Logger',
+    url: 'https://new-url.com',
+});
+
+// Delete
+await client.deleteWebhook({ webhookId: 'wh_123' });
+```
+
+---
+
+### Feedback
+
+```typescript
+await client.feedback({
+    memoryId: 'mem-123',
+    feedback: 'POSITIVE',
+    feedbackReason: 'Accurately captured preference',
+});
+```
+
+---
+
+### Export
+
+```typescript
+const exportReq = await client.createMemoryExport({
+    schema: JSON.stringify({ type: 'object', properties: { name: { type: 'string' } } }),
+    filters: { user_id: 'alice' },
+});
+
+const result = await client.getMemoryExport({ memoryExportId: exportReq.id });
+```
+
+---
+
+### TypeScript Types
+
+Key interfaces from `mem0.types.ts`:
+
+```typescript
+interface Message { role: string; content: string; }
+interface Memory { id: string; memory: string; userId: string; categories: string[]; score?: number; /* ... */ }
+interface MemoryOptions { userId?: string; agentId?: string; appId?: string; runId?: string; metadata?: object; /* ... */ }
+interface SearchOptions { filters?: object; topK?: number; rerank?: boolean; threshold?: number; /* ... */ }
+interface MemoryHistory { id: string; memoryId: string; previousValue: string; newValue: string; action: string; /* ... */ }
+interface FeedbackPayload { memoryId: string; feedback: string; feedbackReason?: string; }
+interface WebhookCreatePayload { url: string; name: string; projectId: string; eventTypes: string[]; }
+```
+
+---
+
+## Open Source / Self-Hosted
+
+### Installation
+
+```bash
+npm install mem0ai
+```
+
+### Memory Class
+
+```typescript
+import { Memory } from 'mem0ai/oss';
+
+const m = new Memory();  // Uses default config
+```
+
+**Import:** `from 'mem0ai/oss'` (NOT the default export -- that is `MemoryClient` for Platform)
+
+### Configuration
+
+```typescript
+const config = {
+    llm: {
+        provider: 'openai',        // openai, groq, anthropic, google, ollama, lmstudio, mistral, azure
+        config: {
+            model: 'gpt-5-mini',
+            apiKey: 'sk-xxx',
+        },
+    },
+    embedder: {
+        provider: 'openai',        // openai, ollama, lmstudio, google, azure, langchain, anthropic
+        config: {
+            model: 'text-embedding-3-small',
+            apiKey: 'sk-xxx',
+        },
+    },
+    vectorStore: {
+        provider: 'qdrant',        // memory, qdrant, redis, supabase, langchain, azure_ai_search, pgvector
+        config: {
+            collectionName: 'my_memories',
+            host: 'localhost',
+            port: 6333,
+        },
+    },
+    historyDbPath: 'history.db',
+    customInstructions: '...',
+    disableHistory: false,
+};
+
+const m = new Memory(config);
+// Or from dict with validation:
+const m2 = Memory.fromConfig(config);
+```
+
+### Methods
+
+All methods are async (return `Promise`):
+
+#### add(messages, config)
+
+```typescript
+await m.add('I prefer dark mode', { userId: 'alice' });
+await m.add([
+    { role: 'user', content: 'I like hiking' },
+    { role: 'assistant', content: 'Great outdoor activity!' },
+], { userId: 'alice' });
+```
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `messages` | `string \| Message[]` | Content to store |
+| `config.userId` | string | User identifier (at least one scope required) |
+| `config.agentId` | string | Agent identifier |
+| `config.runId` | string | Session identifier |
+| `config.metadata` | object | Custom key-value pairs |
+| `config.filters` | object | Additional filters |
+| `config.infer` | boolean | LLM inference (default: true) |
+
+**Returns:** `Promise<{results: [...], relations?: [...]}>`
+
+#### search(query, config)
+
+```typescript
+const results = await m.search('dietary preferences', { filters: { user_id: 'alice' }, topK: 5 });
+```
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `query` | string | Search query |
+| `config.filters` | object | Filter object with entity IDs (`user_id`, `agent_id`, `run_id`, etc.) |
+| `config.topK` | number | Max results (default: 20) |
+
+#### get(memoryId) / getAll(config) / update(memoryId, data) / delete(memoryId) / deleteAll(config) / history(memoryId)
+
+Same interface patterns. Note: OSS `update` takes a string for data, not an object.
+
+```typescript
+await m.update('mem-id', 'new content');
+```
+
+#### reset()
+
+Clear the entire vector store and history.
+
+```typescript
+await m.reset();
+```
+
+---
+
+## Key Differences: Platform vs OSS
+
+| Aspect | Platform (`MemoryClient`) | OSS (`Memory`) |
+|--------|--------------------------|----------------|
+| **Import** | `import MemoryClient from 'mem0ai'` | `import { Memory } from 'mem0ai/oss'` |
+| **Auth** | API key required (`MEM0_API_KEY`) | No API key -- config-based |
+| **Execution** | API calls to `api.mem0.ai` | Local execution |
+| **Infrastructure** | Fully managed | Self-managed vector DB, embedder, LLM |
+| **Param style** | Top-level: `camelCase` (`userId`, `topK`), filter keys: `snake_case` (`user_id`) | Top-level: `camelCase` (`userId`, `topK`), filter keys: `snake_case` (`user_id`) |
+| **Batch ops** | `batchUpdate`, `batchDelete` | Not available |
+| **Webhooks** | Full CRUD | Not available |
+| **Export** | `createMemoryExport` | Not available |
+| **Feedback** | `feedback()` | Not available |
+| **Project mgmt** | `getProject`, `updateProject` | Not available |
+| **User listing** | `users()`, `deleteUser()` | Not available |
+| **History** | Platform-managed | SQLite (configurable) |
+
+---
+
+## v2 Compatibility
+
+If you're using SDK v2.x:
+
+**Naming Changes:**
+- Top-level params now use camelCase: `topK`, `rerank` (not `top_k`)
+- Filter keys use snake_case: `user_id`, `agent_id`
+- OSS: `limit` renamed to `topK`
+
+**API Changes:**
+```typescript
+// v2 - top-level entity IDs, snake_case
+await client.search("query", { user_id: "alice", top_k: 20 });
+
+// v3 - filters object with snake_case keys, camelCase top-level params
+await client.search("query", { filters: { user_id: "alice" }, topK: 20 });
+```
+
+**Default Changes:**
+| Param | v2 | v3 |
+|-------|----|----|
+| `topK` | 100 | 20 |
+| `threshold` | none | 0.1 |
+| `rerank` | true | false |
+
+**Removed:**
+- `OutputFormat` and `API_VERSION` enums
+- `organizationId`, `projectId` from constructor
+- `enableGraph`, `asyncMode`, `outputFormat`, `immutable`, `expirationDate`, `filterMemories`, `batchSize`, `forceAddOnly`, `includes`, `excludes`, `keywordSearch`
+
+See the [v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for details.

mem0-plugin/.opencode-plugin/opencode-skills/mem0/client/python.md

@@ -0,0 +1,487 @@
+# Mem0 Python SDK Reference
+
+Complete reference for the `mem0ai` Python package. Covers both the Platform client (managed API) and the Open Source self-hosted variant.
+
+---
+
+## Platform Client
+
+### Installation
+
+```bash
+pip install mem0ai
+export MEM0_API_KEY="m0-your-api-key"
+```
+
+### MemoryClient (Synchronous)
+
+```python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="m0-xxx")
+```
+
+**Constructor:** `MemoryClient(api_key=None)`. If `api_key` is not provided, reads from `MEM0_API_KEY` environment variable. Raises `ValueError` if no key found.
+
+- HTTP library: `httpx`
+- Timeout: 300 seconds
+- Base URL: `https://api.mem0.ai`
+
+### AsyncMemoryClient (Asynchronous)
+
+```python
+from mem0 import AsyncMemoryClient
+
+client = AsyncMemoryClient(api_key="m0-xxx")
+
+# Or use as context manager
+async with AsyncMemoryClient(api_key="m0-xxx") as client:
+    results = await client.search("query", filters={"user_id": "alice"})
+```
+
+Same methods as `MemoryClient`, all `async`/`await`. Supports async context manager.
+
+---
+
+### Memory Methods
+
+#### add(messages, **kwargs)
+
+Store new memories from messages.
+
+```python
+messages = [
+    {"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
+    {"role": "assistant", "content": "Got it! I'll remember that."}
+]
+client.add(messages, user_id="alice")
+```
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `messages` | str \| dict \| list[dict] | required | Message content. Strings auto-convert to user messages |
+| `user_id` | str | None | User identifier |
+| `agent_id` | str | None | Agent identifier |
+| `app_id` | str | None | Application identifier |
+| `run_id` | str | None | Session/run identifier |
+| `metadata` | dict | None | Custom key-value pairs |
+| `infer` | bool | True | If False, store raw text without LLM inference |
+| `custom_categories` | list | None | Override project categories |
+| `custom_instructions` | str | None | Override extraction instructions |
+| `timestamp` | int \| float \| str | None | Custom timestamp (Unix epoch or ISO 8601) |
+
+**Returns:** `dict` -- list of events: `[{"id": "...", "event": "ADD", "data": {"memory": "..."}}]`
+
+#### search(query, **kwargs)
+
+Search memories by semantic similarity.
+
+```python
+results = client.search("dietary preferences", filters={"user_id": "alice"})
+for mem in results.get("results", []):
+    print(mem["memory"], mem["score"])
+```
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `query` | str | required | Natural language search query |
+| `filters` | dict | None | Filter object with entity IDs and/or `AND`/`OR`/`NOT` conditions (e.g., `{"user_id": "alice"}`) |
+| `top_k` | int | 10 | Number of results |
+| `rerank` | bool | False | Enable deep semantic reranking (+150-200ms) |
+| `threshold` | float | 0.1 | Minimum similarity score |
+| `fields` | list | None | Specific fields to return |
+| `categories` | list | None | Filter by category |
+
+**Returns:** `dict` -- `{"results": [{id, memory, user_id, categories, score, created_at, ...}]}`
+
+#### get(memory_id)
+
+Retrieve a single memory by ID.
+
+```python
+memory = client.get(memory_id="ea925981-...")
+```
+
+**Returns:** `dict` -- full memory object
+
+#### get_all(**kwargs)
+
+Retrieve all memories with optional filtering. Requires at least one entity identifier.
+
+```python
+memories = client.get_all(filters={"user_id": "alice"})
+# With compound filters
+memories = client.get_all(filters={"AND": [{"user_id": "alice"}, {"categories": {"contains": "health"}}]})
+```
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `filters` | dict | None | Filter object with entity IDs and/or `AND`/`OR`/`NOT` conditions |
+| `top_k` | int | None | Limit results |
+| `page` | int | None | Page number |
+| `page_size` | int | None | Results per page |
+
+**Returns:** `dict` -- `{"results": [...]}`
+
+#### update(memory_id, text=None, metadata=None, timestamp=None)
+
+Update a memory's content, metadata, or timestamp. At least one parameter required.
+
+```python
+client.update("ea925981-...", text="Updated: vegan since 2024")
+client.update("ea925981-...", metadata={"verified": True})
+```
+
+**Returns:** `dict` -- updated memory
+
+#### delete(memory_id)
+
+Permanently delete a single memory.
+
+```python
+client.delete("ea925981-...")
+```
+
+#### delete_all(**kwargs)
+
+Delete all memories matching filters. Irreversible.
+
+```python
+client.delete_all(user_id="alice")
+```
+
+#### history(memory_id)
+
+Get the change history of a memory.
+
+```python
+history = client.history("ea925981-...")
+# Returns: [{previous_value, new_value, action, timestamps}]
+```
+
+---
+
+### Batch Methods
+
+#### batch_update(memories)
+
+Update up to 1000 memories in a single request.
+
+```python
+client.batch_update([
+    {"memory_id": "uuid-1", "text": "Updated text"},
+    {"memory_id": "uuid-2", "text": "Another update", "metadata": {"verified": True}},
+])
+```
+
+#### batch_delete(memories)
+
+Delete up to 1000 memories in a single request.
+
+```python
+client.batch_delete([
+    {"memory_id": "uuid-1"},
+    {"memory_id": "uuid-2"},
+])
+```
+
+---
+
+### User/Entity Management
+
+#### users()
+
+List all users, agents, and sessions that have memories.
+
+```python
+users = client.users()
+# Returns: {"results": [{"type": "user", "name": "alice"}, ...]}
+```
+
+#### delete_users(user_id=None, agent_id=None, app_id=None, run_id=None)
+
+Delete a specific entity and all its memories.
+
+```python
+client.delete_users(user_id="alice")
+```
+
+#### reset()
+
+Delete ALL users, agents, sessions, and memories. Complete data reset.
+
+```python
+client.reset()
+```
+
+---
+
+### Export & Summary
+
+#### create_memory_export(schema, **kwargs)
+
+Create a structured export of memories.
+
+```python
+import json
+
+schema = json.dumps({
+    "type": "object",
+    "properties": {
+        "name": {"type": "string"},
+        "preferences": {"type": "array", "items": {"type": "string"}},
+    }
+})
+export = client.create_memory_export(schema=schema, user_id="alice")
+```
+
+#### get_memory_export(**kwargs)
+
+Retrieve a previously created export.
+
+```python
+result = client.get_memory_export(memory_export_id=export["id"])
+```
+
+#### get_summary(filters=None)
+
+Get a summary of memories.
+
+```python
+summary = client.get_summary(filters={"user_id": "alice"})
+```
+
+---
+
+### Feedback
+
+#### feedback(memory_id, feedback=None, feedback_reason=None)
+
+Provide quality feedback on a memory.
+
+```python
+client.feedback(
+    memory_id="mem-123",
+    feedback="POSITIVE",  # POSITIVE | NEGATIVE | VERY_NEGATIVE | None (clear)
+    feedback_reason="Accurately captured preference"
+)
+```
+
+---
+
+### Webhooks
+
+```python
+# List
+webhooks = client.get_webhooks(project_id="proj_123")
+
+# Create
+webhook = client.create_webhook(
+    url="https://your-app.com/webhook",
+    name="Memory Logger",
+    project_id="proj_123",
+    event_types=["memory_add", "memory_update"]
+)
+
+# Update
+client.update_webhook(webhook_id=123, name="Updated", url="https://new-url.com")
+
+# Delete
+client.delete_webhook(webhook_id=123)
+```
+
+---
+
+### Project Management
+
+Access via `client.project.*`:
+
+```python
+# Get project config
+config = client.project.get(fields=["custom_categories", "custom_instructions"])
+
+# Update project settings
+client.project.update(
+    custom_instructions="Extract dietary preferences and health info",
+    custom_categories=[{"health": "Medical and dietary info"}],
+    multilingual=True,
+)
+
+# Create/delete project
+client.project.create(name="My Project", description="...")
+client.project.delete()
+
+# Member management
+members = client.project.get_members()
+client.project.add_member(email="user@example.com", role="READER")  # READER or OWNER
+client.project.update_member(email="user@example.com", role="OWNER")
+client.project.remove_member(email="user@example.com")
+```
+
+---
+
+## Open Source / Self-Hosted
+
+### Installation
+
+```bash
+pip install mem0ai
+```
+
+### Memory Class
+
+```python
+from mem0 import Memory
+
+m = Memory()  # Uses default config (OpenAI embedder + in-memory vector store)
+```
+
+**Import:** `from mem0 import Memory` (NOT `MemoryClient` -- that is the Platform client)
+
+### Configuration
+
+```python
+config = {
+    "llm": {
+        "provider": "openai",        # openai, groq, azure, ollama, lmstudio, google, anthropic, mistral
+        "config": {
+            "model": "gpt-5-mini",
+            "api_key": "sk-xxx",
+        }
+    },
+    "embedder": {
+        "provider": "openai",        # openai, ollama, azure, lmstudio, google, huggingface
+        "config": {
+            "model": "text-embedding-3-small",
+            "api_key": "sk-xxx",
+        }
+    },
+    "vector_store": {
+        "provider": "qdrant",        # faiss, qdrant, pgvector, redis, supabase, azure_ai_search, memory
+        "config": {
+            "collection_name": "my_memories",
+            "host": "localhost",
+            "port": 6333,
+        }
+    },
+    "history_db_path": "history.db",              # SQLite path for change history
+    "custom_instructions": "...",                  # Custom LLM prompt for extraction
+}
+
+m = Memory.from_config(config)
+```
+
+### Context Manager
+
+```python
+with Memory(config) as m:
+    m.add("I prefer dark mode", user_id="alice")
+    results = m.search("preferences", filters={"user_id": "alice"})
+# SQLite connections released automatically
+```
+
+### Methods
+
+All methods mirror the Platform client but run locally:
+
+#### add(messages, *, user_id, agent_id, run_id, metadata, infer=True)
+
+```python
+m.add("I'm a vegetarian", user_id="alice")
+m.add([
+    {"role": "user", "content": "I like hiking"},
+    {"role": "assistant", "content": "Great outdoor activity!"}
+], user_id="alice")
+```
+
+At least one of `user_id`, `agent_id`, `run_id` required.
+
+**Returns:** `{"results": [...], "relations": [...]}`
+
+#### search(query, *, filters=None, top_k=20, threshold=0.1, rerank=False)
+
+```python
+results = m.search("dietary preferences", filters={"user_id": "alice"}, top_k=5)
+```
+
+Entity IDs (`user_id`, `agent_id`, `run_id`) must be passed inside the `filters` dict.
+
+Supports filter operators: `eq`, `ne`, `in`, `nin`, `gt`, `gte`, `lt`, `lte`, `contains`, `not_contains`.
+
+#### get(memory_id) / get_all(**kwargs) / update(memory_id, data, metadata=None) / delete(memory_id) / delete_all(**kwargs) / history(memory_id)
+
+Same interface as Platform client.
+
+#### reset()
+
+Clear the entire vector store collection and history database. Recreates the vector store.
+
+```python
+m.reset()
+```
+
+#### close()
+
+Release SQLite connections. Called automatically when using context manager.
+
+### AsyncMemory
+
+```python
+from mem0 import AsyncMemory
+
+m = AsyncMemory(config)
+await m.add("text", user_id="alice")
+results = await m.search("query", filters={"user_id": "alice"})
+```
+
+---
+
+## Key Differences: Platform vs OSS
+
+| Aspect | Platform (`MemoryClient`) | OSS (`Memory`) |
+|--------|--------------------------|----------------|
+| **Import** | `from mem0 import MemoryClient` | `from mem0 import Memory` |
+| **Auth** | API key required (`MEM0_API_KEY`) | No API key -- config-based |
+| **Execution** | API calls to `api.mem0.ai` | Local execution |
+| **Infrastructure** | Fully managed | Self-managed vector DB, embedder, LLM |
+| **Entity filtering** | `filters={"user_id": "..."}` | `filters={"user_id": "..."}` |
+| **Batch ops** | `batch_update`, `batch_delete` | Not available |
+| **Webhooks** | Full CRUD | Not available |
+| **Export** | `create_memory_export`, `get_memory_export` | Not available |
+| **Feedback** | `feedback()` | Not available |
+| **Project mgmt** | `client.project.*` | Not available |
+| **User listing** | `users()`, `delete_users()` | Not available |
+| **Custom prompts** | Via project settings | Direct config (`custom_instructions`) |
+| **History** | Platform-managed | SQLite (configurable) |
+| **Async** | `AsyncMemoryClient` | `AsyncMemory` |
+
+---
+
+## v2 Compatibility
+
+If you're using SDK v2.x or the v2 API:
+
+**API Changes:**
+- **Entity IDs in search/get_all:** Pass `user_id`, `agent_id` as top-level kwargs instead of inside `filters`
+  ```python
+  # v2
+  results = client.search("query", user_id="alice")
+  # v3
+  results = client.search("query", filters={"user_id": "alice"})
+  ```
+- **add() returns:** v2 returns ADD, UPDATE, DELETE events; v3 returns ADD only
+
+**Default Changes:**
+| Param | v2 | v3 |
+|-------|----|----|
+| `top_k` | 100 | 20 |
+| `threshold` | None | 0.1 |
+| `rerank` | True | False |
+
+**Removed Parameters:**
+- Constructor: `org_id`, `project_id`
+- add(): `async_mode`, `output_format`, `enable_graph`, `immutable`, `expiration_date`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`
+- search()/get_all(): `enable_graph`
+- Config: `enable_graph`, `graph_store`, `custom_fact_extraction_prompt` (renamed to `custom_instructions`)
+
+See the [v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for full details.

mem0-plugin/.opencode-plugin/opencode-skills/mem0/references/api-reference.md

@@ -0,0 +1,150 @@
+# Mem0 Platform API Reference
+
+REST API endpoints for the Mem0 Platform. Base URL: `https://api.mem0.ai`
+
+All endpoints require: `Authorization: Token <MEM0_API_KEY>`
+
+## Endpoints
+
+| Operation | Method | URL |
+|-----------|--------|-----|
+| Add Memories | `POST` | `/v3/memories/add/` |
+| Search Memories | `POST` | `/v3/memories/search/` |
+| Get All Memories | `POST` | `/v3/memories/` |
+| Get Single Memory | `GET` | `/v1/memories/{memory_id}/` |
+| Update Memory | `PUT` | `/v1/memories/{memory_id}/` |
+| Delete Memory | `DELETE` | `/v1/memories/{memory_id}/` |
+| Delete All Memories | `DELETE` | `/v1/memories/?user_id=X&app_id=Y` |
+| Get Event Status | `GET` | `/v1/event/{event_id}/` |
+
+## Memory Object Structure
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | string (UUID) | Unique memory identifier |
+| `memory` | string | Text content of the memory |
+| `user_id` | string | Associated user |
+| `agent_id` | string (nullable) | Agent identifier |
+| `app_id` | string (nullable) | Application identifier |
+| `run_id` | string (nullable) | Run/session identifier |
+| `metadata` | object | Custom key-value pairs |
+| `categories` | array of strings | Auto-assigned category tags |
+| `hash` | string | Content hash |
+| `created_at` | datetime | Creation timestamp |
+| `updated_at` | datetime | Last modification timestamp |
+
+Search results additionally include `score` (relevance metric).
+
+## Scoping Identifiers
+
+Memories can be scoped to different levels:
+
+| Scope | Parameter | Use Case |
+|-------|-----------|----------|
+| User | `user_id` | Per-user memory isolation |
+| Agent | `agent_id` | Per-agent memory partitioning |
+| Application | `app_id` | Cross-agent app-level memory |
+| Run/Session | `run_id` | Session-scoped temporary memory |
+
+**Critical:** Combining `user_id` and `agent_id` in a single AND filter yields empty results. Entities are stored separately. Use `OR` logic or separate queries.
+
+## Processing Model
+
+- Memories are processed **asynchronously** (v3 default)
+- Add responses return queued `ADD` events only (v3 is ADD-only, no UPDATE/DELETE)
+- Poll status via `GET /v1/event/{event_id}/`
+
+## Filter System
+
+Filters use nested JSON with a logical operator at the root:
+
+```json
+{
+    "AND": [
+        {"user_id": "alice"},
+        {"categories": {"contains": "finance"}},
+        {"created_at": {"gte": "2024-01-01"}}
+    ]
+}
+```
+
+Root must be `AND`, `OR`, or `NOT`. Simple shorthand `{"user_id": "alice"}` also works.
+
+### Supported Operators
+
+| Operator | Description |
+|----------|-------------|
+| `eq` | Equal to (default) |
+| `ne` | Not equal to |
+| `in` | Matches any value in array |
+| `gt`, `gte` | Greater than / greater than or equal |
+| `lt`, `lte` | Less than / less than or equal |
+| `contains` | Case-sensitive containment |
+| `icontains` | Case-insensitive containment |
+| `*` | Wildcard -- matches any non-null value |
+
+### Filterable Fields
+
+| Field | Valid Operators |
+|-------|-----------------|
+| `user_id`, `agent_id`, `app_id`, `run_id` | `eq`, `ne`, `in`, `*` |
+| `created_at`, `updated_at`, `timestamp` | `gt`, `gte`, `lt`, `lte`, `eq`, `ne` |
+| `categories` | `eq`, `ne`, `in`, `contains` |
+| `metadata` | `eq`, `ne`, `contains` (top-level keys only) |
+| `keywords` | `contains`, `icontains` |
+| `memory_ids` | `in` |
+
+### Filter Constraints
+
+1. **Entity scope partitioning:** `user_id` AND `agent_id` in one `AND` block yields empty results.
+2. **Metadata limitations:** Only top-level keys. Only `eq`, `contains`, `ne`. No `in` or `gt`.
+3. **Operator syntax:** Use `gte`, `lt`, `ne`. SQL-style (`>=`, `!=`) rejected.
+4. **Entity filter required for get-all:** At least one of `user_id`, `agent_id`, `app_id`, or `run_id`.
+5. **Wildcard excludes null:** `*` matches only non-null values.
+6. **Date format:** ISO 8601 (`YYYY-MM-DDTHH:MM:SSZ`). Timezone-naive defaults to UTC.
+
+## Response Formats
+
+### Add Response (v3)
+
+```json
+{
+  "message": "Memory processing has been queued for background execution",
+  "status": "PENDING",
+  "event_id": "evt-uuid"
+}
+```
+
+v3 is ADD-only. No UPDATE or DELETE events.
+
+### Search Response
+
+```json
+{
+  "results": [
+    {
+      "id": "ea925981-...",
+      "memory": "Is a vegetarian and allergic to nuts.",
+      "user_id": "user123",
+      "categories": ["food", "health"],
+      "score": 0.89,
+      "created_at": "2024-07-26T10:29:36.630547-07:00"
+    }
+  ]
+}
+```
+
+In v3, `score` is a combined multi-signal relevance score.
+
+### Get All Response (v3)
+
+```json
+{
+  "count": 123,
+  "next": "https://api.mem0.ai/v3/memories/?page=2&page_size=50",
+  "previous": null,
+  "results": [...]
+}
+```
+
+v3 returns paginated envelope. Use `page` and `page_size` query params.

mem0-plugin/.opencode-plugin/opencode-skills/mem0/references/architecture.md

@@ -0,0 +1,330 @@
+# Mem0 Platform Architecture
+
+How Mem0 processes, stores, and retrieves memories under the hood.
+
+## Table of Contents
+
+- [Core Concept](#core-concept)
+- [Memory Processing Pipeline](#memory-processing-pipeline)
+- [Retrieval Pipeline](#retrieval-pipeline)
+- [Memory Lifecycle](#memory-lifecycle)
+- [Memory Object Structure](#memory-object-structure)
+- [Scoping & Multi-Tenancy](#scoping--multi-tenancy)
+- [Memory Layers](#memory-layers)
+- [Performance Characteristics](#performance-characteristics)
+
+---
+
+## Core Concept
+
+Mem0 is a managed memory layer that sits between your AI application and users. Every integration follows the same 3-step loop:
+
+```
+User Input → Retrieve relevant memories → Enrich LLM prompt → Generate response → Store new memories
+```
+
+Mem0 handles the complexity of extraction, deduplication, conflict resolution, and semantic retrieval so your application only needs to call `search()` and `add()`.
+
+**Storage architecture:**
+- **Vector store**: Embeddings for semantic similarity search
+- **Entity store**: Automatic entity linking for relationship-aware retrieval
+
+---
+
+## Memory Processing Pipeline
+
+### What happens when you call `client.add()`
+
+```
+Messages In
+    │
+    ▼
+┌─────────────────────┐
+│  1. EXTRACTION       │  Single LLM call extracts all distinct new facts
+│     (infer=True)     │  If infer=False, stores raw text as-is
+└─────────┬───────────┘
+          │
+          ▼
+┌─────────────────────┐
+│  2. DEDUPLICATION    │  Hash-based dedup (MD5 prevents exact duplicates)
+│                      │  No UPDATE/DELETE - v3 is ADD-only
+└─────────┬───────────┘
+          │
+          ▼
+┌─────────────────────┐
+│  3. STORAGE          │  Batch embed → vector store
+│                      │  Entity extraction → entity store
+└─────────┬───────────┘
+          │
+          ▼
+    Memory Object
+```
+
+### Processing (v3)
+
+v3 processes memories asynchronously by default:
+- API returns immediately: `{"status": "PENDING", "event_id": "evt-..."}`
+- Poll status via `GET /v1/event/{event_id}/`
+- Use webhooks for completion notifications
+
+### Extraction modes
+
+**Inferred (`infer=True`, default):**
+- LLM extracts structured facts from conversation
+- Conflict resolution deduplicates and resolves contradictions
+- Best for: natural conversation → memory
+
+**Raw (`infer=False`):**
+- Stores text exactly as provided, no LLM processing
+- Skips conflict resolution — same fact can be stored twice
+- Only `user` role messages are stored; `assistant` messages ignored
+- Best for: bulk imports, pre-structured data, migrations
+
+**Warning:** Don't mix `infer=True` and `infer=False` for the same data — the same fact will be stored twice.
+
+---
+
+## Retrieval Pipeline (v3)
+
+### What happens when you call `client.search()`
+
+```
+Query In
+    │
+    ▼
+┌─────────────────────┐
+│  1. PREPROCESSING    │  Lemmatize keywords, extract entities
+└─────────┬───────────┘
+          │
+          ▼
+┌─────────────────────┐
+│  2. PARALLEL SCORING │  Semantic search (vector similarity)
+│                      │  BM25 keyword search (term matching)
+│                      │  Entity matching (entity graph boost)
+└─────────┬───────────┘
+          │
+          ▼
+┌─────────────────────┐
+│  3. SCORE FUSION     │  Combine signals into single score
+│                      │  Optional: rerank=True for deep reordering
+└─────────┬───────────┘
+          │
+          ▼
+    Results (combined score per memory)
+```
+
+### v3 Search Defaults
+
+| Parameter | Default | Notes |
+|-----------|---------|-------|
+| `top_k` | 20 | Was 100 in v2 |
+| `threshold` | 0.1 | Was None in v2 |
+| `rerank` | False | Was True in v2 |
+
+### Implicit null scoping
+
+When you search with `filters={"user_id": "alice"}` only, Mem0 returns memories where `agent_id`, `app_id`, and `run_id` are all null. This prevents cross-scope leakage by default.
+
+To include memories with non-null fields, use explicit filters:
+```python
+# Gets memories for alice regardless of agent/app/run
+filters={"OR": [{"user_id": "alice"}]}
+```
+
+---
+
+## Memory Lifecycle (v3)
+
+v3 uses ADD-only extraction. Memories accumulate over time rather than being consolidated.
+
+### Creation
+- `client.add(messages, user_id="...")`
+- Single-pass extraction → deduplication → storage
+- Returns `{"event_id": "...", "status": "PENDING"}`
+
+### Updates
+- `client.update(memory_id, text="...")` replaces text
+- Batch: `client.batch_update([...])`
+
+### Deletion
+- Single: `client.delete(memory_id)`
+- Batch: `client.batch_delete([...])`
+- Bulk: `client.delete_all(filters={"user_id": "alice"})`
+
+---
+
+## Memory Object Structure
+
+```json
+{
+  "id": "uuid-string",
+  "memory": "Extracted memory text",
+  "user_id": "user-identifier",
+  "agent_id": null,
+  "app_id": null,
+  "run_id": null,
+  "metadata": { "source": "chat", "priority": "high" },
+  "categories": ["health", "preferences"],
+  "created_at": "2025-03-12T12:34:56Z",
+  "updated_at": "2025-03-12T12:34:56Z",
+  "structured_attributes": {
+    "day": 12, "month": 3, "year": 2025,
+    "hour": 12, "minute": 34,
+    "day_of_week": "wednesday",
+    "is_weekend": false,
+    "quarter": 1, "week_of_year": 11
+  },
+  "score": 0.85
+}
+```
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | UUID | Unique identifier, used for update/delete |
+| `memory` | string | Extracted or stored text content |
+| `user_id` | string | Primary entity scope |
+| `agent_id` | string | Agent scope |
+| `app_id` | string | Application scope |
+| `run_id` | string | Session/run scope |
+| `metadata` | object | Custom key-value pairs for filtering |
+| `categories` | array | Auto-assigned or custom category tags |
+| `created_at` | datetime | Creation timestamp |
+| `updated_at` | datetime | Last modification timestamp |
+| `structured_attributes` | object | Temporal breakdown for time-based queries |
+| `score` | float | Semantic similarity (search results only, 0-1) |
+
+---
+
+## Scoping & Multi-Tenancy
+
+Mem0 separates memories across four dimensions to prevent data mixing:
+
+| Dimension | Field | Purpose | Example |
+|-----------|-------|---------|---------|
+| User | `user_id` | Persistent persona or account | `"customer_6412"` |
+| Agent | `agent_id` | Distinct agent or tool | `"meal_planner"` |
+| App | `app_id` | Product surface or deployment | `"ios_retail_app"` |
+| Session | `run_id` | Short-lived flow or thread | `"ticket-9241"` |
+
+### Storage model
+
+Each entity combination creates separate records. A memory with `user_id="alice"` is stored separately from one with `user_id="alice"` + `agent_id="bot"`.
+
+### Critical: cross-entity queries
+
+```python
+# This returns NOTHING — user and agent memories are stored separately
+filters={"AND": [{"user_id": "alice"}, {"agent_id": "bot"}]}
+
+# Use OR to query multiple scopes
+filters={"OR": [{"user_id": "alice"}, {"agent_id": "bot"}]}
+
+# Use wildcard to include any non-null value
+filters={"AND": [{"user_id": "*"}]}  # All users (excludes null)
+```
+
+### Recommended scoping patterns
+
+```python
+# User-level: persistent preferences
+client.add(messages, user_id="alice")
+
+# Session-level: temporary context
+client.add(messages, user_id="alice", run_id="session_123")
+# Clean up when done: client.delete_all(run_id="session_123")
+
+# Agent-level: agent-specific knowledge
+client.add(messages, agent_id="support_bot", app_id="helpdesk")
+
+# Multi-tenant: full isolation
+client.add(messages, user_id="alice", agent_id="bot", app_id="acme_corp", run_id="ticket_42")
+```
+
+---
+
+## Memory Layers
+
+Mem0 supports three layers of memory, from shortest to longest lived:
+
+### Conversation memory
+- In-flight messages within a single turn
+- Tool calls, chain-of-thought reasoning
+- **Lifetime:** Single response — lost after turn finishes
+- **Managed by:** Your application, not Mem0
+
+### Session memory
+- Short-lived facts for current task or channel
+- Multi-step flows (onboarding, debugging, support tickets)
+- **Lifetime:** Minutes to hours
+- **Managed by:** Mem0 via `run_id` parameter
+- Clean up with `client.delete_all(run_id="session_id")`
+
+### User memory
+- Long-lived knowledge tied to a person or account
+- Personal preferences, account state, compliance details
+- **Lifetime:** Weeks to forever
+- **Managed by:** Mem0 via `user_id` parameter
+- Persists across all sessions and interactions
+
+### How layering works in practice
+
+```python
+def chat(user_input: str, user_id: str, session_id: str) -> str:
+    # 1. Retrieve user memories (long-term preferences)
+    user_mems = mem0.search(user_input, filters={"user_id": user_id})
+
+    # 2. Retrieve session memories (current task context)
+    session_mems = mem0.search(user_input, filters={
+        "AND": [{"user_id": user_id}, {"run_id": session_id}]
+    })
+
+    # 3. Combine both layers for LLM context
+    context = format_memories(user_mems) + format_memories(session_mems)
+
+    # 4. Generate response
+    response = llm.generate(context=context, input=user_input)
+
+    # 5. Store in session scope (temporary) + user scope (persistent)
+    messages = [{"role": "user", "content": user_input}, {"role": "assistant", "content": response}]
+    mem0.add(messages, user_id=user_id, run_id=session_id)
+
+    return response
+```
+
+---
+
+## Performance Characteristics
+
+### Latency
+
+| Operation | Typical Latency |
+|-----------|----------------|
+| Hybrid search (v3 default) | ~100-150ms |
+| + reranking | +150-200ms |
+| Add (async) | < 50ms response |
+
+### Processing
+
+- **Async (default):** Returns immediately, processes in background
+- **Batch operations:** Up to 1000 memories per batch_update/batch_delete
+- **Webhooks:** Real-time notifications when async processing completes
+
+### Scoping strategy for performance
+
+- Use `user_id` for all user-facing queries (most common, fastest)
+- Add `run_id` for session isolation (narrows search space)
+- Avoid wildcard `"*"` filters on large datasets (scans all non-null records)
+- Use `top_k` to limit result count when you only need a few memories
+
+---
+
+## Comparison with Alternatives
+
+| Approach | Pros | Cons |
+|----------|------|------|
+| **Raw vector DB** | Fast, full control | No extraction, no dedup, no conflict resolution |
+| **In-memory chat history** | Zero latency | Lost on restart, no cross-session, grows unbounded |
+| **RAG over documents** | Good for static knowledge | No personalization, no memory updates |
+| **Mem0 Platform** | Managed extraction + dedup + graph + scoping | External dependency, async processing delay |
+
+Mem0 combines the best of vector search (semantic retrieval) with automatic extraction (LLM-powered), conflict resolution (deduplication), and structured scoping (multi-tenancy) — in a single managed API.

mem0-plugin/.opencode-plugin/opencode-skills/mem0/references/features.md

@@ -0,0 +1,425 @@
+# Platform Features -- Mem0 Platform
+
+Additional platform capabilities beyond core CRUD operations.
+
+## Table of Contents
+
+- [Advanced Retrieval](#advanced-retrieval)
+- [Entity Linking](#entity-linking)
+- [Custom Categories](#custom-categories)
+- [Custom Instructions](#custom-instructions)
+- [Criteria Retrieval](#criteria-retrieval)
+- [Feedback Mechanism](#feedback-mechanism)
+- [Memory Export](#memory-export)
+- [Group Chat](#group-chat)
+- [MCP Integration](#mcp-integration)
+- [Webhooks](#webhooks)
+- [Multimodal Support](#multimodal-support)
+
+## Advanced Retrieval
+
+### Hybrid Search (v3 Default)
+
+v3 uses multi-signal hybrid search combining:
+- **Semantic search** (vector similarity)
+- **BM25 keyword search** (normalized term matching)
+- **Entity matching** (entity graph boost)
+
+This is automatic — no configuration needed.
+
+### Reranking (`rerank=True`)
+
+Deep semantic reordering of results — most relevant first.
+
+- Latency: +150-200ms
+- Default: `False` (was `True` in v2)
+- Best for: user-facing results, top-N precision
+
+**Python:**
+```python
+results = client.search(query, filters={"user_id": "user123"}, rerank=True)
+```
+
+**TypeScript:**
+```typescript
+const results = await client.search(query, {
+    filters: { user_id: 'user123' },
+    rerank: true,
+});
+```
+
+---
+
+## Entity Linking
+
+v3 replaces graph memory with built-in entity linking. Entities (proper nouns, quoted text, compound noun phrases) are automatically extracted and linked across memories.
+
+### How It Works
+
+1. **Extraction**: During `add()`, entities are automatically extracted from memory text
+2. **Storage**: Entities are stored in a parallel collection (`{collection}_entities`)
+3. **Retrieval**: During `search()`, query entities are matched and used to boost relevant memories
+
+Entity linking is automatic — no configuration required. The boost is folded into the combined `score` on each result.
+
+### v2 Migration Note
+
+If you were using `enable_graph=True` in v2:
+- Remove `enable_graph` from all API calls
+- Remove `graph_store` from OSS configuration
+- Entity relationships are now consumed through retrieval ranking, not exposed as a separate `relations` array
+
+See the [v2 to v3 migration guide](https://docs.mem0.ai/migration/platform-v2-to-v3) for details.
+
+---
+
+## Custom Categories
+
+Replace Mem0's default 15 labels with domain-specific categories. The system automatically tags memories to the closest matching category.
+
+### Default Categories (15)
+
+`personal_details`, `family`, `professional_details`, `sports`, `travel`, `food`, `music`, `health`, `technology`, `hobbies`, `fashion`, `entertainment`, `milestones`, `user_preferences`, `misc`
+
+### Configuration
+
+**Set project-level categories:**
+```python
+new_categories = [
+    {"lifestyle_management": "Tracks daily routines, habits, wellness activities"},
+    {"seeking_structure": "Documents goals around creating routines and systems"},
+    {"personal_information": "Basic information about the user"}
+]
+client.project.update(custom_categories=new_categories)
+```
+
+```javascript
+await client.updateProject({ customCategories: newCategories });
+```
+
+**Retrieve active categories:**
+```python
+categories = client.project.get(fields=["custom_categories"])
+```
+
+### Key Constraint
+
+Per-request overrides (`custom_categories=...` on `client.add`) are **not supported** on the managed API. Only project-level configuration works. Workaround: store ad-hoc labels in `metadata` field.
+
+---
+
+## Custom Instructions
+
+Natural language filters that control what information Mem0 extracts when creating memories.
+
+### Set Instructions
+
+```python
+client.project.update(custom_instructions="Your guidelines here...")
+```
+
+```javascript
+await client.updateProject({ customInstructions: "Your guidelines here..." });
+```
+
+### Template Structure
+
+1. **Task Description** -- brief extraction overview
+2. **Information Categories** -- numbered sections with specific details to capture
+3. **Processing Guidelines** -- quality and handling rules
+4. **Exclusion List** -- sensitive/irrelevant data to filter out
+
+### Domain Examples
+
+**E-commerce:** Capture product issues, preferences, service experience; exclude payment data.
+
+**Education:** Extract learning progress, student preferences, performance patterns; exclude specific grades.
+
+**Finance:** Track financial goals, life events, investment interests; exclude account numbers and SSNs.
+
+### Best Practices
+
+- Start simply, test with sample messages, iterate based on results
+- Avoid overly lengthy instructions
+- Be specific about what to include AND exclude
+
+---
+
+## Criteria Retrieval
+
+Custom attribute-based memory ranking using LLM-evaluated criteria with weights. Goes beyond semantic similarity to prioritize memories based on domain-specific signals.
+
+### Configuration
+
+```python
+# Define criteria at project level
+retrieval_criteria = [
+    {"name": "joy", "description": "Positive emotions like happiness and excitement", "weight": 3},
+    {"name": "curiosity", "description": "Inquisitiveness and desire to learn", "weight": 2},
+    {"name": "urgency", "description": "Time-sensitive or high-priority items", "weight": 4},
+]
+client.project.update(retrieval_criteria=retrieval_criteria)
+```
+
+```typescript
+await client.updateProject({
+    retrievalCriteria: [
+        { name: 'joy', description: 'Positive emotions', weight: 3 },
+        { name: 'urgency', description: 'Time-sensitive items', weight: 4 },
+    ],
+});
+```
+
+### Usage
+
+Once configured, `client.search()` automatically applies criteria ranking:
+
+```python
+# Criteria-weighted results returned automatically
+results = client.search("Why am I feeling happy?", filters={"user_id": "alice"})
+```
+
+**Best for:** Wellness assistants, tutoring platforms, productivity tools — any app needing intent-aware retrieval.
+
+---
+
+## Feedback Mechanism
+
+Provide feedback on extracted memories to improve system quality over time.
+
+### Feedback Types
+
+| Type | Meaning |
+|------|---------|
+| `POSITIVE` | Memory is useful and accurate |
+| `NEGATIVE` | Memory is not useful |
+| `VERY_NEGATIVE` | Memory is harmful or completely wrong |
+| `None` | Clear existing feedback |
+
+### Usage
+
+**Python:**
+```python
+client.feedback(
+    memory_id="mem-123",
+    feedback="POSITIVE",
+    feedback_reason="Accurately captured dietary preference"
+)
+
+# Bulk feedback
+for item in feedback_data:
+    client.feedback(**item)
+```
+
+**TypeScript:**
+```typescript
+await client.feedback('mem-123', {
+    feedback: 'POSITIVE',
+    feedbackReason: 'Accurately captured dietary preference',
+});
+```
+
+---
+
+## Memory Export
+
+Create structured exports of memories using customizable schemas with filters.
+
+### Usage
+
+```python
+import json
+
+# Define export schema
+schema = {
+    "type": "object",
+    "properties": {
+        "name": {"type": "string"},
+        "preferences": {"type": "array", "items": {"type": "string"}},
+        "health_info": {"type": "string"},
+    }
+}
+
+# Create export
+response = client.create_memory_export(
+    schema=json.dumps(schema),
+    filters={"user_id": "alice"},
+    export_instructions="Create comprehensive profile based on all memories"
+)
+
+# Retrieve export (may take a moment to process)
+result = client.get_memory_export(memory_export_id=response["id"])
+```
+
+**Best for:** Data analytics, user profile generation, compliance audits, CRM sync.
+
+---
+
+## Group Chat
+
+Process multi-participant conversations and automatically attribute memories to individual speakers.
+
+### Usage
+
+```python
+messages = [
+    {"role": "user", "name": "Alice", "content": "I think we should use React for the frontend"},
+    {"role": "user", "name": "Bob", "content": "I prefer Vue.js, it's simpler for our use case"},
+    {"role": "assistant", "content": "Both are great choices. Let me note your preferences."},
+]
+
+# Mem0 automatically attributes memories to each speaker
+response = client.add(messages, run_id="team_meeting_1")
+
+# Retrieve Alice's memories from that session
+alice_mems = client.get_all(
+    filters={"AND": [{"user_id": "alice"}, {"run_id": "team_meeting_1"}]}
+)
+```
+
+Use the `name` field in messages to identify speakers. Mem0 maps names to entity scopes automatically.
+
+---
+
+## MCP Integration
+
+Model Context Protocol integration enables AI clients (Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode) to manage Mem0 memory autonomously.
+
+### Setup
+
+Add Mem0 MCP to your clients with a single command:
+
+```bash
+npx mcp-add \
+  --name mem0-mcp \
+  --type http \
+  --url "https://mcp.mem0.ai/mcp" \
+  --clients "claude,claude code,cursor,windsurf,vscode,opencode"
+```
+
+### Available MCP Tools
+
+The MCP server exposes 9 memory tools that AI agents can use autonomously:
+- Add, search, get, update, delete memories
+- Get history, list users, delete users
+- Search Mem0 documentation
+
+### How It Works
+
+1. Add Mem0 MCP to your AI client using the setup command above
+2. The agent autonomously decides when to store/retrieve memories
+3. No manual API calls needed — the agent manages memory as part of its reasoning
+
+**Best for:** Universal AI client integration — one protocol works everywhere.
+
+---
+
+## Webhooks
+
+Real-time event notifications for memory operations.
+
+### Supported Events
+
+| Event | Trigger |
+|-------|---------|
+| `memory_add` | Memory created |
+| `memory_update` | Memory modified |
+| `memory_delete` | Memory removed |
+| `memory_categorize` | Memory tagged |
+
+### Create Webhook
+
+Note: `project_id` here refers to the Mem0 dashboard project scope for webhooks — not the deprecated client init parameter.
+
+```python
+webhook = client.create_webhook(
+    url="https://your-app.com/webhook",
+    name="Memory Logger",
+    project_id="proj_123",
+    event_types=["memory_add", "memory_categorize"]
+)
+```
+
+### Manage Webhooks
+
+```python
+# Retrieve
+webhooks = client.get_webhooks(project_id="proj_123")
+
+# Update
+client.update_webhook(
+    name="Updated Logger",
+    url="https://your-app.com/new-webhook",
+    event_types=["memory_update", "memory_add"],
+    webhook_id="wh_123"
+)
+
+# Delete
+client.delete_webhook(webhook_id="wh_123")
+```
+
+### Payload Structure
+
+Memory events contain: ID, data object with memory content, event type (`ADD`/`UPDATE`/`DELETE`).
+Categorization events contain: memory ID, event type (`CATEGORIZE`), assigned category labels.
+
+---
+
+## Multimodal Support
+
+Mem0 can process images and documents alongside text.
+
+### Supported Media Types
+
+- Images: JPG, PNG
+- Documents: MDX, TXT, PDF
+
+### Image via URL
+
+```python
+image_message = {
+    "role": "user",
+    "content": {
+        "type": "image_url",
+        "image_url": {"url": "https://example.com/image.jpg"}
+    }
+}
+client.add([image_message], user_id="alice")
+```
+
+### Image via Base64
+
+```python
+import base64
+with open("photo.jpg", "rb") as f:
+    base64_image = base64.b64encode(f.read()).decode("utf-8")
+
+image_message = {
+    "role": "user",
+    "content": {
+        "type": "image_url",
+        "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}
+    }
+}
+client.add([image_message], user_id="alice")
+```
+
+### Document (MDX/TXT)
+
+```python
+doc_message = {
+    "role": "user",
+    "content": {"type": "mdx_url", "mdx_url": {"url": document_url}}
+}
+client.add([doc_message], user_id="alice")
+```
+
+### PDF Document
+
+```python
+pdf_message = {
+    "role": "user",
+    "content": {"type": "pdf_url", "pdf_url": {"url": pdf_url}}
+}
+client.add([pdf_message], user_id="alice")
+```

mem0-plugin/.opencode-plugin/opencode-skills/mem0/references/integration-patterns.md

@@ -0,0 +1,395 @@
+# Mem0 Integration Patterns
+
+Working code examples for integrating Mem0 Platform with popular AI frameworks.
+All examples use `MemoryClient` (Platform API key).
+
+Code examples are sourced from official Mem0 integration docs at docs.mem0.ai, simplified for quick reference.
+
+---
+
+## Common Pattern
+
+Every integration follows the same 3-step loop:
+
+1. **Retrieve** -- search relevant memories before generating a response
+2. **Generate** -- include memories as context in the LLM prompt
+3. **Store** -- save the interaction back to Mem0 for future use
+
+---
+
+## LangChain
+
+Source: [docs.mem0.ai/integrations/langchain](https://docs.mem0.ai/integrations/langchain)
+
+```python
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from mem0 import MemoryClient
+
+llm = ChatOpenAI(model="gpt-5-mini")
+mem0 = MemoryClient()
+
+prompt = ChatPromptTemplate.from_messages([
+    SystemMessage(content="You are a helpful travel agent AI. Use the provided context to personalize your responses."),
+    MessagesPlaceholder(variable_name="context"),
+    HumanMessage(content="{input}")
+])
+
+def retrieve_context(query: str, user_id: str):
+    """Retrieve relevant memories from Mem0"""
+    memories = mem0.search(query, filters={"user_id": user_id})
+    memory_list = memories['results']
+    serialized = ' '.join([m["memory"] for m in memory_list])
+    return [
+        {"role": "system", "content": f"Relevant information: {serialized}"},
+        {"role": "user", "content": query}
+    ]
+
+def chat_turn(user_input: str, user_id: str) -> str:
+    # 1. Retrieve
+    context = retrieve_context(user_input, user_id)
+    # 2. Generate
+    chain = prompt | llm
+    response = chain.invoke({"context": context, "input": user_input})
+    # 3. Store
+    mem0.add(
+        [{"role": "user", "content": user_input}, {"role": "assistant", "content": response.content}],
+        user_id=user_id
+    )
+    return response.content
+```
+
+---
+
+## CrewAI
+
+Source: [docs.mem0.ai/integrations/crewai](https://docs.mem0.ai/integrations/crewai)
+
+CrewAI has native Mem0 integration via `memory_config`:
+
+```python
+from crewai import Agent, Task, Crew, Process
+from mem0 import MemoryClient
+
+client = MemoryClient()
+
+# Store user preferences first
+messages = [
+    {"role": "user", "content": "I am more of a beach person than a mountain person."},
+    {"role": "assistant", "content": "Noted! I'll recommend beach destinations."},
+    {"role": "user", "content": "I like Airbnb more than hotels."},
+]
+client.add(messages, user_id="crew_user_1")
+
+# Create agent
+travel_agent = Agent(
+    role="Personalized Travel Planner",
+    goal="Plan personalized travel itineraries",
+    backstory="You are a seasoned travel planner.",
+    memory=True,
+)
+
+# Create task
+task = Task(
+    description="Find places to live, eat, and visit in San Francisco.",
+    expected_output="A detailed list of places to live, eat, and visit.",
+    agent=travel_agent,
+)
+
+# Setup crew with Mem0 memory
+crew = Crew(
+    agents=[travel_agent],
+    tasks=[task],
+    process=Process.sequential,
+    memory=True,
+    memory_config={
+        "provider": "mem0",
+        "config": {"user_id": "crew_user_1"},
+    }
+)
+
+result = crew.kickoff()
+```
+
+---
+
+## Vercel AI SDK
+
+> **Dedicated skill available.** For comprehensive Vercel AI SDK documentation, see the [mem0-vercel-ai-sdk skill](https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk).
+
+Install: `npm install @mem0/vercel-ai-provider`
+
+Quick example (wrapped model with automatic memory):
+
+```typescript
+import { generateText } from "ai";
+import { createMem0 } from "@mem0/vercel-ai-provider";
+
+const mem0 = createMem0();
+const { text } = await generateText({
+    model: mem0("gpt-5-mini", { user_id: "borat" }),
+    prompt: "Suggest me a good car to buy!",
+});
+```
+
+Supported providers: `openai`, `anthropic`, `google`, `groq`, `cohere`
+
+---
+
+## OpenAI Agents SDK
+
+Source: [docs.mem0.ai/integrations/openai-agents-sdk](https://docs.mem0.ai/integrations/openai-agents-sdk)
+
+```python
+from agents import Agent, Runner, function_tool
+from mem0 import MemoryClient
+
+mem0 = MemoryClient()
+
+@function_tool
+def search_memory(query: str, user_id: str) -> str:
+    """Search through past conversations and memories"""
+    memories = mem0.search(query, filters={"user_id": user_id}, top_k=3)
+    if memories and memories.get('results'):
+        return "\n".join([f"- {mem['memory']}" for mem in memories['results']])
+    return "No relevant memories found."
+
+@function_tool
+def save_memory(content: str, user_id: str) -> str:
+    """Save important information to memory"""
+    mem0.add([{"role": "user", "content": content}], user_id=user_id)
+    return "Information saved to memory."
+
+agent = Agent(
+    name="Personal Assistant",
+    instructions="""You are a helpful personal assistant with memory capabilities.
+    Use search_memory to recall past conversations.
+    Use save_memory to store important information.""",
+    tools=[search_memory, save_memory],
+    model="gpt-5-mini"
+)
+
+result = Runner.run_sync(agent, "I love Italian food and I'm planning a trip to Rome next month")
+print(result.final_output)
+```
+
+### Multi-Agent with Handoffs
+
+```python
+from agents import Agent, Runner, function_tool
+
+travel_agent = Agent(
+    name="Travel Planner",
+    instructions="You are a travel planning specialist. Use search_memory and save_memory tools.",
+    tools=[search_memory, save_memory],
+    model="gpt-5-mini"
+)
+
+health_agent = Agent(
+    name="Health Advisor",
+    instructions="You are a health and wellness advisor. Use search_memory and save_memory tools.",
+    tools=[search_memory, save_memory],
+    model="gpt-5-mini"
+)
+
+triage_agent = Agent(
+    name="Personal Assistant",
+    instructions="""Route travel questions to Travel Planner, health questions to Health Advisor.""",
+    handoffs=[travel_agent, health_agent],
+    model="gpt-5-mini"
+)
+
+result = Runner.run_sync(triage_agent, "Plan a healthy meal for my Italy trip")
+```
+
+---
+
+## Pipecat (Voice / Real-Time)
+
+Source: [docs.mem0.ai/integrations/pipecat](https://docs.mem0.ai/integrations/pipecat)
+
+```python
+from pipecat.services.mem0 import Mem0MemoryService
+
+memory = Mem0MemoryService(
+    api_key=os.getenv("MEM0_API_KEY"),
+    user_id="alice",
+    agent_id="voice_bot",
+    params={
+        "search_limit": 10,
+        "search_threshold": 0.1,
+        "system_prompt": "Here are your past memories:",
+        "add_as_system_message": True,
+    }
+)
+
+# Use in pipeline
+pipeline = Pipeline([
+    transport.input(),
+    stt,
+    user_context,
+    memory,          # Memory enhances context automatically
+    llm,
+    transport.output(),
+    assistant_context
+])
+```
+
+
+
+---
+
+## LangGraph
+
+Source: [docs.mem0.ai/integrations/langgraph](https://docs.mem0.ai/integrations/langgraph)
+
+State-based agent workflows with memory persistence. Best for complex conversation flows with branching logic.
+
+```python
+from typing import Annotated, TypedDict, List
+from langgraph.graph import StateGraph, START
+from langgraph.graph.message import add_messages
+from langchain_openai import ChatOpenAI
+from mem0 import MemoryClient
+from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
+
+llm = ChatOpenAI(model="gpt-5-mini")
+mem0 = MemoryClient()
+
+class State(TypedDict):
+    messages: Annotated[List[HumanMessage | AIMessage], add_messages]
+    mem0_user_id: str
+
+def chatbot(state: State):
+    messages = state["messages"]
+    user_id = state["mem0_user_id"]
+
+    # Retrieve relevant memories
+    memories = mem0.search(messages[-1].content, filters={"user_id": user_id})
+    context = "Relevant context:\n"
+    for memory in memories["results"]:
+        context += f"- {memory['memory']}\n"
+
+    system_message = SystemMessage(content=f"""You are a helpful support assistant.
+{context}""")
+
+    response = llm.invoke([system_message] + messages)
+
+    # Store the interaction
+    mem0.add(
+        [{"role": "user", "content": messages[-1].content},
+         {"role": "assistant", "content": response.content}],
+        user_id=user_id
+    )
+    return {"messages": [response]}
+
+graph = StateGraph(State)
+graph.add_node("chatbot", chatbot)
+graph.add_edge(START, "chatbot")
+app = graph.compile()
+
+# Usage
+result = app.invoke({
+    "messages": [HumanMessage(content="I need help with my order")],
+    "mem0_user_id": "customer_123"
+})
+```
+
+---
+
+## LlamaIndex
+
+Source: [docs.mem0.ai/integrations/llama-index](https://docs.mem0.ai/integrations/llama-index)
+
+Install: `pip install llama-index-core llama-index-memory-mem0`
+
+LlamaIndex has native Mem0 support via `Mem0Memory`. Works with ReAct and FunctionCalling agents.
+
+```python
+from llama_index.memory.mem0 import Mem0Memory
+
+context = {"user_id": "alice", "agent_id": "llama_agent_1"}
+memory = Mem0Memory.from_client(
+    context=context,
+    search_msg_limit=4,  # messages from chat history used for retrieval (default: 5)
+)
+
+# Use with LlamaIndex agent
+from llama_index.core.agent import FunctionCallingAgent
+from llama_index.llms.openai import OpenAI
+
+llm = OpenAI(model="gpt-5-mini")
+agent = FunctionCallingAgent.from_tools(
+    tools=[],
+    llm=llm,
+    memory=memory,
+    verbose=True,
+)
+
+response = agent.chat("I prefer vegetarian restaurants")
+# Memory automatically stores and retrieves context
+response = agent.chat("What kind of food do I like?")
+# Agent retrieves the vegetarian preference from Mem0
+```
+
+---
+
+## AutoGen
+
+Source: [docs.mem0.ai/integrations/autogen](https://docs.mem0.ai/integrations/autogen)
+
+Install: `pip install autogen mem0ai`
+
+Multi-agent conversational systems with memory persistence.
+
+```python
+from autogen import ConversableAgent
+from mem0 import MemoryClient
+
+memory_client = MemoryClient()
+USER_ID = "alice"
+
+agent = ConversableAgent(
+    "chatbot",
+    llm_config={"config_list": [{"model": "gpt-5-mini", "api_key": os.environ["OPENAI_API_KEY"]}]},
+    code_execution_config=False,
+    human_input_mode="NEVER",
+)
+
+def get_context_aware_response(question: str) -> str:
+    # Retrieve memories for context
+    relevant_memories = memory_client.search(question, filters={"user_id": USER_ID})
+    context = "\n".join([m["memory"] for m in relevant_memories.get("results", [])])
+
+    prompt = f"""Answer considering previous interactions:
+    Previous context: {context}
+    Question: {question}"""
+
+    reply = agent.generate_reply(messages=[{"content": prompt, "role": "user"}])
+
+    # Store the new interaction
+    memory_client.add(
+        [{"role": "user", "content": question}, {"role": "assistant", "content": reply}],
+        user_id=USER_ID
+    )
+    return reply
+```
+
+---
+
+## All Supported Frameworks
+
+Beyond the examples above, Mem0 integrates with:
+
+| Framework | Type | Install |
+|-----------|------|---------|
+| [Mastra](https://docs.mem0.ai/integrations/mastra) | TS agent framework | `npm install @mastra/mem0` |
+| [ElevenLabs](https://docs.mem0.ai/integrations/elevenlabs) | Voice AI | `pip install elevenlabs mem0ai` |
+| [LiveKit](https://docs.mem0.ai/integrations/livekit) | Real-time voice/video | `pip install livekit-agents mem0ai` |
+| [Camel AI](https://docs.mem0.ai/integrations/camel-ai) | Multi-agent framework | `pip install camel-ai[all] mem0ai` |
+| [AWS Bedrock](https://docs.mem0.ai/integrations/aws-bedrock) | Cloud LLM provider | `pip install boto3 mem0ai` |
+| [Dify](https://docs.mem0.ai/integrations/dify) | Low-code AI platform | Plugin-based |
+| [Google AI ADK](https://docs.mem0.ai/integrations/google-ai-adk) | Google agent framework | `pip install google-adk mem0ai` |
+
+For the general Python pattern (no framework), see the "Common integration pattern" in [SKILL.md](../SKILL.md).

mem0-plugin/.opencode-plugin/opencode-skills/mem0/references/quickstart.md

@@ -0,0 +1,119 @@
+# Mem0 Platform Quickstart
+
+Get running with Mem0 in 2 minutes. No infrastructure to deploy -- just an API key.
+
+## Prerequisites
+
+- Python 3.10+ or Node.js 18+
+- A Mem0 Platform API key ([Get one here](https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=mem0-plugin-skill-quickstart))
+
+## Python Setup
+
+```bash
+pip install mem0ai
+export MEM0_API_KEY="m0-your-api-key"
+```
+
+```python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+# Add a memory
+messages = [
+    {"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
+    {"role": "assistant", "content": "Got it! I'll remember your dietary preferences."}
+]
+client.add(messages, user_id="user123")
+
+# Search memories
+results = client.search("What are my dietary restrictions?", filters={"user_id": "user123"})
+print(results)
+```
+
+### Async Client
+
+```python
+from mem0 import AsyncMemoryClient
+
+client = AsyncMemoryClient(api_key="your-api-key")
+
+await client.add(messages, user_id="user123")
+results = await client.search("query", filters={"user_id": "user123"})
+```
+
+## TypeScript / JavaScript Setup
+
+```bash
+npm install mem0ai
+export MEM0_API_KEY="m0-your-api-key"
+```
+
+```javascript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: 'your-api-key' });
+
+// Add a memory
+const messages = [
+    {"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
+    {"role": "assistant", "content": "Got it! I'll remember your dietary preferences."}
+];
+await client.add(messages, { userId: "user123" });
+
+// Search memories
+const results = await client.search("What are my dietary restrictions?", {
+    filters: { user_id: "user123" }
+});
+console.log(results);
+```
+
+## cURL
+
+```bash
+export MEM0_API_KEY="m0-your-api-key"
+
+# Add memory
+curl -X POST https://api.mem0.ai/v3/memories/add/ \
+  -H "Authorization: Token $MEM0_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [
+      {"role": "user", "content": "I am a vegetarian and allergic to nuts."},
+      {"role": "assistant", "content": "Got it! I will remember your dietary preferences."}
+    ],
+    "user_id": "user123"
+  }'
+
+# Search memories
+curl -X POST https://api.mem0.ai/v3/memories/search/ \
+  -H "Authorization: Token $MEM0_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "query": "What are my dietary restrictions?",
+    "filters": {"user_id": "user123"}
+  }'
+```
+
+## Sample Response
+
+```json
+{
+  "results": [
+    {
+      "id": "14e1b28a-2014-40ad-ac42-69c9ef42193d",
+      "memory": "Allergic to nuts",
+      "user_id": "user123",
+      "categories": ["health"],
+      "created_at": "2025-10-22T04:40:22.864647-07:00",
+      "score": 0.30
+    }
+  ]
+}
+```
+
+## Next Steps
+
+- [SDK Guide](sdk-guide.md) -- all methods for Python and TypeScript
+- [API Reference](api-reference.md) -- REST endpoints and memory object structure
+- [Integration Patterns](integration-patterns.md) -- LangChain, CrewAI, Vercel AI, etc.

mem0-plugin/.opencode-plugin/opencode-skills/mem0/references/sdk-guide.md

@@ -0,0 +1,353 @@
+# Mem0 SDK Guide
+
+Complete SDK reference for Python and TypeScript. All methods use `MemoryClient` (Platform API).
+
+> **For language-specific deep references (including OSS):** See [client/python.md](../client/python.md) and [client/node.md](../client/node.md). For Python vs TypeScript differences: [client/differences.md](../client/differences.md).
+
+## Initialization
+
+**Python:**
+```python
+from mem0 import MemoryClient
+client = MemoryClient(api_key="m0-your-api-key")
+```
+
+**Python (Async):**
+```python
+from mem0 import AsyncMemoryClient
+client = AsyncMemoryClient(api_key="m0-your-api-key")
+```
+
+**TypeScript:**
+```typescript
+import MemoryClient from 'mem0ai';
+const client = new MemoryClient({ apiKey: 'm0-your-api-key' });
+```
+
+Constructor accepts `apiKey` (required) and `host` (optional, default: `https://api.mem0.ai`).
+
+---
+
+## add() -- Store Memories
+
+**Python:**
+```python
+messages = [
+    {"role": "user", "content": "I'm a vegetarian and allergic to nuts."},
+    {"role": "assistant", "content": "Got it! I'll remember that."}
+]
+client.add(messages, user_id="alice")
+
+# With metadata
+client.add(messages, user_id="alice", metadata={"source": "onboarding"})
+```
+
+**TypeScript:**
+```typescript
+await client.add(messages, { userId: "alice" });
+await client.add(messages, { userId: "alice", metadata: { source: "onboarding" } });
+```
+
+### Parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| `messages` | array | `[{"role": "user", "content": "..."}]` |
+| `user_id` | string | User identifier (recommended) |
+| `agent_id` | string | Agent identifier |
+| `run_id` | string | Session identifier |
+| `metadata` | object | Custom key-value pairs |
+| `infer` | boolean | If `false`, store raw text without inference (default: `true`) |
+
+### Advanced Add Options
+
+```python
+# Agent + session scoping
+client.add(messages, user_id="alice", agent_id="nutrition-agent", run_id="session-456")
+
+# Raw text -- skip LLM inference
+client.add(
+    [{"role": "user", "content": "User prefers dark mode."}],
+    user_id="alice",
+    infer=False,
+)
+```
+
+---
+
+## search() -- Find Memories
+
+**Python:**
+```python
+results = client.search("dietary preferences?", filters={"user_id": "alice"})
+
+# With filters and reranking
+results = client.search(
+    query="work experience",
+    filters={"AND": [{"user_id": "alice"}, {"categories": {"contains": "professional_details"}}]},
+    top_k=5,
+    rerank=True,
+    threshold=0.5
+)
+```
+
+**TypeScript:**
+```typescript
+const results = await client.search("dietary preferences", { filters: { user_id: "alice" } });
+const results = await client.search("work experience", {
+    filters: { AND: [{ user_id: "alice" }, { categories: { contains: "professional_details" } }] },
+    topK: 5,
+    rerank: true,
+});
+```
+
+### Parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| `query` | string | Natural language search query |
+| `filters` | object | Filter object (AND/OR operators). Use `{"user_id": "..."}` to filter by user |
+| `top_k` | number | Number of results (default: 10 for Platform) |
+| `rerank` | boolean | Enable reranking for better relevance (default: `false`) |
+| `threshold` | number | Minimum similarity score (default: 0.1) |
+
+### Common Filter Patterns
+
+**Python:**
+```python
+# Single user filter
+filters={"user_id": "alice"}
+
+# OR across agents
+filters={"OR": [{"user_id": "alice"}, {"agent_id": {"in": ["travel-agent", "sports-agent"]}}]}
+
+# Category filtering (partial match)
+filters={"AND": [{"user_id": "alice"}, {"categories": {"contains": "finance"}}]}
+
+# Category filtering (exact match)
+filters={"AND": [{"user_id": "alice"}, {"categories": {"in": ["personal_information"]}}]}
+
+# Wildcard (match any non-null run)
+filters={"AND": [{"user_id": "alice"}, {"run_id": "*"}]}
+
+# Date range
+filters={"AND": [
+    {"user_id": "alice"},
+    {"created_at": {"gte": "2024-01-01T00:00:00Z"}},
+    {"created_at": {"lt": "2024-02-01T00:00:00Z"}}
+]}
+
+# Exclude categories with NOT
+filters={"AND": [{"user_id": "user_123"}, {"NOT": {"categories": {"in": ["spam", "test"]}}}]}
+
+# Multi-dimensional query
+filters={"AND": [
+    {"user_id": "user_123"},
+    {"keywords": {"icontains": "invoice"}},
+    {"categories": {"in": ["finance"]}},
+    {"created_at": {"gte": "2024-01-01T00:00:00Z"}}
+]}
+```
+
+**TypeScript:**
+```typescript
+// Single user filter
+filters: { user_id: "alice" }
+
+// OR across agents
+filters: { OR: [{ user_id: "alice" }, { agent_id: { in: ["travel-agent", "sports-agent"] } }] }
+
+// Category filtering (partial match)
+filters: { AND: [{ user_id: "alice" }, { categories: { contains: "finance" } }] }
+
+// Category filtering (exact match)
+filters: { AND: [{ user_id: "alice" }, { categories: { in: ["personal_information"] } }] }
+```
+
+---
+
+## get() / getAll() -- Retrieve Memories
+
+**Python:**
+```python
+# Single memory by ID
+memory = client.get(memory_id="ea925981-...")
+
+# All memories for a user
+memories = client.get_all(filters={"user_id": "alice"})
+
+# With date range
+memories = client.get_all(
+    filters={"AND": [
+        {"user_id": "alex"},
+        {"created_at": {"gte": "2024-07-01", "lte": "2024-07-31"}}
+    ]}
+)
+```
+
+**TypeScript:**
+```typescript
+const memory = await client.get("ea925981-...");
+const memories = await client.getAll({ filters: { user_id: "alice" } });
+```
+
+**Note:** `get_all` requires at least one of `user_id`, `agent_id`, `app_id`, or `run_id` in filters.
+
+---
+
+## update() -- Modify Memories
+
+**Python:**
+```python
+client.update(memory_id="ea925981-...", text="Updated: vegan since 2024")
+client.update(memory_id="ea925981-...", text="Updated", metadata={"verified": True})
+```
+
+**TypeScript:**
+```typescript
+await client.update("ea925981-...", { text: "Updated: vegan since 2024" });
+```
+
+---
+
+## delete() / deleteAll() -- Remove Memories
+
+**Python:**
+```python
+client.delete(memory_id="ea925981-...")
+client.delete_all(user_id="alice")  # Irreversible bulk delete
+```
+
+**TypeScript:**
+```typescript
+await client.delete("ea925981-...");
+await client.deleteAll({ userId: "alice" });
+```
+
+---
+
+## history() -- Track Changes
+
+**Python:**
+```python
+history = client.history(memory_id="ea925981-...")
+# Returns: [{previous_value, new_value, action, timestamps}]
+```
+
+**TypeScript:**
+```typescript
+const history = await client.history("ea925981-...");
+```
+
+---
+
+## Batch Operations (TypeScript)
+
+```typescript
+// Batch update
+await client.batchUpdate([
+    { memoryId: "uuid-1", text: "Updated text" },
+    { memoryId: "uuid-2", text: "Another updated text" },
+]);
+
+// Batch delete
+await client.batchDelete(["uuid-1", "uuid-2", "uuid-3"]);
+```
+
+---
+
+## Additional Methods
+
+```python
+# List all users/agents/sessions with memories
+users = client.users()
+
+# Delete a user/agent entity
+client.delete_users(user_id="alice")
+
+# Submit feedback on a memory
+client.feedback(memory_id="...", feedback="POSITIVE", feedback_reason="Accurate extraction")
+
+# Export memories
+export = client.create_memory_export(filters={"AND": [{"user_id": "alice"}]})
+data = client.get_memory_export(memory_export_id=export["id"])
+```
+
+---
+
+## Common Pitfalls
+
+1. **Entity cross-filtering fails silently** -- `AND` with `user_id` + `agent_id` returns empty. Use `OR`.
+2. **SQL operators rejected** -- use `gte`, `lt`, etc. Not `>=`, `<`.
+3. **Metadata filtering is limited** -- only top-level keys with `eq`, `contains`, `ne`.
+4. **Wildcard `*` excludes null** -- only matches non-null values.
+5. **Default threshold is 0.1** -- increase for stricter matching.
+6. **Async processing** -- memories process asynchronously. Wait 2-3s after `add()` before searching.
+
+## Naming Conventions
+
+Python uses `snake_case` everywhere (`user_id`, `memory_id`, `get_all`). TypeScript uses `camelCase` for methods (`getAll`, `deleteAll`, `batchUpdate`) and top-level parameters (`userId`, `topK`, `pageSize`), but filter keys use `snake_case` (`user_id`, `agent_id`).
+
+---
+
+## v2 to v3 Migration
+
+### Breaking Changes in v3
+
+**1. Entity IDs in search() and getAll()**
+
+v3 requires entity IDs (`user_id`, `agent_id`, `run_id`) inside `filters` instead of as top-level parameters:
+
+```python
+# v2 (deprecated)
+client.search("query", user_id="alice")
+client.get_all(user_id="alice")
+
+# v3
+client.search("query", filters={"user_id": "alice"})
+client.get_all(filters={"user_id": "alice"})
+```
+
+```typescript
+// v2 (deprecated)
+await client.search("query", { user_id: "alice" });
+await client.getAll({ user_id: "alice" });
+
+// v3
+await client.search("query", { filters: { user_id: "alice" } });
+await client.getAll({ filters: { user_id: "alice" } });
+```
+
+**2. TypeScript Parameter Naming**
+
+v3 TypeScript uses camelCase for all parameters:
+
+| v2 | v3 |
+|----|-----|
+| `user_id` | `userId` |
+| `agent_id` | `agentId` |
+| `run_id` | `runId` |
+| `top_k` | `topK` |
+| `page_size` | `pageSize` |
+
+**3. Default Values Changed**
+
+| Parameter | v2 Default | v3 Default |
+|-----------|------------|------------|
+| `threshold` | 0.3 | 0.1 |
+| `rerank` | (not specified) | `false` |
+
+**4. Removed Parameters**
+
+The following parameters are no longer supported:
+
+| Parameter | Status |
+|-----------|--------|
+| `enable_graph` | Removed from add/search/getAll |
+| `keyword_search` | Removed from search |
+| `filter_memories` | Removed |
+| `immutable` | Removed from add |
+| `expiration_date` | Removed from add |
+| `includes` | Removed from add |
+| `excludes` | Removed from add |
+| `async_mode` | Removed from add |

mem0-plugin/.opencode-plugin/opencode-skills/mem0/references/use-cases.md

@@ -0,0 +1,720 @@
+# Mem0 Use Cases & Examples
+
+Real-world implementation patterns for Mem0 Platform. Each use case includes complete, runnable code in both Python and TypeScript.
+
+## Table of Contents
+
+- [Personalized AI Companion](#1-personalized-ai-companion)
+- [Customer Support with Categories](#2-customer-support-with-categories)
+- [Healthcare Coach](#3-healthcare-coach)
+- [Content Creation Workflow](#4-content-creation-workflow)
+- [Multi-Agent / Multi-Tenant](#5-multi-agent--multi-tenant)
+- [Personalized Search](#6-personalized-search)
+- [Email Intelligence](#7-email-intelligence)
+- [Common Patterns Across Use Cases](#common-patterns-across-use-cases)
+
+---
+
+## 1. Personalized AI Companion
+
+A fitness coach that remembers goals, preferences, and progress across sessions. Mem0 persists context across app restarts — no session state needed.
+
+### Implementation (Python)
+
+```python
+from mem0 import MemoryClient
+from openai import OpenAI
+
+mem0 = MemoryClient()
+openai_client = OpenAI()
+
+def chat(user_input: str, user_id: str) -> str:
+    # 1. Retrieve relevant memories
+    memories = mem0.search(user_input, user_id=user_id)
+    context = "\n".join([f"- {m['memory']}" for m in memories.get("results", [])])
+
+    # 2. Generate response with memory context
+    system_prompt = f"""You are Ray, a personal fitness coach.
+Use these known facts about the user to personalize your response:
+{context if context else 'No prior context yet.'}"""
+
+    response = openai_client.chat.completions.create(
+        model="gpt-5-mini",
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_input},
+        ]
+    )
+    reply = response.choices[0].message.content
+
+    # 3. Store interaction for future context
+    mem0.add(
+        [{"role": "user", "content": user_input}, {"role": "assistant", "content": reply}],
+        user_id=user_id
+    )
+    return reply
+
+# Usage
+chat("I want to run a marathon in under 4 hours", user_id="max")
+# Next day, app restarted:
+chat("What should I focus on today?", user_id="max")
+# Ray remembers the sub-4 marathon goal
+```
+
+### Implementation (TypeScript)
+
+```typescript
+import MemoryClient from 'mem0ai';
+import OpenAI from 'openai';
+
+const mem0 = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
+const openai = new OpenAI();
+
+async function chat(userInput: string, userId: string): Promise<string> {
+    // 1. Retrieve relevant memories
+    const memories = await mem0.search(userInput, { filters: { user_id: userId } });
+    const context = memories.results
+        ?.map((m: any) => `- ${m.memory}`)
+        .join('\n') || 'No prior context yet.';
+
+    // 2. Generate response with memory context
+    const response = await openai.chat.completions.create({
+        model: 'gpt-5-mini',
+        messages: [
+            { role: 'system', content: `You are Ray, a personal fitness coach.\nUser context:\n${context}` },
+            { role: 'user', content: userInput },
+        ],
+    });
+    const reply = response.choices[0].message.content!;
+
+    // 3. Store interaction
+    await mem0.add(
+        [{ role: 'user', content: userInput }, { role: 'assistant', content: reply }],
+        { userId: userId }
+    );
+    return reply;
+}
+```
+
+### Key Benefits
+
+- Context persists across app restarts — no session management needed
+- Memories are automatically deduplicated and updated
+- Works with any LLM provider (OpenAI, Anthropic, etc.)
+
+**Best for:** Fitness coaches, tutors, therapists — any assistant that needs to remember goals across sessions.
+
+---
+
+## 2. Customer Support with Categories
+
+Auto-categorize support data so teams retrieve the right facts fast. Uses custom categories for structured retrieval.
+
+### Implementation (Python)
+
+```python
+from mem0 import MemoryClient
+
+client = MemoryClient()
+
+# 1. Define categories at the project level (one-time setup)
+custom_categories = [
+    {"support_tickets": "Customer issues and resolutions"},
+    {"account_info": "Account details and preferences"},
+    {"billing": "Payment history and billing questions"},
+    {"product_feedback": "Feature requests and feedback"},
+]
+client.project.update(custom_categories=custom_categories)
+
+# 2. Store interactions — auto-classified into categories
+def log_support_interaction(user_id: str, message: str, priority: str = "normal"):
+    client.add(
+        [{"role": "user", "content": message}],
+        user_id=user_id,
+        metadata={"priority": priority, "source": "support_chat"}
+    )
+
+# 3. Retrieve by category
+def get_billing_issues(user_id: str):
+    return client.get_all(
+        filters={
+            "AND": [
+                {"user_id": user_id},
+                {"categories": {"in": ["billing"]}}
+            ]
+        }
+    )
+
+def search_support_history(user_id: str, query: str):
+    return client.search(
+        query,
+        filters={
+            "AND": [
+                {"user_id": user_id},
+                {"categories": {"contains": "support_tickets"}}
+            ]
+        },
+        top_k=5
+    )
+
+# Usage
+log_support_interaction("maria", "I was charged twice for last month's subscription", priority="high")
+log_support_interaction("maria", "The dashboard is loading slowly on mobile")
+billing = get_billing_issues("maria")  # Returns only billing-related memories
+```
+
+### Implementation (TypeScript)
+
+```typescript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
+
+// Setup categories (one-time)
+await client.updateProject({
+    custom_categories: [
+        { support_tickets: 'Customer issues and resolutions' },
+        { billing: 'Payment history and billing questions' },
+        { product_feedback: 'Feature requests and feedback' },
+    ],
+});
+
+async function logInteraction(userId: string, message: string, priority = 'normal') {
+    await client.add(
+        [{ role: 'user', content: message }],
+        { userId: userId, metadata: { priority, source: 'support_chat' } }
+    );
+}
+
+async function getBillingIssues(userId: string) {
+    return client.getAll({
+        filters: { AND: [{ user_id: userId }, { categories: { in: ['billing'] } }] },
+    });
+}
+```
+
+### Key Benefits
+
+- Automatic categorization — no manual tagging
+- Filter by category for structured retrieval
+- Metadata (`priority`, `source`) enables multi-dimensional queries
+
+**Best for:** Help desks, SaaS support, e-commerce — structured retrieval by category eliminates manual scanning.
+
+---
+
+## 3. Healthcare Coach
+
+Guide patients with an assistant that remembers medical history. Uses high `threshold` for confident retrieval in safety-critical contexts.
+
+### Implementation (Python)
+
+```python
+from mem0 import MemoryClient
+from openai import OpenAI
+
+mem0 = MemoryClient()
+openai_client = OpenAI()
+
+def save_patient_info(user_id: str, information: str):
+    mem0.add(
+        [{"role": "user", "content": information}],
+        user_id=user_id,
+        run_id="healthcare_session",
+        metadata={"type": "patient_information"}
+    )
+
+def consult(user_id: str, question: str) -> str:
+    # High threshold for medical accuracy
+    memories = mem0.search(question, user_id=user_id, top_k=5, threshold=0.7)
+    context = "\n".join([f"- {m['memory']}" for m in memories.get("results", [])])
+
+    response = openai_client.chat.completions.create(
+        model="gpt-5-mini",
+        messages=[
+            {"role": "system", "content": f"You are a health coach. Patient context:\n{context}"},
+            {"role": "user", "content": question},
+        ]
+    )
+    reply = response.choices[0].message.content
+
+    # Store the interaction
+    mem0.add(
+        [{"role": "user", "content": question}, {"role": "assistant", "content": reply}],
+        user_id=user_id,
+        run_id="healthcare_session",
+    )
+    return reply
+
+# Usage
+save_patient_info("alex", "I'm allergic to penicillin and take metformin for type 2 diabetes")
+consult("alex", "Can I take amoxicillin for my sore throat?")
+# Remembers penicillin allergy — amoxicillin is a penicillin-type antibiotic
+```
+
+### Implementation (TypeScript)
+
+```typescript
+import MemoryClient from 'mem0ai';
+import OpenAI from 'openai';
+
+const mem0 = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
+const openai = new OpenAI();
+
+async function savePatientInfo(userId: string, info: string) {
+    await mem0.add(
+        [{ role: 'user', content: info }],
+        { userId: userId, runId: 'healthcare_session', metadata: { type: 'patient_information' } }
+    );
+}
+
+async function consult(userId: string, question: string): Promise<string> {
+    const memories = await mem0.search(question, {
+        filters: { user_id: userId },
+        topK: 5,
+        threshold: 0.7,
+    });
+    const context = memories.results?.map((m: any) => `- ${m.memory}`).join('\n') || '';
+
+    const response = await openai.chat.completions.create({
+        model: 'gpt-5-mini',
+        messages: [
+            { role: 'system', content: `You are a health coach. Patient context:\n${context}` },
+            { role: 'user', content: question },
+        ],
+    });
+    const reply = response.choices[0].message.content!;
+
+    await mem0.add(
+        [{ role: 'user', content: question }, { role: 'assistant', content: reply }],
+        { userId: userId, runId: 'healthcare_session' }
+    );
+    return reply;
+}
+```
+
+### Key Benefits
+
+- High threshold (0.7) ensures only confident matches for safety-critical retrieval
+- Session scoping via `run_id` groups related health interactions
+- Metadata tagging separates patient info from conversation history
+
+**Best for:** Telehealth, wellness apps, patient management — persistent health context across visits.
+
+---
+
+## 4. Content Creation Workflow
+
+Store voice guidelines once and apply them across every draft. Uses `run_id` and `metadata` to scope writing preferences per session.
+
+### Implementation (Python)
+
+```python
+from mem0 import MemoryClient
+from openai import OpenAI
+
+mem0 = MemoryClient()
+openai_client = OpenAI()
+
+def store_writing_preferences(user_id: str, preferences: str):
+    mem0.add(
+        [{"role": "user", "content": preferences}],
+        user_id=user_id,
+        run_id="editing_session",
+        metadata={"type": "preferences", "category": "writing_style"}
+    )
+
+def draft_content(user_id: str, topic: str) -> str:
+    # Retrieve writing preferences
+    prefs = mem0.search(
+        "writing style preferences",
+        filters={"AND": [{"user_id": user_id}, {"run_id": "editing_session"}]}
+    )
+    style_context = "\n".join([f"- {m['memory']}" for m in prefs.get("results", [])])
+
+    response = openai_client.chat.completions.create(
+        model="gpt-5-mini",
+        messages=[
+            {"role": "system", "content": f"Write content matching these style preferences:\n{style_context}"},
+            {"role": "user", "content": f"Write a blog post about: {topic}"},
+        ]
+    )
+    return response.choices[0].message.content
+
+# Usage
+store_writing_preferences("writer_01", "I prefer short sentences. Active voice. No jargon. Use analogies.")
+draft_content("writer_01", "Why AI memory matters for chatbots")
+# Drafts content matching the stored voice guidelines
+```
+
+### Implementation (TypeScript)
+
+```typescript
+import MemoryClient from 'mem0ai';
+import OpenAI from 'openai';
+
+const mem0 = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
+const openai = new OpenAI();
+
+async function storePreferences(userId: string, preferences: string) {
+    await mem0.add(
+        [{ role: 'user', content: preferences }],
+        { userId: userId, runId: 'editing_session', metadata: { type: 'preferences' } }
+    );
+}
+
+async function draftContent(userId: string, topic: string): Promise<string> {
+    const prefs = await mem0.search('writing style preferences', {
+        filters: { AND: [{ user_id: userId }, { run_id: 'editing_session' }] },
+    });
+    const styleContext = prefs.results?.map((m: any) => `- ${m.memory}`).join('\n') || '';
+
+    const response = await openai.chat.completions.create({
+        model: 'gpt-5-mini',
+        messages: [
+            { role: 'system', content: `Write content matching these preferences:\n${styleContext}` },
+            { role: 'user', content: `Write a blog post about: ${topic}` },
+        ],
+    });
+    return response.choices[0].message.content!;
+}
+```
+
+### Key Benefits
+
+- Voice consistency across all content without repeating guidelines
+- Scoped sessions let you maintain different style profiles
+- Preferences update automatically as you refine them
+
+**Best for:** Marketing teams, technical writers, agencies — consistent voice across all content.
+
+---
+
+## 5. Multi-Agent / Multi-Tenant
+
+Keep memories separate using `user_id`, `agent_id`, `app_id`, and `run_id` scoping. Critical for multi-agent workflows and multi-tenant apps.
+
+### Implementation (Python)
+
+```python
+from mem0 import MemoryClient
+
+client = MemoryClient()
+
+# Store memories scoped to user + agent + session
+def store_scoped_memory(messages: list, user_id: str, agent_id: str, run_id: str, app_id: str):
+    client.add(
+        messages,
+        user_id=user_id,
+        agent_id=agent_id,
+        run_id=run_id,
+        app_id=app_id
+    )
+
+# Query within a specific scope
+def search_user_session(query: str, user_id: str, app_id: str, run_id: str):
+    """Search memories for a specific user within a specific session."""
+    return client.search(
+        query,
+        filters={
+            "AND": [
+                {"user_id": user_id},
+                {"app_id": app_id},
+                {"run_id": run_id}
+            ]
+        }
+    )
+
+def search_agent_knowledge(query: str, agent_id: str, app_id: str):
+    """Search all memories an agent has across all users."""
+    return client.search(
+        query,
+        filters={
+            "AND": [
+                {"agent_id": agent_id},
+                {"app_id": app_id}
+            ]
+        }
+    )
+
+# Usage: Travel concierge app with multiple agents
+store_scoped_memory(
+    [{"role": "user", "content": "I'm vegetarian and prefer window seats"}],
+    user_id="traveler_cam",
+    agent_id="travel_planner",
+    run_id="tokyo-2025",
+    app_id="concierge_app"
+)
+
+# User-scoped query: "What does Cam prefer?"
+user_mems = search_user_session("dietary restrictions?", "traveler_cam", "concierge_app", "tokyo-2025")
+
+# Agent-scoped query: "What do all travelers prefer?" (across users)
+agent_mems = search_agent_knowledge("common dietary restrictions?", "travel_planner", "concierge_app")
+```
+
+### Implementation (TypeScript)
+
+```typescript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
+
+async function storeScopedMemory(
+    messages: Array<{ role: string; content: string }>,
+    userId: string, agentId: string, runId: string, appId: string
+) {
+    await client.add(messages, {
+        userId: userId,
+        agentId: agentId,
+        runId: runId,
+        appId: appId,
+    });
+}
+
+async function searchUserSession(query: string, userId: string, appId: string, runId: string) {
+    return client.search(query, {
+        filters: { AND: [{ user_id: userId }, { app_id: appId }, { run_id: runId }] },
+    });
+}
+
+async function searchAgentKnowledge(query: string, agentId: string, appId: string) {
+    return client.search(query, {
+        filters: { AND: [{ agent_id: agentId }, { app_id: appId }] },
+    });
+}
+```
+
+### Key Benefits
+
+- Full isolation between users, agents, sessions, and apps
+- Query at any scope level — user, agent, session, or app-wide
+- No memory leakage between tenants
+
+**Best for:** Multi-agent workflows, multi-tenant SaaS — proper isolation at every level.
+
+---
+
+## 6. Personalized Search
+
+Blend real-time search results with personal context. Uses `custom_instructions` to infer preferences from queries.
+
+### Implementation (Python)
+
+```python
+from mem0 import MemoryClient
+from openai import OpenAI
+
+mem0 = MemoryClient()
+openai_client = OpenAI()
+
+# One-time setup: configure Mem0 to infer from queries
+mem0.project.update(
+    custom_instructions="""Infer user preferences and facts from their search queries.
+Extract dietary preferences, location, interests, and purchase history."""
+)
+
+def personalized_search(user_id: str, query: str, search_results: list) -> str:
+    # Get user context from memory
+    memories = mem0.search(query, user_id=user_id, top_k=5)
+    user_context = "\n".join([f"- {m['memory']}" for m in memories.get("results", [])])
+
+    response = openai_client.chat.completions.create(
+        model="gpt-5-mini",
+        messages=[
+            {"role": "system", "content": f"Personalize search results using user context:\n{user_context}"},
+            {"role": "user", "content": f"Query: {query}\n\nSearch results:\n{search_results}"},
+        ]
+    )
+    reply = response.choices[0].message.content
+
+    # Store the query to learn preferences over time
+    mem0.add(
+        [{"role": "user", "content": query}],
+        user_id=user_id
+    )
+    return reply
+
+# Usage
+personalized_search("user_42", "best restaurants nearby", ["Restaurant A", "Restaurant B"])
+# Over time, Mem0 learns: "user prefers vegetarian, lives in Austin"
+# Future searches are automatically personalized
+```
+
+### Implementation (TypeScript)
+
+```typescript
+import MemoryClient from 'mem0ai';
+import OpenAI from 'openai';
+
+const mem0 = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
+const openai = new OpenAI();
+
+async function personalizedSearch(userId: string, query: string, searchResults: string[]): Promise<string> {
+    const memories = await mem0.search(query, { filters: { user_id: userId }, topK: 5 });
+    const context = memories.results?.map((m: any) => `- ${m.memory}`).join('\n') || '';
+
+    const response = await openai.chat.completions.create({
+        model: 'gpt-5-mini',
+        messages: [
+            { role: 'system', content: `Personalize results using user context:\n${context}` },
+            { role: 'user', content: `Query: ${query}\nResults: ${searchResults.join(', ')}` },
+        ],
+    });
+    const reply = response.choices[0].message.content!;
+
+    await mem0.add([{ role: 'user', content: query }], { userId: userId });
+    return reply;
+}
+```
+
+### Key Benefits
+
+- Learns preferences from queries automatically via `custom_instructions`
+- Personalizes any search provider (Tavily, Google, Bing)
+- Zero manual preference setup — improves over time
+
+**Best for:** Personalized search engines, recommendation systems — search results tailored to individual users.
+
+---
+
+## 7. Email Intelligence
+
+Capture, categorize, and recall inbox threads using persistent memories with rich metadata.
+
+### Implementation (Python)
+
+```python
+from mem0 import MemoryClient
+
+client = MemoryClient()
+
+def store_email(user_id: str, sender: str, subject: str, body: str, date: str):
+    client.add(
+        [{"role": "user", "content": f"Email from {sender}: {subject}\n\n{body}"}],
+        user_id=user_id,
+        metadata={"email_type": "incoming", "sender": sender, "subject": subject, "date": date}
+    )
+
+def search_emails(user_id: str, query: str):
+    return client.search(
+        query,
+        filters={"AND": [{"user_id": user_id}, {"categories": {"contains": "email"}}]},
+        top_k=10
+    )
+
+def get_emails_from_sender(user_id: str, sender: str):
+    return client.get_all(
+        filters={
+            "AND": [
+                {"user_id": user_id},
+                {"metadata": {"contains": sender}}
+            ]
+        }
+    )
+
+# Usage
+store_email("alice", "bob@acme.com", "Q3 Budget Review", "Attached is the Q3 budget...", "2025-01-15")
+store_email("alice", "carol@acme.com", "Sprint Planning", "Here are the priorities...", "2025-01-16")
+
+results = search_emails("alice", "budget discussions")
+sender_emails = get_emails_from_sender("alice", "bob@acme.com")
+```
+
+### Implementation (TypeScript)
+
+```typescript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
+
+async function storeEmail(userId: string, sender: string, subject: string, body: string, date: string) {
+    await client.add(
+        [{ role: 'user', content: `Email from ${sender}: ${subject}\n\n${body}` }],
+        { userId: userId, metadata: { email_type: 'incoming', sender, subject, date } }
+    );
+}
+
+async function searchEmails(userId: string, query: string) {
+    return client.search(query, {
+        filters: { AND: [{ user_id: userId }, { categories: { contains: 'email' } }] },
+        topK: 10,
+    });
+}
+```
+
+### Key Benefits
+
+- Rich metadata enables multi-dimensional queries (sender, date, subject)
+- Category filtering separates emails from other memory types
+- Semantic search across all email content
+
+**Best for:** Inbox management, email automation — searchable email memories with metadata filtering.
+
+---
+
+## Common Patterns Across Use Cases
+
+### Pattern 1: Retrieve → Generate → Store
+
+Every use case follows the same 3-step loop:
+
+```python
+# 1. Retrieve relevant context
+memories = mem0.search(user_input, user_id=user_id)
+context = "\n".join([m["memory"] for m in memories.get("results", [])])
+
+# 2. Generate with context
+response = llm.generate(system_prompt=f"Context:\n{context}", user_input=user_input)
+
+# 3. Store the interaction
+mem0.add(
+    [{"role": "user", "content": user_input}, {"role": "assistant", "content": response}],
+    user_id=user_id
+)
+```
+
+### Pattern 2: Scope with Entity Identifiers
+
+Use `user_id`, `agent_id`, `app_id`, and `run_id` to isolate memories:
+
+```python
+# User-level: personal preferences
+client.add(messages, user_id="alice")
+
+# Session-level: conversation within one session
+client.add(messages, user_id="alice", run_id="session_123")
+
+# Agent-level: agent-specific knowledge
+client.add(messages, agent_id="support_bot", app_id="helpdesk")
+```
+
+### Pattern 3: Rich Metadata for Filtering
+
+Attach structured metadata for multi-dimensional queries:
+
+```python
+# Store with metadata
+client.add(messages, user_id="alice", metadata={"priority": "high", "source": "phone_call"})
+
+# Filter by category + metadata
+client.search("billing issues", filters={
+    "AND": [{"user_id": "alice"}, {"categories": {"contains": "billing"}}]
+})
+```
+
+### Pattern 4: Custom Instructions for Domain-Specific Extraction
+
+Control what Mem0 extracts from conversations:
+
+```python
+client.project.update(
+    custom_instructions="Extract medical conditions, medications, and allergies. Exclude billing info."
+)
+```
+
+---
+
+## More Examples
+
+For 30+ cookbooks with complete working code: [docs.mem0.ai/cookbooks](https://docs.mem0.ai/cookbooks)

mem0-plugin/.opencode-plugin/opencode-skills/mem0/scripts/mem0_doc_search.ts

@@ -0,0 +1,181 @@
+#!/usr/bin/env bun
+export {};
+const DOCS_BASE = "https://docs.mem0.ai";
+const SEARCH_ENDPOINT = `${DOCS_BASE}/api/search`;
+const LLMS_INDEX = `${DOCS_BASE}/llms.txt`;
+
+const SECTION_MAP: Record<string, string[]> = {
+  platform: [
+    "/platform/overview",
+    "/platform/quickstart",
+    "/platform/features",
+    "/platform/features/graph-memory",
+    "/platform/features/selective-memory",
+    "/platform/features/custom-categories",
+    "/platform/features/v2-memory-filters",
+    "/platform/features/async-client",
+    "/platform/features/webhooks",
+    "/platform/features/multimodal-support",
+  ],
+  api: [
+    "/api-reference/memory/add-memories",
+    "/api-reference/memory/v2-search-memories",
+    "/api-reference/memory/v2-get-memories",
+    "/api-reference/memory/get-memory",
+    "/api-reference/memory/update-memory",
+    "/api-reference/memory/delete-memory",
+  ],
+  "open-source": [
+    "/open-source/overview",
+    "/open-source/python-quickstart",
+    "/open-source/node-quickstart",
+    "/open-source/features",
+    "/open-source/features/graph-memory",
+    "/open-source/features/rest-api",
+    "/open-source/configure-components",
+  ],
+  openmemory: [
+    "/openmemory/overview",
+    "/openmemory/quickstart",
+  ],
+  sdks: ["/sdks/python", "/sdks/js"],
+  integrations: ["/integrations"],
+};
+
+async function fetchUrl(url: string): Promise<string> {
+  try {
+    const res = await fetch(url, {
+      headers: { "User-Agent": "Mem0DocSearchAgent/1.0" },
+      signal: AbortSignal.timeout(15000),
+    });
+    if (!res.ok) return `HTTP Error ${res.status}: ${res.statusText}`;
+    return await res.text();
+  } catch (e: any) {
+    return `Error: ${e.message}`;
+  }
+}
+
+async function searchDocs(query: string, section?: string) {
+  const params = new URLSearchParams({ query });
+  try {
+    const result = await fetchUrl(`${SEARCH_ENDPOINT}?${params}`);
+    const data = JSON.parse(result);
+    if (data?.results) {
+      let results = data.results;
+      if (section && SECTION_MAP[section]) {
+        const paths = SECTION_MAP[section];
+        results = results.filter((r: any) =>
+          paths.some((p) => r.url?.startsWith(p)),
+        );
+      }
+      return { source: "mintlify_search", results };
+    }
+  } catch {}
+
+  const indexContent = await fetchUrl(LLMS_INDEX);
+  const queryLower = query.toLowerCase();
+  let matching = indexContent
+    .split("\n")
+    .map((l) => l.trim())
+    .filter((l) => l && !l.startsWith("#") && l.toLowerCase().includes(queryLower));
+
+  if (section && SECTION_MAP[section]) {
+    const paths = SECTION_MAP[section];
+    matching = matching.filter((u) => paths.some((p) => u.includes(p)));
+  }
+
+  return {
+    source: "llms_txt_index",
+    query,
+    matching_urls: matching.slice(0, 20),
+    suggestion: "Fetch specific URLs for detailed content",
+  };
+}
+
+async function fetchPage(pagePath: string) {
+  const url = pagePath.startsWith("/") ? `${DOCS_BASE}${pagePath}` : pagePath;
+  const content = await fetchUrl(url);
+  return { url, content: content.slice(0, 10000), truncated: content.length > 10000 };
+}
+
+async function getIndex() {
+  const content = await fetchUrl(LLMS_INDEX);
+  const urls = content
+    .split("\n")
+    .map((l) => l.trim())
+    .filter((l) => l && !l.startsWith("#"));
+  return { total_pages: urls.length, urls, sections: Object.keys(SECTION_MAP) };
+}
+
+function listSection(section: string) {
+  if (!SECTION_MAP[section]) {
+    return { error: `Unknown section: ${section}`, available: Object.keys(SECTION_MAP) };
+  }
+  return { section, pages: SECTION_MAP[section].map((p) => `${DOCS_BASE}${p}`) };
+}
+
+function printResult(result: any) {
+  if (result.results) {
+    console.log(`Source: ${result.source ?? "unknown"}`);
+    for (const r of result.results) {
+      console.log(`  - ${r.title ?? "N/A"}: ${r.url ?? "N/A"}`);
+      if (r.description) console.log(`    ${r.description.slice(0, 200)}`);
+    }
+  } else if (result.matching_urls) {
+    console.log(`Source: ${result.source}`);
+    console.log(`Query: ${result.query}`);
+    for (const url of result.matching_urls) console.log(`  - ${url}`);
+    if (result.suggestion) console.log(`\n${result.suggestion}`);
+  } else if (result.urls) {
+    console.log(`Total documentation pages: ${result.total_pages}`);
+    console.log(`Sections: ${result.sections.join(", ")}`);
+    for (const url of result.urls.slice(0, 30)) console.log(`  - ${url}`);
+    if (result.total_pages > 30) console.log(`  ... and ${result.total_pages - 30} more`);
+  } else if (result.pages) {
+    console.log(`Section: ${result.section}`);
+    for (const page of result.pages) console.log(`  - ${page}`);
+  } else if (result.content) {
+    console.log(`URL: ${result.url}`);
+    if (result.truncated) console.log("[Content truncated to 10000 chars]");
+    console.log(result.content);
+  } else if (result.error) {
+    console.log(`Error: ${result.error}`);
+    if (result.available) console.log(`Available sections: ${result.available.join(", ")}`);
+  } else {
+    console.log(JSON.stringify(result, null, 2));
+  }
+}
+
+const args = process.argv.slice(2);
+const flags: Record<string, string | boolean> = {};
+for (let i = 0; i < args.length; i++) {
+  if (args[i] === "--query" && args[i + 1]) flags.query = args[++i];
+  else if (args[i] === "--page" && args[i + 1]) flags.page = args[++i];
+  else if (args[i] === "--section" && args[i + 1]) flags.section = args[++i];
+  else if (args[i] === "--index") flags.index = true;
+  else if (args[i] === "--json") flags.json = true;
+}
+
+let result: any;
+if (flags.index) {
+  result = await getIndex();
+} else if (flags.section && !flags.query) {
+  result = listSection(flags.section as string);
+} else if (flags.page) {
+  result = await fetchPage(flags.page as string);
+} else if (flags.query) {
+  result = await searchDocs(flags.query as string, flags.section as string | undefined);
+} else {
+  console.log("Usage:");
+  console.log("  bun scripts/mem0_doc_search.ts --query \"topic\"");
+  console.log("  bun scripts/mem0_doc_search.ts --page \"/platform/features/graph-memory\"");
+  console.log("  bun scripts/mem0_doc_search.ts --index");
+  console.log("  bun scripts/mem0_doc_search.ts --section platform");
+  process.exit(1);
+}
+
+if (flags.json) {
+  console.log(JSON.stringify(result, null, 2));
+} else {
+  printResult(result);
+}

mem0-plugin/.opencode-plugin/opencode-skills/memory-reviewer/SKILL.md

@@ -0,0 +1,63 @@
+---
+name: memory-reviewer
+description: Reviews stored memory quality by detecting duplicates, contradictions, and stale entries with actionable recommendations. Use when search results seem conflicting, before running dream consolidation, or for periodic memory hygiene audits.
+---
+
+# Memory Reviewer
+
+Audits memory quality for the active project. Finds duplicates, contradictions, and low-confidence entries.
+
+## When to use
+
+- User asks "check my memories", "memory quality", "any duplicates?"
+- User runs `/mem0:memory-reviewer` directly
+- After a session with 5+ memory writes (suggest proactively)
+- After `/mem0:health --deep` identifies issues
+
+## Steps
+
+1. **Fetch all memories** for active project via `get_memories` with `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}`, `page_size=200`. Paginate if needed — cap at 200 memories.
+
+2. **Group by `metadata.type`**. Common types: `decision`, `convention`, `anti_pattern`, `task_learning`, `project_profile`, `user_preference`, `session_state`.
+
+3. **Scan each group for issues:**
+
+   | Issue | Detection method |
+   |---|---|
+   | **Near-duplicates** | >60% noun overlap within same type. Compare memory text after stripping stop words. |
+   | **Contradictions** | Opposing facts about same topic (e.g., "use PostgreSQL" vs "use MySQL" for same component) |
+   | **Low-confidence** | `metadata.confidence < 0.3` |
+   | **Missing type** | No `metadata.type` set |
+   | **Stale** | `created_at` older than 180 days with no updates |
+
+4. **Output compact summary:**
+
+```
+memory-reviewer: project=<id> total=<N>
+  duplicates:      <N> found
+  contradictions:  <N> found
+  low_confidence:  <N> found
+  untagged:        <N> found
+  stale:           <N> found
+```
+
+5. **If issues found**, list them with memory IDs:
+
+```
+Issues:
+  [duplicate] "<memory_a>" ≈ "<memory_b>" [mem0:<id_a>, mem0:<id_b>]
+  [contradiction] "<memory_x>" vs "<memory_y>" [mem0:<id_x>, mem0:<id_y>]
+  [low_conf] "<memory_z>" (confidence: 0.1) [mem0:<id_z>]
+```
+
+6. **Suggest action**: "Run `/mem0:dream` to consolidate duplicates and resolve contradictions."
+
+## Constraints
+
+- **Read-only** — never modify or delete memories (that's `/mem0:dream`'s job)
+- **Max 200 memories** per scan
+- Report findings, let user decide on action
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/onboard/SKILL.md

@@ -0,0 +1,181 @@
+---
+name: onboard
+description: Sets up mem0 for a new project including API key configuration, MCP authentication, project file import, and coding categories. Use on first run in a new project, when API key needs updating, or to re-run initial setup after configuration changes.
+---
+
+# Mem0 Onboarding Wizard
+
+Run this wizard to set up the mem0 plugin for the current project. Complete in ~60 seconds.
+
+**IMPORTANT: Execute steps strictly in order (0 → 1 → 2 → 3 → 4 → 5 → 6). Each step depends on the previous one. Do NOT run steps in parallel or skip ahead. Complete one step fully before starting the next.**
+
+## Step 0: Skip dependency install
+
+The plugin lists `mem0ai` as a declared dependency — no install step is needed. Proceed directly to Step 1.
+
+## Step 1: Set up API key
+
+Check if the API key is available:
+
+```bash
+[ -n "${MEM0_API_KEY:-}" ] && echo "SET" || echo "NOT_SET"
+```
+
+IMPORTANT: Never run `echo $MEM0_API_KEY` — that prints the secret in plaintext to the conversation log.
+
+### If API key IS set (output is "SET")
+
+Print: `- API key found.` and proceed to Step 2.
+
+### If API key is NOT set (output is "NOT_SET")
+
+Guide the user through API key setup. Show this message:
+
+```
+Step 1: Setting up API key.
+
+- API key not found. Let's set it up.
+
+  1. Get your API key from https://app.mem0.ai/dashboard/api-keys
+
+  2. Choose ONE method:
+
+     Option A — Shell profile:
+       echo 'export MEM0_API_KEY="m0-your-key-here"' >> ~/.zshrc
+       source ~/.zshrc
+
+     Option B — OpenCode environment config:
+       Add MEM0_API_KEY to your OpenCode environment settings
+       so it is available in all sessions.
+
+  3. Verify:
+     [ -n "${MEM0_API_KEY:-}" ] && echo "SET" || echo "NOT_SET"
+```
+
+After the user confirms, re-run the verify command. If NOT_SET, repeat. If SET, proceed to Step 2.
+
+## Step 2: MCP server connection
+
+First, check if MCP tools are already available using ToolSearch with query `"mem0 search_memories"`. The exact tool name varies by install method (may be `mcp__mem0__search_memories` or `mcp__plugin_mem0_mem0__search_memories`).
+
+**If MCP tools ARE found:** Print `- MCP already connected.` and proceed to Step 3.
+
+**If MCP tools are NOT found:**
+
+The MCP server authenticates using the `MEM0_API_KEY` set in Step 1. No OAuth or browser login is needed.
+
+1. Verify the API key is set (re-run the Step 1 check)
+2. Check the plugin is installed and the MCP server for mem0 is listed in OpenCode's MCP configuration
+3. If the server shows an error, ask the user to restart OpenCode and run `/mem0:onboard` again
+4. If all checks pass but tools are still missing: "Restart OpenCode and run `/mem0:onboard` again."
+
+**STOP here** — do not proceed without MCP tools.
+
+## Step 3: Verify connectivity and show identity
+
+Call `search_memories` with `query="project setup"`, `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}`, `top_k=1` to verify connectivity.
+
+Print:
+```
+- Connected
+  user:    <user_id>
+  project: <project_id>
+  branch:  <branch>
+```
+
+If the search fails, troubleshoot the API key and MCP connection.
+
+## Step 4: Import project files
+
+Check for common project context files in the working directory root. Read any that exist and store their key facts as memories using `add_memory`.
+
+### 4a: Detect project files
+
+```bash
+for f in CLAUDE.md AGENTS.md .cursorrules .windsurfrules mem0.md .mem0.md; do
+  [ -f "$f" ] && echo "FOUND: $f ($(wc -c < "$f") bytes)"
+done || true
+```
+
+If no files found, print `- No project files found. Skipping import.` and proceed to Step 5.
+
+### 4b: Read and import via MCP
+
+For each file found in 4a:
+
+1. Read the file contents with the Read tool.
+2. Extract the key facts, conventions, and decisions documented in the file. Do not import the entire file verbatim — summarize meaningful chunks.
+3. Call `add_memory` for each meaningful chunk with:
+   - `data`: the extracted fact or convention
+   - `user_id`: the active user id
+   - `app_id`: the active project id
+   - `metadata`: `{"source": "<filename>", "type": "project_context"}`
+
+If a file is very large (over 4000 bytes), split it into logical sections (one `add_memory` call per section).
+
+### 4c: Report to user
+
+After processing all files, print a user-friendly summary:
+
+```
+- Importing project files into mem0... done.
+  <N> file(s) read, <M> memories stored.
+  These are available as context for future sessions.
+```
+
+If `add_memory` calls fail, print:
+```
+- Project file import failed. Check API key and MCP connection, then retry with: /mem0:onboard
+```
+
+## Step 5: Verify coding categories
+
+Coding categories are now configured automatically in the background when the plugin starts. This step only verifies they are set up.
+
+Check if the categories are already configured by searching for a project_profile memory:
+
+Call `search_memories` with `query="coding categories project profile"`, `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}`, `top_k=1`.
+
+If a project_profile memory is found, print:
+```
+- Coding categories already configured (17 categories, auto-installed).
+```
+
+If no project_profile memory is found, store one as a fallback. Call `add_memory` once with:
+
+- `data`: a plain-text description of the project profile and the list of active coding categories:
+  ```
+  Project profile for <project_id>.
+  Active coding categories: architecture_decisions, api_design, data_models,
+  algorithms, dependencies, environment_setup, testing_strategy, debugging_notes,
+  performance, security, deployment, code_conventions, error_handling,
+  refactoring_history, integrations, onboarding, project_meta.
+  ```
+- `user_id`: the active user id
+- `app_id`: the active project id
+- `metadata`: `{"type": "project_profile", "source": "onboard"}`
+
+After the `add_memory` call succeeds, print:
+```
+- Coding categories installed (17 categories).
+```
+
+## Step 6: Summary
+
+Print a summary:
+```
+- Onboarding complete.
+  user_id:    <user_id>
+  project_id: <project_id> (app_id)
+  files:      <N> found, <M> memories stored
+  categories: <N installed | already installed | skipped by user>
+
+Memory is now active for this project. Start working — mem0 will
+automatically search relevant context and capture learnings.
+
+Run /mem0:tour to see what mem0 already knows about this project.
+```
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/peek/SKILL.md

@@ -0,0 +1,56 @@
+---
+name: peek
+description: Searches memories and displays compact one-liner results, or looks up a specific memory by ID. Use for quick memory lookups, checking if a decision was recorded, resolving [mem0:id] citations, or browsing memories without full category detail.
+---
+
+# Mem0 Peek
+
+Quick search with compact output. Lighter than `/mem0:tour`.
+
+## Execution
+
+### Step 1: Parse query
+
+The user provides a search query: `/mem0:peek auth middleware`
+
+If no query provided, ask: "What should I search for?"
+
+**Memory ID detection:** If the query matches any of these patterns, treat it as a
+direct memory ID lookup instead of a search:
+- Bare hex: `^[a-f0-9]{8}$` (short ID) or `^[a-f0-9]{8}-[a-f0-9-]+$` (full UUID)
+- Citation ref: `[mem0:<hex>]` — extract the hex portion
+
+When an ID is detected:
+1. Call `get_memory(<id>)` directly (if short ID, try as prefix of full UUID)
+2. If found, skip to Step 3 and display the single result
+3. If not found, fall through to search using the ID as query text
+
+### Step 2: Search
+
+Run 2 parallel `search_memories` calls:
+
+1. Broad: `query=<user's query>`, `filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]}`, `top_k=10`, `rerank=true`
+2. Targeted: `query=<user's query>`, `filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"metadata": {"type": "decision"}}]}`, `top_k=5`, `rerank=true`
+
+### Step 3: Display
+
+Deduplicate by ID, then show compact results:
+
+```
+## mem0 peek: "<query>" (<N> results)
+
+1. [decision] Auth module uses JWT with RS256 keys (2025-05-15) [mem0:a3f8b2c1]
+2. [anti_pattern] Don't use symmetric HS256 — leaked in env (2025-05-10) [mem0:7e2d9f4a]
+3. [convention] All middleware in src/middleware/ (2025-05-08) [mem0:c4d5e6f7]
+```
+
+Format: `<number>. [<type>] <content, 80 chars> (<date>) [mem0:<short_id>]`
+
+If no results:
+```
+No memories matching "<query>" for project <project_id>.
+```
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/pin/SKILL.md

@@ -0,0 +1,71 @@
+---
+name: pin
+description: Pins or unpins a memory to protect it from pruning during dream consolidation. Use when a memory is critical and must never be removed, such as architecture decisions, security constraints, or immutable team conventions.
+---
+
+# Mem0 Pin
+
+Pin a memory to mark it as high-priority and protect from pruning.
+
+## Execution
+
+### Step 1: Find the memory
+
+The user provides either a search query or memory ID.
+
+**If memory ID:**
+- Call `get_memory` with the ID.
+
+**If search query:**
+- Call `search_memories` with the query, `filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]}`, `top_k=5`.
+- Show numbered list with content previews.
+- Ask: "Which memory to pin? Enter a number."
+
+### Step 2: Read current content
+
+Call `get_memory` with the selected memory ID. Store:
+- `original_text` — the memory's text content
+- `original_metadata` — the existing `metadata` dict
+
+### Step 3: Pin it
+
+The MCP `update_memory` tool only accepts `memory_id`, `text`, and `source` — it
+does not accept a `metadata` parameter. To pin, append a pin marker to the text:
+
+```python
+pinned_text = "[PINNED] " + original_text if not original_text.startswith("[PINNED]") else original_text
+update_memory(memory_id=<selected_id>, text=pinned_text)
+```
+
+**For new memories** (user wants to pin text that isn't stored yet):
+1. Call `add_memory` with:
+   - `text="[PINNED] <the user's text>"`
+   - `user_id=<active_user_id>`
+   - `app_id=<active_project_id>`
+   - `metadata={"pinned": true, "type": "decision", "confidence": 1.0}`
+   - `infer=False`
+2. The response contains `event_id`. Call `get_event_status(event_id=<event_id>)` once to retrieve the memory ID, then confirm.
+
+### Step 4: Confirm
+
+```
+Pinned: "<memory content, first 80 chars>"
+Memory ID: <id>
+```
+
+Append `...` only if content exceeds 80 characters.
+
+### Unpin
+
+If the user says "unpin":
+1. Call `get_memory` to read current content.
+2. Remove the pin marker from the text:
+   ```python
+   unpinned_text = original_text.removeprefix("[PINNED] ")
+   update_memory(memory_id=<id>, text=unpinned_text)
+   ```
+3. Print: `Unpinned: "<content>..."`
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/remember/SKILL.md

@@ -0,0 +1,61 @@
+---
+name: remember
+description: Stores a memory verbatim from user input with appropriate type classification and metadata. Use when the user says remember this, save this, store this, note that, or explicitly asks to record a decision, preference, convention, or learning.
+---
+
+# Mem0 Remember
+
+Store a fact or learning directly into mem0.
+
+## Execution
+
+### Step 1: Extract the content
+
+The user provides the content as an argument: `/mem0:remember <text>`
+
+If no text was provided, ask: "What should I remember?"
+
+### Step 2: Classify the memory
+
+Based on the content, pick the best `metadata.type`:
+
+| Content signal | Type |
+|---|---|
+| "we decided...", "always use...", "never..." | `decision` |
+| "X doesn't work because...", "don't try..." | `anti_pattern` |
+| "I prefer...", "use X instead of Y" | `user_preference` |
+| "the convention is...", "we always..." | `convention` |
+| "learned that...", "figured out..." | `task_learning` |
+| setup, env, tooling, config | `environmental` |
+| anything else | `task_learning` |
+
+### Step 3: Store
+
+Call `add_memory` with:
+- `text="<the user's text>"`
+- `user_id=<active_user_id>`
+- `app_id=<active_project_id>`
+- `metadata={"type": "<classified_type>", "branch": "<active_branch>", "confidence": 1.0, "source": "remember_command"}`
+- `infer=False`
+
+`infer=False` because the user stated the fact explicitly — no extraction needed.
+`confidence=1.0` because the user explicitly asked to store this.
+
+### Step 4: Confirm
+
+The `add_memory` response returns `event_id` (not `memory_id`) because writes are async.
+Call `get_event_status(event_id=<event_id>)` once.
+
+- If status is `SUCCEEDED`: print the memory ID from the result.
+- If status is `PENDING` or `processing`: print with the event ID as fallback.
+
+```
+Remembered as <type>: "<content, first 80 chars>"
+Memory ID: <id from event status>
+```
+
+Append `...` only if content was truncated (longer than 80 chars).
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/stats/SKILL.md

@@ -0,0 +1,135 @@
+---
+name: stats
+description: Displays memory usage statistics for the current session and project including counts by category, age distribution, and API latency. Use when checking how many memories exist, reviewing session activity, or auditing memory distribution across categories.
+---
+
+# Mem0 Stats
+
+Show session and lifetime memory statistics.
+
+## Execution
+
+### Step 1: Gather session context
+
+Read the session identity from env vars set by the plugin's shell.env hook:
+
+- `MEM0_USER_ID` (falls back to `$USER` if unset)
+- `MEM0_APP_ID` — the active project identifier
+- `MEM0_SESSION_ID` — current session identifier
+- `MEM0_BRANCH` — current git branch
+
+If `MEM0_USER_ID` is unset, use `$USER`. If `MEM0_APP_ID` is unset, note "No project configured" and stop.
+
+### Step 2: Fetch total memory count
+
+Call `get_memories` MCP tool to get the total count for this project:
+
+`filters={"AND": [{"user_id": "<MEM0_USER_ID>"}, {"app_id": "<MEM0_APP_ID>"}]}`, `page_size=1`
+
+Read the `count` field from the response — this is the total number of memories for the project regardless of page size.
+
+### Step 3: Fetch memories for category breakdown
+
+Call `get_memories` MCP tool to retrieve memories for grouping:
+
+`filters={"AND": [{"user_id": "<MEM0_USER_ID>"}, {"app_id": "<MEM0_APP_ID>"}]}`, `page_size=200`
+
+Group each memory by:
+1. `categories[0]` (platform-assigned) — primary grouping
+2. `metadata.type` (agent-assigned) — secondary if `categories` is empty or absent
+3. `created_at` date — for age analysis
+
+**Category normalization:** Merge `auto_capture` and `uncategorized` into a single `uncategorized` row. Do NOT show `auto_capture` as its own row.
+
+Also run a `search_memories` MCP tool call with `query="project"`, `filters={"AND": [{"user_id": "<MEM0_USER_ID>"}, {"app_id": "<MEM0_APP_ID>"}]}`, `top_k=1` to measure round-trip latency. Note the time before and after the MCP call — do NOT attempt raw HTTP calls to the API.
+
+### Step 4: Display
+
+Print a minimal plain-text dashboard. No markdown formatting — OpenCode TUI renders text verbatim.
+
+Example output shape:
+
+```
+mem0 stats
+
+Session  (<first 12 chars of MEM0_SESSION_ID>)  branch: main
+
+Project: my-project  —  55 memories  —  API: 84ms
+
+Category             Count
+--------------------  -----
+decision                24
+convention              15
+anti_pattern             6
+task_learning            5
+user_preference          3
+session_state            2
+
+Age  —  oldest: 2026-02-15  newest: 2026-05-23
+  < 7 days: 5  |  7-30d: 12  |  30-90d: 10  |  > 90d: 8
+
+Identity  —  user: kartik  project: my-project  branch: main
+```
+
+**Display rules:**
+- Use plain text with spaces to align columns — no markdown tables, no | pipes, no ** bold, no ## headers
+- Category section: sort by count descending, omit categories with 0 memories
+- Age: single line with pipe-separated buckets, computed from `created_at`
+- Session line: show MEM0_SESSION_ID (first 12 chars) and MEM0_BRANCH if available; skip the line entirely if both are unset
+- If only 1-2 total memories, skip the category table — just show the count
+- Keep everything compact — no decorative borders or filler
+
+## Weekly digest mode
+
+When invoked with `--weekly` (e.g., `/mem0:stats --weekly`), append a weekly
+activity digest after the standard stats dashboard.
+
+### W1: Fetch recent memories
+
+Call `search_memories` in parallel with time-scoped queries:
+1. `query="decisions made this week"`, `filters={"AND": [{"user_id": "<MEM0_USER_ID>"}, {"app_id": "<MEM0_APP_ID>"}, {"created_at": {"gte": "<7 days ago YYYY-MM-DD>"}}]}`, `top_k=20`
+2. `query="bugs errors fixes"`, same time filter, `top_k=20`
+3. `query="patterns conventions learnings"`, same time filter, `top_k=20`
+
+### W2: Analyze
+
+Merge by ID. Group into "New this week" by `categories[0]` or `metadata.type`.
+Calculate: memories added last 7 days, most active categories, most active day.
+
+### W3: Display
+
+Append after the standard stats in plain text (no markdown):
+
+```
+This week (May 16 - May 23)
+
++12 memories  —  most active: Wednesday (5)
+
+Category        New
+--------------  ---
+decision          5
+task_learning     4
+bug_fix           3
+
+Highlights
+- <2-3 sentence summary of most important decisions/learnings this week>
+```
+
+### W4: Write digest file
+
+Write to `~/.mem0/weekly-digest.txt` (overwrite). Append one line to
+`~/.mem0/digest-history.log`:
+```
+<YYYY-MM-DD> | <MEM0_APP_ID> | +<new_count> memories | top: <top_category>
+```
+
+### W5: Empty state
+
+If no new memories in 7 days, output:
+```
+No new memories in the past week. Total: <N> memories in <MEM0_APP_ID>.
+```
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/switch-project/SKILL.md

@@ -0,0 +1,107 @@
+---
+name: switch-project
+description: Overrides the auto-detected project scope to read and write memories under a different project ID, or enables global search to access all memories across all users and projects. Use when working across multiple projects, accessing memories from another repo, enabling team-wide memory access, or when auto-detection resolves to the wrong project.
+---
+
+# Mem0 Switch Project
+
+Override the automatic project_id detection for the current directory, or enable global search mode.
+
+## Usage
+
+- `/mem0:switch-project <project-name>` — switch to a specific project scope
+- `/mem0:switch-project --global` — enable global search (all memories, all users, all projects)
+- `/mem0:switch-project --no-global` — disable global search and return to per-project scoping
+
+## Execution
+
+### If `--global` flag is provided:
+
+1. Set `global_search: true` in `~/.mem0/settings.json` using the Bash tool:
+
+   ```bash
+   python3 -c "
+   import json, os
+   settings_file = os.path.expanduser('~/.mem0/settings.json')
+   settings = {}
+   if os.path.isfile(settings_file):
+       with open(settings_file) as f:
+           settings = json.load(f)
+   settings['global_search'] = True
+   with open(settings_file, 'w') as f:
+       json.dump(settings, f, indent=2)
+   print('Global search enabled')
+   "
+   ```
+
+2. Print:
+   ```
+   Global search enabled.
+   Searches now return all memories across all users and projects.
+   Writes still use the current user_id and app_id.
+   Restart the session for the change to take effect.
+   ```
+
+### If `--no-global` flag is provided:
+
+1. Set `global_search: false` in `~/.mem0/settings.json` using the Bash tool:
+
+   ```bash
+   python3 -c "
+   import json, os
+   settings_file = os.path.expanduser('~/.mem0/settings.json')
+   settings = {}
+   if os.path.isfile(settings_file):
+       with open(settings_file) as f:
+           settings = json.load(f)
+   settings['global_search'] = False
+   with open(settings_file, 'w') as f:
+       json.dump(settings, f, indent=2)
+   print('Global search disabled')
+   "
+   ```
+
+2. Print:
+   ```
+   Global search disabled.
+   Searches now return only memories scoped to the current project.
+   Restart the session for the change to take effect.
+   ```
+
+### If a project name is provided (no flags):
+
+1. If no project name was given, ask: "What project_id should this directory use?"
+
+2. Write the mapping to `~/.mem0/project_map.json` using the Bash tool:
+
+   ```bash
+   python3 -c "
+   import json, os
+   map_file = os.path.expanduser('~/.mem0/project_map.json')
+   mapping = {}
+   if os.path.isfile(map_file):
+       with open(map_file) as f:
+           mapping = json.load(f)
+   mapping[os.getcwd()] = '<PROJECT_NAME>'
+   os.makedirs(os.path.dirname(map_file), exist_ok=True)
+   with open(map_file, 'w') as f:
+       json.dump(mapping, f, indent=2)
+   print(f'Mapped {os.getcwd()} -> <PROJECT_NAME>')
+   "
+   ```
+
+   (Replace `<PROJECT_NAME>` with the user's chosen project name.)
+
+3. Verify by searching for existing memories:
+   - Call `search_memories` with `query="project"`, `filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<PROJECT_NAME>"}]}`, `top_k=1`
+
+4. Print:
+   ```
+   Switched to project <PROJECT_NAME>.
+   <N> memories found for this project.
+   Note: This override persists across sessions for this directory.
+   ```
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode-skills/tour/SKILL.md

@@ -0,0 +1,156 @@
+---
+name: tour
+description: Browses all stored memories grouped by category with full content display. Use when reviewing all project memories, exploring stored knowledge, onboarding to a project, or getting an overview of captured decisions, conventions, and learnings.
+---
+
+# Mem0 Project Tour
+
+Show the user what mem0 has stored for the current project.
+
+## Cross-project mode
+
+When invoked with `--all-projects` (e.g., `/mem0:tour --all-projects` or
+`/mem0:tour --all-projects auth middleware`), search across ALL projects:
+
+1. Call `get_memories` with `filters={"AND": [{"user_id": "<active_user_id>"}]}`, `page_size=200` — **no `app_id` filter**.
+2. If a search query was also provided, run `search_memories` with `query=<query>`,
+   `filters={"AND": [{"user_id": "<active_user_id>"}]}`, `top_k=20` — again no `app_id`.
+3. Group results by `app_id` first, then by category within each project.
+4. Display:
+   ```
+   ## <app_id_1> (<N> memories) ← current
+   **Architecture Decisions** — <memory content>
+   ...
+
+   ## <app_id_2> (<N> memories)
+   ...
+
+   <N> memories across <M> projects
+   ```
+5. Mark the current project with `← (current)` in the heading.
+
+If `--all-projects` is NOT present, use the standard single-project flow below.
+
+## Peek mode (compact search)
+
+When `/mem0:tour` receives a search query argument (e.g., `/mem0:tour auth middleware`)
+WITHOUT `--all-projects`, run in **peek mode** — compact one-liner results:
+
+1. Run 2 parallel `search_memories` calls:
+   - Broad: `query=<query>`, `filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]}`, `top_k=10`, `rerank=true`
+   - Targeted: `query=<query>`, `filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"metadata": {"type": "decision"}}]}`, `top_k=5`, `rerank=true`
+2. Deduplicate by ID, display compact results:
+   ```
+   ## mem0 search: "<query>" (<N> results)
+
+   1. [decision] Auth module uses JWT with RS256 keys (2025-05-15) [mem0:a3f8b2c1]
+   2. [anti_pattern] Don't use symmetric HS256 — leaked in env (2025-05-10) [mem0:7e2d9f4a]
+   3. [convention] All middleware in src/middleware/ (2025-05-08) [mem0:c4d5e6f7]
+   ```
+   Format: `<number>. [<type>] <content, 80 chars> (<date>) [mem0:<short_id>]`
+3. If no results: `No memories matching "<query>" for project <project_id>.`
+
+If no query argument and no `--all-projects` flag, use the full tour flow below.
+
+## Execution
+
+### Step 1: Fetch ALL memories for this project
+
+Call `get_memories` to fetch all memories for this project:
+
+`filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}`, `page_size=100`
+
+### Step 2: Run supplementary semantic searches
+
+In parallel, run these `search_memories` calls to get relevance-ranked results for key topics:
+
+- `query="architecture decisions design choices"`, `filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]}`, `top_k=10`, `rerank=true`
+- `query="bugs errors failures anti-patterns"`, `filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]}`, `top_k=10`, `rerank=true`
+- `query="project setup tooling conventions preferences"`, `filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]}`, `top_k=10`, `rerank=true`
+
+**Do NOT filter by `metadata.type` in these calls.** The platform auto-assigns `categories` — filtering on `metadata.type` misses memories that were auto-categorized but don't have an explicit `metadata.type`.
+
+### Step 3: Merge and group
+
+Merge all results by memory ID (deduplicate). For each memory, determine its group using this priority:
+
+1. **Platform `categories` field** (array on each memory, auto-assigned by Mem0). Use the first category value.
+2. **`metadata.type` field** (if present, set explicitly by hooks/agent). Use as fallback if no `categories`.
+3. **"other"** bucket for memories with neither.
+
+Map category names to display names:
+
+| Platform category / metadata.type | Display name |
+|---|---|
+| `architecture decisions`, `architecture_decisions`, `decision` | Architecture Decisions |
+| `anti patterns`, `anti_patterns`, `anti_pattern` | Anti-Patterns |
+| `task learnings`, `task_learnings`, `task_learning` | Task Learnings |
+| `coding conventions`, `coding_conventions`, `convention` | Coding Conventions |
+| `user preferences`, `user_preferences`, `user_preference` | User Preferences |
+| `project profile`, `project_profile` | Project Profile |
+| `tooling setup`, `tooling_setup`, `environmental` | Tooling & Setup |
+| `technology`, `professional_details` | Tooling & Setup |
+| `session_state` | Session State |
+| `compact_summary` | Compact Summaries |
+| anything else | Other |
+
+### Step 4: Display results
+
+Sort groups by descending memory count. Display in compact tabular format:
+
+First show the category summary table:
+
+```
+mem0 tour
+
+Session (ses_abc123)  branch: main
+Project: my-project  -  349 memories
+
+Category                   Count
+-----------------------------------------
+tooling_setup                119
+bug_fixes                     78
+architecture_decisions        32
+task_learnings                14
+...
+```
+
+Then for each category (sorted by count descending), show memories as numbered one-liners. Truncate each memory to 100 chars max:
+
+```
+tooling_setup (119)
+  1. User requires that no git commit or push be performed without explicit permission... 
+  2. OpenCode plugins are loaded from ~/.config/opencode/plugins/ for global installation...
+  3. Assistant determined that the symlink method for loading the Mem0 plugin was failing...
+  ... and 116 more
+
+bug_fixes (78)
+  1. Fixed getAll filter format from flat object to AND-wrapped array for mem0ai TS SDK v3...
+  2. Root cause of user_id mismatch: plugin derived kartik.labhshetwar from git email...
+  ... and 76 more
+```
+
+Show top 5 memories per category by recency. If a group has more than 5, note `... and <N> more`.
+
+Skip empty groups entirely.
+
+### Step 5: Print totals
+
+```
+<N> memories across <M> categories
+project: <project_id>  branch: <active_branch>
+
+Identity - user: <user_id>  project: <project_id>  branch: <branch>
+```
+
+### Step 6: Empty state
+
+If zero memories found for this project, print:
+```
+No memories stored yet for project <project_id>.
+Run /mem0-onboard to import project files, or start working - mem0 captures learnings automatically.
+```
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim - markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

mem0-plugin/.opencode-plugin/opencode.json

@@ -0,0 +1,14 @@
+{
+  "$schema": "https://opencode.ai/config.json",
+  "plugin": ["@mem0/opencode-plugin"],
+  "mcp": {
+    "mem0": {
+      "type": "remote",
+      "url": "https://mcp.mem0.ai/mcp/",
+      "headers": {
+        "Authorization": "Token {env:MEM0_API_KEY}"
+      },
+      "oauth": false
+    }
+  }
+}