docs: remove deprecated Graph Memory references

Graph Memory was deprecated on April 16, 2026. This removes remaining
mentions across docs that could mislead readers into thinking it is still
a supported feature.

- Platform pages (overview, quickstart, faqs, platform-vs-oss, vibecoding):
  remove graph memory from feature lists, comparison table, and intro copy
- Platform features (platform-overview, mcp-integration,
  advanced-memory-operations): rephrase "graph-powered retrieval" to
  "advanced retrieval"; drop "Enable graph memories" tip; remove graph-edge
  verification step from the metadata example
- Core concepts (add, search, memory-types, memory-evaluation): remove
  "optional graph storage" / "graph features" / "graph writes" / "graph
  toggles"; drop "graph" from Entity Search description; remove outdated
  add/search architecture diagrams (replacement pending)
- Open source features (overview, rest-api): remove "graph relationships"
  and "graph backend(s)" from REST server config narrative
- API reference (organizations-projects): drop "graph settings" from
  Update Project Settings description
- Cookbooks: remove "Graph Memory on Neptune" card (page removed) and
  Neptune mention from AWS Bedrock card; remove "Graph Capabilities"
  bullet from Gemini cookbook; remove graph-memory conditional bullet from
  controlling-memory-ingestion
- CLI: remove --graph / --no-graph flags from mem0 add / mem0 search
  flag tables and MEM0_ENABLE_GRAPH env var
- Images: delete orphan add_architecture.png and search_architecture.png

.agents/plugins/marketplace.json

@@ -8,7 +8,7 @@
       "name": "mem0",
       "source": {
         "source": "local",
-        "path": "./integrations/mem0-plugin"
+        "path": "./mem0-plugin"
       },
       "policy": {
         "installation": "AVAILABLE",

.claude-plugin/marketplace.json

@@ -10,9 +10,9 @@
   "plugins": [
     {
       "name": "mem0",
-      "source": "./integrations/mem0-plugin",
+      "source": "./mem0-plugin",
       "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
-      "version": "0.2.12"
+      "version": "0.2.7"
     }
   ]
 }

.codex-plugin/marketplace.json

@@ -8,7 +8,7 @@
       "name": "mem0",
       "source": {
         "source": "local",
-        "path": "./integrations/mem0-plugin"
+        "path": "./mem0-plugin"
       },
       "policy": {
         "installation": "AVAILABLE",

.cursor-plugin/marketplace.json

@@ -10,9 +10,9 @@
   "plugins": [
     {
       "name": "mem0",
-      "source": "./integrations/mem0-plugin",
+      "source": "./mem0-plugin",
       "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search.",
-      "version": "0.2.12"
+      "version": "0.2.7"
     }
   ]
 }

.github/workflows/cd.yml

@@ -1,33 +1,18 @@
 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:
-  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
+  release:
+    types: [published]
 
 jobs:
   build-n-publish:
     name: Build and publish Python 🐍 distributions 📦 to PyPI and TestPyPI
-    # 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')
+    if: startsWith(github.event.release.tag_name, '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
@@ -54,6 +39,7 @@ 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/ci-gate.yml

@@ -1,171 +0,0 @@
-name: CI Gate
-
-# Single required status check for all PRs.
-#
-# Path-filtered CI workflows can't be marked as required in branch
-# protection: on a PR that doesn't touch their paths they never report, and
-# the required check hangs at "Expected" forever. This gate solves that. It
-# runs on every PR, detects which packages changed, calls only the relevant
-# package CI workflows (as reusable workflows), and the final "CI Gate" job
-# reports the aggregate result — success when every invoked pipeline passed
-# (skipped pipelines are fine), failure when any failed.
-#
-# Branch protection should require exactly one status check: "CI Gate".
-#
-# Package CI workflows keep their own push-to-main and workflow_dispatch
-# triggers; only their pull_request triggers moved here. To wire in a new
-# package: add a filter under the `changes` job, a call job that `uses:` the
-# package workflow, and list the call job in the gate's `needs`.
-
-on:
-  pull_request:
-
-concurrency:
-  group: ci-gate-${{ github.event.pull_request.number }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-  pull-requests: read
-
-jobs:
-  changes:
-    name: Detect changed packages
-    runs-on: ubuntu-latest
-    outputs:
-      python_sdk: ${{ steps.filter.outputs.python_sdk }}
-      ts_sdk: ${{ steps.filter.outputs.ts_sdk }}
-      cli_python: ${{ steps.filter.outputs.cli_python }}
-      cli_node: ${{ steps.filter.outputs.cli_node }}
-      openclaw: ${{ steps.filter.outputs.openclaw }}
-      opencode_plugin: ${{ steps.filter.outputs.opencode_plugin }}
-      pi_agent_plugin: ${{ steps.filter.outputs.pi_agent_plugin }}
-      docs_llms_txt: ${{ steps.filter.outputs.docs_llms_txt }}
-    steps:
-      - uses: dorny/paths-filter@v3
-        id: filter
-        with:
-          # Each filter mirrors the package workflow's old pull_request
-          # paths, plus the package workflow file itself and this gate file
-          # (changing either must re-exercise the pipeline).
-          filters: |
-            python_sdk:
-              - 'mem0/**'
-              - 'tests/**'
-              - 'pyproject.toml'
-              - '.github/workflows/ci.yml'
-              - '.github/workflows/ci-gate.yml'
-            ts_sdk:
-              - 'mem0-ts/**'
-              - '.github/workflows/ts-sdk-ci.yml'
-              - '.github/workflows/ci-gate.yml'
-            cli_python:
-              - 'cli/python/**'
-              - '.github/workflows/cli-python-ci.yml'
-              - '.github/workflows/ci-gate.yml'
-            cli_node:
-              - 'cli/node/**'
-              - '.github/workflows/cli-node-ci.yml'
-              - '.github/workflows/ci-gate.yml'
-            openclaw:
-              - 'integrations/openclaw/**'
-              - '.github/workflows/openclaw-checks.yml'
-              - '.github/workflows/ci-gate.yml'
-            opencode_plugin:
-              - 'integrations/mem0-plugin/.opencode-plugin/**'
-              - '.github/workflows/opencode-plugin-checks.yml'
-              - '.github/workflows/ci-gate.yml'
-            pi_agent_plugin:
-              - 'integrations/pi-agent-plugin/**'
-              - '.github/workflows/pi-agent-plugin-checks.yml'
-              - '.github/workflows/ci-gate.yml'
-            docs_llms_txt:
-              - 'docs/**/*.mdx'
-              - 'docs/llms.txt'
-              - 'scripts/check-llms-txt-coverage.py'
-              - 'scripts/llms-txt-ignore.txt'
-              - '.github/workflows/docs-llms-txt-check.yml'
-              - '.github/workflows/ci-gate.yml'
-
-  python-sdk:
-    name: Python SDK
-    needs: changes
-    if: needs.changes.outputs.python_sdk == 'true'
-    uses: ./.github/workflows/ci.yml
-    secrets: inherit
-
-  ts-sdk:
-    name: TypeScript SDK
-    needs: changes
-    if: needs.changes.outputs.ts_sdk == 'true'
-    uses: ./.github/workflows/ts-sdk-ci.yml
-    secrets: inherit
-
-  cli-python:
-    name: Python CLI
-    needs: changes
-    if: needs.changes.outputs.cli_python == 'true'
-    uses: ./.github/workflows/cli-python-ci.yml
-    secrets: inherit
-
-  cli-node:
-    name: Node CLI
-    needs: changes
-    if: needs.changes.outputs.cli_node == 'true'
-    uses: ./.github/workflows/cli-node-ci.yml
-    secrets: inherit
-
-  openclaw:
-    name: OpenClaw
-    needs: changes
-    if: needs.changes.outputs.openclaw == 'true'
-    uses: ./.github/workflows/openclaw-checks.yml
-    secrets: inherit
-
-  opencode-plugin:
-    name: OpenCode Plugin
-    needs: changes
-    if: needs.changes.outputs.opencode_plugin == 'true'
-    uses: ./.github/workflows/opencode-plugin-checks.yml
-    secrets: inherit
-
-  pi-agent-plugin:
-    name: Pi Agent Plugin
-    needs: changes
-    if: needs.changes.outputs.pi_agent_plugin == 'true'
-    uses: ./.github/workflows/pi-agent-plugin-checks.yml
-    secrets: inherit
-
-  docs-llms-txt:
-    name: docs llms.txt
-    needs: changes
-    if: needs.changes.outputs.docs_llms_txt == 'true'
-    uses: ./.github/workflows/docs-llms-txt-check.yml
-    secrets: inherit
-
-  gate:
-    name: CI Gate
-    needs:
-      - changes
-      - python-sdk
-      - ts-sdk
-      - cli-python
-      - cli-node
-      - openclaw
-      - opencode-plugin
-      - pi-agent-plugin
-      - docs-llms-txt
-    if: always()
-    runs-on: ubuntu-latest
-    steps:
-      - name: Evaluate pipeline results
-        env:
-          NEEDS: ${{ toJSON(needs) }}
-        run: |
-          echo "$NEEDS" | jq -r 'to_entries[] | "\(.key): \(.value.result)"'
-          failed=$(echo "$NEEDS" | jq -r '[to_entries[] | select(.value.result == "failure" or .value.result == "cancelled") | .key] | join(", ")')
-          if [ -n "$failed" ]; then
-            echo "::error::Failing pipelines: $failed"
-            exit 1
-          fi
-          echo "All pipelines relevant to this change passed."

.github/workflows/ci.yml

@@ -1,11 +1,9 @@
 name: ci
 
-# On PRs this is invoked by ci-gate.yml (the single required check);
-# push-to-main runs remain standalone.
 on:
   push:
     branches: [main]
-  workflow_call:
+  pull_request:
 
 jobs:
   changelog_check:

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

@@ -1,25 +1,13 @@
 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:
-  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
+  release:
+    types: [published]
 
 jobs:
   build-n-publish:
     name: Build and publish @mem0/cli 📦 to npm
-    if: startsWith(inputs.tag, 'cli-node-v')
+    if: startsWith(github.event.release.tag_name, 'cli-node-v')
     runs-on: ubuntu-latest
     permissions:
       id-token: write
@@ -28,8 +16,6 @@ jobs:
         working-directory: cli/node
     steps:
       - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.tag }}
 
       - name: Install pnpm
         uses: pnpm/action-setup@v4
@@ -52,7 +38,7 @@ jobs:
 
       - name: Publish to npm
         run: |
-          if [ "${{ inputs.prerelease }}" = "true" ]; then
+          if [ "${{ github.event.release.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-node-ci.yml

@@ -1,7 +1,5 @@
 name: CLI Node CI
 
-# On PRs this is invoked by ci-gate.yml (the single required check);
-# push-to-main and manual runs remain standalone.
 on:
   workflow_dispatch:
   push:
@@ -9,7 +7,10 @@ on:
     paths:
       - 'cli/node/**'
       - '.github/workflows/cli-node-ci.yml'
-  workflow_call:
+  pull_request:
+    paths:
+      - 'cli/node/**'
+      - '.github/workflows/cli-node-ci.yml'
 
 jobs:
   lint:

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

@@ -1,24 +1,13 @@
 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:
-  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
+  release:
+    types: [published]
 
 jobs:
   build-n-publish:
     name: Build and publish mem0-cli 📦 to PyPI
-    if: startsWith(inputs.tag, 'cli-v')
+    if: startsWith(github.event.release.tag_name, 'cli-v')
     runs-on: ubuntu-latest
     permissions:
       id-token: write
@@ -27,8 +16,6 @@ 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/cli-python-ci.yml

@@ -1,7 +1,5 @@
 name: CLI Python CI
 
-# On PRs this is invoked by ci-gate.yml (the single required check);
-# push-to-main and manual runs remain standalone.
 on:
   workflow_dispatch:
   push:
@@ -9,7 +7,10 @@ on:
     paths:
       - 'cli/python/**'
       - '.github/workflows/cli-python-ci.yml'
-  workflow_call:
+  pull_request:
+    paths:
+      - 'cli/python/**'
+      - '.github/workflows/cli-python-ci.yml'
 
 jobs:
   lint:

.github/workflows/docs-llms-txt-check.yml

@@ -6,10 +6,13 @@ name: docs - llms.txt check
 #   python scripts/check-llms-txt-coverage.py           # read-only
 #   python scripts/check-llms-txt-coverage.py --write   # scaffold placeholders
 
-# On PRs this is invoked by ci-gate.yml (the single required check);
-# manual runs remain standalone.
 on:
-  workflow_call:
+  pull_request:
+    paths:
+      - 'docs/**/*.mdx'
+      - 'docs/llms.txt'
+      - 'scripts/check-llms-txt-coverage.py'
+      - 'scripts/llms-txt-ignore.txt'
   workflow_dispatch: {}
 
 permissions:

.github/workflows/openclaw-cd.yml

@@ -1,35 +1,21 @@
 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:
-  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
+  release:
+    types: [published]
 
 jobs:
   build-n-publish:
     name: Build and publish @mem0/openclaw-mem0 📦 to npm
-    if: startsWith(inputs.tag, 'openclaw-v')
+    if: startsWith(github.event.release.tag_name, 'openclaw-v')
     runs-on: ubuntu-latest
     permissions:
       id-token: write
     defaults:
       run:
-        working-directory: integrations/openclaw
+        working-directory: openclaw
     steps:
       - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.tag }}
 
       - name: Install pnpm
         uses: pnpm/action-setup@v4
@@ -42,7 +28,7 @@ jobs:
           node-version: '22'
           registry-url: 'https://registry.npmjs.org'
           cache: 'pnpm'
-          cache-dependency-path: integrations/openclaw/pnpm-lock.yaml
+          cache-dependency-path: openclaw/pnpm-lock.yaml
 
       - name: Install dependencies
         run: pnpm install --frozen-lockfile
@@ -52,7 +38,7 @@ jobs:
 
       - name: Publish to npm
         run: |
-          if [ "${{ inputs.prerelease }}" = "true" ]; then
+          if [ "${{ github.event.release.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/openclaw-checks.yml

@@ -1,15 +1,16 @@
 name: openclaw checks
 
-# On PRs this is invoked by ci-gate.yml (the single required check);
-# push-to-main and manual runs remain standalone.
 on:
   workflow_dispatch:
   push:
     branches: [main]
     paths:
-      - 'integrations/openclaw/**'
+      - 'openclaw/**'
+      - '.github/workflows/openclaw-checks.yml'
+  pull_request:
+    paths:
+      - 'openclaw/**'
       - '.github/workflows/openclaw-checks.yml'
-  workflow_call:
 
 jobs:
   lint:
@@ -27,13 +28,13 @@ jobs:
         with:
           node-version: 20
           cache: 'pnpm'
-          cache-dependency-path: integrations/openclaw/pnpm-lock.yaml
+          cache-dependency-path: openclaw/pnpm-lock.yaml
 
       - name: Install dependencies
-        run: cd integrations/openclaw && pnpm install --frozen-lockfile
+        run: cd openclaw && pnpm install --frozen-lockfile
 
       - name: Type check
-        run: cd integrations/openclaw && pnpm exec tsc --noEmit
+        run: cd openclaw && pnpm exec tsc --noEmit
 
   test:
     runs-on: ubuntu-latest
@@ -53,20 +54,20 @@ jobs:
         with:
           node-version: ${{ matrix.node-version }}
           cache: 'pnpm'
-          cache-dependency-path: integrations/openclaw/pnpm-lock.yaml
+          cache-dependency-path: openclaw/pnpm-lock.yaml
 
       - name: Install dependencies
-        run: cd integrations/openclaw && pnpm install --frozen-lockfile
+        run: cd openclaw && pnpm install --frozen-lockfile
 
       - name: Run tests with coverage
-        run: cd integrations/openclaw && pnpm exec vitest run --coverage
+        run: cd openclaw && pnpm exec vitest run --coverage
 
       - name: Upload coverage to Codecov
         if: matrix.node-version == 20
         uses: codecov/codecov-action@v4
         with:
           flags: openclaw
-          directory: integrations/openclaw/coverage
+          directory: openclaw/coverage
         env:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
 
@@ -85,15 +86,15 @@ jobs:
         with:
           node-version: 20
           cache: 'pnpm'
-          cache-dependency-path: integrations/openclaw/pnpm-lock.yaml
+          cache-dependency-path: openclaw/pnpm-lock.yaml
 
       - name: Install dependencies
-        run: cd integrations/openclaw && pnpm install --frozen-lockfile
+        run: cd openclaw && pnpm install --frozen-lockfile
 
       - name: Build
-        run: cd integrations/openclaw && pnpm build
+        run: cd openclaw && pnpm build
 
       - name: Verify dist output exists
         run: |
-          test -f integrations/openclaw/dist/index.js || (echo "Build output missing: dist/index.js" && exit 1)
-          test -f integrations/openclaw/dist/index.d.ts || (echo "Build output missing: dist/index.d.ts" && exit 1)
+          test -f openclaw/dist/index.js || (echo "Build output missing: dist/index.js" && exit 1)
+          test -f openclaw/dist/index.d.ts || (echo "Build output missing: dist/index.d.ts" && exit 1)

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

@@ -1,58 +0,0 @@
-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: integrations/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

@@ -1,39 +0,0 @@
-name: opencode-plugin checks
-
-# On PRs this is invoked by ci-gate.yml (the single required check);
-# push-to-main and manual runs remain standalone.
-on:
-  workflow_dispatch:
-  push:
-    branches: [main]
-    paths:
-      - 'integrations/mem0-plugin/.opencode-plugin/**'
-      - '.github/workflows/opencode-plugin-checks.yml'
-  workflow_call:
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: integrations/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

@@ -1,60 +0,0 @@
-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: integrations/pi-agent-plugin
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.tag }}
-
-      - name: Install pnpm
-        uses: pnpm/action-setup@v4
-        with:
-          version: 9
-
-      - name: Set up Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: '22'
-          registry-url: 'https://registry.npmjs.org'
-          cache: 'pnpm'
-          cache-dependency-path: integrations/pi-agent-plugin/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: pnpm install --frozen-lockfile
-
-      - name: Build
-        run: pnpm build
-
-      - name: Publish to npm
-        run: |
-          if [ "${{ 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

@@ -1,92 +0,0 @@
-name: pi-agent-plugin checks
-
-# On PRs this is invoked by ci-gate.yml (the single required check);
-# push-to-main and manual runs remain standalone.
-on:
-  workflow_dispatch:
-  push:
-    branches: [main]
-    paths:
-      - 'integrations/pi-agent-plugin/**'
-      - '.github/workflows/pi-agent-plugin-checks.yml'
-  workflow_call:
-
-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: integrations/pi-agent-plugin/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: cd integrations/pi-agent-plugin && pnpm install --frozen-lockfile
-
-      - name: Type check
-        run: cd integrations/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: integrations/pi-agent-plugin/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: cd integrations/pi-agent-plugin && pnpm install --frozen-lockfile
-
-      - name: Run tests
-        run: cd integrations/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: integrations/pi-agent-plugin/pnpm-lock.yaml
-
-      - name: Install dependencies
-        run: cd integrations/pi-agent-plugin && pnpm install --frozen-lockfile
-
-      - name: Build
-        run: cd integrations/pi-agent-plugin && pnpm build
-
-      - name: Verify dist output exists
-        run: |
-          test -f integrations/pi-agent-plugin/dist/index.js || (echo "Build output missing: dist/index.js" && exit 1)
-          test -f integrations/pi-agent-plugin/dist/index.d.ts || (echo "Build output missing: dist/index.d.ts" && exit 1)
-          test -f integrations/pi-agent-plugin/dist/entry.js || (echo "Build output missing: dist/entry.js" && exit 1)
-          test -f integrations/pi-agent-plugin/dist/entry.d.ts || (echo "Build output missing: dist/entry.d.ts" && exit 1)

.github/workflows/release.yml

@@ -1,68 +0,0 @@
-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

@@ -1,24 +1,13 @@
 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:
-  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
+  release:
+    types: [published]
 
 jobs:
   build-n-publish:
     name: Build and publish mem0ai 📦 to npm
-    if: startsWith(inputs.tag, 'ts-v')
+    if: startsWith(github.event.release.tag_name, 'ts-v')
     runs-on: ubuntu-latest
     permissions:
       id-token: write
@@ -27,8 +16,6 @@ jobs:
         working-directory: mem0-ts
     steps:
       - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.tag }}
 
       - name: Install pnpm
         uses: pnpm/action-setup@v4
@@ -51,7 +38,7 @@ jobs:
 
       - name: Publish to npm
         run: |
-          if [ "${{ inputs.prerelease }}" = "true" ]; then
+          if [ "${{ github.event.release.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/ts-sdk-ci.yml

@@ -1,14 +1,14 @@
 name: TypeScript SDK CI
 
-# On PRs this is invoked by ci-gate.yml (the single required check);
-# push-to-main runs remain standalone.
 on:
   push:
     branches: [main]
     paths:
       - 'mem0-ts/**'
       - '.github/workflows/ts-sdk-ci.yml'
-  workflow_call:
+  pull_request:
+    paths:
+      - 'mem0-ts/**'
 
 jobs:
   check_changes:

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

@@ -1,35 +1,21 @@
 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:
-  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
+  release:
+    types: [published]
 
 jobs:
   build-n-publish:
     name: Build and publish @mem0/vercel-ai-provider 📦 to npm
-    if: startsWith(inputs.tag, 'vercel-ai-v')
+    if: startsWith(github.event.release.tag_name, 'vercel-ai-v')
     runs-on: ubuntu-latest
     permissions:
       id-token: write
     defaults:
       run:
-        working-directory: integrations/vercel-ai-sdk
+        working-directory: vercel-ai-sdk
     steps:
       - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.tag }}
 
       - name: Install pnpm
         uses: pnpm/action-setup@v4
@@ -42,7 +28,7 @@ jobs:
           node-version: '22'
           registry-url: 'https://registry.npmjs.org'
           cache: 'pnpm'
-          cache-dependency-path: integrations/vercel-ai-sdk/pnpm-lock.yaml
+          cache-dependency-path: vercel-ai-sdk/pnpm-lock.yaml
 
       - name: Install dependencies
         run: pnpm install --frozen-lockfile
@@ -52,7 +38,7 @@ jobs:
 
       - name: Publish to npm
         run: |
-          if [ "${{ inputs.prerelease }}" = "true" ]; then
+          if [ "${{ github.event.release.prerelease }}" = "true" ]; then
             PREID=$(node -p "require('./package.json').version.split('-')[1].split('.')[0]")
             npx npm@latest publish --provenance --access public --tag "$PREID"
           else

.gitignore

@@ -189,5 +189,3 @@ eval/
 qdrant_storage/
 .crossnote
 testing.ipynb
-.weave/
-

.gitmodules

@@ -1,4 +0,0 @@
-[submodule "evaluation"]
-	path = evaluation
-	url = https://github.com/mem0ai/memory-benchmarks
-	branch = main

AGENTS.md

@@ -12,7 +12,7 @@ This file provides context for AI coding assistants (Claude Code, Cursor, GitHub
 
 ## Repository Structure
 
-This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs, servers, plugins, and documentation.
+This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs, servers, plugins, documentation, and evaluation tooling.
 
 ### Key Directories
 
@@ -22,18 +22,17 @@ This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs,
 | `mem0-ts/` | TypeScript SDK (`mem0ai` on npm) — client + OSS memory |
 | `cli/python/` | Python CLI (`mem0-cli` on PyPI) — Typer-based, entry point `mem0` |
 | `cli/node/` | Node CLI (`@mem0/cli` on npm) — Commander-based, entry point `mem0` |
-| `integrations/` | **Agent & editor integrations**, one directory per integration (see "Adding a New Integration") |
-| `integrations/mem0-plugin/` | AI editor plugins (Claude Code, Cursor, Codex) — MCP server connection, lifecycle hooks, skills. Contains nested `.opencode-plugin/` (`@mem0/opencode-plugin`) |
-| `integrations/openclaw/` | `@mem0/openclaw-mem0` — OpenClaw plugin for Claude Code / AI editors |
-| `integrations/pi-agent-plugin/` | `@mem0/pi-agent-plugin` — Pi Agent plugin |
-| `integrations/vercel-ai-sdk/` | `@mem0/vercel-ai-provider` — Vercel AI SDK memory provider |
+| `vercel-ai-sdk/` | `@mem0/vercel-ai-provider` — Vercel AI SDK memory provider |
+| `openclaw/` | `@mem0/openclaw-mem0` — OpenClaw plugin for Claude Code / AI editors |
 | `server/` | FastAPI REST server for self-hosted Mem0 (Docker: FastAPI + PostgreSQL/pgvector + Neo4j) |
 | `openmemory/` | Self-hosted memory platform — `api/` (FastAPI + Alembic + MCP server) and `ui/` (Next.js 15 + React 19) |
-| `skills/` | Claude Code skill definitions. Reference skills (SDK knowledge, always-on): `mem0/`, `mem0-cli/`, `mem0-vercel-ai-sdk/`. Pipeline skills (run on demand): `mem0-integrate/`, `mem0-test-integration/`, `mem0-oss-to-platform/` |
+| `mem0-plugin/` | AI editor plugins (Claude Code, Cursor, Codex) — MCP server connection, lifecycle hooks, skills |
+| `skills/` | Claude Code skill definitions. Reference skills (SDK knowledge, always-on): `mem0/`, `mem0-cli/`, `mem0-vercel-ai-sdk/`. Pipeline skills (run on demand): `mem0-integrate/`, `mem0-test-integration/` |
 | `docs/` | Documentation site (Mintlify) |
 | `tests/` | Python SDK tests (pytest) |
-| `evaluation/` | Submodule → [`mem0ai/memory-benchmarks`](https://github.com/mem0ai/memory-benchmarks) — benchmarking (LOCOMO, LongMemEval, BEAM) lives in that repo |
-| `examples/` | Sample projects & runnable demos — apps, Chrome extension, multi-agent patterns, and Jupyter notebooks (`notebooks/`) |
+| `evaluation/` | Benchmarking framework — LOCOMO evals, experiment runner, score generation |
+| `examples/` | Sample projects — demo apps, Chrome extension, multi-agent patterns |
+| `cookbooks/` | Jupyter notebooks — customer support chatbot, AutoGen integration |
 | `pr-reviews/` | Pull request review materials |
 | `scripts/` | Repo-wide utility scripts (e.g., `check-llms-txt-coverage.py` for docs/llms.txt sync) |
 
@@ -50,8 +49,8 @@ mem0 (Python SDK)          mem0-ts (TypeScript SDK)
 
 cli/python/ ──▶ mem0ai (optional, for OSS mode)
 cli/node/   ──▶ mem0ai (npm, for API calls)
-integrations/vercel-ai-sdk/ ──▶ ai, @ai-sdk/* providers
-integrations/openclaw/ ──▶ mem0ai (npm)
+vercel-ai-sdk/ ──▶ ai, @ai-sdk/* providers
+openclaw/   ──▶ mem0ai (npm)
 ```
 
 ## Development Setup
@@ -74,8 +73,8 @@ pre-commit install                # install git hooks
 # TypeScript packages
 cd mem0-ts && pnpm install        # TS SDK
 cd cli/node && pnpm install       # Node CLI
-cd integrations/vercel-ai-sdk && pnpm install  # Vercel AI provider
-cd integrations/openclaw && pnpm install       # OpenClaw plugin
+cd vercel-ai-sdk && pnpm install  # Vercel AI provider
+cd openclaw && pnpm install       # OpenClaw plugin
 ```
 
 ## Build, Lint, and Test Commands
@@ -163,10 +162,10 @@ pnpm run dev                       # tsx src/index.ts (development)
 - **Test:** vitest (not jest)
 - **Framework:** Commander + Chalk + ora + cli-table3
 
-### Vercel AI SDK Provider (`integrations/vercel-ai-sdk/`)
+### Vercel AI SDK Provider (`vercel-ai-sdk/`)
 
 ```bash
-cd integrations/vercel-ai-sdk
+cd vercel-ai-sdk
 pnpm install
 pnpm run build                     # tsup
 pnpm run lint                      # eslint
@@ -181,10 +180,10 @@ pnpm run test:node                 # vitest (node runtime)
 - **Lint:** ESLint + Prettier
 - **Test:** jest + vitest (edge/node configs)
 
-### OpenClaw Plugin (`integrations/openclaw/`)
+### OpenClaw Plugin (`openclaw/`)
 
 ```bash
-cd integrations/openclaw
+cd openclaw
 pnpm install
 pnpm run build                     # tsup
 pnpm run test                      # vitest run
@@ -246,19 +245,18 @@ make docs                          # or: cd docs && mintlify dev
 - **API spec:** `docs/openapi.json`
 - **Structure:** `api-reference/`, `open-source/`, `platform/`, `integrations/`, `cookbooks/`, `core-concepts/`
 
-### Evaluation / Benchmarking
-
-Benchmarking lives in the external [`mem0ai/memory-benchmarks`](https://github.com/mem0ai/memory-benchmarks) repo (LOCOMO + LongMemEval + BEAM). The in-repo `evaluation/` path is a **git submodule** pinned to that repo's `main` — populate it with `git submodule update --init evaluation` (or clone mem0 with `--recurse-submodules`), or clone the benchmarks repo standalone:
+### Evaluation (`evaluation/`)
 
 ```bash
-git clone https://github.com/mem0ai/memory-benchmarks.git
-cd memory-benchmarks
-pip install -r requirements.txt
-
-# Run a benchmark (Mem0 Cloud; use docker compose for OSS)
-python -m benchmarks.locomo.run --project-name my-test --backend cloud --mem0-api-key $MEM0_API_KEY
-python -m benchmarks.longmemeval.run --project-name my-test --backend cloud --mem0-api-key $MEM0_API_KEY --all-questions
-python -m benchmarks.beam.run --project-name my-test --backend cloud --mem0-api-key $MEM0_API_KEY --chat-sizes 100K --conversations 0-9
+cd evaluation
+make run-mem0-add                  # Run mem0 add experiments
+make run-mem0-search               # Run mem0 search experiments
+make run-mem0-plus-add             # With graph memory
+make run-mem0-plus-search          # With graph memory
+make run-rag                       # RAG baseline
+make run-full-context              # Full context baseline
+make run-langmem                   # LangMem comparison
+make run-openai                    # OpenAI comparison
 ```
 
 ## Core APIs
@@ -344,8 +342,8 @@ python -m benchmarks.beam.run --project-name my-test --backend cloud --mem0-api-
 |---------|--------|-----------|---------------|
 | `mem0-ts/` | — | Prettier | jest |
 | `cli/node/` | Biome | Biome | vitest |
-| `integrations/vercel-ai-sdk/` | ESLint | Prettier | jest + vitest |
-| `integrations/openclaw/` | — | — | vitest |
+| `vercel-ai-sdk/` | ESLint | Prettier | jest + vitest |
+| `openclaw/` | — | — | vitest |
 
 ### Type Checking
 
@@ -383,14 +381,14 @@ Model Context Protocol support in multiple places:
 
 - **Remote:** MCP server at `mcp.mem0.ai`
 - **Local:** MCP server in `openmemory/api/` (FastAPI-based)
-- **Plugin:** MCP tools in `integrations/mem0-plugin/` — 9 tools: `add_memory`, `search_memories`, `get_memories`, `get_memory`, `update_memory`, `delete_memory`, `delete_all_memories`, `delete_entities`, `list_entities`
+- **Plugin:** MCP tools in `mem0-plugin/` — 9 tools: `add_memory`, `search_memories`, `get_memories`, `get_memory`, `update_memory`, `delete_memory`, `delete_all_memories`, `delete_entities`, `list_entities`
 
 ### Plugin & Skills System
 
-- `integrations/mem0-plugin/` provides integrations for Claude Code, Cursor, and Codex via MCP server connections and lifecycle hooks for automatic memory capture.
+- `mem0-plugin/` provides integrations for Claude Code, Cursor, and Codex via MCP server connections and lifecycle hooks for automatic memory capture.
 - `skills/` contains structured skill definitions for AI agents, split into two categories:
   - **Reference skills** (always-on SDK knowledge): `mem0` (Python + TS SDKs, framework integrations), `mem0-cli` (terminal workflows), `mem0-vercel-ai-sdk` (Vercel AI provider).
-  - **Pipeline skills** (run on demand): `mem0-integrate` wires Mem0 into an existing repo via a TDD pipeline; `mem0-test-integration` verifies what the integrator produced on the same branch (the two are loosely coupled via `.mem0-integration/` artifacts); `mem0-oss-to-platform` migrates an existing project from Mem0 OSS to the hosted Platform SDK (plan, then execute on approval).
+  - **Pipeline skills** (run on demand): `mem0-integrate` wires Mem0 into an existing repo via a TDD pipeline; `mem0-test-integration` verifies what the integrator produced on the same branch. The two are loosely coupled via `.mem0-integration/` artifacts.
 
 ### Adding a New Provider
 
@@ -404,58 +402,31 @@ To add a new LLM, embedding, vector store, or reranker provider:
 6. Add any new dependencies to the appropriate optional group in `pyproject.toml` (never to core `dependencies`)
 7. Follow the exact pattern of existing providers in the same category — match method signatures, error handling, and config structure
 
-### Adding a New Integration
-
-Agent/editor integrations live under `integrations/`. Each is a self-contained directory (its own `package.json`/lockfile, build, and tests). To add one:
-
-1. Create `integrations/<name>/` and build the integration there.
-2. If it publishes to a registry, set `repository.directory: "integrations/<name>"` in its `package.json` so npm provenance links to the correct subdirectory.
-3. Add CI/CD under `.github/workflows/` (`<name>-checks.yml`, `<name>-cd.yml`). Use `integrations/<name>` in `paths:` triggers, `working-directory`, and `cache-dependency-path`. Register the release tag prefix in the `case` block in `release.yml` (keep the bare `v*` arm last). Keep workflow **filenames** stable — npm OIDC trusted publishing is pinned to repo + workflow filename.
-4. If it is a Claude Code / editor marketplace plugin, register its path in the five `marketplace.json` files (root + `.claude-plugin/`, `.cursor-plugin/`, `.codex-plugin/`, `.agents/plugins/`).
-5. Document it under `docs/integrations/` and add the page to `docs/docs.json` and `docs/llms.txt`.
-6. Add rows to the "Key Directories" table and the CI/CD tables in this file.
-
 ## CI/CD
 
 ### CI Workflows (automated testing)
 
-PR testing is orchestrated by a single entry point: **`ci-gate.yml` (CI Gate)** runs on every PR, detects which packages changed, and invokes only the relevant package workflows below as reusable workflows (`workflow_call`). Its final **`CI Gate`** job aggregates the results (skipped pipelines pass; failed or cancelled ones fail) and is the **only status check that needs to be required** in branch protection. Package workflows keep their own push-to-main and manual triggers; their `pull_request` triggers moved into the gate's path filters.
-
-| Workflow | File | Standalone Triggers | Tests |
-|----------|------|---------------------|-------|
-| CI Gate | `ci-gate.yml` | All PRs | Routes to and aggregates the workflows below |
-| Python SDK | `ci.yml` | Push to main | Ruff lint + pytest on Python 3.10, 3.11, 3.12 |
-| TypeScript SDK | `ts-sdk-ci.yml` | Push to main (on `mem0-ts/`) | Prettier + build + jest on Node 20, 22 |
-| Python CLI | `cli-python-ci.yml` | Push to main (on `cli/python/`), manual | Ruff lint + pytest + hatch build on Python 3.10, 3.11, 3.12 |
-| Node CLI | `cli-node-ci.yml` | Push to main (on `cli/node/`), manual | Biome lint + tsc + vitest + tsup build on Node 20, 22 |
-| OpenClaw | `openclaw-checks.yml` | Push to main (on `integrations/openclaw/`), manual | tsc + vitest (with Codecov) + tsup build on Node 20, 22 |
-| OpenCode Plugin | `opencode-plugin-checks.yml` | Push to main (on `integrations/mem0-plugin/.opencode-plugin/`), manual | Bun: tsc type-check + build + dist artifact check |
-| Pi Agent Plugin | `pi-agent-plugin-checks.yml` | Push to main (on `integrations/pi-agent-plugin/`), manual | tsc + vitest + tsup build (dist artifact check) on Node 20, 22 |
-| docs llms.txt | `docs-llms-txt-check.yml` | Manual | `docs/llms.txt` coverage check |
-
-When adding a new package CI workflow: give it `workflow_call` (plus `push`/`workflow_dispatch` as needed, but no `pull_request` trigger), then register it in `ci-gate.yml` — a path filter under the `changes` job, a call job, and an entry in the gate job's `needs` list.
+| Workflow | File | Triggers | Tests |
+|----------|------|----------|-------|
+| Python SDK | `ci.yml` | Push to main, PRs on `mem0/`, `tests/`, `pyproject.toml` | Ruff lint + pytest on Python 3.10, 3.11, 3.12 |
+| TypeScript SDK | `ts-sdk-ci.yml` | Push to main, PRs on `mem0-ts/` | Prettier + build + jest on Node 20, 22 |
+| 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 |
 
 ### 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
 

CONTRIBUTING.md

@@ -1,157 +1,72 @@
-# Contributing to Mem0
+# Contributing to mem0
 
-First off, thank you for taking the time to contribute! 🎉 Mem0 is a
-community-driven project and we welcome contributions of all kinds — bug fixes,
-new features, documentation, examples, and integrations.
+Let us make contribution easy, collaborative and fun.
 
-Mem0 is a polyglot monorepo, and this guide covers contributing to both the
-**Python SDK** and the **TypeScript SDK** (and the rest of the repository).
+## Submit your Contribution through PR
 
-## Before You Start
+To make a contribution, follow these steps:
 
-### 1. Open an Issue First
+1. Fork and clone this repository
+2. Do the changes on your fork with dedicated feature branch `feature/f1`
+3. If you modified the code (new feature or bug-fix), please add tests for it
+4. Include proper documentation / docstring and examples to run the feature
+5. Ensure that all tests pass
+6. Submit a pull request
 
-**Always open an issue before opening a pull request.** This lets us discuss the
-change, avoid duplicate effort, and agree on the approach before you invest time
-in code.
+For more details about pull requests, please read [GitHub's guides](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request).
 
-- Search [existing issues](https://github.com/mem0ai/mem0/issues) first to see if
-  your bug or idea already exists.
-- If it doesn't, open a
-  [bug report](https://github.com/mem0ai/mem0/issues/new?template=bug_report.yml) or
-  [feature request](https://github.com/mem0ai/mem0/issues/new?template=feature_request.yml).
-- For anything beyond a trivial fix, wait for a maintainer to confirm the approach
-  before starting significant work.
 
-Every pull request must link to an issue using `Closes #<issue-number>`.
+### 📦 Development Environment
 
-### 2. Sign the Contributor License Agreement (CLA)
-
-**We cannot accept or merge any pull request until you have signed our Contributor
-License Agreement (CLA).**
-
-When you open your first PR, the CLA bot will automatically comment with a link to
-sign. Signing takes less than a minute and only needs to be done once. Pull
-requests from contributors who have not signed the CLA will be blocked from
-merging.
-
-## Repository Layout
-
-The two most common contribution targets are the SDKs:
-
-| Package               | Path       | Language     | Package manager |
-| --------------------- | ---------- | ------------ | --------------- |
-| Python SDK (`mem0ai`) | `mem0/`    | Python 3.9+  | `hatch`         |
-| TypeScript SDK (`mem0ai`) | `mem0-ts/` | TypeScript | `pnpm`        |
-
-Other packages include the CLIs (`cli/python/`, `cli/node/`), integrations
-(`integrations/`), the self-hosted `server/`, `openmemory/`, and the docs site
-(`docs/`). See [AGENTS.md](./AGENTS.md) for a full map of the repository.
-
-## Development Workflow
-
-1. **Fork** the repository and **clone** your fork.
-2. Create a **feature branch** from `main` (e.g. `feature/my-new-feature` or
-   `fix/issue-1234`).
-3. Make your changes — add **tests**, **documentation**, and **examples** as
-   appropriate.
-4. Run **linting and tests** for every package you touched (see below).
-5. Commit using [Conventional Commits](https://www.conventionalcommits.org/)
-   (e.g. `feat:`, `fix:`, `docs:`, `refactor:`, `test:`).
-6. Push and open a **pull request** against `main`, linking the issue with
-   `Closes #<number>` and filling out the
-   [PR template](./.github/PULL_REQUEST_TEMPLATE.md).
-
-### Contributing to the Python SDK (`mem0/`)
-
-We use [`hatch`](https://hatch.pypa.io/latest/install/) to manage environments.
-**Do not use `pip` or `conda` for dependency management.**
+We use `hatch` for managing development environments. To set up:
 
 ```bash
-# Activate a dev environment (3.9 / 3.10 / 3.11 / 3.12)
-hatch shell dev_py_3_11
+# Activate environment for specific Python version:
+hatch shell dev_py_3_9   # Python 3.9
+hatch shell dev_py_3_10  # Python 3.10  
+hatch shell dev_py_3_11  # Python 3.11
+hatch shell dev_py_3_12  # Python 3.12
 
-# Install pre-commit hooks (runs ruff + isort on commit)
-pre-commit install
-
-# Lint, format, and sort imports
-make lint
-make format
-make sort
-
-# Run the test suite (run `make install_all` first if deps are missing)
+# The environment will automatically install all dev dependencies
+# Run tests within the activated shell:
 make test
 ```
 
-- **Linter / formatter:** Ruff (line length **120**)
-- **Import sorting:** isort (`profile = "black"`)
-- **Tests:** pytest (in `tests/`)
+### 📌 Pre-commit
 
-See the full [Development guide](https://docs.mem0.ai/contributing/development) for
-environment details.
-
-### Contributing to the TypeScript SDK (`mem0-ts/`)
-
-We use [`pnpm`](https://pnpm.io/) (v10+) for all TypeScript packages. **Do not use
-`npm` or `yarn`.**
+To ensure our standards, make sure to install pre-commit before starting to contribute.
 
 ```bash
-cd mem0-ts
-pnpm install
-
-pnpm run build        # tsup (CJS + ESM)
-pnpm run test         # jest (all tests)
-pnpm run test:unit    # unit tests with coverage
+pre-commit install
 ```
 
-- **Build:** tsup
-- **Formatter:** Prettier
-- **Tests:** jest
-- Always run type checking after changes: `pnpm run typecheck` (or `tsc --noEmit`).
-- Use ES module `import` syntax — never `require()`.
+### 🧪 Testing
 
-## Good Contribution Practices
+We use `pytest` to test our code across multiple Python versions. You can run tests using:
 
-- **Keep PRs small and focused.** One logical change per PR is easier to review and
-  merge.
-- **Follow existing patterns.** Match the style, structure, and conventions of the
-  code around you. Don't introduce new frameworks or abstractions without
-  discussion.
-- **Write tests** that would fail without your change — regression tests for bugs,
-  coverage for new features.
-- **Update documentation** in `docs/` for any user-facing change. New `.mdx` pages
-  must be added to `docs/llms.txt` (run
-  `python scripts/check-llms-txt-coverage.py --write` to scaffold entries).
-- **Add examples** when introducing new user-facing behavior.
-- **Run linters and tests locally** before pushing — CI re-runs them on every PR
-  via the CI Gate.
-- **Never commit secrets** — no `.env` files, API keys, or credentials.
-- **Don't add core dependencies lightly.** New Python dependencies belong in an
-  optional group in `pyproject.toml`, not the core `dependencies` list.
-- **Be responsive** to review feedback and keep your branch up to date with `main`.
+```bash
+# Run tests with default Python version
+make test
 
-## Pull Request Checklist
+# Test specific Python versions:
+make test-py-3.9   # Python 3.9 environment
+make test-py-3.10  # Python 3.10 environment
+make test-py-3.11  # Python 3.11 environment
+make test-py-3.12  # Python 3.12 environment
 
-Before requesting review, make sure:
+# When using hatch shells, run tests with:
+make test  # After activating a shell with hatch shell test_XX
+```
 
-- [ ] An issue exists and is linked with `Closes #<number>`
-- [ ] You have signed the CLA
-- [ ] Your code follows the project's style guidelines (lint passes)
-- [ ] You performed a self-review of your changes
-- [ ] Tests are added/updated and pass locally
-- [ ] Documentation is updated if needed
+Make sure that all tests pass across all supported Python versions before submitting a pull request.
 
-## Reporting Security Issues
+We look forward to your pull requests and can't wait to see your contributions!
 
-**Do not report security vulnerabilities through public issues or pull requests.**
-Please follow our [Security Policy](./SECURITY.md) to report them privately.
+### 🚀 Releasing
 
-## Releasing
+All packages are published automatically via GitHub Actions when a GitHub Release is created with the correct tag prefix.
 
-All packages are published automatically via GitHub Actions when a GitHub Release
-is created with the correct tag prefix.
-
-### Tag Prefixes
+#### Tag Prefixes
 
 | Package | Registry | Tag Prefix | Example |
 |---------|----------|------------|---------|
@@ -162,17 +77,15 @@ is created with the correct tag prefix.
 | `@mem0/vercel-ai-provider` | npm | `vercel-ai-v*` | `vercel-ai-v2.0.6` |
 | `@mem0/openclaw-mem0` | npm | `openclaw-v*` | `openclaw-v1.0.1` |
 
-### How to Release
+#### How to Release
 
 1. Bump the version in `pyproject.toml` (Python) or `package.json` (Node)
 2. Create a [GitHub Release](https://github.com/mem0ai/mem0/releases/new) with the matching tag prefix
 3. The correct workflow will trigger automatically — verify in the [Actions tab](https://github.com/mem0ai/mem0/actions)
 
-### Publishing Details
+#### Publishing Details
 
 - **PyPI packages** use OIDC trusted publishing via `pypa/gh-action-pypi-publish`
 - **npm packages** use OIDC trusted publishing via npm CLI (>= 11.5.1) — no tokens or secrets required
 - All workflows require `permissions: id-token: write` for OIDC authentication
 - First publish of a new npm package must be done manually; OIDC works for subsequent versions
-
-We look forward to your pull requests and can't wait to see your contributions!

LLM.md

@@ -1026,8 +1026,7 @@ def get_user_preferences(user_id: str):
 ### AutoGen Integration
 
 ```python
-# Mem0Teachability lives in examples/notebooks/helper/ — see examples/notebooks/mem0-autogen.ipynb
-from helper.mem0_teachability import Mem0Teachability
+from cookbooks.helper.mem0_teachability import Mem0Teachability
 from mem0 import Memory
 
 # Add memory capability to AutoGen agents

README.md

@@ -186,10 +186,9 @@ npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk
 ```bash
 npx skills add https://github.com/mem0ai/mem0 --skill mem0-integrate
 npx skills add https://github.com/mem0ai/mem0 --skill mem0-test-integration
-npx skills add https://github.com/mem0ai/mem0 --skill mem0-oss-to-platform
 ```
 
-Use `/mem0-integrate` to wire Mem0 into an existing repo via a test-first pipeline, then `/mem0-test-integration` to verify. Use `/mem0-oss-to-platform` to migrate an existing project from Mem0 OSS to the hosted Platform SDK. See the [skills catalog](./skills/) or [Vibecoding with Mem0](https://docs.mem0.ai/vibecoding) for the full picture.
+Use `/mem0-integrate` to wire Mem0 into an existing repo via a test-first pipeline, then `/mem0-test-integration` to verify. See the [skills catalog](./skills/) or [Vibecoding with Mem0](https://docs.mem0.ai/vibecoding) for the full picture.
 
 ### Basic Usage
 

SECURITY.md

@@ -1,48 +0,0 @@
-# Security Policy
-
-We take the security of Mem0 and our community seriously. Thank you for helping
-keep Mem0 and its users safe by disclosing vulnerabilities responsibly.
-
-## Reporting a Vulnerability
-
-Please **do not** report security vulnerabilities through public GitHub issues,
-pull requests, or discussions.
-
-If you believe you have found a security vulnerability in Mem0, please report it
-privately through one of the following channels:
-
-1. **GitHub Private Vulnerability Reporting** — open a
-   [private security advisory](https://github.com/mem0ai/mem0/security/advisories/new)
-   directly on this repository.
-2. **Email** the maintainers at **support@mem0.ai** with the subject line:
-
-   `SECURITY: Mem0 vulnerability report`
-
-To help us triage and resolve the issue quickly, please include as much of the
-following as you can:
-
-- Affected component or package (e.g. Python SDK, TypeScript SDK, server, OpenMemory)
-- Affected version, tag, or commit
-- Clear, step-by-step reproduction instructions
-- The security impact and a proof of concept, if available
-- Any suggested fix or mitigation
-
-## Response Process
-
-- We will acknowledge receipt of your report within **72 hours**.
-- We will work with you privately to confirm the issue and assess its impact.
-- Once a fix or mitigation is ready, we will coordinate a disclosure timeline
-  with you and credit you for the discovery, unless you prefer to remain anonymous.
-
-## Public Disclosure
-
-Please avoid sharing technical details of the vulnerability publicly until the
-maintainers have reviewed the issue and a fix or mitigation has been released. We
-are committed to resolving valid reports promptly and keeping you informed
-throughout the process.
-
-## Supported Versions
-
-We release security fixes against the latest published version of each package.
-Whenever possible, please reproduce the issue on the most recent release before
-reporting.

cli/node/CHANGELOG.md

@@ -0,0 +1,36 @@
+# Changelog
+
+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).
+
+## [0.2.7] — 2026-05-20
+
+### Added
+
+- `mem0 whoami` — print the active agent's `default_user_id` (the AGENTRUSH
+  leaderboard identifier). Reads from local config, no network call.
+- `mem0 agent-rush <add | search>` — subcommand group that wraps the new
+  `/v1/agent-rush/` platform endpoints for the 7-day AGENTRUSH game. Project
+  routing is implicit (resolved server-side); no flags exposed. Pretty-prints
+  platform error codes into actionable hints (e.g. `agentrush_search_first`
+  → "Run 3 'mem0 agent-rush search' commands before adding.").
+- PII safety prompt on first `mem0 agent-rush add`. Interactive runs require
+  explicit `y` to acknowledge that AGENTRUSH memories are public; the
+  acknowledgement is persisted in `~/.mem0/config.json` under
+  `agent_rush.acknowledged_at` so the prompt only appears once per machine.
+  Non-interactive (agent) invocations surface the warning to stderr without
+  blocking.
+- New config schema field: `agent_rush.acknowledged_at` (ISO timestamp,
+  empty until first interactive acknowledgement).
+
+### Changed
+
+- HTTP requests from the new agent-rush commands send `X-Mem0-Mode: agent-rush`
+  in addition to the existing source headers, so platform telemetry can split
+  game traffic from regular CLI usage.
+
+## [0.2.6] and earlier
+
+Unlogged historical releases. See git history under `cli/node/`.

cli/node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mem0/cli",
-  "version": "0.2.10",
+  "version": "0.2.7",
   "description": "The official CLI for mem0 — the memory layer for AI agents",
   "type": "module",
   "bin": {
@@ -40,19 +40,8 @@
     "typescript": "^5.4.0",
     "tsup": "^8.0.0",
     "tsx": "^4.7.0",
-    "vite": "^6.0.0",
-    "vitest": "^4.1.0",
+    "vitest": "^1.5.0",
     "@biomejs/biome": "^1.7.0",
     "@types/node": "^20.0.0"
-  },
-  "pnpm": {
-    "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",
-      "postcss@<8.5.10": ">=8.5.10",
-      "esbuild": ">=0.28.1"
-    }
   }
 }

cli/node/pnpm-workspace.yaml

@@ -1,14 +0,0 @@
-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
-  "postcss@<8.5.10": ">=8.5.10"
-  "esbuild": ">=0.28.1"

cli/node/src/backend/platform.ts

@@ -316,18 +316,16 @@ export class PlatformBackend implements Backend {
 		if (entities.length === 0) {
 			throw new Error("At least one entity ID is required for deleteEntities.");
 		}
-		// Delete each provided entity via the v2 path-based endpoint. Key each
-		// response by entity type so a multi-entity delete (e.g. --user-id and
-		// --agent-id together) doesn't discard everything but the last result.
-		const results: Record<string, unknown> = {};
+		// Delete each provided entity via the v2 path-based endpoint
+		let result: Record<string, unknown> = {};
 		for (const [entityType, entityId] of entities) {
-			results[entityType] = (await this._request(
+			result = (await this._request(
 				"DELETE",
 				`/v2/entities/${entityType}/${entityId}/`,
 				{ params: { source: "CLI" } },
 			)) as Record<string, unknown>;
 		}
-		return results;
+		return result;
 	}
 
 	async ping(): Promise<Record<string, unknown>> {

cli/node/src/telemetry.ts

@@ -145,11 +145,11 @@ export function captureEvent(
 			anonDistinctIdToAlias: anonIdToAlias,
 		};
 
-		const child = spawn(process.execPath, [SENDER_SCRIPT], {
-			detached: true,
-			stdio: ["pipe", "ignore", "ignore"],
-		});
-		child.stdin?.end(JSON.stringify(context));
+		const child = spawn(
+			process.execPath,
+			[SENDER_SCRIPT, JSON.stringify(context)],
+			{ detached: true, stdio: "ignore" },
+		);
 		child.unref();
 	} catch {
 		/* silently swallow */

cli/node/telemetry-sender.cjs

@@ -1,8 +1,7 @@
 /**
  * Standalone telemetry sender — runs as a detached child process.
  *
- * Usage: node telemetry-sender.cjs   (JSON context is read from stdin; a single
- * argv argument is still accepted as a legacy fallback)
+ * Usage: node telemetry-sender.cjs '<json context>'
  *
  * This script is spawned by telemetry.captureEvent() and runs independently
  * of the parent CLI process. It:
@@ -20,31 +19,6 @@
 const https = require("https");
 const fs = require("fs");
 
-function loadContext() {
-	return new Promise((resolve, reject) => {
-		if (process.argv[2]) {
-			try {
-				resolve(JSON.parse(process.argv[2]));
-			} catch (err) {
-				reject(err);
-			}
-			return;
-		}
-
-		let data = "";
-		process.stdin.setEncoding("utf8");
-		process.stdin.on("data", (chunk) => (data += chunk));
-		process.stdin.on("end", () => {
-			try {
-				resolve(JSON.parse(data));
-			} catch (err) {
-				reject(err);
-			}
-		});
-		process.stdin.on("error", reject);
-	});
-}
-
 function httpsRequest(url, method, headers, body) {
 	return new Promise((resolve, reject) => {
 		const u = new URL(url);
@@ -134,7 +108,7 @@ async function sendIdentifyEvent(ctx, payload, anonId) {
 }
 
 async function main() {
-	const ctx = await loadContext();
+	const ctx = JSON.parse(process.argv[2]);
 	const payload = ctx.payload;
 
 	if (ctx.needsEmail && ctx.mem0ApiKey) {

cli/node/tests/platform-backend.test.ts

@@ -1,52 +0,0 @@
-/**
- * Tests for the Platform backend (mem0 Platform API client).
- */
-
-import { describe, it, expect, vi } from "vitest";
-import { PlatformBackend } from "../src/backend/platform.js";
-import { createDefaultConfig } from "../src/config.js";
-
-function makeBackend(): PlatformBackend {
-  // apiKey/baseUrl only build request headers; every test spies on _request,
-  // so no real network calls are made.
-  return new PlatformBackend(createDefaultConfig().platform);
-}
-
-describe("deleteEntities", () => {
-  it("returns all results keyed by entity type for a multi-entity delete", async () => {
-    const backend = makeBackend();
-    const responses: Record<string, unknown> = {
-      "/v2/entities/user/alice/": { message: "user deleted" },
-      "/v2/entities/agent/bob/": { message: "agent deleted" },
-    };
-    const spy = vi
-      // biome-ignore lint/suspicious/noExplicitAny: spying on a private method
-      .spyOn(backend as any, "_request")
-      .mockImplementation(async (_method: string, path: string) => responses[path]);
-
-    const result = await backend.deleteEntities({ userId: "alice", agentId: "bob" });
-
-    // Regression: previously only the last entity's response survived.
-    expect(result).toEqual({
-      user: { message: "user deleted" },
-      agent: { message: "agent deleted" },
-    });
-    expect(spy).toHaveBeenCalledTimes(2);
-  });
-
-  it("keys a single-entity delete by its type", async () => {
-    const backend = makeBackend();
-    // biome-ignore lint/suspicious/noExplicitAny: spying on a private method
-    vi.spyOn(backend as any, "_request").mockResolvedValue({ message: "user deleted" });
-
-    const result = await backend.deleteEntities({ userId: "alice" });
-    expect(result).toEqual({ user: { message: "user deleted" } });
-  });
-
-  it("throws when no entity id is provided", async () => {
-    const backend = makeBackend();
-    await expect(backend.deleteEntities({})).rejects.toThrow(
-      "At least one entity ID is required",
-    );
-  });
-});

cli/node/tests/telemetry.test.ts

@@ -1,59 +0,0 @@
-import { beforeEach, describe, expect, it, vi } from "vitest";
-
-const mockLoadConfig = vi.fn();
-const mockSaveConfig = vi.fn();
-const mockSpawn = vi.fn();
-
-vi.mock("../src/config.js", () => ({
-  CONFIG_FILE: "/tmp/mem0-config.json",
-  loadConfig: mockLoadConfig,
-  saveConfig: mockSaveConfig,
-}));
-
-vi.mock("node:child_process", () => ({
-  spawn: mockSpawn,
-}));
-
-describe("captureEvent", () => {
-  beforeEach(() => {
-    vi.resetModules();
-    mockLoadConfig.mockReset();
-    mockSaveConfig.mockReset();
-    mockSpawn.mockReset();
-    delete process.env.MEM0_TELEMETRY;
-  });
-
-  it("pipes the telemetry context through stdin instead of argv", async () => {
-    mockLoadConfig.mockReturnValue({
-      platform: {
-        apiKey: "m0-node-secret",
-        baseUrl: "https://api.mem0.ai",
-        userEmail: "",
-      },
-      telemetry: {
-        anonymousId: "cli-anon-node",
-      },
-    });
-
-    const stdin = { end: vi.fn() };
-    const child = { stdin, unref: vi.fn() };
-    mockSpawn.mockReturnValue(child);
-
-    const { captureEvent } = await import("../src/telemetry.js");
-    captureEvent("node_test_event", { case: "stdin-secret" });
-
-    expect(mockSpawn).toHaveBeenCalledTimes(1);
-    const [execPath, args, options] = mockSpawn.mock.calls[0];
-    expect(execPath).toBe(process.execPath);
-    expect(args).toHaveLength(1);
-    expect(String(args[0])).toContain("telemetry-sender.cjs");
-    expect(JSON.stringify(args)).not.toContain("m0-node-secret");
-    expect(options).toMatchObject({ detached: true, stdio: ["pipe", "ignore", "ignore"] });
-
-    expect(stdin.end).toHaveBeenCalledTimes(1);
-    const payload = JSON.parse(stdin.end.mock.calls[0][0]);
-    expect(payload.mem0ApiKey).toBe("m0-node-secret");
-    expect(payload.payload.event).toBe("node_test_event");
-    expect(child.unref).toHaveBeenCalledTimes(1);
-  });
-});

cli/node/vitest.config.ts

@@ -8,10 +8,4 @@ export default defineConfig({
 	define: {
 		__CLI_VERSION__: JSON.stringify(pkg.version),
 	},
-	test: {
-		// Integration tests spawn the CLI via `npx tsx` (15s subprocess
-		// timeout); the first spawn in a file pays a cold-start cost that can
-		// exceed vitest's 5s default on CI runners.
-		testTimeout: 30_000,
-	},
 });

cli/python/CHANGELOG.md

@@ -0,0 +1,36 @@
+# Changelog
+
+All notable changes to `mem0-cli` (Python) 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).
+
+## [0.2.7] — 2026-05-20
+
+### Added
+
+- `mem0 whoami` — print the active agent's `default_user_id` (the AGENTRUSH
+  leaderboard identifier). Reads from local config, no network call.
+- `mem0 agent-rush <add | search>` — subcommand group that wraps the new
+  `/v1/agent-rush/` platform endpoints for the 7-day AGENTRUSH game. Project
+  routing is implicit (resolved server-side); no flags exposed. Pretty-prints
+  platform error codes into actionable hints (e.g. `agentrush_search_first`
+  → "Run 3 'mem0 agent-rush search' commands before adding.").
+- PII safety prompt on first `mem0 agent-rush add`. Interactive runs require
+  explicit `y` to acknowledge that AGENTRUSH memories are public; the
+  acknowledgement is persisted in `~/.mem0/config.json` under
+  `agent_rush.acknowledged_at` so the prompt only appears once per machine.
+  Non-interactive (agent) invocations surface the warning to stderr without
+  blocking.
+- New config schema field: `agent_rush.acknowledged_at` (ISO timestamp,
+  empty until first interactive acknowledgement).
+
+### Changed
+
+- HTTP requests from the new agent-rush commands send `X-Mem0-Mode: agent-rush`
+  in addition to the existing source headers, so platform telemetry can split
+  game traffic from regular CLI usage.
+
+## [0.2.6] and earlier
+
+Unlogged historical releases. See git history under `cli/python/`.

cli/python/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "mem0-cli"
-version = "0.2.9"
+version = "0.2.7"
 description = "The official CLI for mem0 — the memory layer for AI agents"
 readme = "README.md"
 license = "Apache-2.0"

cli/python/src/mem0_cli/__init__.py

@@ -1,3 +1,3 @@
 """mem0 CLI — the command-line interface for the mem0 memory layer."""
 
-__version__ = "0.2.9"
+__version__ = "0.2.4"

cli/python/src/mem0_cli/backend/platform.py

@@ -296,15 +296,13 @@ class PlatformBackend(Backend):
         entities = {t: v for t, v in type_map.items() if v}
         if not entities:
             raise ValueError("At least one entity ID is required for delete_entities.")
-        # Delete each provided entity via the v2 path-based endpoint. Key each
-        # response by entity type so a multi-entity delete (e.g. --user-id and
-        # --agent-id together) doesn't discard everything but the last result.
-        results: dict = {}
+        # Delete each provided entity via the v2 path-based endpoint
+        result: dict = {}
         for entity_type, entity_id in entities.items():
-            results[entity_type] = self._request(
+            result = self._request(
                 "DELETE", f"/v2/entities/{entity_type}/{entity_id}/", params={"source": "CLI"}
             )
-        return results
+        return result
 
     def ping(self, timeout: float | None = None) -> dict:
         """Call the ping endpoint and return the raw response.

cli/python/src/mem0_cli/config.py

@@ -235,10 +235,7 @@ def set_nested_value(config: Mem0Config, dotted_key: str, value: str) -> bool:
     if isinstance(current, bool):
         value = value.lower() in ("true", "1", "yes")  # type: ignore[assignment]
     elif isinstance(current, int):
-        try:
-            value = int(value)  # type: ignore[assignment]
-        except ValueError:
-            return False
+        value = int(value)  # type: ignore[assignment]
 
     setattr(obj, final_key, value)
     return True

cli/python/src/mem0_cli/output.py

@@ -20,8 +20,8 @@ def format_memories_text(console: Console, memories: list[dict], title: str = "m
     console.print(f"\n[{BRAND_COLOR}]Found {count} {title}:[/]\n")
 
     for i, mem in enumerate(memories, 1):
-        memory_text = mem.get("memory") or mem.get("text") or ""
-        mem_id = (mem.get("id") or "")[:8]
+        memory_text = mem.get("memory", mem.get("text", ""))
+        mem_id = mem.get("id", "")[:8]
         score = mem.get("score")
         created = _format_date(mem.get("created_at"))
         category = mem.get("categories", [None])
@@ -67,8 +67,8 @@ def format_memories_table(
     table.add_column("Created", max_width=12)
 
     for mem in memories:
-        mem_id = mem.get("id") or ""
-        memory_text = mem.get("memory") or mem.get("text") or ""
+        mem_id = mem.get("id", "")
+        memory_text = mem.get("memory", mem.get("text", ""))
         if len(memory_text) > 60:
             memory_text = memory_text[:57] + "..."
         categories = mem.get("categories", [])
@@ -104,8 +104,8 @@ def format_single_memory(console: Console, mem: dict, output: str = "text") -> N
         format_json(console, mem)
         return
 
-    memory_text = mem.get("memory") or mem.get("text") or ""
-    mem_id = mem.get("id") or ""
+    memory_text = mem.get("memory", mem.get("text", ""))
+    mem_id = mem.get("id", "")
 
     lines = []
     lines.append(f"  [white bold]{memory_text}[/]")

cli/python/src/mem0_cli/telemetry.py

@@ -137,19 +137,12 @@ def capture_event(
             "anon_distinct_id_to_alias": anon_id_to_alias,
         }
 
-        child = subprocess.Popen(
-            [sys.executable, "-m", "mem0_cli.telemetry_sender"],
-            stdin=subprocess.PIPE,
+        subprocess.Popen(
+            [sys.executable, "-m", "mem0_cli.telemetry_sender", json.dumps(context)],
             stdout=subprocess.DEVNULL,
             stderr=subprocess.DEVNULL,
             start_new_session=True,
             close_fds=True,
-            text=True,
         )
-        if child.stdin:
-            with contextlib.suppress(Exception):
-                child.stdin.write(json.dumps(context))
-            with contextlib.suppress(Exception):
-                child.stdin.close()
     except Exception:
         pass

cli/python/src/mem0_cli/telemetry_sender.py

@@ -1,7 +1,6 @@
 """Standalone telemetry sender — runs as a detached subprocess.
 
-Usage: python -m mem0_cli.telemetry_sender   (JSON context is read from stdin;
-a single argv argument is still accepted as a legacy fallback)
+Usage: python -m mem0_cli.telemetry_sender '<json context>'
 
 This module is spawned by telemetry.capture_event() and runs independently
 of the parent CLI process. It:
@@ -21,18 +20,8 @@ import sys
 import urllib.request
 
 
-def _load_context() -> dict:
-    """Load telemetry context from stdin, falling back to argv for compatibility."""
-    raw = ""
-    if not sys.stdin.isatty():
-        raw = sys.stdin.read().strip()
-    if not raw and len(sys.argv) > 1:
-        raw = sys.argv[1]
-    return json.loads(raw)
-
-
 def main() -> None:
-    ctx = _load_context()
+    ctx = json.loads(sys.argv[1])
     payload = ctx["payload"]
 
     if ctx.get("needs_email") and ctx.get("mem0_api_key"):

cli/python/tests/test_agent_mode.py

@@ -32,13 +32,12 @@ def _run(args: list[str], home_dir: str | None = None) -> subprocess.CompletedPr
         if key.startswith("MEM0_"):
             del env[key]
     env.pop("FORCE_COLOR", None)
-    env["PYTHONIOENCODING"] = "utf-8"
     if home_dir:
         env["HOME"] = home_dir
     result = subprocess.run(
         [sys.executable, "-m", "mem0_cli", *args],
         capture_output=True,
-        encoding="utf-8",
+        text=True,
         env=env,
         timeout=15,
     )
@@ -100,13 +99,12 @@ class TestArgvPreprocessing:
         result = subprocess.run(
             [sys.executable, "-m", "mem0_cli", "init", "--agent"],
             capture_output=True,
-            encoding="utf-8",
+            text=True,
             env={
                 **{k: v for k, v in os.environ.items() if not k.startswith("MEM0_")},
                 "HOME": clean_home,
                 "MEM0_BASE_URL": "http://127.0.0.1:1",  # blackhole
                 "FORCE_COLOR": "0",
-                "PYTHONIOENCODING": "utf-8",
             },
             timeout=15,
         )
@@ -135,13 +133,12 @@ class TestJsonEnvelopeParity:
         result = subprocess.run(
             [sys.executable, "-m", "mem0_cli", "init", "--agent", "--json"],
             capture_output=True,
-            encoding="utf-8",
+            text=True,
             env={
                 **{k: v for k, v in os.environ.items() if not k.startswith("MEM0_")},
                 "HOME": clean_home,
                 "MEM0_BASE_URL": "http://127.0.0.1:1",
                 "FORCE_COLOR": "0",
-                "PYTHONIOENCODING": "utf-8",
             },
             timeout=15,
         )

cli/python/tests/test_cli_integration.py

@@ -49,7 +49,6 @@ def _run(
         if key.startswith("MEM0_"):
             del env[key]
     env.pop("FORCE_COLOR", None)
-    env["PYTHONIOENCODING"] = "utf-8"
     if home_dir:
         env["HOME"] = home_dir
     if env_override:
@@ -57,7 +56,7 @@ def _run(
     result = subprocess.run(
         [sys.executable, "-m", "mem0_cli", *args],
         capture_output=True,
-        encoding="utf-8",
+        text=True,
         env=env,
     )
     return subprocess.CompletedProcess(

cli/python/tests/test_commands.py

@@ -8,8 +8,8 @@ from io import StringIO
 from unittest.mock import patch
 
 import pytest
+from click.exceptions import Exit as ClickExit
 from rich.console import Console
-from typer import Exit as TyperExit
 
 from mem0_cli.commands.config_cmd import (
     cmd_config_get,
@@ -181,7 +181,7 @@ class TestAddCommand:
             patch("mem0_cli.commands.memory.console", console),
             patch("mem0_cli.commands.memory.err_console", err_console),
             patch("mem0_cli.commands.memory._stdin_is_piped", return_value=False),
-            pytest.raises((SystemExit, TyperExit)),
+            pytest.raises((SystemExit, ClickExit)),
         ):
             cmd_add(
                 mock_backend,
@@ -206,7 +206,7 @@ class TestAddCommand:
         with (
             patch("mem0_cli.commands.memory.console", console),
             patch("mem0_cli.commands.memory.err_console", err_console),
-            pytest.raises((SystemExit, TyperExit)),
+            pytest.raises((SystemExit, ClickExit)),
         ):
             cmd_add(
                 mock_backend,
@@ -764,7 +764,7 @@ class TestImportCommand:
         with (
             patch("mem0_cli.commands.utils.console", console),
             patch("mem0_cli.commands.utils.err_console", err_console),
-            pytest.raises((SystemExit, TyperExit)),
+            pytest.raises((SystemExit, ClickExit)),
         ):
             cmd_import(mock_backend, "/nonexistent/file.json", user_id=None, agent_id=None)
 
@@ -801,7 +801,7 @@ class TestEntitiesListCommand:
         with (
             patch("mem0_cli.commands.entities.console", console),
             patch("mem0_cli.commands.entities.err_console", err_console),
-            pytest.raises((SystemExit, TyperExit)),
+            pytest.raises((SystemExit, ClickExit)),
         ):
             cmd_entities_list(mock_backend, "invalid", output="table")
 
@@ -944,7 +944,7 @@ class TestEntitiesDeleteCommand:
         with (
             patch("mem0_cli.commands.entities.console", console),
             patch("mem0_cli.commands.entities.err_console", err_console),
-            pytest.raises((SystemExit, TyperExit)),
+            pytest.raises((SystemExit, ClickExit)),
         ):
             cmd_entities_delete(
                 mock_backend,
@@ -1308,7 +1308,7 @@ class TestAgentMode:
             patch("mem0_cli.commands.memory.console", console),
             patch("mem0_cli.commands.memory.err_console", err_console),
             patch("sys.stdout", captured_stdout),
-            pytest.raises((SystemExit, TyperExit)),
+            pytest.raises((SystemExit, ClickExit)),
         ):
             cmd_get(mock_backend, "bad-id", output="text")
 

cli/python/tests/test_config.py

@@ -67,8 +67,7 @@ class TestConfig:
         from mem0_cli.config import CONFIG_FILE
 
         mode = os.stat(CONFIG_FILE).st_mode & 0o777
-        if os.name != "nt":
-            assert mode == 0o600
+        assert mode == 0o600
 
     def test_defaults_save_and_load(self, isolate_config):
         config = Mem0Config()
@@ -139,11 +138,6 @@ class TestNestedAccess:
         assert set_nested_value(config, "platform.api_key", "new-key")
         assert config.platform.api_key == "new-key"
 
-    def test_set_int_value_rejects_invalid_input(self):
-        config = Mem0Config()
-        assert set_nested_value(config, "version", "abc") is False
-        assert config.version == 1
-
     def test_set_nonexistent_key(self):
         config = Mem0Config()
         assert set_nested_value(config, "nonexistent.key", "val") is False

cli/python/tests/test_output.py

@@ -54,11 +54,6 @@ class TestTextFormat:
         output = buf.getvalue()
         assert "Found 0" in output
 
-    def test_format_memories_text_handles_null_fields(self):
-        console, buf = _make_console()
-        format_memories_text(console, [{"id": None, "memory": None, "created_at": None}])
-        assert "Found 1 memories" in buf.getvalue()
-
 
 class TestTableFormat:
     def test_format_memories_table(self):
@@ -75,13 +70,6 @@ class TestTableFormat:
         # Should still render (empty table)
         assert "ID" in output
 
-    def test_format_memories_table_handles_null_fields(self):
-        console, buf = _make_console()
-        format_memories_table(console, [{"id": None, "memory": None, "created_at": None}])
-        output = buf.getvalue()
-        assert "ID" in output
-        assert "Memory" in output
-
 
 class TestSingleMemory:
     def test_format_single_memory_text(self):
@@ -99,17 +87,6 @@ class TestSingleMemory:
         output = buf.getvalue()
         assert '"memory"' in output
 
-    def test_format_single_memory_handles_null_fields(self):
-        console, buf = _make_console()
-        format_single_memory(
-            console,
-            {"id": None, "memory": None, "text": "Fallback memory", "created_at": None},
-            "text",
-        )
-        output = buf.getvalue()
-        assert "Fallback memory" in output
-        assert "ID:" not in output
-
 
 class TestAddResult:
     def test_format_add_result_text(self):

cli/python/tests/test_platform_backend.py

@@ -1,46 +0,0 @@
-"""Tests for the Platform backend (mem0 Platform API client)."""
-
-from __future__ import annotations
-
-from unittest.mock import patch
-
-from mem0_cli.backend.platform import PlatformBackend
-from mem0_cli.config import PlatformConfig
-
-
-def _make_backend() -> PlatformBackend:
-    # api_key/base_url are only used to build the httpx client; every test here
-    # patches _request, so no real network calls are made.
-    return PlatformBackend(PlatformConfig(api_key="test-key", base_url="https://api.mem0.ai"))
-
-
-class TestDeleteEntities:
-    def test_multiple_entities_returns_all_results(self):
-        backend = _make_backend()
-        responses = {
-            "/v2/entities/user/alice/": {"message": "user deleted"},
-            "/v2/entities/agent/bob/": {"message": "agent deleted"},
-        }
-        with patch.object(backend, "_request") as mock_request:
-            mock_request.side_effect = lambda method, path, **kw: responses[path]
-            result = backend.delete_entities(user_id="alice", agent_id="bob")
-
-        # Regression: previously only the last entity's response survived.
-        assert result == {
-            "user": {"message": "user deleted"},
-            "agent": {"message": "agent deleted"},
-        }
-        assert mock_request.call_count == 2
-
-    def test_single_entity_keyed_by_type(self):
-        backend = _make_backend()
-        with patch.object(backend, "_request", return_value={"message": "user deleted"}):
-            result = backend.delete_entities(user_id="alice")
-        assert result == {"user": {"message": "user deleted"}}
-
-    def test_no_entities_raises(self):
-        backend = _make_backend()
-        import pytest
-
-        with pytest.raises(ValueError):
-            backend.delete_entities()

cli/python/tests/test_telemetry.py

@@ -1,80 +0,0 @@
-"""Tests for telemetry subprocess secret handling."""
-
-from __future__ import annotations
-
-import io
-import json
-import subprocess
-import sys
-
-from mem0_cli.config import Mem0Config, save_config
-from mem0_cli.telemetry import capture_event
-from mem0_cli.telemetry_sender import _load_context
-
-
-class _CaptureStdin:
-    def __init__(self):
-        self.buffer = ""
-        self.closed = False
-
-    def write(self, value: str) -> None:
-        self.buffer += value
-
-    def close(self) -> None:
-        self.closed = True
-
-
-class _DummyProcess:
-    def __init__(self):
-        self.stdin = _CaptureStdin()
-
-
-def test_capture_event_writes_context_to_stdin_not_argv(isolate_config, monkeypatch):
-    config = Mem0Config()
-    config.platform.api_key = "m0-test-secret"
-    config.telemetry.anonymous_id = "cli-anon-test"
-    save_config(config)
-
-    captured: dict[str, object] = {}
-    proc = _DummyProcess()
-
-    def fake_popen(args, **kwargs):
-        captured["args"] = args
-        captured["kwargs"] = kwargs
-        return proc
-
-    monkeypatch.setattr("mem0_cli.telemetry.subprocess.Popen", fake_popen)
-
-    capture_event("unit_test_event", {"case": "stdin-secret"})
-
-    argv = captured["args"]
-    assert argv == [sys.executable, "-m", "mem0_cli.telemetry_sender"]
-    assert all("m0-test-secret" not in arg for arg in argv)
-
-    kwargs = captured["kwargs"]
-    assert kwargs["stdin"] == subprocess.PIPE
-    assert kwargs["text"] is True
-
-    ctx = json.loads(proc.stdin.buffer)
-    assert ctx["mem0_api_key"] == "m0-test-secret"
-    assert ctx["payload"]["event"] == "unit_test_event"
-
-    assert proc.stdin.closed
-
-
-def test_load_context_reads_from_stdin(monkeypatch):
-    monkeypatch.setattr("sys.argv", ["telemetry_sender"])
-    monkeypatch.setattr("sys.stdin", io.StringIO('{"payload": {"event": "stdin"}}'))
-
-    ctx = _load_context()
-
-    assert ctx["payload"]["event"] == "stdin"
-
-
-def test_load_context_falls_back_to_argv(monkeypatch):
-    monkeypatch.setattr("sys.argv", ["telemetry_sender", '{"payload": {"event": "argv"}}'])
-    monkeypatch.setattr("sys.stdin", io.StringIO(""))
-
-    ctx = _load_context()
-
-    assert ctx["payload"]["event"] == "argv"

cookbooks/mem0-autogen.ipynb

@@ -555,7 +555,7 @@
     "# - Enables creation of AI agents with long-term memory and learning abilities.\n",
     "# - Improves consistency and reduces repetition in user-agent interactions.\n",
     "\n",
-    "from helper.mem0_teachability import Mem0Teachability\n",
+    "from cookbooks.helper.mem0_teachability import Mem0Teachability\n",
     "\n",
     "teachability = Mem0Teachability(\n",
     "    verbosity=2,  # for visibility of what's happening\n",

docs/api-reference/memory/add-memories.mdx

@@ -4,7 +4,7 @@ description: "Add facts, messages, or metadata to a user memory store with async
 openapi: post /v3/memories/add/
 ---
 
-Extract and store memories from a conversation using the V3 additive pipeline. The endpoint uses single-pass ADD-only extraction: one LLM call, no UPDATE/DELETE. Memories accumulate over time; nothing is overwritten.
+Extract and store memories from a conversation using the V3 additive pipeline. The endpoint uses single-pass ADD-only extraction — one LLM call, no UPDATE/DELETE. Memories accumulate over time; nothing is overwritten.
 
 ## Endpoint
 
@@ -50,7 +50,6 @@ Provide conversation messages for Mem0 to extract memories from. At least one en
 | `app_id` | string | No* | Associates the memory with an app. |
 | `metadata` | object | Optional | Custom key/value metadata (e.g., `{"topic": "preferences"}`). |
 | `infer` | boolean (default `true`) | Optional | Set to `false` to skip inference and store the provided text as-is. |
-| `expiration_date` | string | Optional | Date in `YYYY-MM-DD` format. The memory is visible through this date and hidden by default after it passes. |
 
 > \* At least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`) is required.
 
@@ -84,11 +83,3 @@ The request is queued for background processing. The response contains an `event
 <Info>
 Poll the event status via `GET /v1/event/{event_id}/`. Status will be `SUCCEEDED` or `FAILED` once processing completes.
 </Info>
-
-<Info>
-Memories with `expiration_date` remain stored after they expire. Search and get-all hide them by default; pass `show_expired: true` to include them.
-</Info>
-
-<Info>
-Python uses `expiration_date`; TypeScript uses `expirationDate`.
-</Info>

docs/api-reference/memory/create-memory-export.mdx

@@ -4,4 +4,4 @@ description: "Submit an export job to create a structured memory export using a
 openapi: post /v1/exports/
 ---
 
-Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you're exporting a large number of memories. You can tailor the export by applying various filters (e.g., `user_id`, `agent_id`, `app_id`, or `run_id`) and by modifying the Pydantic schema to ensure the final data matches your exact needs.
+Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you're exporting a large number of memories. You can tailor the export by applying various filters (e.g., `user_id`, `agent_id`, `run_id`, or `session_id`) and by modifying the Pydantic schema to ensure the final data matches your exact needs.

docs/api-reference/memory/get-memories.mdx

@@ -4,11 +4,7 @@ description: "Retrieve memories with paginated results and advanced filtering us
 openapi: post /v3/memories/
 ---
 
-List memories scoped by filters with paginated results. Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object: top-level entity IDs are rejected with 400.
-
-Expired memories are hidden by default. Pass `show_expired: true` to include memories whose `expiration_date` has passed.
-
-Python uses `show_expired`; TypeScript uses `showExpired`.
+List memories scoped by filters with paginated results. Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object — top-level entity IDs are rejected with 400.
 
 The `filters` object supports complex logical operations (AND, OR, NOT) and comparison operators:
 
@@ -36,7 +32,6 @@ memories = client.get_all(
             }
         ]
     },
-    show_expired=False,
     page=1,
     page_size=50
 )
@@ -51,14 +46,12 @@ memories = client.get_all(
         {
             "id": "f4cbdb08-7062-4f3e-8eb2-9f5c80dfe64c",
             "memory": "Alex is planning a trip to San Francisco from July 1st to July 10th",
-            "expiration_date": null,
             "created_at": "2024-07-01T12:00:00Z",
             "updated_at": "2024-07-01T12:00:00Z"
         },
         {
             "id": "a2b8c3d4-5e6f-7g8h-9i0j-1k2l3m4n5o6p",
             "memory": "Alex prefers vegetarian restaurants",
-            "expiration_date": null,
             "created_at": "2024-07-05T15:30:00Z",
             "updated_at": "2024-07-05T15:30:00Z"
         }

docs/api-reference/memory/get-memory-export.mdx

@@ -4,4 +4,4 @@ description: "Retrieve the latest structured memory export after submitting an e
 openapi: post /v1/exports/get
 ---
 
-Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `agent_id`, `app_id`, `run_id`, `created_at`, or `updated_at` to get the most recent export matching your filters.
+Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `run_id`, `session_id`, or `app_id` to get the most recent export matching your filters.

docs/api-reference/memory/search-memories.mdx

@@ -4,13 +4,9 @@ description: "Search memories with hybrid retrieval (semantic + BM25 + entity ma
 openapi: post /v3/memories/search/
 ---
 
-Relevance-ranked hybrid search across stored memories. V3 uses multi-signal retrieval: semantic, BM25 keyword, and entity matching scored in parallel and fused. The returned `score` is a combined `[0, 1]` value.
+Relevance-ranked hybrid search across stored memories. V3 uses multi-signal retrieval — semantic, BM25 keyword, and entity matching scored in parallel and fused. The returned `score` is a combined `[0, 1]` value.
 
-Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object: top-level entity IDs are rejected with 400. At least one entity ID is required.
-
-Expired memories are hidden by default. Pass `show_expired: true` to include memories whose `expiration_date` has passed.
-
-Python uses `show_expired`; TypeScript uses `showExpired`.
+Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object — top-level entity IDs are rejected with 400. At least one entity ID is required.
 
 The `filters` object supports complex logical operations (AND, OR, NOT) and comparison operators:
 - `in`: Matches any of the values specified
@@ -24,17 +20,16 @@ The `filters` object supports complex logical operations (AND, OR, NOT) and comp
 
 ### Search parameter defaults
 
-| Parameter | Default |
-| --- | --- |
-| `top_k` | `10` (range 1–1000) |
-| `threshold` | `0.1` (pass `0.0` to disable) |
-| `rerank` | `false` (pass `true` to enable) |
+| Parameter | V1/V2 | V3 |
+| --- | --- | --- |
+| `top_k` | Supported (default 10) | Supported (1-1000, default 10) |
+| `threshold` | No default | Default `0.1` (pass `0.0` to disable) |
+| `rerank` | Default `true` | Default `false` (pass `true` to enable) |
 
 <CodeGroup>
 ```python Platform API Example
 related_memories = client.search(
     query="What are Alice's hobbies?",
-    show_expired=False,
     filters={
         "OR": [
             {
@@ -59,7 +54,6 @@ related_memories = client.search(
         "category": "hobbies"
       },
       "score": 0.82,
-      "expiration_date": null,
       "created_at": "2024-07-26T10:29:36.630547-07:00",
       "updated_at": null,
       "categories": ["hobbies"]

docs/api-reference/memory/update-memory.mdx

@@ -1,14 +1,5 @@
 ---
 title: 'Update Memory'
-description: "Update the content, metadata, timestamp, or expiration date of a single memory by its unique ID using the PUT endpoint."
+description: "Update the content or metadata of a single memory by its unique ID using the PUT endpoint."
 openapi: put /v1/memories/{memory_id}/
----
-
-Use this endpoint to update mutable memory fields. To make a memory expire, set `expiration_date` to a `YYYY-MM-DD` date. To make it permanent again, send `expiration_date: null`.
-
-```python
-client.update("mem_123", expiration_date="2030-01-31")
-client.update("mem_123", expiration_date=None)
-```
-
-TypeScript uses `expirationDate`.
+---

docs/api-reference/organization/remove-org-member.mdx

@@ -1,5 +0,0 @@
----
-title: "Remove Organization Member"
-description: "Remove a member from an organization to revoke their access to its projects and resources."
-openapi: "delete /api/v1/orgs/organizations/{org_id}/members/"
----

docs/api-reference/organization/update-org-member.mdx

@@ -1,5 +0,0 @@
----
-title: "Update Organization Member"
-description: "Update an existing member's role within an organization to change their permissions and access level."
-openapi: "put /api/v1/orgs/organizations/{org_id}/members/"
----

docs/api-reference/organizations-projects.mdx

@@ -14,7 +14,7 @@ Organizations and projects are **optional** features. You can use Mem0 without t
 
 ## Key Capabilities
 
-- **Multi-org/project Support**: Organization and project are resolved automatically from your API key via `/v1/ping/`: no org or project params are accepted by `MemoryClient.__init__`. Use a project-specific API key to target a particular project.
+- **Multi-org/project Support**: Specify organization and project when initializing the Mem0 client to attribute API usage appropriately
 - **Member Management**: Control access to data through organization and project membership
 - **Access Control**: Only members can access memories and data within their organization/project scope
 - **Team Isolation**: Maintain data separation between different teams and projects for secure collaboration
@@ -79,7 +79,7 @@ new_project = client.project.create(
 
 ### Update Project Settings
 
-Modify project configuration including custom instructions, categories, language preferences, retrieval criteria, and memory decay:
+Modify project configuration including custom instructions, categories, and language preferences:
 
 ```python
 # Update project with custom categories
@@ -98,17 +98,6 @@ client.project.update(
 # Use the input language for memory storage and retrieval
 client.project.update(multilingual=True)
 
-# Set retrieval criteria to control which memories are surfaced in search
-client.project.update(
-    retrieval_criteria=[
-        {"name": "relevance", "description": "How directly relevant this memory is to the current topic or user query", "weight": 3},
-        {"name": "access_frequency", "description": "How often this memory has been accessed or surfaced recently", "weight": 1}
-    ]
-)
-
-# Enable Memory Decay (boosts recently-accessed memories at search time)
-client.project.update(decay=True)
-
 # Update multiple settings at once
 client.project.update(
     custom_instructions="...",
@@ -120,37 +109,9 @@ client.project.update(
 )
 ```
 
-#### Set Retrieval Criteria
-
-`retrieval_criteria` is a per-project list of dictionaries (`List[Dict]`) that shapes how memories are ranked and filtered during search. Each dictionary has three fields: `name` (identifier), `description` (interpreted by the LLM to score each memory), and `weight` (relative influence on the final score). Use this to focus retrieval on intent-aligned or signal-specific memories:
-
-```python
-client.project.update(
-    retrieval_criteria=[
-        {
-            "name": "joy",
-            "description": "Measure the intensity of positive emotions such as happiness, excitement, or amusement expressed in the memory. A higher score reflects greater joy.",
-            "weight": 3
-        },
-        {
-            "name": "curiosity",
-            "description": "Assess the extent to which the memory reflects inquisitiveness or interest in exploring new information. A higher score reflects stronger curiosity.",
-            "weight": 2
-        },
-        {
-            "name": "access_frequency",
-            "description": "How often this memory has been accessed or surfaced recently.",
-            "weight": 1
-        }
-    ]
-)
-```
-
-Pass an empty list to clear all criteria and restore default retrieval behaviour.
-
 #### Toggle Memory Decay
 
-`decay` is a per-project boolean that turns on [Memory Decay](/platform/features/memory-decay): a search-time ranking bias that reinforces recently-accessed memories and gently dampens stale ones. The flag is `false` by default; set it via the same project-update endpoint:
+`decay` is a per-project boolean that turns on [Memory Decay](/platform/features/memory-decay) — a search-time ranking bias that reinforces recently-accessed memories and gently dampens stale ones. The flag is `false` by default; set it via the same project-update endpoint:
 
 ```bash cURL
 curl -X PATCH https://api.mem0.ai/api/v1/orgs/organizations/$ORG_ID/projects/$PROJECT_ID/ \

docs/api-reference/project/remove-project-member.mdx

@@ -1,5 +0,0 @@
----
-title: "Remove Project Member"
-description: "Remove a member from a project to revoke their access to its memories, configuration, and resources."
-openapi: "delete /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
----

docs/api-reference/project/update-project-member.mdx

@@ -1,5 +0,0 @@
----
-title: "Update Project Member"
-description: "Update an existing member's role within a project to change their permissions and access level."
-openapi: "put /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
----

docs/api-reference/project/update-project.mdx

@@ -1,5 +0,0 @@
----
-title: "Update Project"
-description: "Update a project's settings, including name, custom instructions, and other configuration options."
-openapi: "patch /api/v1/orgs/organizations/{org_id}/projects/{project_id}/"
----

docs/changelog/highlights.mdx

@@ -4,121 +4,16 @@ description: "Major product launches, headline features, and milestones for Mem0
 mode: "wide"
 ---
 
-<Update label="2026-06-27" description="SDK memory expiration">
-
-**SDK Memory Expiration: Expiring Memories Across Python and TypeScript**
-
-The latest SDK releases add first-class expiration controls to memory writes, updates, and reads, plus new TypeScript provider coverage for production deployments.
-
-- **Python client updates:** `MemoryClient.update()` and `AsyncMemoryClient.update()` now accept `expiration_date`, including `None` to clear an existing expiration.
-- **TypeScript client updates:** `AddMemoryOptions`, `update()`, and `Memory` now support `expirationDate`; `search()` and `getAll()` can include expired memories with `showExpired`.
-- **New TypeScript LLM providers:** `MiniMaxLLM` and `LiteLLM` are now available for OpenAI-compatible MiniMax and LiteLLM proxy deployments.
-- **PGVector deployment flexibility:** TypeScript PGVector config now supports `connectionString` and `ssl`, so apps can use a managed Postgres URI instead of separate connection fields.
-
-See [SDK & Tools](/changelog/sdk) for version details and PR links.
-
-</Update>
-
-<Update label="2026-06-25" description="Mem0 Plugin v0.2.11">
-
-**Mem0 Plugin: Shared Memory for Claude Code, Cursor, Codex, and Antigravity**
-
-The shared Mem0 editor plugin is current through v0.2.11:
-
-- **Automatic context injection:** File reads, bash errors, session resume prompts, and startup timelines can retrieve relevant memories automatically.
-- **Project and global scopes:** Project-scoped memories remain the default, while `global_search` supports team-wide recall across users and app scopes.
-- **Coding categories:** A 17-category coding taxonomy installs in the background and is cached per Mem0 account.
-- **Reliable capture:** Auto-capture, compaction summaries, session summaries, and metadata defaults keep memories scoped and readable.
-- **Editor correctness:** Telemetry now reports Claude Code, Cursor, Codex, and Antigravity separately with each editor's real plugin version.
-
-</Update>
-
-<Update label="2026-06-25" description="Antigravity Plugin v0.1.3">
-
-**Antigravity Plugin: Mem0 for Google Antigravity**
-
-Antigravity support in the shared Mem0 editor plugin family is current through v0.1.3:
-
-- **Self-contained plugin:** Includes its own plugin manifest, MCP config, hooks, shared scripts, and skills.
-- **AGENTS.md convention:** Uses Antigravity's `contextFileName: "AGENTS.md"` convention.
-- **Lifecycle hooks:** Wires session start, prompt recall, file-read context, memory-tool metadata enforcement, bash-error lookup, post-tool tracking, and stop summaries.
-- **Shared memory layer:** Reuses the Mem0 Platform MCP tools and the shared 16-skill command bundle.
-- **Better automatic recall:** v0.1.3 adds reranked injected context and clean `files_touched` metadata in session summaries.
-- **Telemetry correctness:** v0.1.2 reports Antigravity as `antigravity` with the plugin's own version.
-
-</Update>
-
-<Update label="2026-06-22" description="OpenCode Plugin v0.2.0">
-
-**OpenCode Plugin: Native SDK Memory Tools for OpenCode**
-
-`@mem0/opencode-plugin` adds memory to OpenCode and is current through v0.2.0:
-
-- **Native SDK tools:** Memory tools register through `@opencode-ai/plugin` and call the `mem0ai` SDK directly, so the plugin no longer depends on `mcp.mem0.ai`.
-- **Memory scopes:** Operations support `project`, `session`, and `global` scope, with `/mem0-scope` to change the default.
-- **Automatic context:** File-context injection, structured compaction summaries, and a recent-activity timeline surface relevant memories without manual recall.
-- **Auto-dream consolidation:** Gated consolidation merges duplicates, drops stale or sensitive entries, and rewrites vague memories.
-- **Skills load in place:** The `config` hook adds bundled skills to `skills.paths` instead of copying them into user config directories.
-- **Safety controls:** Blocks `MEMORY.md` writes and redacts secrets before storing memories.
-
-</Update>
-
-<Update label="2026-06-12" description="Pi Agent Plugin v0.1.2">
-
-**Pi Agent Plugin: Persistent Memory for Pi Agent**
-
-`@mem0/pi-agent-plugin` adds semantic memory to Pi Agent and has been updated through v0.1.2:
-
-- **Agent memory tool:** Registers `mem0_memory` for scoped search, add, get, delete, and delete-all operations.
-- **Slash commands:** Adds `/mem0-remember`, `/mem0-search`, `/mem0-forget`, `/mem0-tour`, `/mem0-dream`, `/mem0-pin`, `/mem0-scope`, and `/mem0-status`.
-- **Auto-capture:** Stores user and assistant memories after agent turns.
-- **Dream consolidation:** Merges duplicates, resolves contradictions, and prunes stale memories behind session, time, and memory-count gates.
-- **Project scoping:** Uses git-root detection for stable `app_id` values across monorepos.
-- **Relevant command results:** v0.1.2 adds visible command feedback and thresholded, reranked search for search, forget, and pin commands.
-
-</Update>
-
-<Update label="2026-06-12" description="OpenClaw v1.0.13">
-
-**OpenClaw Plugin: Current Memory Backend for OpenClaw**
-
-`@mem0/openclaw-mem0` is current through v1.0.13, with the original production-ready memory backend plus newer setup, security, and runtime work:
-
-- **Skills-based memory architecture:** Triage, recall, and dream skills handle extraction, recall, consolidation, and tool guidance.
-- **Chat and CLI setup:** Supports chat-based platform setup, `openclaw mem0 init`, direct API keys, email OTP, autonomous agent setup, and OSS onboarding.
-- **Platform and OSS modes:** Works with Mem0 Platform or self-hosted OSS providers including OpenAI, Anthropic, Ollama, Qdrant, and PGVector.
-- **Agent-friendly CLI:** All 16 CLI commands support `--json` for machine-driven setup and diagnostics.
-- **Runtime integration:** Exposes OpenClaw memory capability APIs for search manager and backend config status.
-- **Security and compliance:** Added path containment checks, sensitive config metadata, dependency overrides, telemetry hashing, and metadata-only registration safety.
-
-</Update>
-
-<Update label="2026-06-10" description="Vercel AI SDK Provider v3.0.0">
-
-**Vercel AI SDK Provider: Memory-Augmented Generation for AI SDK v6**
-
-The Vercel AI SDK provider moved to the v6 provider contract and Mem0 v3 APIs:
-
-- **AI SDK v6 support:** Migrated to `LanguageModelV3` / `ProviderV3`, including v3 stream lifecycle events and content arrays.
-- **Mem0 v3 API support:** Memory writes and searches now use `/v3/memories/add/` and `/v3/memories/search/`.
-- **Mem0 sources in responses:** `generateText` and `streamText` responses include memories as sources with `providerMetadata.mem0.memories`.
-- **Safer prompt handling:** Prompts are cloned before memory injection, avoiding caller-side mutation.
-- **Async storage fix:** `addMemories` is awaited so generated memories are not silently dropped.
-- **Raw memory utilities:** Exports `searchMemories`, `retrieveMemories`, `getMemories`, and `addMemories` for apps that need direct memory control.
-- **Deployment controls:** Per-request `mem0ApiKey` and `host` support Mem0 Platform, custom API keys, and self-hosted API endpoints.
-
-</Update>
-
 <Update label="2026-05-13" description="Temporal Reasoning for Mem0 Platform v3">
 
-**Temporal Reasoning: Time-Aware Retrieval for Platform v3**
+**Temporal Reasoning — Time-Aware Retrieval for Platform v3**
 
 Mem0 Platform v3 can now interpret time-aware memories and queries so assistants retrieve the right information for questions about the past, upcoming plans, and current state.
 
-- **Search intent parsing:** Queries like `last week`, `upcoming`, `right now`, and `as of March 2025` now resolve against memory timestamps automatically.
-- **Default v3 behavior:** No per-request toggle is needed for v3 writes or searches.
-- **Deterministic testing:** Pass `reference_date` to anchor relative phrases in tests, backfills, and demos.
-- **Stable API shape:** Temporal reasoning changes ranking, not the client response contract.
+- **Time-aware search intent** — Queries like `last week`, `upcoming`, `right now`, and `as of March 2025` return contextually appropriate results automatically
+- **Enabled by default** — No per-request toggle required for v3 writes or searches
+- **Anchored relative queries** — `reference_date` anchors relative search phrases for tests, backfills, and reproducible demos
+- **Normal response shape** — Temporal reasoning affects ranking while preserving existing client response patterns
 
 See [Temporal Reasoning](/platform/features/temporal-reasoning) for usage details.
 
@@ -126,11 +21,11 @@ See [Temporal Reasoning](/platform/features/temporal-reasoning) for usage detail
 
 <Update label="2026-05-08" description="Memory Decay">
 
-**Memory Decay: Recently-Used Memories Surface Higher**
+**Memory Decay — Recently-Used Memories Surface Higher, Automatically**
 
 Per-project search-time ranking bias that boosts recently-touched memories and gently dampens stale ones. Off by default; opt in per project via the `decay` field on the project endpoint, or via `client.project.update(decay=True)` in the SDKs (Python `v2.0.2` / TypeScript `v3.0.3`).
 
-- **Soft bias, never a filter.** The scaling factor stays in `0.3×–1.5×`. Decay can reorder candidates but never removes them; anything that surfaced before decay can still surface after.
+- **Soft bias, never a filter.** The scaling factor stays in `0.3×–1.5×`. Decay can reorder candidates but never zeros them out — anything that surfaced before decay can still surface after.
 - **Reinforcement loop.** Every memory returned in a search has its access history updated, so frequently-used facts naturally float to the top over time.
 - **Public score still clamped to `[0, 1]`.** Existing API contract preserved; no client-side changes needed.
 - **v3 search only**, fully reversible. See [Memory Decay docs](/platform/features/memory-decay).
@@ -139,48 +34,76 @@ Per-project search-time ranking bias that boosts recently-touched memories and g
 
 <Update label="2026-04-14" description="Mem0 SDK v2.0.0 / v3.0.0">
 
-**New Memory Algorithm: State-of-the-Art Accuracy at ~3-4x Lower Cost**
+**New Memory Algorithm — State-of-the-Art Accuracy at ~3-4x Lower Cost**
 
 Ground-up rewrite of the memory pipeline with 20+ point benchmark improvements:
 
-- **LoCoMo:** 71.4 → **91.6** (+20) for multi-turn conversation recall.
-- **LongMemEval:** 67.8 → **93.4** (+26) for long-term memory across sessions.
-- **BEAM (1M tokens):** **64.1** on production-scale memory evaluation.
-- **Agent memories:** Assistant recall moves from 46% to **100%**.
-- **Temporal reasoning:** "Where did I live before SF?" improves from 51% to **93%**.
-- **Lower token use:** Retrieval stays under 7K tokens versus 25K+ for full-context approaches.
-- **ADD-only extraction:** Memories accumulate; nothing is overwritten or deleted.
-- **Hybrid retrieval:** Semantic search, BM25 keyword search, and entity boost are scored in parallel.
-- **Graph memory (built-in)**: entities extracted, embedded, and linked across memories, with no external graph store required
+- **LoCoMo:** 71.4 → **91.6** (+20) — multi-turn conversation recall
+- **LongMemEval:** 67.8 → **93.4** (+26) — long-term memory across sessions
+- **BEAM (1M tokens):** **64.1** — production-scale memory evaluation
+- **Agent memories are first-class** — Previous algorithm: 46% on assistant recall. New: **100%**
+- **Temporal reasoning works** — "Where did I live before SF?" Previous: 51%. New: **93%**
+- **~3-4x fewer tokens** — Under 7K tokens per retrieval vs 25K+ for full-context approaches
+- **ADD-only extraction** — Memories accumulate; nothing is overwritten or deleted
+- **Hybrid retrieval** — Semantic + BM25 keyword + entity boost, scored in parallel
+- **Entity linking** — Entities extracted, embedded, and linked across memories
 
-Breaking changes: external graph stores removed from OSS (replaced by built-in graph memory), `search()` defaults changed, deprecated params removed. See [migration guide](/migration/oss-v2-to-v3).
+Breaking changes: Graph memory removed from OSS, `search()` defaults changed, deprecated params removed. See [migration guide](/migration/oss-v2-to-v3).
 
 </Update>
 
 <Update label="2026-04-06" description="Mem0 Skill Graph">
 
-**Mem0 Skill Graph: In-Context Documentation for AI Agents**
+**Mem0 Skill Graph — In-Context Documentation for AI Agents**
 
-AI coding agents in Claude Code, Cursor, and Codex can now access Mem0 knowledge directly in their workflow without leaving the editor. Three interconnected skills launched:
+AI coding agents in Claude Code, Cursor, and Codex can now access Mem0 knowledge directly in their workflow — no doc searching required. Three interconnected skills launched:
 
-- **mem0 Core Skill:** Python and TypeScript SDK reference, REST API patterns, and integration guides for LangChain, CrewAI, Autogen, and more.
-- **mem0-cli Skill:** Terminal command reference, configuration walkthroughs, and CI/CD recipes.
-- **mem0-vercel-ai-sdk Skill:** Vercel AI SDK provider API, memory-augmented generation patterns, and multi-provider setup.
+- **mem0 Core Skill** — Complete Python and TypeScript SDK reference, REST API patterns, and integration guides for LangChain, CrewAI, Autogen, and more
+- **mem0-cli Skill** — Terminal command reference, configuration walkthroughs, and CI/CD recipes
+- **mem0-vercel-ai-sdk Skill** — Vercel AI SDK provider API, memory-augmented generation patterns, and multi-provider setup
 
 </Update>
 
 <Update label="2026-04-06" description="Mem0 CLI v0.2.2">
 
-**Official Mem0 CLI: Now on PyPI and npm**
+**Official Mem0 CLI — Now on PyPI and npm**
 
 A full-featured command-line interface for Mem0, available in both Python and Node.js:
 
 - **Install:** `pip install mem0-cli` or `npm install -g @mem0/cli`
-- **Full command suite:** `add`, `search`, `list`, `get`, `update`, `delete`, `import`, `config`, `init`, `status`, `entity`, `event`.
-- **Interactive setup:** `mem0 init` supports email verification and direct API key entry.
-- **Runtime coverage:** Works with Mem0 Platform and self-hosted OSS modes.
-- **Automation support:** Use `--json` for CI/CD pipelines and agent workflows.
-- **Dual implementation:** Same commands and behavior across Python and Node.js.
+- **Full command suite** — `add`, `search`, `list`, `get`, `update`, `delete`, `import`, `config`, `init`, `status`, `entity`, `event`
+- **Interactive setup** — `mem0 init` with email verification or direct API key entry
+- **Works everywhere** — Platform (Mem0 Cloud) and self-hosted OSS modes
+- **Scriptable** — `--json` flag for CI/CD pipelines and automation
+- **Dual SDK** — Same commands, same experience across Python and Node.js
+
+</Update>
+
+<Update label="2026-04-06" description="OpenClaw v1.0.4">
+
+**OpenClaw Plugin — Production-Ready**
+
+The OpenClaw Mem0 plugin went from initial release to production-ready in one week (v1.0.0 → v1.0.4):
+
+- **Skills-based memory architecture** — New extraction pipeline with skill-loader, batched extraction, and domain-aware memory triage
+- **Dream gate** — Automatic memory consolidation during idle periods for higher-quality long-term recall
+- **Interactive CLI** — `openclaw mem0 init`, `status`, `config`, `import`, and `event` commands
+- **Unified tool naming** — `memory_add` and `memory_delete` replace 4 legacy tools, matching the platform API
+- **Security hardened** — Path traversal protection, pinned dependencies, 329 tests across 10 files
+
+</Update>
+
+<Update label="2026-04-02" description="Mem0 Plugin for AI Editors">
+
+**Mem0 Plugin for Claude Code, Cursor, and Codex**
+
+Launched a unified Mem0 plugin across three major AI development environments — Claude Code and Cursor first (March 25), then Codex (April 2):
+
+- **9 MCP memory tools** — add, search, get, update, delete, bulk delete, entity management via `mcp.mem0.ai`
+- **Lifecycle hooks** — Automatic memory capture at session start, context compaction, task completion, and session end
+- **Cloud MCP server** — Managed endpoint replaces local MCP and Smithery setup
+- **Streamable HTTP transport** — New MCP transport protocol for real-time streaming
+- **Codex-specific skill** — Dedicated skill in `mem0-plugin/skills/mem0-codex` for Codex workflows
 
 </Update>
 
@@ -190,11 +113,11 @@ A full-featured command-line interface for Mem0, available in both Python and No
 
 Major expansion of the provider ecosystem:
 
-- **Apache AGE:** New graph store support, bringing the total to 4 graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE). **Note:** All external graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE) were subsequently removed in v2.0.0 (2026-04-14). Graph memory is now built-in entity linking with no external graph store required; see the [v2.0.0 entry above](#mem0-sdk-v2-0-0-v3-0-0).
-- **Turbopuffer:** New vector database provider for Python SDK.
-- **MiniMax:** New LLM provider with dedicated AWS Bedrock support.
-- **pgvector for Node.js:** PostgreSQL vector support added to the TypeScript OSS SDK.
-- **Reasoning models:** `reasoning_effort` parameter for OpenAI o1/o3-style models.
+- **Apache AGE** — New graph store support, bringing the total to 4 graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE)
+- **Turbopuffer** — New vector database provider for Python SDK
+- **MiniMax** — New LLM provider with dedicated AWS Bedrock support
+- **pgvector for Node.js** — PostgreSQL vector support added to the TypeScript OSS SDK
+- **Reasoning models** — `reasoning_effort` parameter for OpenAI o1/o3-style models
 
 </Update>
 
@@ -202,6 +125,6 @@ Major expansion of the provider ecosystem:
 
 **Mem0 Platform Skill on skills.sh**
 
-First skill launch: a dedicated Mem0 skill providing platform API reference, quickstart patterns, and integration examples directly inside agent sessions. Available on [skills.sh](https://skills.sh) for any compatible AI coding agent.
+First skill launch — a dedicated Mem0 skill providing platform API reference, quickstart patterns, and integration examples directly inside agent sessions. Available on [skills.sh](https://skills.sh) for any compatible AI coding agent.
 
 </Update>

docs/changelog/openclaw.mdx

@@ -0,0 +1,303 @@
+---
+title: "OpenClaw"
+description: "Release notes for the OpenClaw plugin and agent harness."
+mode: "wide"
+---
+
+<Update label="2026-04-29" description="v1.0.11">
+
+**New Features:**
+- **Skills-mode auto-setup:** `enableSkillsConfig()` now runs automatically after onboarding — enables triage, recall (with reranking + keyword search), and dream consolidation with `tools.profile = "full"` and disables the built-in session-memory hook to avoid conflicts
+- **Memory runtime capability:** Plugin now exposes `runtime.getMemorySearchManager()` and `resolveMemoryBackendConfig()` on the registered memory capability, enabling OpenClaw gateway to query memory status and backend config directly
+- **Dimension-aware collections:** OSS wizard detects embedder dimension changes and creates a new collection (`mem0_<dims>d`) automatically, with a warning about old memories being inaccessible under the new embedder
+- **Tool documentation in skills:** Both `memory-triage` and `memory-dream` SKILL.md files now include full tool reference sections listing all available tools with parameters
+
+**Improvements:**
+- **Auto-capture and auto-recall default to enabled:** `autoCapture` and `autoRecall` now default to `true` (was `false`). Manifest descriptions updated accordingly. Ignored in skills mode
+- **`memory_update` over delete+add:** Skills now prefer `memory_update` for in-place edits — atomic and preserves edit history. Consolidation pattern updated: update best memory, delete redundant ones
+- **Search threshold lowered:** Default `searchThreshold` reduced from `0.5` to `0.1` for broader recall. Removed hardcoded `0.6` recall-specific override — all searches now use the configured threshold
+- **Embedder dimension propagation:** Vector store config auto-resolves dimensions from embedder config when not explicitly set. Syncs `dimension` and `embeddingModelDims` fields for Qdrant/PGVector compatibility
+- **Config file write safety:** `writeFullConfig()` now re-reads and deep-merges the `plugins` section before writing, preserving `installs` and `slots` written by the OpenClaw gateway
+- **Additional embedder models:** Added `mxbai-embed-large` (1024), `all-minilm` (384), and `snowflake-arctic-embed` (1024) to known embedder dimensions
+
+**Security:**
+- Bumped `protobufjs` to `>=7.5.5` via pnpm overrides (GHSA-xq3m-2v4x-88gg) ([#5012](https://github.com/mem0ai/mem0/pull/5012))
+
+**Fixes:**
+- Moved `bootstrapTelemetryFlag()` and removed `ensureInstallRecord()` from module-level side effects — both now run inside `register()` to avoid crashes when loaded outside OpenClaw gateway
+- Fixed OSS history DB path resolution: absolute paths no longer passed through `resolvePath()`, preventing double-prefix bugs
+- Manifest `providerAuthEnvVars` replaced with spec-compliant `setup.providers` format using `id` + `envVars`
+
+**Dependencies:**
+- Bumped `mem0ai` from `3.0.1` to `3.0.2`
+- Bumped `pluginApi` and `minGatewayVersion` compat to `>=2026.4.24`
+
+</Update>
+
+<Update label="2026-04-23" description="v1.0.10">
+
+**Security:**
+- Telemetry `distinct_id` now uses SHA-256 instead of MD5 — prevents rainbow-table reversal of API key hashes
+- User email is now SHA-256 hashed before sending as `distinct_id` — no PII in telemetry payloads
+- Declared PostHog telemetry endpoint (`us.i.posthog.com`) in `providerEndpoints`
+
+**Fixes:**
+- Fixed version-pinned install records preventing plugin updates. `ensureInstallRecord()` now detects semver-pinned specs (e.g. `@mem0/openclaw-mem0@1.0.7`) and rewrites them to `@latest` or `clawhub:` prefix so `openclaw plugins update` resolves to the newest release
+- Fixed `searchThreshold` default inconsistency: standardized to `0.3` across docs, README, and manifest
+- `PLUGIN_VERSION` now injected at build time via tsup `define` from `package.json` — no more hardcoded version strings
+
+**Manifest Compliance:**
+- Removed non-spec fields: `requiredEnvVars`, `dataLocations`, `privacy`, `setup` (with `externalEndpoints`, `providers`, `requiresRuntime`, `postInstallHint`)
+- Replaced `setup.externalEndpoints` with spec-compliant `providerEndpoints` using `endpointClass` + `hosts` format
+- Env var declarations now rely solely on `providerAuthEnvVars` (already spec-compliant)
+
+**Docs:**
+- Fixed `openclaw plugins update` command: uses plugin ID (`openclaw-mem0`), not npm package name (`@mem0/openclaw-mem0`)
+- Added update section to README
+- Removed redundant "Key Features" and "Conclusion" sections from integration docs
+
+</Update>
+
+<Update label="2026-04-22" description="v1.0.9">
+
+**Security & Compliance:**
+- Added top-level `requiredEnvVars` to plugin manifest, declaring env vars per mode (platform, OSS OpenAI, OSS Anthropic, OSS Ollama). Fixes ClaHub scanner "required env vars: none" mismatch
+- Added `sensitive: true` and descriptions to `apiKey` and `userEmail` in `configSchema` — previously only declared in `uiHints`
+- Added `default: false` with descriptions to `autoCapture` and `autoRecall` in `configSchema` so scanner can confirm opt-in defaults
+- Added `dataLocations` field to manifest declaring all persistence paths (config, vectorStore, historyDb, dreamState)
+- Added `privacy` field to manifest documenting data flow for platform vs open-source mode and credential storage guidance
+- Added `externalEndpoints` to `setup` section declaring api.mem0.ai and app.mem0.ai with purpose and requirement context
+
+**Tests:**
+- Replaced direct `process.env` access in `tests/cli-commands.test.ts` and `tests/fs-safe.test.ts` with `vi.stubEnv`/`vi.unstubAllEnvs`. Fixes ClaHub static analysis flag for "environment variable access combined with network send"
+- 421 tests across 15 test files
+
+</Update>
+
+<Update label="2026-04-21" description="v1.0.8">
+
+**New Features:**
+- **OSS Onboarding Wizard:** New guided 4-step interactive setup for open-source mode — walks through LLM provider, embedding provider, vector store, and user ID selection with prefilled defaults
+- **Agent-Friendly CLI:** Added `--json` flag to all 16 CLI commands for machine-readable output. Agents can call `openclaw mem0 help --json` to discover every command and flag
+- **Non-Interactive OSS Setup:** Added `--mode open-source` with `--oss-llm`, `--oss-embedder`, `--oss-vector` flags for fully automated OSS configuration without prompts
+- **JSON Helpers Module:** New `cli/json-helpers.ts` with `jsonOut`, `jsonErr`, and `redactSecrets` utilities for consistent structured output
+
+**Improvements:**
+- **Init Flow Redesigned:** Replaced 3-option flat menu with 2-level structure: Platform (email login or API key) and Open Source (guided wizard)
+- **Provider Selection:** LLM providers: OpenAI, Ollama, Anthropic. Embedding providers: OpenAI, Ollama. Vector stores: Qdrant, PGVector
+- **Input Prefill:** All prompts with defaults (base URL, user ID) now prefill the input field instead of showing defaults in brackets
+- **Smart Reuse:** When LLM and embedder use the same provider, API key and base URL are automatically reused from the LLM step
+- **Default Model:** Updated default LLM model to `gpt-5-mini`
+- **Manifest Compliance:** Removed undocumented fields, aligned env var declarations between SKILL.md and manifest, fixed `configSchema.required` for clean installs
+
+**Tests:**
+- 404 tests across 15 test files (+3 new: `json-helpers.test.ts`, `oss-wizard.test.ts`, `cli-commands.test.ts`)
+
+</Update>
+
+<Update label="2026-04-20" description="v1.0.7">
+
+**New Features:**
+- **Chat-Based Setup:** Added chat-based Platform setup flow — users can now configure the plugin conversationally instead of editing config files manually
+- **Installation Docs Rewrite:** Rewrote README and integration docs with chat-first setup, numbered manual steps.
+
+**Improvements:**
+- **SDK Upgrade:** Bumped `mem0ai` dependency to 3.0.1 for V3 API compatibility
+- **Config Cleanup:** Dropped deprecated `orgId`, `projectId`, `enableGraph` config options; updated CLI prompts ([#4734](https://github.com/mem0ai/mem0/pull/4734), [#4764](https://github.com/mem0ai/mem0/pull/4764))
+- **Noise Filtering:** Expanded noise patterns in memory add tool; handle leading text in JSON extraction
+
+</Update>
+
+<Update label="2026-04-11" description="v1.0.6">
+
+**Bug Fixes:**
+- **Telemetry:** Replaced shared `"anonymous-openclaw"` fallback with a persistent per-machine random hash (`openclaw-anon-<uuid>`), so anonymous plugin users are counted individually in PostHog ([#4790](https://github.com/mem0ai/mem0/pull/4790))
+- **Telemetry:** Added PostHog `$identify` event on first authenticated run to stitch anonymous history onto the authenticated profile ([#4790](https://github.com/mem0ai/mem0/pull/4790))
+- **Telemetry:** Fixed event loss on short-lived CLI invocations — added `beforeExit` handler to flush queued events before the process exits ([#4790](https://github.com/mem0ai/mem0/pull/4790))
+- **Telemetry:** Added lazy `/v1/ping/` email resolution so users who configure API key outside `mem0 init` show as their email in PostHog, not an md5 hash ([#4790](https://github.com/mem0ai/mem0/pull/4790))
+- **Telemetry:** Unified CLI event prefix from `openclaw.<cmd>` to `openclaw.cli.<cmd>` on the needsSetup branch to match the authenticated branch ([#4790](https://github.com/mem0ai/mem0/pull/4790))
+
+**Improvements:**
+- **API:** Added `source: "OPENCLAW"` to all provider calls (`add`, `search`, `getAll`) across tools, CLI commands, recall, and the OSS backend adapter ([#4790](https://github.com/mem0ai/mem0/pull/4790))
+
+</Update>
+
+<Update label="2026-04-07" description="v1.0.5">
+
+**Bug Fixes:**
+- **Init interactive choice bug**: Fixed number selection in `openclaw mem0 init` — entering 1/2/3 now correctly selects the corresponding option (was broken by readline prefill concatenating with user input)
+- **OSS pgvector crash** ([#4727](https://github.com/mem0ai/mem0/issues/4727)): Fixed "Client has already been connected" cascade when using pgvector in OSS mode. The warmup call swallowed errors leaving a half-initialized pg client; concurrent recall/capture then all hit `client.connect()` on the same client. Fix: let warmup errors propagate (so `initPromise` resets and retries with a fresh Memory + fresh pg client) and build fresh config objects per attempt instead of mutating shared state.
+
+**Removed:**
+- **`orgId` / `projectId` config parameters**: Removed from config schema, CLI (`config show/get/set`), init display, and providers. The API key is project-scoped, so separate org/project IDs are unnecessary and could cause access errors if mismatched.
+- **`enableGraph` config parameter**: Removed from all config surfaces, providers, backend, and tools. Graph memory is being deprecated — removing the flag avoids unnecessary exposure.
+
+</Update>
+
+<Update label="2026-04-04" description="v1.0.4">
+
+**New Features:**
+- **Interactive init flow**: `openclaw mem0 init` with interactive menu (email verification or direct API key). Non-interactive modes: `--api-key`, `--email`, `--email --code`
+- **`memory_add` tool**: Replaces `memory_store` — name now matches `mem0` CLI and platform API
+- **`memory_delete` tool**: Unified delete — single ID, search-then-delete, bulk, entity cascade. Replaces `memory_forget` and `memory_delete_all`
+- **CLI subcommands**: `openclaw mem0 init`, `openclaw mem0 status`, `openclaw mem0 config show`, `openclaw mem0 config set`
+- **`import` CLI command**: Bulk-import memories from a JSON file with `--user-id` and `--agent-id` overrides
+- **`event list` / `event status` CLI commands**: Monitor background processing events
+- **`fs-safe.ts` module**: Isolated filesystem wrappers in a separate entry point
+- **`backend/` module**: `PlatformBackend` with direct HTTP API access for CLI commands
+- **Plugin manifest**: Added `contracts.tools`, `configSchema`, and `uiHints` to `openclaw.plugin.json`
+- **Test suite**: 329 tests across 10 test files
+
+**Changes:**
+- **Modular architecture**: Extracted tools into `tools/` directory (6 files) and CLI into `cli/commands.ts`
+- **Code splitting**: tsup builds with `splitting: true` and two entry points
+- **Skills updated**: All SKILL.md files reference new tool names (`memory_add`, `memory_delete`)
+- **Auto-recall timeout**: Recall wrapped in 8-second `Promise.race`
+- **Auto-capture fire-and-forget**: `provider.add()` runs in background via `.then()/.catch()`
+- **Auto-capture minimum content gate**: Skips extraction when total user content is fewer than 50 chars
+
+**Removed:**
+- `memory_store` tool — replaced by `memory_add`
+- `memory_forget` tool — replaced by `memory_delete`
+- `memory_delete_all` tool — merged into `memory_delete`
+- `memory_history` tool and `history` CLI command — deprecated
+
+</Update>
+
+<Update label="2026-04-03" description="v1.0.3">
+
+**Bug Fixes:**
+- **Security**: Added `safePath()` containment helper to `readSkillFile` and `readDomainOverlay` in `skill-loader.ts` — prevents directory traversal
+- **Noise filter**: Reverted incorrect `After-Compaction` regex rename back to `Post-Compaction`
+
+**Changes:**
+- **Supply-chain hardening**: Pinned `mem0ai` dependency to exact `2.3.0` (was `^2.3.0`)
+
+**Tests:**
+- 12 new tests covering `safePath`, `readSkillFile`, `readDomainOverlay`, and `loadSkill` with traversal inputs
+
+</Update>
+
+<Update label="2026-04-02" description="v1.0.2">
+
+**Bug Fixes:**
+- **Security**: Removed `resolveEnvVars()` and `resolveEnvVarsDeep()` from `config.ts` — plugin-side env resolution was redundant and triggered static analysis warnings ([#4676](https://github.com/mem0ai/mem0/pull/4676))
+
+</Update>
+
+<Update label="2026-04-02" description="v1.0.1">
+
+**New Features:**
+- **CD workflow**: Added continuous deployment workflow with OIDC trusted publishing ([#4672](https://github.com/mem0ai/mem0/pull/4672))
+- **Plugin configuration manifest**: Added `compat` and `build` metadata to `package.json` ([#4667](https://github.com/mem0ai/mem0/pull/4667))
+- **LICENSE**: Added Apache-2.0 license file ([#4667](https://github.com/mem0ai/mem0/pull/4667))
+
+**Bug Fixes:**
+- **Dream gate**: Fixed cheap-first ordering, session isolation, and verified completion ([#4666](https://github.com/mem0ai/mem0/pull/4666))
+- **Graceful startup**: Plugin now starts gracefully when no API key is configured ([#4669](https://github.com/mem0ai/mem0/pull/4669))
+
+</Update>
+
+<Update label="2026-04-01" description="v1.0.0">
+
+**New Features:**
+- **Skills-based memory architecture**: New skill-loader and skill-based extraction pipeline with batched extraction ([#4624](https://github.com/mem0ai/mem0/pull/4624))
+- **Dream gate**: Memory consolidation and dream-cycle processing during idle periods
+- **Enhanced recall**: New `recall.ts` module with improved recall logic and skill-aware retrieval
+- **Memory triage skill**: Domain-aware memory triage with companion domain support and recall protocol
+- **Memory dream skill**: Skill for memory consolidation during idle periods
+- **Plugin configuration**: Added `openclaw.plugin.json` manifest and `scripts/configure.py` setup helper
+
+**Changes:**
+- Extraction pipeline refactored to use skills-based architecture for more contextual and higher quality memory capture
+
+</Update>
+
+<Update label="2026-03-26" description="v0.4.1">
+
+**New Features:**
+- **Improved extraction quality**: Enhanced noise filtering, deduplication, and better extraction instructions
+
+**Bug Fixes:**
+- **Credential detection**: Improved detection of credentials, API keys, and secrets in extraction instructions (#4552)
+- **Standalone timestamps**: Prevented extraction of standalone timestamps as memories (#4550)
+
+</Update>
+
+<Update label="2026-03-16" description="v0.4.0">
+
+**New Features:**
+- **Non-interactive trigger filtering**: Skips recall and capture for `cron`, `heartbeat`, `automation`, and `schedule` triggers
+- **Subagent hallucination prevention**: Detects ephemeral subagent sessions and routes recall to parent namespace
+- **Dynamic recall thresholding**: Memories scoring less than 50% of top result are dropped
+- **SQLite resilience**: Init error recovery with automatic retry for OSS mode
+- **`disableHistory` config option**: New `oss.disableHistory` flag
+- 78 unit tests covering filtering, isolation, trigger filtering, subagent detection, and SQLite resilience
+
+**Changes:**
+- Auto-recall threshold raised from 0.5 to 0.6 for stricter precision
+- Recall candidate pool increased to `topK * 2` for better filtering headroom
+- Relaxed extraction instructions: related facts kept together to preserve context
+
+**Bug Fixes:**
+- **Concurrent session race condition**: Lifecycle hooks now use `ctx.sessionKey` directly instead of a shared mutable variable
+
+</Update>
+
+<Update label="2026-03-12" description="v0.3.1">
+
+**New Features:**
+- **Message filtering pipeline**: Multi-stage noise removal before extraction
+- **Broad recall for new sessions**: Short or new-session prompts trigger secondary broad search
+- **Client-side threshold filtering**: Safety net that drops low-relevance results
+- **Temporal anchoring**: Extraction instructions now include current date
+- 55 unit tests covering filtering and isolation helpers
+
+**Changes:**
+- Extraction window expanded from last 10 to last 20 messages
+- Rewritten custom extraction instructions for conciseness and deduplication
+- Refactored monolithic `index.ts` (1772 lines) into 6 focused modules
+
+</Update>
+
+<Update label="2026-03-10" description="v0.3.0">
+
+**Bug Fixes:**
+- Updated `mem0ai` dependency with sqlite3 to better-sqlite3 migration (#4270)
+
+</Update>
+
+<Update label="2026-03-09" description="v0.2.0">
+
+**New Features:**
+- Per-agent memory isolation for multi-agent setups via `agentId`
+- "Understanding userId" section in docs
+
+**Changes:**
+- Updated config examples to use concrete `userId` values instead of placeholders
+
+**Bug Fixes:**
+- Migrated platform search to Mem0 v2 API
+
+</Update>
+
+<Update label="2026-02-19" description="v0.1.2">
+
+**New Features:**
+- Source field for openclaw memory entries
+
+**Bug Fixes:**
+- Auto-recall injection and auto-capture message drop
+
+</Update>
+
+<Update label="2026-02-02" description="v0.1.0">
+
+**New Features:**
+- Initial release of the OpenClaw Mem0 plugin
+- Platform mode (Mem0 Cloud) and open-source mode support
+- Auto-recall: inject relevant memories before each turn
+- Auto-capture: store facts after each turn
+- Configurable `topK`, `threshold`, and `apiVersion` options
+
+</Update>

docs/changelog/platform.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Platform"
-description: "Release notes for the Mem0 hosted platform: backend, dashboard, billing, and infrastructure changes."
+description: "Release notes for the Mem0 hosted platform — backend, dashboard, billing, and infrastructure changes."
 mode: "wide"
 ---
 
@@ -25,7 +25,7 @@ mode: "wide"
 <Update label="2026-04-16" description="">
 
 **Improvements:**
-- **UI:** Removed the legacy external-graph-store visualization tab, page, and its references from dashboard, sidebar, project settings, playground, and billing
+- **UI:** Removed Graph Memory tab, page, and all references from dashboard, sidebar, project settings, playground, and billing
 
 </Update>
 

docs/components/embedders/models/aws_bedrock.mdx

@@ -59,9 +59,5 @@ Here are the parameters available for configuring AWS Bedrock embedder:
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `model` | The name of the embedding model to use | `amazon.titan-embed-text-v1` |
-| `aws_region` | AWS region for the Bedrock client | `us-west-2` |
-| `aws_access_key_id` | AWS access key ID for authentication | `None` |
-| `aws_secret_access_key` | AWS secret access key for authentication | `None` |
-| `aws_session_token` | AWS session token for temporary credentials | `None` |
 </Tab>
 </Tabs>

docs/components/embedders/models/fastembed.mdx

@@ -1,50 +0,0 @@
----
-title: "FastEmbed"
-description: "Configure FastEmbed as an embedding provider in Mem0 to generate embeddings locally using ONNX-based models without a GPU."
----
-
-You can use FastEmbed to run embedding models locally in Mem0. FastEmbed is an ONNX-based embedding library that runs efficiently on CPU without requiring a GPU or an external API key.
-
-### Installation
-
-```bash
-pip install fastembed
-```
-
-### Usage
-
-<CodeGroup>
-```python Python
-import os
-from mem0 import Memory
-
-os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
-
-config = {
-    "embedder": {
-        "provider": "fastembed",
-        "config": {
-            "model": "thenlper/gte-large"
-        }
-    }
-}
-
-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="john")
-```
-</CodeGroup>
-
-### Config
-
-Here are the parameters available for configuring FastEmbed embedder:
-
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `model` | The name of the FastEmbed model to use | `thenlper/gte-large` |
-| `embedding_dims` | Dimensions of the embedding model (auto-derived from the model if not set) | `None` |

docs/components/embedders/models/google_AI.mdx

@@ -67,15 +67,14 @@ Here are the parameters available for configuring Gemini embedder:
 | Parameter        | Description                          | Default Value           |
 | ---------------- | ------------------------------------ | ----------------------- |
 | `model`          | The name of the embedding model to use| `models/gemini-embedding-001` |
-| `embedding_dims` | Dimensions of the embedding model     | `768`                   |
+| `embedding_dims` | Dimensions of the embedding model     | `1536`                  |
 | `api_key`        | The Google API key                   | `None`                  |
-| `output_dimensionality` | Output dimensionality for the embedding model (Gemini-specific; used when `embedding_dims` is not set) | `None` |
 </Tab>
 <Tab title="TypeScript">
 | Parameter         | Description                                   | Default Value              |
 | ----------------- | --------------------------------------------- | -------------------------- |
 | `model`           | The name of the embedding model to use        | `gemini-embedding-001`     |
-| `embeddingDims`   | Dimensions of the embedding model. When not set, uses the model's native output dimensionality (3072 for `gemini-embedding-001`; MRL truncation to 768, 1536, or 3072 is supported) | `None`                     |
+| `embeddingDims`   | Dimensions of the embedding model             | `1536`                     |
 | `apiKey`          | Google API key                                | `None`                     |
 </Tab>
 </Tabs>

docs/components/embedders/models/lmstudio.mdx

@@ -16,7 +16,7 @@ config = {
     "embedder": {
         "provider": "lmstudio",
         "config": {
-            "model": "nomic-ai/nomic-embed-text-v1.5-GGUF"
+            "model": "nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
         }
     }
 }
@@ -37,6 +37,6 @@ Here are the parameters available for configuring LM Studio embedder:
 
 | Parameter | Description | Default Value |
 | --- | --- | --- |
-| `model` | The name of the LM Studio model to use | `nomic-ai/nomic-embed-text-v1.5-GGUF` |
+| `model` | The name of the LM Studio model to use | `nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf` |
 | `embedding_dims` | Dimensions of the embedding model | `1536` |
 | `lmstudio_base_url` | Base URL for LM Studio connection | `http://localhost:1234/v1` |

docs/components/embedders/overview.mdx

@@ -10,7 +10,7 @@ Mem0 offers support for various embedding models, allowing users to choose the o
 See the list of supported embedders below.
 
 <Note>
-  All embedders listed below are supported in the Python implementation. The TypeScript implementation supports: **OpenAI**, **Azure OpenAI**, **Google AI**, **Langchain**, **LM Studio**, and **Ollama**.
+  The following embedders are supported in the Python implementation. The TypeScript implementation currently only supports OpenAI.
 </Note>
 
 <CardGroup cols={4}>
@@ -24,7 +24,6 @@ See the list of supported embedders below.
   <Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>
   <Card title="Langchain" href="/components/embedders/models/langchain"></Card>
   <Card title="AWS Bedrock" href="/components/embedders/models/aws_bedrock"></Card>
-  <Card title="FastEmbed" href="/components/embedders/models/fastembed"></Card>
 </CardGroup>
 
 ## Usage

docs/components/llms/config.mdx

@@ -98,7 +98,7 @@ Here's a comprehensive list of all parameters that can be used across different
     | `max_tokens`         | Tokens to generate                            | All               |
     | `top_p`              | Probability threshold for nucleus sampling    | All               |
     | `top_k`              | Number of highest probability tokens to keep  | All               |
-    | `http_client_proxies`| Allow proxy server settings                   | All               |
+    | `http_client_proxies`| Allow proxy server settings                   | AzureOpenAI       |
     | `models`             | List of models                                | Openrouter        |
     | `route`              | Routing strategy                              | Openrouter        |
     | `openrouter_base_url`| Base URL for Openrouter API                   | Openrouter        |
@@ -110,7 +110,7 @@ Here's a comprehensive list of all parameters that can be used across different
     | `deepseek_base_url`  | Base URL for DeepSeek API                     | DeepSeek          |
     | `xai_base_url`       | Base URL for XAI API                          | XAI               |
     | `sarvam_base_url`    | Base URL for Sarvam API                       | Sarvam            |
-    | `reasoning_effort`   | Reasoning level (low, medium, high)           | All               |
+    | `reasoning_effort`   | Reasoning level (low, medium, high)           | Sarvam            |
     | `frequency_penalty`  | Penalize frequent tokens (-2.0 to 2.0)        | Sarvam            |
     | `presence_penalty`   | Penalize existing tokens (-2.0 to 2.0)        | Sarvam            |
     | `seed`               | Seed for deterministic sampling               | Sarvam            |

docs/components/llms/models/anthropic.mdx

@@ -20,7 +20,7 @@ config = {
     "llm": {
         "provider": "anthropic",
         "config": {
-            "model": "claude-sonnet-4-6",
+            "model": "claude-sonnet-4-20250514",
             "temperature": 0.1,
             "max_tokens": 2000,
         }
@@ -45,7 +45,7 @@ const config = {
     provider: 'anthropic',
     config: {
       apiKey: process.env.ANTHROPIC_API_KEY || '',
-      model: 'claude-sonnet-4-6',
+      model: 'claude-sonnet-4-20250514',
       temperature: 0.1,
       maxTokens: 2000,
     },

docs/components/llms/models/aws_bedrock.mdx

@@ -6,7 +6,7 @@ description: "Configure AWS Bedrock as an LLM provider in Mem0 with IAM authenti
 ### Setup
 - Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
 - You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials)
-- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY_ID`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
+- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
 
 ### Usage
 

docs/components/llms/models/deepseek.mdx

@@ -7,8 +7,7 @@ To use DeepSeek LLM models, you have to set the `DEEPSEEK_API_KEY` environment v
 
 ## Usage
 
-<CodeGroup>
-```python Python
+```python
 import os
 from mem0 import Memory
 
@@ -37,32 +36,6 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
-```typescript TypeScript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  llm: {
-    provider: 'deepseek',
-    config: {
-      apiKey: process.env.DEEPSEEK_API_KEY || '',
-      model: 'deepseek-chat',
-      temperature: 0.2,
-      maxTokens: 2000,
-      top_p: 1.0,
-    },
-  },
-};
-const memory = new Memory(config);
-const 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."}
-];
-await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
-``` 
-</CodeGroup>
-
 You can also configure the API base URL in the config:
 
 ```python

docs/components/llms/models/groq.mdx

@@ -21,7 +21,7 @@ config = {
     "llm": {
         "provider": "groq",
         "config": {
-            "model": "llama-3.3-70b-versatile",
+            "model": "mixtral-8x7b-32768",
             "temperature": 0.1,
             "max_tokens": 2000,
         }
@@ -46,7 +46,7 @@ const config = {
     provider: 'groq',
     config: {
       apiKey: process.env.GROQ_API_KEY || '',
-      model: 'llama3-70b-8192',
+      model: 'mixtral-8x7b-32768',
       temperature: 0.1,
       maxTokens: 1000,
     },

docs/components/llms/models/litellm.mdx

@@ -4,12 +4,9 @@ description: "Use LiteLLM as an LLM provider in Mem0 to access over 100 language
 ---
 [Litellm](https://litellm.vercel.app/docs/) is compatible with over 100 large language models (LLMs), all using a standardized input/output format. You can explore the [available models](https://litellm.vercel.app/docs/providers) to use with Litellm. Ensure you set the `API_KEY` for the model you choose to use.
 
-In the TypeScript SDK, run LiteLLM as a [proxy server](https://docs.litellm.ai/docs/simple_proxy) (an OpenAI-compatible endpoint) and point Mem0 at it via `LITELLM_API_BASE` (defaults to `http://localhost:4000`).
-
 ## Usage
 
-<CodeGroup>
-```python Python
+```python
 import os
 from mem0 import Memory
 
@@ -36,33 +33,6 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
-```typescript TypeScript
-import { Memory } from 'mem0ai/oss';
-
-// Point Mem0 at your LiteLLM proxy. apiKey defaults to "sk-anything"
-// (the proxy handles real auth); baseURL defaults to http://localhost:4000.
-const config = {
-  llm: {
-    provider: 'litellm',
-    config: {
-      apiKey: process.env.LITELLM_API_KEY || 'sk-anything',
-      baseURL: process.env.LITELLM_API_BASE || 'http://localhost:4000',
-      model: 'gpt-5-mini',
-    },
-  },
-};
-
-const memory = new Memory(config);
-const 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."}
-];
-await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
-```
-</CodeGroup>
-
 ## Config
 
 All available parameters for the `litellm` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/minimax.mdx

@@ -7,8 +7,7 @@ To use MiniMax LLM models, you have to set the `MINIMAX_API_KEY` environment var
 
 ## Usage
 
-<CodeGroup>
-```python Python
+```python
 import os
 from mem0 import Memory
 
@@ -37,37 +36,9 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
-```typescript TypeScript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  llm: {
-    provider: 'minimax',
-    config: {
-      apiKey: process.env.MINIMAX_API_KEY || '',
-      model: 'MiniMax-M2.7',
-      temperature: 0.2,
-      maxTokens: 2000,
-      topP: 1.0,
-    },
-  },
-};
-const memory = new Memory(config);
-const 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." },
-];
-await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
-```
-
-</CodeGroup>
-
 You can also configure the API base URL in the config:
 
-<CodeGroup>
-```python Python
+```python
 config = {
     "llm": {
         "provider": "minimax",
@@ -80,20 +51,6 @@ config = {
 }
 ```
 
-```typescript TypeScript
-const config = {
-  llm: {
-    provider: 'minimax',
-    config: {
-      model: 'MiniMax-M2.7',
-      baseURL: 'https://your-custom-endpoint.com',
-      apiKey: 'your-api-key', // alternatively to using the environment variable
-    },
-  },
-};
-```
-</CodeGroup>
-
 ## Config
 
 All available parameters for the `minimax` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/xAI.mdx

@@ -20,7 +20,7 @@ config = {
     "llm": {
         "provider": "xai",
         "config": {
-            "model": "grok-4.3",
+            "model": "grok-3-beta",
             "temperature": 0.1,
             "max_tokens": 2000,
         }

docs/components/llms/overview.mdx

@@ -16,7 +16,7 @@ For a comprehensive list of available parameters for llm configuration, please r
 See the list of supported LLMs below.
 
 <Note>
-  All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, **Groq**, **Azure OpenAI**, **DeepSeek**, **Google AI**, **Langchain**, **LM Studio**, **Mistral AI**, and **Ollama**.
+  All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, and **Groq**.
 </Note>
 
 <CardGroup cols={4}>

docs/components/rerankers/custom-prompts.mdx

@@ -198,4 +198,4 @@ for i, prompt in enumerate(prompts):
 - **Too Long**: Keep prompts under token limits for your chosen LLM
 - **Too Vague**: Be specific about scoring criteria
 - **Wrong Scale**: Use 0.0-1.0 scale to match the default score extractor
-- **Extra Output**: Ask for only the numeric score: extra text can confuse score extraction
+- **Extra Output**: Ask for only the numeric score — extra text can confuse score extraction

docs/components/rerankers/models/llm.mdx

@@ -0,0 +1,226 @@
+---
+title: LLM as Reranker
+description: "Use any LLM as a flexible reranker in Mem0 with custom prompts and domain-specific scoring logic."
+---
+
+<Warning>
+**This page has been superseded.** Please see [LLM Reranker](/components/rerankers/models/llm_reranker) for the complete and up-to-date documentation on using LLMs for reranking.
+</Warning>
+
+LLM-based reranker provides maximum flexibility by using any Large Language Model to score document relevance. This approach allows for custom prompts and domain-specific scoring logic.
+
+## Supported LLM Providers
+
+Any LLM provider supported by Mem0 can be used for reranking:
+
+- **OpenAI**: GPT-4, GPT-3.5-turbo, etc.
+- **Anthropic**: Claude models
+- **Together**: Open-source models
+- **Groq**: Fast inference
+- **Ollama**: Local models
+- And more...
+
+## Configuration
+
+```python Python
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "my_memories",
+            "path": "./chroma_db"
+        }
+    },
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o-mini"
+        }
+    },
+    "reranker": {
+        "provider": "llm",
+        "config": {
+            "model": "gpt-4o-mini",
+            "provider": "openai",
+            "api_key": "your-openai-api-key",  # or set OPENAI_API_KEY
+            "top_k": 5,
+            "temperature": 0.0
+        }
+    }
+}
+
+memory = Memory.from_config(config)
+```
+
+## Custom Scoring Prompt
+
+You can provide a custom prompt for relevance scoring:
+
+```python Python
+custom_prompt = """You are a relevance scoring assistant. Rate how well this document answers the query.
+
+Query: "{query}"
+Document: "{document}"
+
+Score from 0.0 to 1.0 where:
+- 1.0: Perfect match, directly answers the query
+- 0.8-0.9: Highly relevant, good match  
+- 0.6-0.7: Moderately relevant, partial match
+- 0.4-0.5: Slightly relevant, limited useful information
+- 0.0-0.3: Not relevant or no useful information
+
+Provide only a single numerical score between 0.0 and 1.0."""
+
+config["reranker"]["config"]["scoring_prompt"] = custom_prompt
+```
+
+## Usage Example
+
+```python Python
+import os
+from mem0 import Memory
+
+# Set API key
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+# Initialize memory with LLM reranker
+config = {
+    "vector_store": {"provider": "chroma"},
+    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
+    "reranker": {
+        "provider": "llm",
+        "config": {
+            "model": "gpt-4o-mini",
+            "provider": "openai",
+            "temperature": 0.0
+        }
+    }
+}
+
+memory = Memory.from_config(config)
+
+# Add memories
+messages = [
+    {"role": "user", "content": "I'm learning Python programming"},
+    {"role": "user", "content": "I find object-oriented programming challenging"}, 
+    {"role": "user", "content": "I love hiking in national parks"}
+]
+
+memory.add(messages, user_id="david")
+
+# Search with LLM reranking
+results = memory.search("What programming topics is the user studying?", filters={"user_id": "david"})
+
+for result in results['results']:
+    print(f"Memory: {result['memory']}")
+    print(f"Vector Score: {result['score']:.3f}")
+    print(f"Rerank Score: {result['rerank_score']:.3f}")
+    print()
+```
+
+```text Output
+Memory: I'm learning Python programming
+Vector Score: 0.856
+Rerank Score: 0.920
+
+Memory: I find object-oriented programming challenging
+Vector Score: 0.782
+Rerank Score: 0.850
+```
+
+## Domain-Specific Scoring
+
+Create specialized scoring for your domain:
+
+```python Python
+medical_prompt = """You are a medical relevance expert. Score how relevant this medical record is to the clinical query.
+
+Clinical Query: "{query}"
+Medical Record: "{document}"
+
+Consider:
+- Clinical relevance and accuracy
+- Patient safety implications
+- Diagnostic value
+- Treatment relevance
+
+Score from 0.0 to 1.0. Provide only the numerical score."""
+
+config = {
+    "reranker": {
+        "provider": "llm",
+        "config": {
+            "model": "gpt-4o-mini",
+            "provider": "openai",
+            "scoring_prompt": medical_prompt,
+            "temperature": 0.0
+        }
+    }
+}
+```
+
+## Multiple LLM Providers
+
+Use different LLM providers for reranking:
+
+```python Python
+# Using Anthropic Claude
+anthropic_config = {
+    "reranker": {
+        "provider": "llm",
+        "config": {
+            "model": "claude-3-haiku-20240307",
+            "provider": "anthropic",
+            "temperature": 0.0
+        }
+    }
+}
+
+# Using local Ollama model
+ollama_config = {
+    "reranker": {
+        "provider": "llm",
+        "config": {
+            "model": "llama2:7b",
+            "provider": "ollama",
+            "temperature": 0.0
+        }
+    }
+}
+```
+
+## Configuration Parameters
+
+| Parameter | Description | Type | Default |
+|-----------|-------------|------|---------|
+| `model` | LLM model to use for scoring | `str` | `"gpt-4o-mini"` |
+| `provider` | LLM provider name | `str` | `"openai"` |
+| `api_key` | API key for the LLM provider | `str` | `None` |
+| `top_k` | Maximum documents to return | `int` | `None` |
+| `temperature` | Temperature for LLM generation | `float` | `0.0` |
+| `max_tokens` | Maximum tokens for LLM response | `int` | `100` |
+| `scoring_prompt` | Custom prompt template | `str` | Default prompt |
+
+## Advantages
+
+- **Maximum Flexibility**: Custom prompts for any use case
+- **Domain Expertise**: Leverage LLM knowledge for specialized domains
+- **Interpretability**: Understand scoring through prompt engineering
+- **Multi-criteria**: Score based on multiple relevance factors
+
+## Considerations
+
+- **Latency**: Higher latency than specialized rerankers
+- **Cost**: LLM API costs per reranking operation
+- **Consistency**: May have slight variations in scoring
+- **Prompt Engineering**: Requires careful prompt design
+
+## Best Practices
+
+1. **Temperature**: Use 0.0 for consistent scoring
+2. **Prompt Design**: Be specific about scoring criteria
+3. **Token Efficiency**: Keep prompts concise to reduce costs
+4. **Caching**: Cache results for repeated queries when possible
+5. **Fallback**: Handle API errors gracefully

docs/components/vectordbs/dbs/azure.mdx

@@ -100,7 +100,7 @@ To enable Role-Based Access Control (RBAC) for Azure AI Search, follow these ste
 
 1. In the Azure Portal, navigate to your **Azure AI Search** service.
 2. In the left menu, select **Settings** > **Keys**.
-3. Change the authentication setting to **Role-based access control**, or **Both** if you need API key compatibility. The default is “Key-based authentication”: you must switch it to use Azure roles.
+3. Change the authentication setting to **Role-based access control**, or **Both** if you need API key compatibility. The default is “Key-based authentication”—you must switch it to use Azure roles.
 4. **Go to Access Control (IAM):**
     - In the Azure Portal, select your Search service.
     - Click **Access Control (IAM)** on the left.

docs/components/vectordbs/dbs/baidu.mdx

@@ -46,7 +46,7 @@ Here are the parameters available for configuring Baidu VectorDB:
 | `account` | Baidu VectorDB account name | `root` |
 | `api_key` | API key for accessing Baidu VectorDB | Required |
 | `database_name` | Name of the database | `mem0` |
-| `table_name` | Name of the table | `mem0` |
+| `table_name` | Name of the table | `mem0_table` |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 | `metric_type` | Distance metric for similarity search | `L2` |
 

docs/components/vectordbs/dbs/elasticsearch.mdx

@@ -56,8 +56,6 @@ Here are the parameters available for configuring Elasticsearch:
 | `api_key`              | API key for authentication                         | `None`        |
 | `user`                 | Username for basic authentication                  | `None`        |
 | `password`             | Password for basic authentication                  | `None`        |
-| `use_ssl`              | Whether to use SSL for the connection              | `True`        |
-| `ca_certs`             | Path to CA bundle for SSL certificate verification | `None`        |
 | `verify_certs`         | Whether to verify SSL certificates                 | `True`        |
 | `auto_create_index`    | Whether to automatically create the index          | `True`        |
 | `custom_search_query`  | Function returning a custom search query           | `None`        |

docs/components/vectordbs/dbs/faiss.mdx

@@ -55,7 +55,6 @@ Here are the parameters available for configuring FAISS:
 | `path` | Path to store FAISS index and metadata | `/tmp/faiss/<collection_name>` |
 | `distance_strategy` | Distance metric strategy to use (options: 'euclidean', 'inner_product', 'cosine') | `euclidean` |
 | `normalize_L2` | Whether to normalize L2 vectors (only applicable for euclidean distance) | `False` |
-| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 
 ### Performance Considerations
 

docs/components/vectordbs/dbs/langchain.mdx

@@ -47,12 +47,12 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
 ```typescript TypeScript
-import { Memory } from "mem0ai/oss";
+import { Memory } from "mem0ai";
 import { OpenAIEmbeddings } from "@langchain/openai";
-import { MemoryVectorStore } from "langchain/vectorstores/memory";
+import { MemoryVectorStore as LangchainMemoryStore } from "langchain/vectorstores/memory";
 
 const embeddings = new OpenAIEmbeddings();
-const vectorStore = new MemoryVectorStore(embeddings);
+const vectorStore = new LangchainVectorStore(embeddings);
 
 const config = {
     "vector_store": {

docs/components/vectordbs/dbs/mongodb.mdx

@@ -42,8 +42,8 @@ Here are the parameters available for configuring MongoDB:
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | db_name | Name of the MongoDB database | `"mem0_db"` |
-| collection_name | Name of the MongoDB collection | `"mem0"` |
+| collection_name | Name of the MongoDB collection | `"mem0_collection"` |
 | embedding_model_dims | Dimensions of the embedding vectors | `1536` |
-| mongo_uri | The MongoDB URI connection string | `mongodb://localhost:27017` |
+| mongo_uri | The MongoDB URI connection string | `mongodb://username:password@localhost:27017` |
 
-> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://localhost:27017`.
+> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://username:password@localhost:27017`.

docs/components/vectordbs/dbs/neon.mdx

@@ -1,153 +0,0 @@
----
-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 m = new Memory({
-  vectorStore: {
-    provider: "pgvector",
-    config: {
-      connectionString: process.env.DATABASE_URL!,
-      ssl: {
-        rejectUnauthorized: false,
-      },
-      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">
-Use the Neon `DATABASE_URL` directly with `connectionString`. Set `ssl` if your runtime needs an explicit TLS config object.
-
-| Parameter            | Description                                    | Default        |
-| -------------------- | ---------------------------------------------- | -------------- |
-| `connectionString`   | Neon Postgres connection string.               | Required       |
-| `ssl`                | Optional TLS settings passed directly to `pg`. | Driver default |
-| `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`        |
-
-**TLS note:** `ssl: true` is sufficient for most Neon connections since Neon uses valid certificates. Use `ssl: { rejectUnauthorized: false }` only when connecting through Neon's connection pooler on certain edge runtimes (e.g. Cloudflare Workers) that require it, or when your environment does not trust the Neon CA chain.
-
-</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/components/vectordbs/dbs/neptune_analytics.mdx

@@ -10,7 +10,7 @@ description: "Use AWS Neptune Analytics as a vector store in Mem0, combining gra
 ## Installation
 
 ```bash
-pip install mem0ai[vector-stores]
+pip install mem0ai[vector_stores]
 ```
 
 ## Usage

docs/components/vectordbs/dbs/opensearch.mdx

@@ -56,30 +56,6 @@ config = {
 }
 ```
 
-### Configuration Options
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `collection_name` | string | required | Name of the OpenSearch index |
-| `host` | string | required | OpenSearch endpoint URL |
-| `port` | int | 9200 | Port number |
-| `http_auth` | object | None | Authentication credentials (e.g., AWSV4SignerAuth) |
-| `embedding_model_dims` | int | 1536 | Dimension of embedding vectors |
-| `use_ssl` | bool | False | Enable SSL/TLS connection |
-| `verify_certs` | bool | False | Verify SSL certificates |
-| `auto_refresh` | bool | False | Automatically refresh index after insert. OpenSearch refreshes every ~1 second by default, so this is rarely needed. |
-
-<Note>
-  The defaults above match a local OpenSearch instance. The AWS OpenSearch Serverless
-  example earlier on this page intentionally overrides them with `port=443`, `use_ssl=True`,
-  and `verify_certs=True`, which are required when connecting to a Serverless collection.
-</Note>
-
-<Note>
-  For **AWS OpenSearch Serverless**, keep `auto_refresh=False` (the default).
-  The `indices.refresh()` API is not supported on Serverless collections.
-</Note>
-
 ### Add Memories
 
 ```python

docs/components/vectordbs/dbs/pgvector.mdx

@@ -2,7 +2,6 @@
 title: "pgvector"
 description: "Use pgvector as a vector store in Mem0 for PostgreSQL-based vector similarity search with open-source simplicity."
 ---
-
 [pgvector](https://github.com/pgvector/pgvector) is an open-source vector similarity search extension for Postgres. After connecting to Postgres, run `CREATE EXTENSION IF NOT EXISTS vector;` to create the vector extension.
 
 ### Usage
@@ -22,7 +21,7 @@ config = {
             "password": "123",
             "host": "127.0.0.1",
             "port": "5432",
-        },
+        }
     }
 }
 
@@ -31,22 +30,25 @@ 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."},
+    {"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"})
-
 ```
 
 ```typescript TypeScript
-import { Memory } from "mem0ai/oss";
+import { Memory } from 'mem0ai/oss';
 
 const config = {
   vectorStore: {
-    provider: "pgvector",
+    provider: 'pgvector',
     config: {
-      collectionName: "memories",
+      collectionName: 'memories',
       embeddingModelDims: 1536,
-      connectionString: "postgresql://test:123@localhost:5432/vector_store",
+      user: 'test',
+      password: '123',
+      host: '127.0.0.1',
+      port: 5432,
+      dbname: 'vector_store', // Optional; TypeScript OSS defaults to `vector_store` when omitted
       diskann: false, // Optional, requires pgvectorscale extension
       hnsw: false, // Optional, for HNSW indexing
     },
@@ -55,44 +57,37 @@ const config = {
 
 const memory = new Memory(config);
 const 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." },
-];
-
+    {"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."}
+]
 await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
 ```
-
 </CodeGroup>
 
 ### Config
 
 Here are the parameters available for configuring pgvector:
 
-| Parameter            | SDK                     | Description                                                                                                                                                    | Default Value                           |
-| -------------------- | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
-| `connectionString`   | TypeScript OSS          | PostgreSQL connection string for direct connections. When set, Mem0 connects to the target database directly and skips the bootstrap `postgres` database flow. | `None`                                  |
-| `ssl`                | TypeScript OSS          | SSL option passed directly to `pg`, either `true` or an SSL config object, for both `connectionString` and split-field connections.                            | `None`                                  |
-| `dbname`             | TypeScript OSS          | Split-field database name. This is only used when `connectionString` is absent.                                                                                | `vector_store`                          |
-| `collectionName`     | TypeScript OSS          | Collection name.                                                                                                                                               | `memories`                              |
-| `embeddingModelDims` | TypeScript OSS          | Dimensions of the embedding model.                                                                                                                             | Required                                |
-| `user`               | TypeScript OSS + Python | Database user for split-field connections.                                                                                                                     | `None`                                  |
-| `password`           | TypeScript OSS + Python | Database password for split-field connections.                                                                                                                 | `None`                                  |
-| `host`               | TypeScript OSS + Python | Database host for split-field connections.                                                                                                                     | `None`                                  |
-| `port`               | TypeScript OSS + Python | Database port for split-field connections.                                                                                                                     | `None`                                  |
-| `diskann`            | TypeScript OSS + Python | Whether to use DiskANN for vector similarity search, requires pgvectorscale.                                                                                   | `False`                                 |
-| `hnsw`               | TypeScript OSS + Python | Whether to use HNSW for vector similarity search.                                                                                                              | TypeScript OSS: `False`, Python: `True` |
-| `connection_string`  | Python only             | PostgreSQL connection string, overrides individual connection parameters.                                                                                      | `None`                                  |
-| `sslmode`            | Python only             | SSL mode for PostgreSQL connections, such as `require`, `prefer`, or `disable`.                                                                                | `None`                                  |
-| `connection_pool`    | Python only             | psycopg connection pool object, overrides connection string and individual connection parameters.                                                              | `None`                                  |
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `dbname` | The name of the database | `postgres` |
+| `collection_name` | The name of the collection | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `user` | User name to connect to the database | `None` |
+| `password` | Password to connect to the database | `None` |
+| `host` | The host where the Postgres server is running | `None` |
+| `port` | The port where the Postgres server is running | `None` |
+| `diskann` | Whether to use diskann for vector similarity search (requires pgvectorscale) | `True` |
+| `hnsw` | Whether to use hnsw for vector similarity search | `False` |
+| `sslmode` | SSL mode for PostgreSQL connection (e.g., 'require', 'prefer', 'disable') | `None` |
+| `connection_string` | PostgreSQL connection string (overrides individual connection parameters) | `None` |
+| `connection_pool` | psycopg2 connection pool object (overrides connection string and individual parameters) | `None` |
 
-**TypeScript OSS:** Use `connectionString` plus optional `ssl` for managed Postgres setups. If you omit `connectionString`, Mem0 falls back to split fields and uses `dbname`, `user`, `password`, `host`, `port`, and optional `ssl`.
-
-**Python:** The Python SDK uses snake_case keys such as `connection_string`, `sslmode`, `collection_name`, and `embedding_model_dims`.
-
-**Python connection priority**:
+**Note (TypeScript OSS):** If you omit `dbname`, the TypeScript client uses the database name `vector_store`. Python defaults to `postgres` for `dbname`, as in the table above.
 
+**Note**: The connection parameters have the following priority:
 1. `connection_pool` (highest priority)
 2. `connection_string`
-3. Individual connection parameters (`user`, `password`, `host`, `port`, `sslmode`)
+3. Individual connection parameters (`user`, `password`, `host`, `port`, `sslmode`)