chore: update changelog, bump SDK versions to Python 2.0.7 and TypeScript 3.0.9 (#5615)

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

.agents/plugins/marketplace.json

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

.claude-plugin/marketplace.json

@@ -10,9 +10,9 @@
   "plugins": [
     {
       "name": "mem0",
-      "source": "./mem0-plugin",
+      "source": "./integrations/mem0-plugin",
       "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
-      "version": "0.2.9"
+      "version": "0.2.10"
     }
   ]
 }

.codex-plugin/marketplace.json

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

.cursor-plugin/marketplace.json

@@ -10,9 +10,9 @@
   "plugins": [
     {
       "name": "mem0",
-      "source": "./mem0-plugin",
+      "source": "./integrations/mem0-plugin",
       "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search.",
-      "version": "0.2.9"
+      "version": "0.2.10"
     }
   ]
 }

.github/workflows/ci-gate.yml

@@ -0,0 +1,171 @@
+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

@@ -1,9 +1,11 @@
 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]
-  pull_request:
+  workflow_call:
 
 jobs:
   changelog_check:

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

@@ -1,5 +1,7 @@
 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:
@@ -7,10 +9,7 @@ on:
     paths:
       - 'cli/node/**'
       - '.github/workflows/cli-node-ci.yml'
-  pull_request:
-    paths:
-      - 'cli/node/**'
-      - '.github/workflows/cli-node-ci.yml'
+  workflow_call:
 
 jobs:
   lint:

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

@@ -1,5 +1,7 @@
 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:
@@ -7,10 +9,7 @@ on:
     paths:
       - 'cli/python/**'
       - '.github/workflows/cli-python-ci.yml'
-  pull_request:
-    paths:
-      - 'cli/python/**'
-      - '.github/workflows/cli-python-ci.yml'
+  workflow_call:
 
 jobs:
   lint:

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

@@ -6,13 +6,10 @@ 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:
-  pull_request:
-    paths:
-      - 'docs/**/*.mdx'
-      - 'docs/llms.txt'
-      - 'scripts/check-llms-txt-coverage.py'
-      - 'scripts/llms-txt-ignore.txt'
+  workflow_call:
   workflow_dispatch: {}
 
 permissions:

.github/workflows/openclaw-cd.yml

@@ -25,7 +25,7 @@ jobs:
       id-token: write
     defaults:
       run:
-        working-directory: openclaw
+        working-directory: integrations/openclaw
     steps:
       - uses: actions/checkout@v4
         with:
@@ -42,7 +42,7 @@ jobs:
           node-version: '22'
           registry-url: 'https://registry.npmjs.org'
           cache: 'pnpm'
-          cache-dependency-path: openclaw/pnpm-lock.yaml
+          cache-dependency-path: integrations/openclaw/pnpm-lock.yaml
 
       - name: Install dependencies
         run: pnpm install --frozen-lockfile

.github/workflows/openclaw-checks.yml

@@ -1,16 +1,15 @@
 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:
-      - 'openclaw/**'
-      - '.github/workflows/openclaw-checks.yml'
-  pull_request:
-    paths:
-      - 'openclaw/**'
+      - 'integrations/openclaw/**'
       - '.github/workflows/openclaw-checks.yml'
+  workflow_call:
 
 jobs:
   lint:
@@ -28,13 +27,13 @@ jobs:
         with:
           node-version: 20
           cache: 'pnpm'
-          cache-dependency-path: openclaw/pnpm-lock.yaml
+          cache-dependency-path: integrations/openclaw/pnpm-lock.yaml
 
       - name: Install dependencies
-        run: cd openclaw && pnpm install --frozen-lockfile
+        run: cd integrations/openclaw && pnpm install --frozen-lockfile
 
       - name: Type check
-        run: cd openclaw && pnpm exec tsc --noEmit
+        run: cd integrations/openclaw && pnpm exec tsc --noEmit
 
   test:
     runs-on: ubuntu-latest
@@ -54,20 +53,20 @@ jobs:
         with:
           node-version: ${{ matrix.node-version }}
           cache: 'pnpm'
-          cache-dependency-path: openclaw/pnpm-lock.yaml
+          cache-dependency-path: integrations/openclaw/pnpm-lock.yaml
 
       - name: Install dependencies
-        run: cd openclaw && pnpm install --frozen-lockfile
+        run: cd integrations/openclaw && pnpm install --frozen-lockfile
 
       - name: Run tests with coverage
-        run: cd openclaw && pnpm exec vitest run --coverage
+        run: cd integrations/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: openclaw/coverage
+          directory: integrations/openclaw/coverage
         env:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
 
@@ -86,15 +85,15 @@ jobs:
         with:
           node-version: 20
           cache: 'pnpm'
-          cache-dependency-path: openclaw/pnpm-lock.yaml
+          cache-dependency-path: integrations/openclaw/pnpm-lock.yaml
 
       - name: Install dependencies
-        run: cd openclaw && pnpm install --frozen-lockfile
+        run: cd integrations/openclaw && pnpm install --frozen-lockfile
 
       - name: Build
-        run: cd openclaw && pnpm build
+        run: cd integrations/openclaw && pnpm build
 
       - name: Verify dist output exists
         run: |
-          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)
+          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)

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

@@ -25,7 +25,7 @@ jobs:
       id-token: write
     defaults:
       run:
-        working-directory: mem0-plugin/.opencode-plugin
+        working-directory: integrations/mem0-plugin/.opencode-plugin
     steps:
       - uses: actions/checkout@v4
         with:

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

@@ -1,23 +1,22 @@
 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:
-      - 'mem0-plugin/.opencode-plugin/**'
-      - '.github/workflows/opencode-plugin-checks.yml'
-  pull_request:
-    paths:
-      - 'mem0-plugin/.opencode-plugin/**'
+      - 'integrations/mem0-plugin/.opencode-plugin/**'
       - '.github/workflows/opencode-plugin-checks.yml'
+  workflow_call:
 
 jobs:
   build:
     runs-on: ubuntu-latest
     defaults:
       run:
-        working-directory: mem0-plugin/.opencode-plugin
+        working-directory: integrations/mem0-plugin/.opencode-plugin
     steps:
       - uses: actions/checkout@v4
 

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

@@ -25,7 +25,7 @@ jobs:
       id-token: write
     defaults:
       run:
-        working-directory: pi-agent-plugin
+        working-directory: integrations/pi-agent-plugin
     steps:
       - uses: actions/checkout@v4
         with:
@@ -42,7 +42,7 @@ jobs:
           node-version: '22'
           registry-url: 'https://registry.npmjs.org'
           cache: 'pnpm'
-          cache-dependency-path: pi-agent-plugin/pnpm-lock.yaml
+          cache-dependency-path: integrations/pi-agent-plugin/pnpm-lock.yaml
 
       - name: Install dependencies
         run: pnpm install --frozen-lockfile

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

@@ -1,16 +1,15 @@
 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:
-      - 'pi-agent-plugin/**'
-      - '.github/workflows/pi-agent-plugin-checks.yml'
-  pull_request:
-    paths:
-      - 'pi-agent-plugin/**'
+      - 'integrations/pi-agent-plugin/**'
       - '.github/workflows/pi-agent-plugin-checks.yml'
+  workflow_call:
 
 jobs:
   lint:
@@ -28,13 +27,13 @@ jobs:
         with:
           node-version: 20
           cache: 'pnpm'
-          cache-dependency-path: pi-agent-plugin/pnpm-lock.yaml
+          cache-dependency-path: integrations/pi-agent-plugin/pnpm-lock.yaml
 
       - name: Install dependencies
-        run: cd pi-agent-plugin && pnpm install --frozen-lockfile
+        run: cd integrations/pi-agent-plugin && pnpm install --frozen-lockfile
 
       - name: Type check
-        run: cd pi-agent-plugin && pnpm exec tsc --noEmit
+        run: cd integrations/pi-agent-plugin && pnpm exec tsc --noEmit
 
   test:
     runs-on: ubuntu-latest
@@ -54,13 +53,13 @@ jobs:
         with:
           node-version: ${{ matrix.node-version }}
           cache: 'pnpm'
-          cache-dependency-path: pi-agent-plugin/pnpm-lock.yaml
+          cache-dependency-path: integrations/pi-agent-plugin/pnpm-lock.yaml
 
       - name: Install dependencies
-        run: cd pi-agent-plugin && pnpm install --frozen-lockfile
+        run: cd integrations/pi-agent-plugin && pnpm install --frozen-lockfile
 
       - name: Run tests
-        run: cd pi-agent-plugin && pnpm exec vitest run
+        run: cd integrations/pi-agent-plugin && pnpm exec vitest run
 
   build:
     runs-on: ubuntu-latest
@@ -77,17 +76,17 @@ jobs:
         with:
           node-version: 20
           cache: 'pnpm'
-          cache-dependency-path: pi-agent-plugin/pnpm-lock.yaml
+          cache-dependency-path: integrations/pi-agent-plugin/pnpm-lock.yaml
 
       - name: Install dependencies
-        run: cd pi-agent-plugin && pnpm install --frozen-lockfile
+        run: cd integrations/pi-agent-plugin && pnpm install --frozen-lockfile
 
       - name: Build
-        run: cd pi-agent-plugin && pnpm build
+        run: cd integrations/pi-agent-plugin && pnpm build
 
       - name: Verify dist output exists
         run: |
-          test -f pi-agent-plugin/dist/index.js || (echo "Build output missing: dist/index.js" && exit 1)
-          test -f pi-agent-plugin/dist/index.d.ts || (echo "Build output missing: dist/index.d.ts" && exit 1)
-          test -f pi-agent-plugin/dist/entry.js || (echo "Build output missing: dist/entry.js" && exit 1)
-          test -f pi-agent-plugin/dist/entry.d.ts || (echo "Build output missing: dist/entry.d.ts" && exit 1)
+          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/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'
-  pull_request:
-    paths:
-      - 'mem0-ts/**'
+  workflow_call:
 
 jobs:
   check_changes:

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

@@ -25,7 +25,7 @@ jobs:
       id-token: write
     defaults:
       run:
-        working-directory: vercel-ai-sdk
+        working-directory: integrations/vercel-ai-sdk
     steps:
       - uses: actions/checkout@v4
         with:
@@ -42,7 +42,7 @@ jobs:
           node-version: '22'
           registry-url: 'https://registry.npmjs.org'
           cache: 'pnpm'
-          cache-dependency-path: vercel-ai-sdk/pnpm-lock.yaml
+          cache-dependency-path: integrations/vercel-ai-sdk/pnpm-lock.yaml
 
       - name: Install dependencies
         run: pnpm install --frozen-lockfile

.gitignore

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

.gitmodules

@@ -0,0 +1,4 @@
+[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, documentation, and evaluation tooling.
+This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs, servers, plugins, and documentation.
 
 ### Key Directories
 
@@ -22,17 +22,18 @@ 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` |
-| `vercel-ai-sdk/` | `@mem0/vercel-ai-provider` — Vercel AI SDK memory provider |
-| `openclaw/` | `@mem0/openclaw-mem0` — OpenClaw plugin for Claude Code / AI editors |
+| `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 |
 | `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) |
-| `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/` |
+| `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/` |
 | `docs/` | Documentation site (Mintlify) |
 | `tests/` | Python SDK tests (pytest) |
-| `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 |
+| `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/`) |
 | `pr-reviews/` | Pull request review materials |
 | `scripts/` | Repo-wide utility scripts (e.g., `check-llms-txt-coverage.py` for docs/llms.txt sync) |
 
@@ -49,8 +50,8 @@ mem0 (Python SDK)          mem0-ts (TypeScript SDK)
 
 cli/python/ ──▶ mem0ai (optional, for OSS mode)
 cli/node/   ──▶ mem0ai (npm, for API calls)
-vercel-ai-sdk/ ──▶ ai, @ai-sdk/* providers
-openclaw/   ──▶ mem0ai (npm)
+integrations/vercel-ai-sdk/ ──▶ ai, @ai-sdk/* providers
+integrations/openclaw/ ──▶ mem0ai (npm)
 ```
 
 ## Development Setup
@@ -73,8 +74,8 @@ pre-commit install                # install git hooks
 # TypeScript packages
 cd mem0-ts && pnpm install        # TS SDK
 cd cli/node && pnpm install       # Node CLI
-cd vercel-ai-sdk && pnpm install  # Vercel AI provider
-cd openclaw && pnpm install       # OpenClaw plugin
+cd integrations/vercel-ai-sdk && pnpm install  # Vercel AI provider
+cd integrations/openclaw && pnpm install       # OpenClaw plugin
 ```
 
 ## Build, Lint, and Test Commands
@@ -162,10 +163,10 @@ pnpm run dev                       # tsx src/index.ts (development)
 - **Test:** vitest (not jest)
 - **Framework:** Commander + Chalk + ora + cli-table3
 
-### Vercel AI SDK Provider (`vercel-ai-sdk/`)
+### Vercel AI SDK Provider (`integrations/vercel-ai-sdk/`)
 
 ```bash
-cd vercel-ai-sdk
+cd integrations/vercel-ai-sdk
 pnpm install
 pnpm run build                     # tsup
 pnpm run lint                      # eslint
@@ -180,10 +181,10 @@ pnpm run test:node                 # vitest (node runtime)
 - **Lint:** ESLint + Prettier
 - **Test:** jest + vitest (edge/node configs)
 
-### OpenClaw Plugin (`openclaw/`)
+### OpenClaw Plugin (`integrations/openclaw/`)
 
 ```bash
-cd openclaw
+cd integrations/openclaw
 pnpm install
 pnpm run build                     # tsup
 pnpm run test                      # vitest run
@@ -245,18 +246,19 @@ make docs                          # or: cd docs && mintlify dev
 - **API spec:** `docs/openapi.json`
 - **Structure:** `api-reference/`, `open-source/`, `platform/`, `integrations/`, `cookbooks/`, `core-concepts/`
 
-### Evaluation (`evaluation/`)
+### 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:
 
 ```bash
-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
+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
 ```
 
 ## Core APIs
@@ -342,8 +344,8 @@ make run-openai                    # OpenAI comparison
 |---------|--------|-----------|---------------|
 | `mem0-ts/` | — | Prettier | jest |
 | `cli/node/` | Biome | Biome | vitest |
-| `vercel-ai-sdk/` | ESLint | Prettier | jest + vitest |
-| `openclaw/` | — | — | vitest |
+| `integrations/vercel-ai-sdk/` | ESLint | Prettier | jest + vitest |
+| `integrations/openclaw/` | — | — | vitest |
 
 ### Type Checking
 
@@ -381,14 +383,14 @@ 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 `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 `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 & Skills System
 
-- `mem0-plugin/` provides integrations for Claude Code, Cursor, and Codex via MCP server connections and lifecycle hooks for automatic memory capture.
+- `integrations/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.
+  - **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).
 
 ### Adding a New Provider
 
@@ -402,19 +404,36 @@ 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)
 
-| 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 |
-| OpenCode Plugin | `opencode-plugin-checks.yml` | Push to `mem0-plugin/.opencode-plugin/`, PRs, manual | Bun: tsc type-check + build + dist artifact check |
-| Pi Agent Plugin | `pi-agent-plugin-checks.yml` | Push to `pi-agent-plugin/`, PRs, manual | tsc + vitest + tsup build (dist artifact check) on Node 20, 22 |
+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.
 
 ### CD Workflows (automated publishing)
 

LLM.md

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

README.md

@@ -186,9 +186,10 @@ 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. 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. 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.
 
 ### Basic Usage
 

cli/node/package.json

@@ -44,5 +44,15 @@
     "vitest": "^4.1.0",
     "@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-lock.yaml

@@ -9,6 +9,8 @@ overrides:
   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'
 
 importers:
 
@@ -38,7 +40,7 @@ importers:
         version: 20.19.37
       tsup:
         specifier: ^8.0.0
-        version: 8.5.1(postcss@8.5.8)(tsx@4.21.0)(typescript@5.9.3)
+        version: 8.5.1(postcss@8.5.15)(tsx@4.21.0)(typescript@5.9.3)
       tsx:
         specifier: ^4.7.0
         version: 4.21.0
@@ -76,28 +78,24 @@ packages:
     engines: {node: '>=14.21.3'}
     cpu: [arm64]
     os: [linux]
-    libc: [musl]
 
   '@biomejs/cli-linux-arm64@1.9.4':
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     engines: {node: '>=14.21.3'}
     cpu: [arm64]
     os: [linux]
-    libc: [glibc]
 
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     engines: {node: '>=14.21.3'}
     cpu: [x64]
     os: [linux]
-    libc: [musl]
 
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     engines: {node: '>=14.21.3'}
     cpu: [x64]
     os: [linux]
-    libc: [glibc]
 
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@@ -115,314 +113,158 @@ packages:
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+  '@esbuild/aix-ppc64@0.28.1':
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     engines: {node: '>=18'}
     cpu: [ppc64]
     os: [aix]
 
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-    os: [aix]
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+  '@esbuild/android-arm64@0.28.1':
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+  '@esbuild/darwin-x64@0.28.1':
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     cpu: [arm64]
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-    libc: [musl]
 
   '@rollup/rollup-openbsd-x64@4.60.0':
     resolution: {integrity: sha512-1o/0/pIhozoSaDJoDcec+IVLbnRtQmHwPV730+AOD29lHEEo4F5BEUB24H0OBdhbBBDwIOSuf7vgg0Ywxdfiiw==}
@@ -657,7 +486,7 @@ packages:
     resolution: {integrity: sha512-3WrrOuZiyaaZPWiEt4G3+IffISVC9HYlWueJEBWED4ZH4aIAC2PnkdnuRrR94M+w6yGWn4AglWtJtBI8YqvgoA==}
     engines: {node: ^12.20.0 || ^14.13.1 || >=16.0.0}
     peerDependencies:
-      esbuild: '>=0.18'
+      esbuild: '>=0.28.1'
 
   cac@6.7.14:
     resolution: {integrity: sha512-b6Ilus+c3RrdDk+JhLKUAQfzzgLEPy6wcXqS7f/xe1EETvsDP6GORG7SFuOs6cID5YkqchW/LXZbX5bc8j7ZcQ==}
@@ -737,13 +566,8 @@ packages:
   es-module-lexer@2.1.0:
     resolution: {integrity: sha512-n27zTYMjYu1aj4MjCWzSP7G9r75utsaoc8m61weK+W8JMBGGQybd43GstCXZ3WNmSFtGT9wi59qQTW6mhTR5LQ==}
 
-  esbuild@0.25.12:
-    resolution: {integrity: sha512-bbPBYYrtZbkt6Os6FiTLCTFxvq4tt3JKall1vRwshA3fdVztsLAatFaZobhkBC8/BrPetoa0oksYoKXoG4ryJg==}
-    engines: {node: '>=18'}
-    hasBin: true
-
-  esbuild@0.27.4:
-    resolution: {integrity: sha512-Rq4vbHnYkK5fws5NF7MYTU68FPRE1ajX7heQ/8QXXWqNgqqJ/GkmmyxIzUnf2Sr/bakf8l54716CcMGHYhMrrQ==}
+  esbuild@0.28.1:
+    resolution: {integrity: sha512-HrJrvZv5ayxBzPfwphOoNzkzOIIlifzk0KJrGK2c8R4+LKpMtpYLQeUdjnwjWv/LZlkH2laZk+4w78pi99D4Vw==}
     engines: {node: '>=18'}
     hasBin: true
 
@@ -829,8 +653,8 @@ packages:
   mz@2.7.0:
     resolution: {integrity: sha512-z81GNO7nnYMEhrGh9LeymoE4+Yr0Wn5McHIZMK5cfQCl+NDX08sCZgUc9/6MHni9IWuFLm1Z3HTCXu2z9fN62Q==}
 
-  nanoid@3.3.11:
-    resolution: {integrity: sha512-N8SpfPUnUp1bK+PMYW8qSWdl9U+wwNWI4QKxOYDy9JAro3WMX7p2OeVRF9v+347pnakNevPmiHhNmZ2HbFA76w==}
+  nanoid@3.3.12:
+    resolution: {integrity: sha512-ZB9RH/39qpq5Vu6Y+NmUaFhQR6pp+M2Xt76XBnEwDaGcVAqhlvxrl3B2bKS5D3NH3QR76v3aSrKaF/Kiy7lEtQ==}
     engines: {node: ^10 || ^12 || ^13.7 || ^14 || >=15.0.1}
     hasBin: true
 
@@ -871,7 +695,7 @@ packages:
     engines: {node: '>= 18'}
     peerDependencies:
       jiti: '>=1.21.0'
-      postcss: '>=8.0.9'
+      postcss: '>=8.5.10'
       tsx: ^4.8.1
       yaml: ^2.4.2
     peerDependenciesMeta:
@@ -884,8 +708,8 @@ packages:
       yaml:
         optional: true
 
-  postcss@8.5.8:
-    resolution: {integrity: sha512-OW/rX8O/jXnm82Ey1k44pObPtdblfiuWnrd8X7GJ7emImCOstunGbXUpp7HdBrFQX6rJzn3sPT397Wp5aCwCHg==}
+  postcss@8.5.15:
+    resolution: {integrity: sha512-FfR8sjd4em2T6fb3I2MwAJU7HWVMr9zba+enmQeeWFfCbm+UOC/0X4DS8XtpUTMwWMGbjKYP7xjfNekzyGmB3A==}
     engines: {node: ^10 || ^12 || >=14}
 
   readdirp@4.1.2:
@@ -997,7 +821,7 @@ packages:
     peerDependencies:
       '@microsoft/api-extractor': ^7.36.0
       '@swc/core': ^1
-      postcss: ^8.4.12
+      postcss: '>=8.5.10'
       typescript: '>=4.5.0'
     peerDependenciesMeta:
       '@microsoft/api-extractor':
@@ -1163,160 +987,82 @@ snapshots:
   '@colors/colors@1.5.0':
     optional: true
 
-  '@esbuild/aix-ppc64@0.25.12':
+  '@esbuild/aix-ppc64@0.28.1':
     optional: true
 
-  '@esbuild/aix-ppc64@0.27.4':
+  '@esbuild/android-arm64@0.28.1':
     optional: true
 
-  '@esbuild/android-arm64@0.25.12':
+  '@esbuild/android-arm@0.28.1':
     optional: true
 
-  '@esbuild/android-arm64@0.27.4':
+  '@esbuild/android-x64@0.28.1':
     optional: true
 
-  '@esbuild/android-arm@0.25.12':
+  '@esbuild/darwin-arm64@0.28.1':
     optional: true
 
-  '@esbuild/android-arm@0.27.4':
+  '@esbuild/darwin-x64@0.28.1':
     optional: true
 
-  '@esbuild/android-x64@0.25.12':
+  '@esbuild/freebsd-arm64@0.28.1':
     optional: true
 
-  '@esbuild/android-x64@0.27.4':
+  '@esbuild/freebsd-x64@0.28.1':
     optional: true
 
-  '@esbuild/darwin-arm64@0.25.12':
+  '@esbuild/linux-arm64@0.28.1':
     optional: true
 
-  '@esbuild/darwin-arm64@0.27.4':
+  '@esbuild/linux-arm@0.28.1':
     optional: true
 
-  '@esbuild/darwin-x64@0.25.12':
+  '@esbuild/linux-ia32@0.28.1':
     optional: true
 
-  '@esbuild/darwin-x64@0.27.4':
+  '@esbuild/linux-loong64@0.28.1':
     optional: true
 
-  '@esbuild/freebsd-arm64@0.25.12':
+  '@esbuild/linux-mips64el@0.28.1':
     optional: true
 
-  '@esbuild/freebsd-arm64@0.27.4':
+  '@esbuild/linux-ppc64@0.28.1':
     optional: true
 
-  '@esbuild/freebsd-x64@0.25.12':
+  '@esbuild/linux-riscv64@0.28.1':
     optional: true
 
-  '@esbuild/freebsd-x64@0.27.4':
+  '@esbuild/linux-s390x@0.28.1':
     optional: true
 
-  '@esbuild/linux-arm64@0.25.12':
+  '@esbuild/linux-x64@0.28.1':
     optional: true
 
-  '@esbuild/linux-arm64@0.27.4':
+  '@esbuild/netbsd-arm64@0.28.1':
     optional: true
 
-  '@esbuild/linux-arm@0.25.12':
+  '@esbuild/netbsd-x64@0.28.1':
     optional: true
 
-  '@esbuild/linux-arm@0.27.4':
+  '@esbuild/openbsd-arm64@0.28.1':
     optional: true
 
-  '@esbuild/linux-ia32@0.25.12':
+  '@esbuild/openbsd-x64@0.28.1':
     optional: true
 
-  '@esbuild/linux-ia32@0.27.4':
+  '@esbuild/openharmony-arm64@0.28.1':
     optional: true
 
-  '@esbuild/linux-loong64@0.25.12':
+  '@esbuild/sunos-x64@0.28.1':
     optional: true
 
-  '@esbuild/linux-loong64@0.27.4':
+  '@esbuild/win32-arm64@0.28.1':
     optional: true
 
-  '@esbuild/linux-mips64el@0.25.12':
+  '@esbuild/win32-ia32@0.28.1':
     optional: true
 
-  '@esbuild/linux-mips64el@0.27.4':
-    optional: true
-
-  '@esbuild/linux-ppc64@0.25.12':
-    optional: true
-
-  '@esbuild/linux-ppc64@0.27.4':
-    optional: true
-
-  '@esbuild/linux-riscv64@0.25.12':
-    optional: true
-
-  '@esbuild/linux-riscv64@0.27.4':
-    optional: true
-
-  '@esbuild/linux-s390x@0.25.12':
-    optional: true
-
-  '@esbuild/linux-s390x@0.27.4':
-    optional: true
-
-  '@esbuild/linux-x64@0.25.12':
-    optional: true
-
-  '@esbuild/linux-x64@0.27.4':
-    optional: true
-
-  '@esbuild/netbsd-arm64@0.25.12':
-    optional: true
-
-  '@esbuild/netbsd-arm64@0.27.4':
-    optional: true
-
-  '@esbuild/netbsd-x64@0.25.12':
-    optional: true
-
-  '@esbuild/netbsd-x64@0.27.4':
-    optional: true
-
-  '@esbuild/openbsd-arm64@0.25.12':
-    optional: true
-
-  '@esbuild/openbsd-arm64@0.27.4':
-    optional: true
-
-  '@esbuild/openbsd-x64@0.25.12':
-    optional: true
-
-  '@esbuild/openbsd-x64@0.27.4':
-    optional: true
-
-  '@esbuild/openharmony-arm64@0.25.12':
-    optional: true
-
-  '@esbuild/openharmony-arm64@0.27.4':
-    optional: true
-
-  '@esbuild/sunos-x64@0.25.12':
-    optional: true
-
-  '@esbuild/sunos-x64@0.27.4':
-    optional: true
-
-  '@esbuild/win32-arm64@0.25.12':
-    optional: true
-
-  '@esbuild/win32-arm64@0.27.4':
-    optional: true
-
-  '@esbuild/win32-ia32@0.25.12':
-    optional: true
-
-  '@esbuild/win32-ia32@0.27.4':
-    optional: true
-
-  '@esbuild/win32-x64@0.25.12':
-    optional: true
-
-  '@esbuild/win32-x64@0.27.4':
+  '@esbuild/win32-x64@0.28.1':
     optional: true
 
   '@jridgewell/gen-mapping@0.3.13':
@@ -1491,9 +1237,9 @@ snapshots:
       widest-line: 4.0.1
       wrap-ansi: 8.1.0
 
-  bundle-require@5.1.0(esbuild@0.27.4):
+  bundle-require@5.1.0(esbuild@0.28.1):
     dependencies:
-      esbuild: 0.27.4
+      esbuild: 0.28.1
       load-tsconfig: 0.2.5
 
   cac@6.7.14: {}
@@ -1546,63 +1292,34 @@ snapshots:
 
   es-module-lexer@2.1.0: {}
 
-  esbuild@0.25.12:
+  esbuild@0.28.1:
     optionalDependencies:
-      '@esbuild/aix-ppc64': 0.25.12
-      '@esbuild/android-arm': 0.25.12
-      '@esbuild/android-arm64': 0.25.12
-      '@esbuild/android-x64': 0.25.12
-      '@esbuild/darwin-arm64': 0.25.12
-      '@esbuild/darwin-x64': 0.25.12
-      '@esbuild/freebsd-arm64': 0.25.12
-      '@esbuild/freebsd-x64': 0.25.12
-      '@esbuild/linux-arm': 0.25.12
-      '@esbuild/linux-arm64': 0.25.12
-      '@esbuild/linux-ia32': 0.25.12
-      '@esbuild/linux-loong64': 0.25.12
-      '@esbuild/linux-mips64el': 0.25.12
-      '@esbuild/linux-ppc64': 0.25.12
-      '@esbuild/linux-riscv64': 0.25.12
-      '@esbuild/linux-s390x': 0.25.12
-      '@esbuild/linux-x64': 0.25.12
-      '@esbuild/netbsd-arm64': 0.25.12
-      '@esbuild/netbsd-x64': 0.25.12
-      '@esbuild/openbsd-arm64': 0.25.12
-      '@esbuild/openbsd-x64': 0.25.12
-      '@esbuild/openharmony-arm64': 0.25.12
-      '@esbuild/sunos-x64': 0.25.12
-      '@esbuild/win32-arm64': 0.25.12
-      '@esbuild/win32-ia32': 0.25.12
-      '@esbuild/win32-x64': 0.25.12
-
-  esbuild@0.27.4:
-    optionalDependencies:
-      '@esbuild/aix-ppc64': 0.27.4
-      '@esbuild/android-arm': 0.27.4
-      '@esbuild/android-arm64': 0.27.4
-      '@esbuild/android-x64': 0.27.4
-      '@esbuild/darwin-arm64': 0.27.4
-      '@esbuild/darwin-x64': 0.27.4
-      '@esbuild/freebsd-arm64': 0.27.4
-      '@esbuild/freebsd-x64': 0.27.4
-      '@esbuild/linux-arm': 0.27.4
-      '@esbuild/linux-arm64': 0.27.4
-      '@esbuild/linux-ia32': 0.27.4
-      '@esbuild/linux-loong64': 0.27.4
-      '@esbuild/linux-mips64el': 0.27.4
-      '@esbuild/linux-ppc64': 0.27.4
-      '@esbuild/linux-riscv64': 0.27.4
-      '@esbuild/linux-s390x': 0.27.4
-      '@esbuild/linux-x64': 0.27.4
-      '@esbuild/netbsd-arm64': 0.27.4
-      '@esbuild/netbsd-x64': 0.27.4
-      '@esbuild/openbsd-arm64': 0.27.4
-      '@esbuild/openbsd-x64': 0.27.4
-      '@esbuild/openharmony-arm64': 0.27.4
-      '@esbuild/sunos-x64': 0.27.4
-      '@esbuild/win32-arm64': 0.27.4
-      '@esbuild/win32-ia32': 0.27.4
-      '@esbuild/win32-x64': 0.27.4
+      '@esbuild/aix-ppc64': 0.28.1
+      '@esbuild/android-arm': 0.28.1
+      '@esbuild/android-arm64': 0.28.1
+      '@esbuild/android-x64': 0.28.1
+      '@esbuild/darwin-arm64': 0.28.1
+      '@esbuild/darwin-x64': 0.28.1
+      '@esbuild/freebsd-arm64': 0.28.1
+      '@esbuild/freebsd-x64': 0.28.1
+      '@esbuild/linux-arm': 0.28.1
+      '@esbuild/linux-arm64': 0.28.1
+      '@esbuild/linux-ia32': 0.28.1
+      '@esbuild/linux-loong64': 0.28.1
+      '@esbuild/linux-mips64el': 0.28.1
+      '@esbuild/linux-ppc64': 0.28.1
+      '@esbuild/linux-riscv64': 0.28.1
+      '@esbuild/linux-s390x': 0.28.1
+      '@esbuild/linux-x64': 0.28.1
+      '@esbuild/netbsd-arm64': 0.28.1
+      '@esbuild/netbsd-x64': 0.28.1
+      '@esbuild/openbsd-arm64': 0.28.1
+      '@esbuild/openbsd-x64': 0.28.1
+      '@esbuild/openharmony-arm64': 0.28.1
+      '@esbuild/sunos-x64': 0.28.1
+      '@esbuild/win32-arm64': 0.28.1
+      '@esbuild/win32-ia32': 0.28.1
+      '@esbuild/win32-x64': 0.28.1
 
   estree-walker@3.0.3:
     dependencies:
@@ -1671,7 +1388,7 @@ snapshots:
       object-assign: 4.1.1
       thenify-all: 1.6.0
 
-  nanoid@3.3.11: {}
+  nanoid@3.3.12: {}
 
   object-assign@4.1.1: {}
 
@@ -1707,16 +1424,16 @@ snapshots:
       mlly: 1.8.2
       pathe: 2.0.3
 
-  postcss-load-config@6.0.1(postcss@8.5.8)(tsx@4.21.0):
+  postcss-load-config@6.0.1(postcss@8.5.15)(tsx@4.21.0):
     dependencies:
       lilconfig: 3.1.3
     optionalDependencies:
-      postcss: 8.5.8
+      postcss: 8.5.15
       tsx: 4.21.0
 
-  postcss@8.5.8:
+  postcss@8.5.15:
     dependencies:
-      nanoid: 3.3.11
+      nanoid: 3.3.12
       picocolors: 1.1.1
       source-map-js: 1.2.1
 
@@ -1837,18 +1554,18 @@ snapshots:
 
   ts-interface-checker@0.1.13: {}
 
-  tsup@8.5.1(postcss@8.5.8)(tsx@4.21.0)(typescript@5.9.3):
+  tsup@8.5.1(postcss@8.5.15)(tsx@4.21.0)(typescript@5.9.3):
     dependencies:
-      bundle-require: 5.1.0(esbuild@0.27.4)
+      bundle-require: 5.1.0(esbuild@0.28.1)
       cac: 6.7.14
       chokidar: 4.0.3
       consola: 3.4.2
       debug: 4.4.3
-      esbuild: 0.27.4
+      esbuild: 0.28.1
       fix-dts-default-cjs-exports: 1.0.1
       joycon: 3.1.1
       picocolors: 1.1.1
-      postcss-load-config: 6.0.1(postcss@8.5.8)(tsx@4.21.0)
+      postcss-load-config: 6.0.1(postcss@8.5.15)(tsx@4.21.0)
       resolve-from: 5.0.0
       rollup: 4.60.0
       source-map: 0.7.6
@@ -1857,7 +1574,7 @@ snapshots:
       tinyglobby: 0.2.15
       tree-kill: 1.2.2
     optionalDependencies:
-      postcss: 8.5.8
+      postcss: 8.5.15
       typescript: 5.9.3
     transitivePeerDependencies:
       - jiti
@@ -1867,7 +1584,7 @@ snapshots:
 
   tsx@4.21.0:
     dependencies:
-      esbuild: 0.27.4
+      esbuild: 0.28.1
       get-tsconfig: 4.13.7
     optionalDependencies:
       fsevents: 2.3.3
@@ -1882,10 +1599,10 @@ snapshots:
 
   vite@6.4.3(@types/node@20.19.37)(tsx@4.21.0):
     dependencies:
-      esbuild: 0.25.12
+      esbuild: 0.28.1
       fdir: 6.5.0(picomatch@4.0.4)
       picomatch: 4.0.4
-      postcss: 8.5.8
+      postcss: 8.5.15
       rollup: 4.60.0
       tinyglobby: 0.2.15
     optionalDependencies:

cli/node/pnpm-workspace.yaml

@@ -10,3 +10,5 @@ overrides:
   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/vitest.config.ts

@@ -8,4 +8,10 @@ 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/tests/test_agent_mode.py

@@ -32,12 +32,13 @@ 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,
-        text=True,
+        encoding="utf-8",
         env=env,
         timeout=15,
     )
@@ -99,12 +100,13 @@ class TestArgvPreprocessing:
         result = subprocess.run(
             [sys.executable, "-m", "mem0_cli", "init", "--agent"],
             capture_output=True,
-            text=True,
+            encoding="utf-8",
             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,
         )
@@ -133,12 +135,13 @@ class TestJsonEnvelopeParity:
         result = subprocess.run(
             [sys.executable, "-m", "mem0_cli", "init", "--agent", "--json"],
             capture_output=True,
-            text=True,
+            encoding="utf-8",
             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

@@ -49,6 +49,7 @@ 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:
@@ -56,7 +57,7 @@ def _run(
     result = subprocess.run(
         [sys.executable, "-m", "mem0_cli", *args],
         capture_output=True,
-        text=True,
+        encoding="utf-8",
         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, ClickExit)),
+            pytest.raises((SystemExit, TyperExit)),
         ):
             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, ClickExit)),
+            pytest.raises((SystemExit, TyperExit)),
         ):
             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, ClickExit)),
+            pytest.raises((SystemExit, TyperExit)),
         ):
             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, ClickExit)),
+            pytest.raises((SystemExit, TyperExit)),
         ):
             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, ClickExit)),
+            pytest.raises((SystemExit, TyperExit)),
         ):
             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, ClickExit)),
+            pytest.raises((SystemExit, TyperExit)),
         ):
             cmd_get(mock_backend, "bad-id", output="text")
 

cli/python/tests/test_config.py

@@ -67,7 +67,8 @@ class TestConfig:
         from mem0_cli.config import CONFIG_FILE
 
         mode = os.stat(CONFIG_FILE).st_mode & 0o777
-        assert mode == 0o600
+        if os.name != "nt":
+            assert mode == 0o600
 
     def test_defaults_save_and_load(self, isolate_config):
         config = Mem0Config()

docs/changelog/highlights.mdx

@@ -46,9 +46,9 @@ Ground-up rewrite of the memory pipeline with 20+ point benchmark improvements:
 - **~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
+- **Graph memory (built-in)**: entities extracted, embedded, and linked across memories, with no external graph store required
 
-Breaking changes: Graph memory removed from OSS, `search()` defaults changed, deprecated params removed. See [migration guide](/migration/oss-v2-to-v3).
+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).
 
 </Update>
 

docs/changelog/openclaw.mdx

@@ -4,6 +4,39 @@ description: "Release notes for the OpenClaw plugin and agent harness."
 mode: "wide"
 ---
 
+<Update label="2026-06-12" description="v1.0.13">
+
+**Fixes:**
+- **Custom categories payload:** `customCategories` (a `Record<string, string>` map) is now converted via the new `customCategoryMapToList()` helper into the `Array<Record<string, string>>` shape the Mem0 SDK expects on `add` calls — previously the raw object was passed as `custom_categories` and silently ignored ([#5345](https://github.com/mem0ai/mem0/pull/5345))
+- **Skip runtime setup during metadata registration:** `register()` now detects `registrationMode === "cli-metadata"`, registers only the CLI commands, and returns early — avoiding backend initialization, service/tool registration, and hook installation during OpenClaw's metadata-only registration pass ([#5383](https://github.com/mem0ai/mem0/pull/5383))
+
+**Security:**
+- Bumped `mem0ai` from `3.0.3` to `3.0.7` (latest Node SDK) — includes the transitive axios CVE remediation shipped in `3.0.6` ([#5460](https://github.com/mem0ai/mem0/pull/5460))
+- Added pnpm override `uuid@<11.1.1` → `>=11.1.1` to resolve an open MEDIUM Dependabot alert ([#5489](https://github.com/mem0ai/mem0/pull/5489))
+
+**Improvements:**
+- **Repo consolidation:** Plugin moved from repo-root `openclaw/` to `integrations/openclaw/`; `package.json` `repository.directory` updated to match so npm provenance links to the correct subdirectory ([#5491](https://github.com/mem0ai/mem0/pull/5491))
+
+**Tests:**
+- Added `customCategoryMapToList` unit tests and a `PlatformProvider` test asserting `custom_categories` is passed to the Mem0 SDK as a list ([#5345](https://github.com/mem0ai/mem0/pull/5345))
+- Added a regression test asserting `cli-metadata` registration registers only CLI commands and triggers no runtime side effects ([#5383](https://github.com/mem0ai/mem0/pull/5383))
+
+</Update>
+
+<Update label="2026-06-02" description="v1.0.12">
+
+**Docs:**
+- **Agent Mode onboarding:** README now documents an autonomous setup path for AI agents — `mem0 init --agent --json` mints an evaluation Mem0 API key with no email, OTP, or browser and exports it as `MEM0_API_KEY` for `openclaw mem0 init`; a human owner can later run `mem0 init --email <email>` to claim ownership without disrupting the agent ([#5123](https://github.com/mem0ai/mem0/pull/5123))
+
+**Security:**
+- Added pnpm overrides to remediate advisories in transitive dependencies: `langsmith@<0.6.0` → `^0.6.0`, `picomatch@<2.3.2` → `^2.3.2`, `vite` → `^8.0.5`, and `@qdrant/js-client-rest` → `^1.18.0` ([#5294](https://github.com/mem0ai/mem0/pull/5294))
+
+**Dependencies:**
+- Bumped `mem0ai` from `3.0.2` to `3.0.3` ([#5212](https://github.com/mem0ai/mem0/pull/5212))
+- Bumped dev dependencies `@vitest/coverage-v8` and `vitest` from `^4.0.18` to `^4.1.7`; added `vite@^8.0.5` and `@qdrant/js-client-rest@^1.18.0` ([#5294](https://github.com/mem0ai/mem0/pull/5294))
+
+</Update>
+
 <Update label="2026-04-29" description="v1.0.11">
 
 **New Features:**

docs/changelog/platform.mdx

@@ -25,7 +25,7 @@ mode: "wide"
 <Update label="2026-04-16" description="">
 
 **Improvements:**
-- **UI:** Removed Graph Memory tab, page, and all references from dashboard, sidebar, project settings, playground, and billing
+- **UI:** Removed the legacy external-graph-store visualization tab, page, and its references from dashboard, sidebar, project settings, playground, and billing
 
 </Update>
 

docs/changelog/sdk.mdx

@@ -7,6 +7,57 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
+<Update label="2026-06-17" description="v2.0.7">
+
+**New Features:**
+- **LLMs:** Add Gemini via Vertex AI as LLM provider ([#4030](https://github.com/mem0ai/mem0/pull/4030))
+- **Embeddings:** Add native `embed_batch` to `OllamaEmbedding` for batched embedding requests ([#5415](https://github.com/mem0ai/mem0/pull/5415))
+
+**Bug Fixes:**
+- **Core:** Fix `api_error_handler` silently dropping return values from async methods ([#5540](https://github.com/mem0ai/mem0/pull/5540))
+- **Core:** Fix `AsyncMemory.reset()` not resetting the entity store ([#5535](https://github.com/mem0ai/mem0/pull/5535))
+- **Core:** Fix `async delete_all` aborting on first error, leaving partial deletion ([#5529](https://github.com/mem0ai/mem0/pull/5529))
+- **Core:** Skip messages without a `content` key in message parsers to prevent `KeyError` crashes ([#5575](https://github.com/mem0ai/mem0/pull/5575))
+- **Core:** Preserve custom metadata fields during memory update ([#5480](https://github.com/mem0ai/mem0/pull/5480))
+- **LLMs:** Fix Anthropic `tool_choice` format and tool response parsing ([#5537](https://github.com/mem0ai/mem0/pull/5537))
+- **LLMs:** Fix Ollama `json` format mutating the caller's messages list in-place ([#5539](https://github.com/mem0ai/mem0/pull/5539))
+- **LLMs:** Omit `None` config values from Gemini `GenerateContentConfig` to prevent validation errors ([#5528](https://github.com/mem0ai/mem0/pull/5528))
+- **LLMs:** Honor reasoning-model params in `AzureOpenAIStructuredLLM` ([#5548](https://github.com/mem0ai/mem0/pull/5548))
+- **LLMs:** Honor reasoning-model params in `OpenAIStructuredLLM` ([#5458](https://github.com/mem0ai/mem0/pull/5458))
+- **LLMs:** Send `max_completion_tokens` for the GPT-5 family across all providers ([#5547](https://github.com/mem0ai/mem0/pull/5547))
+- **LLMs:** Accept and forward `**kwargs` in Together, LangChain, and Sarvam providers ([#5556](https://github.com/mem0ai/mem0/pull/5556))
+- **LLMs:** Fix Bedrock AI21 response parse default using `dict` literal instead of `set` ([#5527](https://github.com/mem0ai/mem0/pull/5527))
+- **LLMs:** Fix LiteLLM function-calling check blocking all calls on non-tool models ([#5536](https://github.com/mem0ai/mem0/pull/5536))
+- **LLMs:** Fix HuggingFace provider using `self.config` instead of raw `config` parameter ([#5538](https://github.com/mem0ai/mem0/pull/5538))
+- **Embeddings:** Honor `aws_session_token` in AWS Bedrock embeddings ([#5566](https://github.com/mem0ai/mem0/pull/5566))
+- **Rerankers:** Respect `config.top_k` in Cohere and ZeroEntropy fallback paths ([#5560](https://github.com/mem0ai/mem0/pull/5560))
+- **Vector Stores:** Fix FAISS filtered search dropping over-fetched candidates before filtering ([#5453](https://github.com/mem0ai/mem0/pull/5453))
+- **Vector Stores:** Fix Weaviate `reset()` crashing with missing `vector_size` argument ([#5531](https://github.com/mem0ai/mem0/pull/5531))
+- **Vector Stores:** Pass embedding dims in Weaviate `reset()` to avoid re-init crash ([#5570](https://github.com/mem0ai/mem0/pull/5570))
+- **Vector Stores:** Fix MongoDB `reset()` passing wrong argument to `create_col()` ([#5532](https://github.com/mem0ai/mem0/pull/5532))
+- **Vector Stores:** Fix Pinecone hybrid search crashing when `filters` is `None` ([#5533](https://github.com/mem0ai/mem0/pull/5533))
+- **Vector Stores:** Fix Redis crashing on empty or `None` filters in `search()` and `list()` ([#5446](https://github.com/mem0ai/mem0/pull/5446))
+- **Vector Stores:** Return `None` from `get()` for missing IDs in Milvus, Weaviate, and Supabase ([#5562](https://github.com/mem0ai/mem0/pull/5562))
+- **Vector Stores:** Return `None` from ChromaDB `get()` for missing IDs ([#5561](https://github.com/mem0ai/mem0/pull/5561))
+
+</Update>
+
+<Update label="2026-06-13" description="v2.0.6">
+
+**New Features:**
+- **Memory:** Add a contextual OSS-to-Platform notices system that surfaces occasional, situation-aware messages (first run, scale/performance thresholds, slow queries, and when temporal/decay features are relevant) pointing to the corresponding Mem0 Platform capabilities; disable via `MEM0_TELEMETRY=false` ([#5494](https://github.com/mem0ai/mem0/pull/5494))
+
+**Bug Fixes:**
+- **Memory:** Prevent a crash in `parse_vision_messages` when vision support is disabled ([#5487](https://github.com/mem0ai/mem0/pull/5487))
+- **Vector Stores:** Expose the `https` option on the Qdrant vector store configuration so TLS endpoints can be targeted explicitly ([#5380](https://github.com/mem0ai/mem0/pull/5380))
+- **Vector Stores:** Use valid S3 Vectors entity index names, fixing index operations that failed on invalid names ([#5416](https://github.com/mem0ai/mem0/pull/5416))
+- **Vector Stores:** Fix `search()` crashing with a `TypeError` in the LangChain vector store when a result score is `None` ([#5072](https://github.com/mem0ai/mem0/pull/5072))
+- **Vector Stores:** Use `is not None` instead of a truthiness check for vector/payload in the PGVector `update()` path, so valid empty/zero values are no longer skipped ([#5488](https://github.com/mem0ai/mem0/pull/5488))
+- **Vector Stores:** Index the Valkey `memory` field as `TEXT` rather than `TAG` so full-text search behaves correctly ([#5443](https://github.com/mem0ai/mem0/pull/5443))
+- **Vector Stores:** Implement `$not` filter support in the ChromaDB vector store ([#5485](https://github.com/mem0ai/mem0/pull/5485))
+
+</Update>
+
 <Update label="2026-06-10" description="v2.0.5">
 
 **New Features:**
@@ -98,8 +149,8 @@ mode: "wide"
 - **`messages` in `Memory.add()` rejects invalid types:** Passing `None` or non-`(str | dict | list)` values raises `Mem0ValidationError` (`error_code="VALIDATION_003"`) ([#4843](https://github.com/mem0ai/mem0/pull/4843))
 - **`qdrant-client>=1.12.0` required** — Upgrade from `>=1.9.1` ([#4805](https://github.com/mem0ai/mem0/pull/4805))
 - **`org_id` and `project_id` removed** — Removed from `MemoryClient` constructor and all method signatures ([#4740](https://github.com/mem0ai/mem0/pull/4740))
-- **Graph Memory Removed (OSS):** `mem0/memory/graph_memory.py`, `memgraph_memory.py`, `kuzu_memory.py`, `apache_age_memory.py`, and `mem0/graphs/` (Neo4j / Memgraph / Kuzu / Apache AGE / Neptune drivers) deleted — ~4,000 lines. Graph memory is no longer supported in the OSS SDK; graph drivers (neo4j, memgraph, kuzu, etc.) can be uninstalled. Use the Platform API for graph features. Remove `enable_graph` and `graph_store` from your config ([#4805](https://github.com/mem0ai/mem0/pull/4805))
-- **`enable_graph` removed from Client SDK** — Graph memory is now a project-level setting on the Platform. Remove `enable_graph` from `MemoryClient.add()` / `search()` / `get_all()` / `update_project()` calls ([#4776](https://github.com/mem0ai/mem0/pull/4776))
+- **External Graph Store Removed (OSS):** `mem0/memory/graph_memory.py`, `memgraph_memory.py`, `kuzu_memory.py`, `apache_age_memory.py`, and `mem0/graphs/` (Neo4j / Memgraph / Kuzu / Apache AGE / Neptune drivers) deleted, about 4,000 lines. The external graph store integration is no longer part of the OSS SDK; graph drivers (neo4j, memgraph, kuzu, etc.) can be uninstalled. Graph memory now runs natively as built-in entity linking. Remove `enable_graph` and `graph_store` from your config ([#4805](https://github.com/mem0ai/mem0/pull/4805))
+- **`enable_graph` removed from Client SDK:** Graph memory now runs automatically and no longer needs a flag. Remove `enable_graph` from `MemoryClient.add()` / `search()` / `get_all()` / `update_project()` calls ([#4776](https://github.com/mem0ai/mem0/pull/4776))
 - **`custom_fact_extraction_prompt` renamed to `custom_instructions`** — Update config and memory module references ([#4740](https://github.com/mem0ai/mem0/pull/4740))
 - **Typed option classes** — Added Pydantic v2 typed classes: `AddMemoryOptions`, `SearchMemoryOptions`, `GetAllMemoryOptions`, `DeleteAllMemoryOptions`, `UpdateMemoryOptions`, `ProjectUpdateOptions` ([#4740](https://github.com/mem0ai/mem0/pull/4740))
 
@@ -960,6 +1011,29 @@ See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-
 
 <Tab title="TypeScript">
 
+<Update label="2026-06-17" description="v3.0.9">
+
+**Bug Fixes:**
+- **LLMs:** Fix Anthropic `tool_choice` format — was incorrectly sent as a bare string `"auto"` (rejected by the API); now correctly sent as `{ type: "auto" }`. Also fixes tool response parsing: `tool_use` blocks are now parsed into `toolCalls` objects instead of throwing. Updated default model to `claude-sonnet-4-6` and default `max_tokens` to `2000` to match the Python provider. Added `temperature`, `topP`, and `maxTokens` to `LLMConfig` so Anthropic params can be configured ([#5537](https://github.com/mem0ai/mem0/pull/5537))
+- **Memory (OSS):** Preserve custom metadata fields during `update()` — fields such as `category`, `priority`, and other user-defined keys were previously dropped on update; the existing payload is now spread before applying the new data ([#5480](https://github.com/mem0ai/mem0/pull/5480))
+- **Client:** Preserve user-defined schema keys in `createMemoryExport` ([#5594](https://github.com/mem0ai/mem0/pull/5594))
+
+**Security:**
+- **Dependencies:** Bump `esbuild` to `>=0.28.1` across all npm packages via pnpm overrides to remediate upstream vulnerability ([#5563](https://github.com/mem0ai/mem0/pull/5563))
+
+</Update>
+
+<Update label="2026-06-13" description="v3.0.8">
+
+**New Features:**
+- **Memory:** Add a contextual OSS-to-Platform notices system that surfaces occasional, situation-aware messages (first run, scale/performance thresholds, slow queries, and when temporal/decay features are relevant) pointing to the corresponding Mem0 Platform capabilities; disable via `MEM0_TELEMETRY=false` ([#5494](https://github.com/mem0ai/mem0/pull/5494))
+
+**Security:**
+- **Dependencies:** Upgrade `@langchain/community` to `^1.1.18` to remediate CVE-2026-27795 and CVE-2026-26019 ([#5510](https://github.com/mem0ai/mem0/pull/5510))
+- **Dependencies:** Resolve all open MEDIUM Dependabot alerts via pnpm overrides ([#5489](https://github.com/mem0ai/mem0/pull/5489))
+
+</Update>
+
 <Update label="2026-06-10" description="v3.0.7">
 
 **New Features:**
@@ -1039,7 +1113,7 @@ See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-
 - **Default model:** `gpt-5-mini` is now the default in `OpenAI`, `OpenAIStructured`, and `Azure` LLM providers ([#4829](https://github.com/mem0ai/mem0/pull/4829))
 
 **Breaking Changes:**
-- **Graph Memory Removed (OSS):** `graph_memory.ts` (675 lines), `graphs/tools.ts` (267 lines), `graphs/utils.ts` (116 lines), `graphs/configs.ts` (30 lines) deleted. Graph memory is no longer supported in the OSS SDK — use Platform API for graph features ([#4805](https://github.com/mem0ai/mem0/pull/4805))
+- **External Graph Store Removed (OSS):** `graph_memory.ts` (675 lines), `graphs/tools.ts` (267 lines), `graphs/utils.ts` (116 lines), `graphs/configs.ts` (30 lines) deleted. The external graph store integration is no longer part of the OSS SDK; graph memory now runs natively as built-in entity linking ([#4805](https://github.com/mem0ai/mem0/pull/4805))
 - **camelCase Parameters (Client SDK):** All user-facing parameters converted from snake_case to camelCase. Mapping is transparent at API boundary via `camelToSnakeKeys()` / `snakeToCamelKeys()` ([#4776](https://github.com/mem0ai/mem0/pull/4776))
   ```typescript
   // Before

docs/components/vectordbs/dbs/qdrant.mdx

@@ -76,6 +76,7 @@ Let's see the available parameters for the `qdrant` config:
 | `path` | Path for the qdrant database | `/tmp/qdrant` |
 | `url` | Full URL for the qdrant server | `None` |
 | `api_key` | API key for the qdrant server | `None` |
+| `https` | Whether to force HTTPS on or off. `None` lets the client decide; set `False` for plain HTTP Qdrant with API key authentication. | `None` |
 | `on_disk` | For enabling persistent storage | `False` |
 </Tab>
 <Tab title="TypeScript">
@@ -90,4 +91,4 @@ Let's see the available parameters for the `qdrant` config:
 | `apiKey` | API key for the Qdrant server | `None` |
 | `onDisk` | For enabling persistent storage | `False` |
 </Tab>
-</Tabs>
+</Tabs>

docs/core-concepts/memory-evaluation.mdx

@@ -17,7 +17,7 @@ Some benchmarks today — particularly smaller ones like LoCoMo and LongMemEval
 
 ## Architecture Overview
 
-Mem0's memory system operates across two phases — **extraction** (writing) and **retrieval** (reading) — with an entity linking layer connecting them.
+Mem0's memory system operates across two phases, **extraction** (writing) and **retrieval** (reading), with a graph memory layer (entity linking) connecting them.
 
 ### Memory Extraction (Distillation)
 
@@ -27,14 +27,14 @@ When new conversations arrive, the extraction pipeline processes them through fi
 2. **Context Lookup** — Find related existing memories to avoid duplicates
 3. **Distill Memories** — Single-pass LLM extraction produces ADD-only facts from input + context
 4. **Deduplicate + Embed** — Hash-based deduplication, then vectorize new memories
-5. **Entity Linking** — Identify entities (proper nouns, quoted text, compound noun phrases) and link them across memories
+5. **Graph Memory (Entity Linking)**: Identify entities (proper nouns, quoted text, compound noun phrases) and link them across memories into a graph
 
 Memories are distributed across three storage layers, each tuned for a specific retrieval pattern:
 
 | Store | Contents | Purpose |
 |---|---|---|
 | **Vector Database** | Memory text, embeddings, metadata (timestamps, hash, categories, attributed_to) | Primary fact storage + semantic retrieval |
-| **Entity Store** | Entities + embeddings + linked memory IDs | Entity-based retrieval boost |
+| **Graph / Entity Store** | Entities + embeddings + linked memory IDs | Graph connections across memories + entity-based retrieval boost |
 | **SQL Database** | History log (ADD events) + rolling message window | Audit trail + extraction dedup context |
 
 <Info>
@@ -76,7 +76,7 @@ The combined score outperformed every individual signal across every category te
 
 *Mean tokens: 6,956*
 
-The two largest gains are **temporal queries (+29.6)** and **multi-hop reasoning (+23.1)**. Both categories directly test the ADD-only architecture (preserving temporal context) and entity linking (connecting facts across memories).
+The two largest gains are **temporal queries (+29.6)** and **multi-hop reasoning (+23.1)**. Both categories directly test the ADD-only architecture (preserving temporal context) and graph memory / entity linking (connecting facts across memories).
 
 ### LongMemEval
 

docs/docs.json

@@ -71,6 +71,7 @@
                         "pages": [
                           "platform/features/v2-memory-filters",
                           "platform/features/entity-scoped-memory",
+                          "platform/features/graph-memory",
                           "platform/features/async-client",
                           "platform/features/multimodal-support",
                           "platform/features/custom-categories",
@@ -647,10 +648,6 @@
       "source": "/open-source/features/custom-fact-extraction-prompt",
       "destination": "/open-source/features/custom-instructions"
     },
-    {
-      "source": "/platform/features/graph-memory",
-      "destination": "/migration/oss-v2-to-v3"
-    },
     {
       "source": "/cookbooks/essentials/choosing-memory-architecture-vector-vs-graph",
       "destination": "/migration/oss-v2-to-v3"
@@ -1025,7 +1022,7 @@
     },
     {
       "source": "/features/graph-memory",
-      "destination": "/migration/oss-v2-to-v3"
+      "destination": "/platform/features/graph-memory"
     },
     {
       "source": "/features/:slug",

docs/integrations/antigravity.mdx

@@ -28,7 +28,7 @@ echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.bashrc && source ~/.bashrc
 
 ```bash
 # Install the plugin (MCP server, hooks, scripts)
-npx degit mem0ai/mem0/mem0-plugin ~/.gemini/config/plugins/mem0
+npx degit mem0ai/mem0/integrations/mem0-plugin ~/.gemini/config/plugins/mem0
 ```
 
 This installs the MCP server, lifecycle hooks, and shared scripts.

docs/integrations/opencode.mdx

@@ -1,6 +1,6 @@
 ---
 title: OpenCode
-description: "Add persistent memory to OpenCode with the Mem0 plugin — MCP server, lifecycle hooks, and slash commands."
+description: "Add persistent memory to OpenCode with the Mem0 plugin — native SDK-backed memory tools, lifecycle hooks, and skills."
 ---
 
 Add persistent memory to [**OpenCode**](https://opencode.ai) with the Mem0 plugin. Your agent forgets everything between sessions — Mem0 fixes that by storing decisions, preferences, and learnings so they carry over automatically.
@@ -30,27 +30,17 @@ echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.bashrc && source ~/.bashrc
 opencode plugin @mem0/opencode-plugin
 ```
 
-
-Or using this command which does the same thing:
-
-
-```bash
-bunx @mem0/opencode-plugin@latest install
-```
-
-
-
 **Or let your agent do it** — paste this into OpenCode:
 
 ```
-Install @mem0/opencode-plugin by following https://raw.githubusercontent.com/mem0ai/mem0/main/mem0-plugin/.opencode-plugin/README.md
+Install @mem0/opencode-plugin by following https://raw.githubusercontent.com/mem0ai/mem0/main/integrations/mem0-plugin/.opencode-plugin/README.md
 ```
 
-All commands auto-add the plugin and MCP server to your `~/.config/opencode/opencode.json`. Restart OpenCode — you get the MCP server, lifecycle hooks, and all `/mem0:` slash commands.
+This adds the plugin to your `~/.config/opencode/opencode.json`. Restart OpenCode — you get the native memory tools, lifecycle hooks, and all `/mem0-*` slash commands. The memory tools are registered by the plugin itself via the `mem0ai` SDK — no MCP server to configure.
 
-### Option B — MCP Only
+### Option B — Standalone MCP Server
 
-If you only need the memory tools without hooks or skills, add this to your `opencode.json` (project-level or global at `~/.config/opencode/opencode.json`):
+If you only need the memory tools without the plugin's hooks or skills, point OpenCode at Mem0's hosted MCP server directly. Add this to your `opencode.json` (project-level or global at `~/.config/opencode/opencode.json`):
 
 ```json
 {
@@ -69,13 +59,13 @@ If you only need the memory tools without hooks or skills, add this to your `ope
 
 ## What's Included
 
-| Component | Plugin (A) | MCP Only (B) |
-|-----------|:----------:|:------------:|
-| MCP Server (9 memory tools) | Yes | Yes |
+| Component | Plugin (A) | Standalone MCP (B) |
+|-----------|:----------:|:------------------:|
+| 9 memory tools | Native (SDK) | Remote MCP server |
 | Lifecycle Hooks | Yes | No |
-| 16 Slash Commands | Yes | No |
+| 9 Skills | Yes | No |
 
-## Available MCP Tools
+## Available Memory Tools
 
 | Tool | Description |
 |------|-------------|
@@ -89,25 +79,70 @@ If you only need the memory tools without hooks or skills, add this to your `ope
 | `delete_entities` | Delete a user/agent/app/run entity and its memories |
 | `list_entities` | List users/agents/apps/runs stored in Mem0 |
 
+## Memory scope
+
+`search_memories`, `get_memories`, `add_memory`, and `delete_all_memories` accept an optional **`scope`** that controls how widely they read or write:
+
+| Scope | Reads | Writes |
+|-------|-------|--------|
+| `project` *(default)* | this repo (`user_id` + `app_id`) | this repo |
+| `session` | this run only (`+ run_id`) | this run |
+| `global` | **all your projects in the workspace** (`app_id: "*"`) | user-wide |
+
+Just ask naturally — e.g. *"search my memories across all my projects"* — and the agent passes `scope: "global"`. For normal questions it stays scoped to the current project automatically.
+
+To change the **default** scope (used when no scope is passed), run the `/mem0-scope` skill:
+
+```
+/mem0-scope            # show the current default scope + identity
+/mem0-scope global     # save & search across all your projects by default
+/mem0-scope project    # back to repo-only (the default)
+```
+
+The default persists in `~/.mem0/settings.json` (`default_scope`) and is read fresh on each memory operation, so a change applies immediately — no restart. `delete_all_memories` always requires an explicit `scope: "global"` to delete user-wide, so changing the default can't trigger a cross-project wipe.
+
+The project id (`app_id`) is derived from your git remote (`owner-repo`), falling back to the git repo's root directory name, then the current directory. Launch OpenCode from inside your repo so memories scope to the project rather than your home directory.
+
 ## Lifecycle Hooks
 
 The plugin uses the [mem0ai](https://www.npmjs.com/package/mem0ai) TypeScript SDK directly — pure TypeScript, no Python, no shell scripts.
 
 | OpenCode Event | Hook | What happens |
 |----------------|------|-------------|
+| `config` | **Config** | Registers the `/mem0-*` slash commands (`config.command`) and adds the plugin's own `opencode-skills/` dir to OpenCode's `skills.paths` for in-place skill discovery (no copying) |
 | `chat.message` | **Chat message** | Searches prior memories on session start, searches relevant memories before each prompt, auto-captures learnings periodically |
-| `tool.execute.before` | **Pre-tool** | Blocks MEMORY.md writes, injects `user_id`/`app_id` on mem0 tool calls |
-| `tool.execute.after` | **Post-tool** | Tracks stats, scans Bash errors and pre-fetches related error memories |
-| `experimental.chat.system.transform` | **System transform** | Injects memory context (session memories, search results, error lookups) into the system prompt |
+| `tool.execute.before` | **Pre-tool** | Blocks MEMORY.md writes, steering them to the `add_memory` tool |
+| `tool.execute.after` | **Post-tool** | Scans Bash errors and pre-fetches related error memories |
+| `experimental.chat.messages.transform` | **Messages transform** | Injects memory context (session memories, search results, error lookups) into the prompt |
 | `experimental.session.compacting` | **Compaction** | Stores session state memory, then injects prior memories into compaction context so nothing is lost |
 | `shell.env` | **Shell env** | Exports `MEM0_USER_ID`, `MEM0_APP_ID`, `MEM0_SESSION_ID`, and `MEM0_BRANCH` to all shell executions |
 
+## Auto-dream (memory consolidation)
+
+The plugin can automatically consolidate stored memories — merging duplicates, dropping stale/sensitive entries, and rewriting vague ones — so your memory set stays clean over time. It runs at most once per session, and only when **all** gates pass:
+
+- **Time** — at least `minHours` (default 24) since the last consolidation
+- **Sessions** — at least `minSessions` (default 5) sessions since then
+- **Memories** — at least `minMemories` (default 20) stored for the project
+
+A filesystem lock (`~/.mem0/mem0-dream.lock`) keeps two sessions from consolidating at once. Tune the thresholds with a `dream` block in `~/.mem0/settings.json`, or disable entirely with `MEM0_DREAM=false`:
+
+```json
+{
+  "dream": { "enabled": true, "auto": true, "minHours": 24, "minSessions": 5, "minMemories": 20 }
+}
+```
+
+If auto-dream hasn't run yet, it's almost always because a gate hasn't been met (most often too few memories). Run `/mem0-status` to see the exact gate progress (e.g. `sessions 2/5, memories 3/20`), `/mem0-dream` to consolidate **now** regardless of the gates, or lower the thresholds above.
+
 ## Troubleshooting
 
 - **No tools appearing** — Restart OpenCode after installing
 - **"Connection failed"** — Verify your key is set: `echo $MEM0_API_KEY`
 - **Plugin not loading** — Run `opencode plugin @mem0/opencode-plugin` again, then restart
 - **Hooks not firing** — Hooks require the plugin install (Option A). MCP-only installs don't include hooks.
+- **Auto-dream never runs** — It's gated (time + sessions + memories). Run `/mem0-status` to see which gate is blocking, or `/mem0-dream` to consolidate now.
+- **Wrong project name / memories not found** — The project id comes from your git remote; launch OpenCode from inside the repo (not your home directory). Check the resolved id with `/mem0-status`.
 
 <CardGroup cols={2}>
   <Card title="Mem0 MCP Setup" icon="puzzle-piece" href="/platform/mem0-mcp">

docs/integrations/pi-agent.mdx

@@ -59,6 +59,7 @@ For advanced settings, create `~/.pi/agent/mem0-config.json`:
   "userId": "your-username",
   "autoCapture": true,
   "defaultScope": "project",
+  "searchThreshold": 0.3,
   "dream": {
     "enabled": true,
     "auto": true,
@@ -75,12 +76,14 @@ For advanced settings, create `~/.pi/agent/mem0-config.json`:
 | `userId` | `string` | `$MEM0_USER_ID` or `"default"` | User identity for memory scoping |
 | `autoCapture` | `boolean` | `true` | Store facts from conversations automatically |
 | `defaultScope` | `string` | `"project"` | Default memory scope: `project`, `session`, or `global` |
+| `searchThreshold` | `number` | `0.3` | Minimum similarity score (0–1) a memory must reach to count as a match for `/mem0-search`, `/mem0-forget`, and `/mem0-pin`, enforced on each result's relevance score. Raise it to be stricter; lower it if relevant results are missed. |
 | `dream.enabled` | `boolean` | `true` | Enable dream consolidation |
 | `dream.auto` | `boolean` | `true` | Auto-trigger dreams when thresholds are met |
 | `dream.minHours` | `number` | `24` | Minimum hours between auto-dreams |
 | `dream.minSessions` | `number` | `5` | Minimum sessions before first auto-dream |
 | `dream.minMemories` | `number` | `20` | Minimum memories before auto-dream triggers |
 
+
 ## What's Included
 
 | Component | Description |

docs/llms.txt

@@ -197,6 +197,7 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 - [Platform Features Overview](https://docs.mem0.ai/platform/features/platform-overview) [Platform]: Use when surveying what managed offers beyond CRUD.
 - [V2 Memory Filters](https://docs.mem0.ai/platform/features/v2-memory-filters) [Platform]: Use when compound filters (AND/OR on metadata, entity, time) are needed at search.
 - [Entity-Scoped Memory](https://docs.mem0.ai/platform/features/entity-scoped-memory) [Platform]: Use when partitioning memories by user, agent, app, or run.
+- [Graph Memory](https://docs.mem0.ai/platform/features/graph-memory) [Platform]: Use when connecting facts across memories through shared entities for entity-centric or multi-hop questions.
 - [Async Client](https://docs.mem0.ai/platform/features/async-client) [Platform]: Use when the app issues many concurrent Mem0 calls and needs non-blocking I/O.
 - [Multimodal Support](https://docs.mem0.ai/platform/features/multimodal-support) [Platform]: Use when storing images or PDFs as memory input.
 - [Custom Categories](https://docs.mem0.ai/platform/features/custom-categories) [Platform]: Use when the default categories do not match the domain.
@@ -398,9 +399,9 @@ Each subdirectory is a Claude Code Skill (`SKILL.md` + supporting assets). Load
 
 ### Editor Plugin (shared glue)
 
-Source: https://github.com/mem0ai/mem0/tree/main/mem0-plugin
+Source: https://github.com/mem0ai/mem0/tree/main/integrations/mem0-plugin
 
-The `mem0-plugin/` directory provides MCP server connection, lifecycle hooks, and skill bundling for Claude Code, Cursor, Codex, OpenCode, and Antigravity. It exposes 9 MCP tools: `add_memory`, `search_memories`, `get_memories`, `get_memory`, `update_memory`, `delete_memory`, `delete_all_memories`, `delete_entities`, `list_entities`.
+The `integrations/mem0-plugin/` directory provides MCP server connection, lifecycle hooks, and skill bundling for Claude Code, Cursor, Codex, OpenCode, and Antigravity. It exposes 9 MCP tools: `add_memory`, `search_memories`, `get_memories`, `get_memory`, `update_memory`, `delete_memory`, `delete_all_memories`, `delete_entities`, `list_entities`.
 
 Editor-specific setup docs (already listed above under `## Integrations > AI Coding Tools`):
 

docs/migration/oss-to-platform.mdx

@@ -6,9 +6,7 @@ versionFrom: "Open Source"
 versionTo: "Platform"
 ---
 
-# Migrate from Open Source to Platform
-
-Move your Mem0 implementation to managed infrastructure with enterprise features.
+## Overview
 
 | Scope                 | Effort         | Downtime                     |
 | --------------------- | -------------- | ---------------------------- |
@@ -30,12 +28,29 @@ Move your Mem0 implementation to managed infrastructure with enterprise features
   - **Production Grade**: Auto-scaling, high availability, dedicated support
 </Info>
 
-## Plan
+### Plan
 
 1. **Sign up**: Create an account on <a href="https://app.mem0.ai?utm_source=oss&utm_medium=migration-oss-to-platform" rel="nofollow">Mem0 Platform</a>.
 2. **Get API Key**: Navigate to **Settings > API Keys** and generate a new key.
 3. **Review Usage**: Identify where you instantiate `Memory` and where you call `search` or `get_all`.
 
+## Migrate with Agent Skill
+
+Paste this prompt into your coding agent. It uses a migration skill to produce a plan; once you review and approve it, the agent implements the changes.
+
+```text
+Migrate my project from Mem0 OSS to the Mem0 Platform SDK using the
+mem0-oss-to-platform skill in the mem0ai/mem0 repo, at
+skills/mem0-oss-to-platform/
+
+Get the skill whichever way is easiest:
+- install it: npx skills add https://github.com/mem0ai/mem0 --skill mem0-oss-to-platform
+- if the mem0 repo is cloned locally, read it from skills/mem0-oss-to-platform/
+- otherwise fetch that folder from github.com/mem0ai/mem0 (SKILL.md + references/)
+
+Then read SKILL.md and begin the migration.
+```
+
 ## Migrate
 
 ### 1. Import Memories Into Platform

docs/migration/oss-v2-to-v3.mdx

@@ -328,27 +328,27 @@ The new algorithm automatically creates a parallel entity store collection named
 Make sure your vector store user/credentials have permission to create new collections. If you're using a managed vector database with restricted permissions, pre-create the `{collection_name}_entities` collection with the same embedding dimensions as your main collection.
 </Warning>
 
-## Graph Memory → Entity Linking
+## Graph Memory: Now Built-In
 
-Graph store support has been removed from the open-source SDK. It is replaced by **built-in entity linking**, which runs natively with no external dependencies.
+External graph **store** support has been removed from the open-source SDK and replaced by **built-in graph memory** (entity linking), which runs natively with no external dependencies.
 
 **What was removed:**
 - `enable_graph` / `enableGraph` config flag
 - `graph_store` / `graphStore` configuration block (Neo4j, Memgraph, Kuzu, Apache AGE, Neptune)
-- All graph memory code paths (~4000 lines)
+- All external graph store code paths (~4000 lines)
 
 **What replaces it:**
 
-Entity linking extracts entities (proper nouns, quoted text, compound noun phrases) from every memory during the add pipeline and stores them in a parallel collection (`{collection}_entities`) inside your existing vector store. At search time, entities from the query are matched against this collection and used to boost relevant memories. The boost is folded into the combined `score` on each result.
+Mem0 now builds the graph itself. It extracts entities (proper nouns, quoted text, compound noun phrases) from every memory during the add pipeline and stores them in a parallel collection (`{collection}_entities`) inside your existing vector store. Memories that share an entity are linked, and at search time entities from the query are matched against this collection to boost connected memories. The boost is folded into the combined `score` on each result.
 
 **Migration:**
 - Remove `enable_graph` / `enableGraph` from your config
 - Remove the `graph_store` / `graphStore` block — it is no longer read
-- Uninstall graph drivers (neo4j, memgraph, etc.) if you were using them only for Mem0
-- No data migration is required. Entity linking activates automatically on the next `add()` call.
+- Uninstall external graph drivers (neo4j, memgraph, etc.) if you were using them only for Mem0
+- No data migration is required. Built-in graph memory activates automatically on the next `add()` call.
 
 <Warning>
-Graph relationships exposed via the old `relations` field on search results are no longer populated. Entity relationships are consumed indirectly through retrieval ranking, not exposed as a queryable graph structure. If your application depended on traversing graph relationships directly, you will need to redesign that part against the new API.
+The old `relations` field on search results (populated by the external graph store) is no longer returned. Entity connections are now applied through retrieval ranking rather than exposed as a separate, directly traversable structure. If your application read or traversed the `relations` array, you will need to redesign that part against the new API.
 </Warning>
 
 ## How the New Algorithm Works

docs/migration/platform-v2-to-v3.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Platform: Migrating to the New Memory Algorithm"
-description: "Guide for Mem0 Platform users to adopt the new memory algorithm with single-pass extraction, entity linking, and multi-signal retrieval."
+description: "Guide for Mem0 Platform users to adopt the new memory algorithm with single-pass extraction, built-in graph memory, and multi-signal retrieval."
 icon: "arrow-right"
 iconType: "solid"
 ---
@@ -18,8 +18,7 @@ The new Mem0 memory algorithm is a ground-up redesign of how memories are extrac
 | **Extraction** | Two LLM passes (extract + merge) | Single-pass ADD-only (one LLM call) |
 | **Memory mutations** | ADD, UPDATE, DELETE | ADD only — nothing is overwritten or deleted |
 | **Agent-generated facts** | Often ignored | First-class, stored with equal weight |
-| **Entity linking** | Not available | Entities extracted and linked across memories |
-| **Graph memory** | Separate graph store + dashboard visualization | Replaced by built-in entity linking, no graph visuals on platform dashboard |
+| **Graph memory** | External graph store (Neo4j, etc.) + manual setup | Built-in and automatic; entities extracted and linked across memories natively, no external store |
 | **Retrieval** | Semantic (vector) only | Hybrid retrieval combining multiple signals |
 
 ## What This Means for Your Application
@@ -249,19 +248,18 @@ await client.search("query", {
 For the full list of parameter changes across all SDKs, see the [OSS migration guide](/migration/oss-v2-to-v3#removed-parameters-reference).
 </Info>
 
-## Graph Memory → Entity Linking
+## Graph Memory Is Now Built-In
 
-Graph memory has been replaced by **built-in entity linking**. The changes:
+Graph memory no longer requires an external graph database. It is now **native to the platform** and automatic. The changes:
 
-- **Graph visualizations removed from the platform dashboard.** The graph view in your project dashboard is no longer available.
-- **`enable_graph` project setting removed.** The toggle is gone from the dashboard; the API parameter is ignored.
-- **No external graph store to configure.** Previously graph memory required a separate Neo4j (or similar) deployment. Entity linking runs natively inside the platform — nothing to provision, no connection strings to manage.
-- **Entity linking is the native replacement.** Entities (proper nouns, quoted text, compound noun phrases) are automatically extracted from every memory and linked across memories belonging to the same user. At search time, entities from the query are matched against this index and used to boost ranking. The boost is folded into the combined `score` returned on each result.
+- **No external graph store to configure.** Previously, graph memory required a separate Neo4j (or similar) deployment. Mem0 now builds the graph itself from your memories, so there is nothing to provision and no connection strings to manage.
+- **Always on, no flag.** The `enable_graph` project setting is no longer needed; graph memory activates automatically. (The API parameter is now ignored if sent.)
+- **Connections power retrieval directly.** Entities (proper nouns, quoted text, compound noun phrases) are automatically extracted from every memory and linked across memories belonging to the same user. At search time, entities from the query are matched against the graph and used to boost ranking. The boost is folded into the combined `score` returned on each result.
 
-**No migration work is required.** Entity linking activates automatically for all projects on the new algorithm. Existing memories are not re-processed, but any new memories you add will be indexed for entity-based retrieval going forward.
+**No migration work is required.** Graph memory activates automatically for all projects on the new algorithm. Existing memories are not re-processed, but any new memories you add are added to the graph going forward. See [Graph Memory](/platform/features/graph-memory) for how the built-in graph works.
 
 <Note>
-If your application previously read graph relations from the API response (`relations` field on search results), note that this field is no longer populated. Entity relationships are now consumed indirectly through retrieval ranking, not exposed as a separate graph structure.
+If your application previously read graph relations from the API response (`relations` field on search results), note that this field is no longer populated. Entity connections are now applied through retrieval ranking rather than returned as a separate `relations` array.
 </Note>
 
 ## Migration Checklist

docs/platform/features/entity-scoped-memory.mdx

@@ -5,6 +5,10 @@ description: Scope conversations by user, agent, app, and session so memories la
 
 Mem0's Platform API lets you separate memories for different users, agents, and apps. By tagging each write and query with the right identifiers, you can prevent data from mixing between them, maintain clear audit trails, and control data retention.
 
+<Note>
+**Entity IDs vs. graph entities.** This page covers the `user_id` / `agent_id` / `app_id` / `run_id` identifiers used to *scope* memories. These are different from the **graph entities** (the people, places, and concepts surfaced in [Graph Memory](/platform/features/graph-memory)).
+</Note>
+
 <Tip icon="layers">
 Want the long-form tutorial? The <Link href="/cookbooks/essentials/entity-partitioning-playbook">Partition Memories by Entity</Link> cookbook walks through multi-agent storage, debugging, and cleanup step by step.
 </Tip>

docs/platform/features/graph-memory.mdx

@@ -0,0 +1,93 @@
+---
+title: "Graph Memory"
+description: "Mem0 Platform builds a native graph linking people, places, and concepts across your memories, with no external graph database to provision."
+icon: "circle-nodes"
+iconType: "solid"
+---
+
+Mem0 Platform automatically organizes your memories into a **graph**: the **graph entities** mentioned across your memories (the people, places, organizations, and concepts they refer to) become nodes, and memories that share an entity are connected. This is how Mem0 reasons across separate facts, for example linking everything it knows about a person, a company, or a project, without you defining any schema.
+
+Graph Memory is **built in**. There is no Neo4j, Memgraph, or other graph store to deploy, no connection strings to manage, and nothing to enable. It runs natively inside the platform and is always on.
+
+<Info>
+  **Graph Memory matters when…**
+  - You ask entity-centric questions like "what do we know about Alice?" and expect facts pulled from many different conversations
+  - Your app needs multi-hop recall, connecting a fact in one memory to a related fact in another
+  - You previously used an external graph store and want the same cross-memory connections with zero infrastructure
+</Info>
+
+<Note>
+  **Graph entities vs. entity IDs.** The entities in your graph (people, places, and concepts extracted from memory text) are different from the *entity IDs* (`user_id`, `agent_id`, `app_id`, `run_id`) used to scope memories. Those are covered in [Entity-Scoped Memory](/platform/features/entity-scoped-memory).
+</Note>
+
+<Note>
+  Graph Memory is the native successor to Mem0's earlier graph store integration. Earlier versions connected an external graph database (Neo4j and others) and exposed a `relations` field. Mem0 now builds the graph itself from your memories. See [What changed from the external graph store](#what-changed-from-the-external-graph-store) below.
+</Note>
+
+## How it works
+
+Graph Memory is built and used across the two phases of the memory pipeline: **extraction** (when you add memories) and **retrieval** (when you search).
+
+### 1. Entities become nodes
+
+Every time you add a memory, Mem0 extracts the **entities** it contains: the proper nouns, names, and key phrases that identify a specific person, place, organization, product, or concept (for example *Alice*, *San Francisco*, *Acme Corp*, *the Q1 roadmap*). Each distinct entity is stored once and embedded, so entities that refer to the same thing can be matched even when they are phrased differently.
+
+### 2. Shared entities become connections
+
+When the same entity appears in more than one memory, those memories are **linked** through that entity. Over time this forms a graph: a web of entities, each connecting all the memories that mention it. The connections are derived directly from your data. There is no relationship schema to define and nothing to label by hand.
+
+### 3. The graph powers retrieval
+
+At search time, Mem0 extracts the entities from your query and matches them against the graph. Memories connected to those entities receive a ranking boost, which is combined with semantic (vector) and keyword (BM25) scores into the single `score` returned on each result.
+
+This is what lets Mem0 answer entity-centric and multi-hop questions: a query about *Alice* surfaces facts about Alice that live in completely different memories, because the graph connects them. The connecting-facts-across-memories behavior contributes to Mem0's gains on multi-hop and temporal benchmarks. See [Memory Evaluation](/core-concepts/memory-evaluation).
+
+<Info>
+Graph Memory affects **ranking**, not the response shape. Search results come back in the normal format with a combined `score`; there is no separate graph payload to parse.
+</Info>
+
+## What's in the graph
+
+| Element | What it is |
+| --- | --- |
+| **Graph entity** (node) | A distinct person, place, organization, product, or concept extracted from your memories (e.g. *Alice*, *Acme Corp*). Distinct from the user/agent/app/run *entity IDs* used to scope memories. |
+| **Memory node** | An individual memory (fact) stored for a user, agent, or session. |
+| **Connection** | A link between an entity and every memory that mentions it. Two entities are related when they co-occur in one or more memories. |
+
+Graph Memory captures **which entities your memories are about and how they connect through shared context**. It does not assign typed, labeled relationships between entities (it won't, for example, record a "manages" edge from one person to another); connections are inferred from co-occurrence rather than declared. This is what makes it schema-free and zero-configuration.
+
+## Availability
+
+Graph Memory is **automatic and included on all plans**. It activates on the new memory algorithm with no flag, no configuration, and no external dependencies. You don't need to do anything to benefit from it.
+
+```python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+# Entities are extracted and linked into the graph automatically on add
+client.add(
+    messages=[
+        {"role": "user", "content": "I work at Acme Corp with Alice on the Q1 roadmap"}
+    ],
+    user_id="jordan",
+)
+
+# Entity matches from the query are used to connect and boost related memories
+results = client.search(
+    query="who does jordan work with?",
+    filters={"user_id": "jordan"},
+)
+```
+
+## What changed from the external graph store
+
+Earlier versions of Mem0 offered graph memory by connecting an **external graph database** (Neo4j, Memgraph, Kuzu, Apache AGE, or Neptune) through an `enable_graph` flag and a `graph_store` configuration block. That integration has been replaced by **native, built-in Graph Memory**:
+
+- **No external graph store.** The graph is built inside Mem0 from your memories. There is nothing to provision or connect.
+- **Always on, all plans.** The `enable_graph` flag is no longer needed; Graph Memory is automatic. (If you still send the parameter, it is ignored.)
+- **Connections power retrieval directly.** Entity connections are folded into the combined `score` on each result. The standalone `relations` field that the external graph store returned is no longer populated. If your application read that field, see the migration guide below.
+
+<Card title="Platform Migration Guide" icon="arrow-right" href="/migration/platform-v2-to-v3">
+  Full details on the move to the new algorithm, including the `relations` field change.
+</Card>

docs/vibecoding.mdx

@@ -45,10 +45,12 @@ Let your assistant execute an end-to-end workflow in an existing repo. Invoked a
 ```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
 ```
 
 - `/mem0-integrate` — wire Mem0 into an existing repository using a goal-driven, test-first pipeline. Detects the stack, asks whether to use Platform or OSS, writes failing tests first, and keeps the integration additive and feature-flagged.
 - `/mem0-test-integration` — verify what `/mem0-integrate` produced. Runs the repo's native test suite and a real end-to-end smoke flow against your API key, then produces a scorecard.
+- `/mem0-oss-to-platform` — migrate an existing project from Mem0 OSS to the hosted Platform SDK. Audits where Mem0 is used, writes a reviewable migration plan, then executes it on approval.
 
 See the [skills index](https://github.com/mem0ai/mem0/tree/main/skills) for the full catalog.
 

evaluation/Makefile

@@ -1,31 +0,0 @@
-
-# Run the experiments
-run-mem0-add:
-	python run_experiments.py --technique_type mem0 --method add
-
-run-mem0-search:
-	python run_experiments.py --technique_type mem0 --method search --output_folder results/ --top_k 30
-
-run-mem0-plus-add:
-	python run_experiments.py --technique_type mem0 --method add --is_graph
-
-run-mem0-plus-search:
-	python run_experiments.py --technique_type mem0 --method search --is_graph --output_folder results/ --top_k 30
-
-run-rag:
-	python run_experiments.py --technique_type rag --chunk_size 500 --num_chunks 1 --output_folder results/
-
-run-full-context:
-	python run_experiments.py --technique_type rag --chunk_size -1 --num_chunks 1 --output_folder results/
-
-run-langmem:
-	python run_experiments.py --technique_type langmem --output_folder results/
-
-run-zep-add:
-	python run_experiments.py --technique_type zep --method add --output_folder results/
-
-run-zep-search:
-	python run_experiments.py --technique_type zep --method search --output_folder results/
-
-run-openai:
-	python run_experiments.py --technique_type openai --output_folder results/

evaluation/README.md

@@ -1,198 +0,0 @@
-# Mem0: Building Production‑Ready AI Agents with Scalable Long‑Term Memory
-
-[![arXiv](https://img.shields.io/badge/arXiv-Paper-b31b1b.svg)](https://arxiv.org/abs/2504.19413)
-[![Website](https://img.shields.io/badge/Website-Project-blue)](https://mem0.ai/research)
-
-This repository contains the code and dataset for our paper: **Mem0: Building Production‑Ready AI Agents with Scalable Long‑Term Memory**.
-
-## 📋 Overview
-
-This project evaluates Mem0 and compares it with different memory and retrieval techniques for AI systems:
-
-1. **Established LOCOMO Benchmarks**: We evaluate against five established approaches from the literature: LoCoMo, ReadAgent, MemoryBank, MemGPT, and A-Mem.
-2. **Open-Source Memory Solutions**: We test promising open-source memory architectures including LangMem, which provides flexible memory management capabilities.
-3. **RAG Systems**: We implement Retrieval-Augmented Generation with various configurations, testing different chunk sizes and retrieval counts to optimize performance.
-4. **Full-Context Processing**: We examine the effectiveness of passing the entire conversation history within the context window of the LLM as a baseline approach.
-5. **Proprietary Memory Systems**: We evaluate OpenAI's built-in memory feature available in their ChatGPT interface to compare against commercial solutions.
-6. **Third-Party Memory Providers**: We incorporate Zep, a specialized memory management platform designed for AI agents, to assess the performance of dedicated memory infrastructure.
-
-We test these techniques on the LOCOMO dataset, which contains conversational data with various question types to evaluate memory recall and understanding.
-
-## 🔍 Dataset
-
-The LOCOMO dataset used in our experiments can be downloaded from our Google Drive repository:
-
-[Download LOCOMO Dataset](https://drive.google.com/drive/folders/1L-cTjTm0ohMsitsHg4dijSPJtqNflwX-?usp=drive_link)
-
-The dataset contains conversational data specifically designed to test memory recall and understanding across various question types and complexity levels.
-
-Place the dataset files in the `dataset/` directory:
-- `locomo10.json`: Original dataset
-- `locomo10_rag.json`: Dataset formatted for RAG experiments
-
-## 📁 Project Structure
-
-```
-.
-├── src/                  # Source code for different memory techniques
-│   ├── mem0/             # Implementation of the Mem0 technique
-│   ├── openai/           # Implementation of the OpenAI memory
-│   ├── zep/              # Implementation of the Zep memory
-│   ├── rag.py            # Implementation of the RAG technique
-│   └── langmem.py        # Implementation of the Language-based memory
-├── metrics/              # Code for evaluation metrics
-├── results/              # Results of experiments
-├── dataset/              # Dataset files
-├── evals.py              # Evaluation script
-├── run_experiments.py    # Script to run experiments
-├── generate_scores.py    # Script to generate scores from results
-└── prompts.py            # Prompts used for the models
-```
-
-## 🚀 Getting Started
-
-### Prerequisites
-
-Create a `.env` file with your API keys and configurations. The following keys are required:
-
-```
-# OpenAI API key for GPT models and embeddings
-OPENAI_API_KEY="your-openai-api-key"
-
-# Mem0 API keys (for Mem0 and Mem0+ techniques)
-MEM0_API_KEY="your-mem0-api-key"
-MEM0_PROJECT_ID="your-mem0-project-id"
-MEM0_ORGANIZATION_ID="your-mem0-organization-id"
-
-# Model configuration
-MODEL="gpt-4o-mini"  # or your preferred model
-EMBEDDING_MODEL="text-embedding-3-small"  # or your preferred embedding model
-ZEP_API_KEY="api-key-from-zep"
-```
-
-### Running Experiments
-
-You can run experiments using the provided Makefile commands:
-
-#### Memory Techniques
-
-```bash
-# Run Mem0 experiments
-make run-mem0-add         # Add memories using Mem0
-make run-mem0-search      # Search memories using Mem0
-
-# Run Mem0+ experiments (with graph-based search)
-make run-mem0-plus-add    # Add memories using Mem0+
-make run-mem0-plus-search # Search memories using Mem0+
-
-# Run RAG experiments
-make run-rag              # Run RAG with chunk size 500
-make run-full-context     # Run RAG with full context
-
-# Run LangMem experiments
-make run-langmem          # Run LangMem
-
-# Run Zep experiments
-make run-zep-add          # Add memories using Zep
-make run-zep-search       # Search memories using Zep
-
-# Run OpenAI experiments
-make run-openai           # Run OpenAI experiments
-```
-
-Alternatively, you can run experiments directly with custom parameters:
-
-```bash
-python run_experiments.py --technique_type [mem0|rag|langmem] [additional parameters]
-```
-
-#### Command-line Parameters:
-
-| Parameter | Description | Default |
-|-----------|-------------|---------|
-| `--technique_type` | Memory technique to use (mem0, rag, langmem) | mem0 |
-| `--method` | Method to use (add, search) | add |
-| `--chunk_size` | Chunk size for processing | 1000 |
-| `--top_k` | Number of top memories to retrieve | 30 |
-| `--filter_memories` | Whether to filter memories | False |
-| `--is_graph` | Whether to use graph-based search | False |
-| `--num_chunks` | Number of chunks to process for RAG | 1 |
-
-### 📊 Evaluation
-
-To evaluate results, run:
-
-```bash
-python evals.py --input_file [path_to_results] --output_file [output_path]
-```
-
-This script:
-1. Processes each question-answer pair
-2. Calculates BLEU and F1 scores automatically
-3. Uses an LLM judge to evaluate answer correctness
-4. Saves the combined results to the output file
-
-### 📈 Generating Scores
-
-Generate final scores with:
-
-```bash
-python generate_scores.py
-```
-
-This script:
-1. Loads the evaluation metrics data
-2. Calculates mean scores for each category (BLEU, F1, LLM)
-3. Reports the number of questions per category
-4. Calculates overall mean scores across all categories
-
-Example output:
-```
-Mean Scores Per Category:
-         bleu_score  f1_score  llm_score  count
-category                                       
-1           0.xxxx    0.xxxx     0.xxxx     xx
-2           0.xxxx    0.xxxx     0.xxxx     xx
-3           0.xxxx    0.xxxx     0.xxxx     xx
-
-Overall Mean Scores:
-bleu_score    0.xxxx
-f1_score      0.xxxx
-llm_score     0.xxxx
-```
-
-## 📏 Evaluation Metrics
-
-We use several metrics to evaluate the performance of different memory techniques:
-
-1. **BLEU Score**: Measures the similarity between the model's response and the ground truth
-2. **F1 Score**: Measures the harmonic mean of precision and recall
-3. **LLM Score**: A binary score (0 or 1) determined by an LLM judge evaluating the correctness of responses
-4. **Token Consumption**: Number of tokens required to generate final answer.
-5. **Latency**: Time required during search and to generate response.
-
-## 📚 Citation
-
-If you use this code or dataset in your research, please cite our paper:
-
-```bibtex
-@article{mem0,
-  title={Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory},
-  author={Chhikara, Prateek and Khant, Dev and Aryan, Saket and Singh, Taranjeet and Yadav, Deshraj},
-  journal={arXiv preprint arXiv:2504.19413},
-  year={2025}
-}
-```
-
-## 📄 License
-
-[MIT License](LICENSE)
-
-## 👥 Contributors
-
-- [Prateek Chhikara](https://github.com/prateekchhikara)
-- [Dev Khant](https://github.com/Dev-Khant)
-- [Saket Aryan](https://github.com/whysosaket)
-- [Taranjeet Singh](https://github.com/taranjeet)
-- [Deshraj Yadav](https://github.com/deshraj)
-

evaluation/evals.py

@@ -1,81 +0,0 @@
-import argparse
-import concurrent.futures
-import json
-import threading
-from collections import defaultdict
-
-from metrics.llm_judge import evaluate_llm_judge
-from metrics.utils import calculate_bleu_scores, calculate_metrics
-from tqdm import tqdm
-
-
-def process_item(item_data):
-    k, v = item_data
-    local_results = defaultdict(list)
-
-    for item in v:
-        gt_answer = str(item["answer"])
-        pred_answer = str(item["response"])
-        category = str(item["category"])
-        question = str(item["question"])
-
-        # Skip category 5
-        if category == "5":
-            continue
-
-        metrics = calculate_metrics(pred_answer, gt_answer)
-        bleu_scores = calculate_bleu_scores(pred_answer, gt_answer)
-        llm_score = evaluate_llm_judge(question, gt_answer, pred_answer)
-
-        local_results[k].append(
-            {
-                "question": question,
-                "answer": gt_answer,
-                "response": pred_answer,
-                "category": category,
-                "bleu_score": bleu_scores["bleu1"],
-                "f1_score": metrics["f1"],
-                "llm_score": llm_score,
-            }
-        )
-
-    return local_results
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Evaluate RAG results")
-    parser.add_argument(
-        "--input_file", type=str, default="results/rag_results_500_k1.json", help="Path to the input dataset file"
-    )
-    parser.add_argument(
-        "--output_file", type=str, default="evaluation_metrics.json", help="Path to save the evaluation results"
-    )
-    parser.add_argument("--max_workers", type=int, default=10, help="Maximum number of worker threads")
-
-    args = parser.parse_args()
-
-    with open(args.input_file, "r") as f:
-        data = json.load(f)
-
-    results = defaultdict(list)
-    results_lock = threading.Lock()
-
-    # Use ThreadPoolExecutor with specified workers
-    with concurrent.futures.ThreadPoolExecutor(max_workers=args.max_workers) as executor:
-        futures = [executor.submit(process_item, item_data) for item_data in data.items()]
-
-        for future in tqdm(concurrent.futures.as_completed(futures), total=len(futures)):
-            local_results = future.result()
-            with results_lock:
-                for k, items in local_results.items():
-                    results[k].extend(items)
-
-    # Save results to JSON file
-    with open(args.output_file, "w") as f:
-        json.dump(results, f, indent=4)
-
-    print(f"Results saved to {args.output_file}")
-
-
-if __name__ == "__main__":
-    main()

evaluation/generate_scores.py

@@ -1,34 +0,0 @@
-import json
-
-import pandas as pd
-
-# Load the evaluation metrics data
-with open("evaluation_metrics.json", "r") as f:
-    data = json.load(f)
-
-# Flatten the data into a list of question items
-all_items = []
-for key in data:
-    all_items.extend(data[key])
-
-# Convert to DataFrame
-df = pd.DataFrame(all_items)
-
-# Convert category to numeric type
-df["category"] = pd.to_numeric(df["category"])
-
-# Calculate mean scores by category
-result = df.groupby("category").agg({"bleu_score": "mean", "f1_score": "mean", "llm_score": "mean"}).round(4)
-
-# Add count of questions per category
-result["count"] = df.groupby("category").size()
-
-# Print the results
-print("Mean Scores Per Category:")
-print(result)
-
-# Calculate overall means
-overall_means = df.agg({"bleu_score": "mean", "f1_score": "mean", "llm_score": "mean"}).round(4)
-
-print("\nOverall Mean Scores:")
-print(overall_means)

evaluation/metrics/llm_judge.py

@@ -1,130 +0,0 @@
-import argparse
-import json
-from collections import defaultdict
-
-import numpy as np
-from openai import OpenAI
-
-from mem0.memory.utils import extract_json
-
-client = OpenAI()
-
-ACCURACY_PROMPT = """
-Your task is to label an answer to a question as ’CORRECT’ or ’WRONG’. You will be given the following data:
-    (1) a question (posed by one user to another user), 
-    (2) a ’gold’ (ground truth) answer, 
-    (3) a generated answer
-which you will score as CORRECT/WRONG.
-
-The point of the question is to ask about something one user should know about the other user based on their prior conversations.
-The gold answer will usually be a concise and short answer that includes the referenced topic, for example:
-Question: Do you remember what I got the last time I went to Hawaii?
-Gold answer: A shell necklace
-The generated answer might be much longer, but you should be generous with your grading - as long as it touches on the same topic as the gold answer, it should be counted as CORRECT. 
-
-For time related questions, the gold answer will be a specific date, month, year, etc. The generated answer might be much longer or use relative time references (like "last Tuesday" or "next month"), but you should be generous with your grading - as long as it refers to the same date or time period as the gold answer, it should be counted as CORRECT. Even if the format differs (e.g., "May 7th" vs "7 May"), consider it CORRECT if it's the same date.
-
-Now it's time for the real question:
-Question: {question}
-Gold answer: {gold_answer}
-Generated answer: {generated_answer}
-
-First, provide a short (one sentence) explanation of your reasoning, then finish with CORRECT or WRONG. 
-Do NOT include both CORRECT and WRONG in your response, or it will break the evaluation script.
-
-Just return the label CORRECT or WRONG in a json format with the key as "label".
-"""
-
-
-def evaluate_llm_judge(question, gold_answer, generated_answer):
-    """Evaluate the generated answer against the gold answer using an LLM judge."""
-    response = client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=[
-            {
-                "role": "user",
-                "content": ACCURACY_PROMPT.format(
-                    question=question, gold_answer=gold_answer, generated_answer=generated_answer
-                ),
-            }
-        ],
-        response_format={"type": "json_object"},
-        temperature=0.0,
-    )
-    label = json.loads(extract_json(response.choices[0].message.content))["label"]
-    return 1 if label == "CORRECT" else 0
-
-
-def main():
-    """Main function to evaluate RAG results using LLM judge."""
-    parser = argparse.ArgumentParser(description="Evaluate RAG results using LLM judge")
-    parser.add_argument(
-        "--input_file",
-        type=str,
-        default="results/default_run_v4_k30_new_graph.json",
-        help="Path to the input dataset file",
-    )
-
-    args = parser.parse_args()
-
-    dataset_path = args.input_file
-    output_path = f"results/llm_judge_{dataset_path.split('/')[-1]}"
-
-    with open(dataset_path, "r") as f:
-        data = json.load(f)
-
-    LLM_JUDGE = defaultdict(list)
-    RESULTS = defaultdict(list)
-
-    index = 0
-    for k, v in data.items():
-        for x in v:
-            question = x["question"]
-            gold_answer = x["answer"]
-            generated_answer = x["response"]
-            category = x["category"]
-
-            # Skip category 5
-            if int(category) == 5:
-                continue
-
-            # Evaluate the answer
-            label = evaluate_llm_judge(question, gold_answer, generated_answer)
-            LLM_JUDGE[category].append(label)
-
-            # Store the results
-            RESULTS[index].append(
-                {
-                    "question": question,
-                    "gt_answer": gold_answer,
-                    "response": generated_answer,
-                    "category": category,
-                    "llm_label": label,
-                }
-            )
-
-            # Save intermediate results
-            with open(output_path, "w") as f:
-                json.dump(RESULTS, f, indent=4)
-
-            # Print current accuracy for all categories
-            print("All categories accuracy:")
-            for cat, results in LLM_JUDGE.items():
-                if results:  # Only print if there are results for this category
-                    print(f"  Category {cat}: {np.mean(results):.4f} ({sum(results)}/{len(results)})")
-            print("------------------------------------------")
-        index += 1
-
-    # Save final results
-    with open(output_path, "w") as f:
-        json.dump(RESULTS, f, indent=4)
-
-    # Print final summary
-    print("PATH: ", dataset_path)
-    print("------------------------------------------")
-    for k, v in LLM_JUDGE.items():
-        print(k, np.mean(v))
-
-
-if __name__ == "__main__":
-    main()

evaluation/metrics/utils.py

@@ -1,211 +0,0 @@
-"""
-Borrowed from https://github.com/WujiangXu/AgenticMemory/blob/main/utils.py
-
-@article{xu2025mem,
-    title={A-mem: Agentic memory for llm agents},
-    author={Xu, Wujiang and Liang, Zujie and Mei, Kai and Gao, Hang and Tan, Juntao
-           and Zhang, Yongfeng},
-    journal={arXiv preprint arXiv:2502.12110},
-    year={2025}
-}
-"""
-
-import statistics
-from collections import defaultdict
-from typing import Dict, List, Union
-
-import nltk
-from bert_score import score as bert_score
-from nltk.translate.bleu_score import SmoothingFunction, sentence_bleu
-from nltk.translate.meteor_score import meteor_score
-from rouge_score import rouge_scorer
-from sentence_transformers import SentenceTransformer
-
-# from load_dataset import load_locomo_dataset, QA, Turn, Session, Conversation
-from sentence_transformers.util import pytorch_cos_sim
-
-# Download required NLTK data
-try:
-    nltk.download("punkt", quiet=True)
-    nltk.download("wordnet", quiet=True)
-except Exception as e:
-    print(f"Error downloading NLTK data: {e}")
-
-# Initialize SentenceTransformer model (this will be reused)
-try:
-    sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
-except Exception as e:
-    print(f"Warning: Could not load SentenceTransformer model: {e}")
-    sentence_model = None
-
-
-def simple_tokenize(text):
-    """Simple tokenization function."""
-    # Convert to string if not already
-    text = str(text)
-    return text.lower().replace(".", " ").replace(",", " ").replace("!", " ").replace("?", " ").split()
-
-
-def calculate_rouge_scores(prediction: str, reference: str) -> Dict[str, float]:
-    """Calculate ROUGE scores for prediction against reference."""
-    scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=True)
-    scores = scorer.score(reference, prediction)
-    return {
-        "rouge1_f": scores["rouge1"].fmeasure,
-        "rouge2_f": scores["rouge2"].fmeasure,
-        "rougeL_f": scores["rougeL"].fmeasure,
-    }
-
-
-def calculate_bleu_scores(prediction: str, reference: str) -> Dict[str, float]:
-    """Calculate BLEU scores with different n-gram settings."""
-    pred_tokens = nltk.word_tokenize(prediction.lower())
-    ref_tokens = [nltk.word_tokenize(reference.lower())]
-
-    weights_list = [(1, 0, 0, 0), (0.5, 0.5, 0, 0), (0.33, 0.33, 0.33, 0), (0.25, 0.25, 0.25, 0.25)]
-    smooth = SmoothingFunction().method1
-
-    scores = {}
-    for n, weights in enumerate(weights_list, start=1):
-        try:
-            score = sentence_bleu(ref_tokens, pred_tokens, weights=weights, smoothing_function=smooth)
-        except Exception as e:
-            print(f"Error calculating BLEU score: {e}")
-            score = 0.0
-        scores[f"bleu{n}"] = score
-
-    return scores
-
-
-def calculate_bert_scores(prediction: str, reference: str) -> Dict[str, float]:
-    """Calculate BERTScore for semantic similarity."""
-    try:
-        P, R, F1 = bert_score([prediction], [reference], lang="en", verbose=False)
-        return {"bert_precision": P.item(), "bert_recall": R.item(), "bert_f1": F1.item()}
-    except Exception as e:
-        print(f"Error calculating BERTScore: {e}")
-        return {"bert_precision": 0.0, "bert_recall": 0.0, "bert_f1": 0.0}
-
-
-def calculate_meteor_score(prediction: str, reference: str) -> float:
-    """Calculate METEOR score for the prediction."""
-    try:
-        return meteor_score([reference.split()], prediction.split())
-    except Exception as e:
-        print(f"Error calculating METEOR score: {e}")
-        return 0.0
-
-
-def calculate_sentence_similarity(prediction: str, reference: str) -> float:
-    """Calculate sentence embedding similarity using SentenceBERT."""
-    if sentence_model is None:
-        return 0.0
-    try:
-        # Encode sentences
-        embedding1 = sentence_model.encode([prediction], convert_to_tensor=True)
-        embedding2 = sentence_model.encode([reference], convert_to_tensor=True)
-
-        # Calculate cosine similarity
-        similarity = pytorch_cos_sim(embedding1, embedding2).item()
-        return float(similarity)
-    except Exception as e:
-        print(f"Error calculating sentence similarity: {e}")
-        return 0.0
-
-
-def calculate_metrics(prediction: str, reference: str) -> Dict[str, float]:
-    """Calculate comprehensive evaluation metrics for a prediction."""
-    # Handle empty or None values
-    if not prediction or not reference:
-        return {
-            "exact_match": 0,
-            "f1": 0.0,
-            "rouge1_f": 0.0,
-            "rouge2_f": 0.0,
-            "rougeL_f": 0.0,
-            "bleu1": 0.0,
-            "bleu2": 0.0,
-            "bleu3": 0.0,
-            "bleu4": 0.0,
-            "bert_f1": 0.0,
-            "meteor": 0.0,
-            "sbert_similarity": 0.0,
-        }
-
-    # Convert to strings if they're not already
-    prediction = str(prediction).strip()
-    reference = str(reference).strip()
-
-    # Calculate exact match
-    exact_match = int(prediction.lower() == reference.lower())
-
-    # Calculate token-based F1 score
-    pred_tokens = set(simple_tokenize(prediction))
-    ref_tokens = set(simple_tokenize(reference))
-    common_tokens = pred_tokens & ref_tokens
-
-    if not pred_tokens or not ref_tokens:
-        f1 = 0.0
-    else:
-        precision = len(common_tokens) / len(pred_tokens)
-        recall = len(common_tokens) / len(ref_tokens)
-        f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
-
-    # Calculate all scores
-    bleu_scores = calculate_bleu_scores(prediction, reference)
-
-    # Combine all metrics
-    metrics = {
-        "exact_match": exact_match,
-        "f1": f1,
-        **bleu_scores,
-    }
-
-    return metrics
-
-
-def aggregate_metrics(
-    all_metrics: List[Dict[str, float]], all_categories: List[int]
-) -> Dict[str, Dict[str, Union[float, Dict[str, float]]]]:
-    """Calculate aggregate statistics for all metrics, split by category."""
-    if not all_metrics:
-        return {}
-
-    # Initialize aggregates for overall and per-category metrics
-    aggregates = defaultdict(list)
-    category_aggregates = defaultdict(lambda: defaultdict(list))
-
-    # Collect all values for each metric, both overall and per category
-    for metrics, category in zip(all_metrics, all_categories):
-        for metric_name, value in metrics.items():
-            aggregates[metric_name].append(value)
-            category_aggregates[category][metric_name].append(value)
-
-    # Calculate statistics for overall metrics
-    results = {"overall": {}}
-
-    for metric_name, values in aggregates.items():
-        results["overall"][metric_name] = {
-            "mean": statistics.mean(values),
-            "std": statistics.stdev(values) if len(values) > 1 else 0.0,
-            "median": statistics.median(values),
-            "min": min(values),
-            "max": max(values),
-            "count": len(values),
-        }
-
-    # Calculate statistics for each category
-    for category in sorted(category_aggregates.keys()):
-        results[f"category_{category}"] = {}
-        for metric_name, values in category_aggregates[category].items():
-            if values:  # Only calculate if we have values for this category
-                results[f"category_{category}"][metric_name] = {
-                    "mean": statistics.mean(values),
-                    "std": statistics.stdev(values) if len(values) > 1 else 0.0,
-                    "median": statistics.median(values),
-                    "min": min(values),
-                    "max": max(values),
-                    "count": len(values),
-                }
-
-    return results

evaluation/prompts.py

@@ -1,147 +0,0 @@
-ANSWER_PROMPT_GRAPH = """
-    You are an intelligent memory assistant tasked with retrieving accurate information from 
-    conversation memories.
-
-    # CONTEXT:
-    You have access to memories from two speakers in a conversation. These memories contain 
-    timestamped information that may be relevant to answering the question. You also have 
-    access to knowledge graph relations for each user, showing connections between entities, 
-    concepts, and events relevant to that user.
-
-    # INSTRUCTIONS:
-    1. Carefully analyze all provided memories from both speakers
-    2. Pay special attention to the timestamps to determine the answer
-    3. If the question asks about a specific event or fact, look for direct evidence in the 
-       memories
-    4. If the memories contain contradictory information, prioritize the most recent memory
-    5. If there is a question about time references (like "last year", "two months ago", 
-       etc.), calculate the actual date based on the memory timestamp. For example, if a 
-       memory from 4 May 2022 mentions "went to India last year," then the trip occurred 
-       in 2021.
-    6. Always convert relative time references to specific dates, months, or years. For 
-       example, convert "last year" to "2022" or "two months ago" to "March 2023" based 
-       on the memory timestamp. Ignore the reference while answering the question.
-    7. Focus only on the content of the memories from both speakers. Do not confuse 
-       character names mentioned in memories with the actual users who created those 
-       memories.
-    8. The answer should be less than 5-6 words.
-    9. Use the knowledge graph relations to understand the user's knowledge network and 
-       identify important relationships between entities in the user's world.
-
-    # APPROACH (Think step by step):
-    1. First, examine all memories that contain information related to the question
-    2. Examine the timestamps and content of these memories carefully
-    3. Look for explicit mentions of dates, times, locations, or events that answer the 
-       question
-    4. If the answer requires calculation (e.g., converting relative time references), 
-       show your work
-    5. Analyze the knowledge graph relations to understand the user's knowledge context
-    6. Formulate a precise, concise answer based solely on the evidence in the memories
-    7. Double-check that your answer directly addresses the question asked
-    8. Ensure your final answer is specific and avoids vague time references
-
-    Memories for user {{speaker_1_user_id}}:
-
-    {{speaker_1_memories}}
-
-    Relations for user {{speaker_1_user_id}}:
-
-    {{speaker_1_graph_memories}}
-
-    Memories for user {{speaker_2_user_id}}:
-
-    {{speaker_2_memories}}
-
-    Relations for user {{speaker_2_user_id}}:
-
-    {{speaker_2_graph_memories}}
-
-    Question: {{question}}
-
-    Answer:
-    """
-
-
-ANSWER_PROMPT = """
-    You are an intelligent memory assistant tasked with retrieving accurate information from conversation memories.
-
-    # CONTEXT:
-    You have access to memories from two speakers in a conversation. These memories contain 
-    timestamped information that may be relevant to answering the question.
-
-    # INSTRUCTIONS:
-    1. Carefully analyze all provided memories from both speakers
-    2. Pay special attention to the timestamps to determine the answer
-    3. If the question asks about a specific event or fact, look for direct evidence in the memories
-    4. If the memories contain contradictory information, prioritize the most recent memory
-    5. If there is a question about time references (like "last year", "two months ago", etc.), 
-       calculate the actual date based on the memory timestamp. For example, if a memory from 
-       4 May 2022 mentions "went to India last year," then the trip occurred in 2021.
-    6. Always convert relative time references to specific dates, months, or years. For example, 
-       convert "last year" to "2022" or "two months ago" to "March 2023" based on the memory 
-       timestamp. Ignore the reference while answering the question.
-    7. Focus only on the content of the memories from both speakers. Do not confuse character 
-       names mentioned in memories with the actual users who created those memories.
-    8. The answer should be less than 5-6 words.
-
-    # APPROACH (Think step by step):
-    1. First, examine all memories that contain information related to the question
-    2. Examine the timestamps and content of these memories carefully
-    3. Look for explicit mentions of dates, times, locations, or events that answer the question
-    4. If the answer requires calculation (e.g., converting relative time references), show your work
-    5. Formulate a precise, concise answer based solely on the evidence in the memories
-    6. Double-check that your answer directly addresses the question asked
-    7. Ensure your final answer is specific and avoids vague time references
-
-    Memories for user {{speaker_1_user_id}}:
-
-    {{speaker_1_memories}}
-
-    Memories for user {{speaker_2_user_id}}:
-
-    {{speaker_2_memories}}
-
-    Question: {{question}}
-
-    Answer:
-    """
-
-
-ANSWER_PROMPT_ZEP = """
-    You are an intelligent memory assistant tasked with retrieving accurate information from conversation memories.
-
-    # CONTEXT:
-    You have access to memories from a conversation. These memories contain
-    timestamped information that may be relevant to answering the question.
-
-    # INSTRUCTIONS:
-    1. Carefully analyze all provided memories
-    2. Pay special attention to the timestamps to determine the answer
-    3. If the question asks about a specific event or fact, look for direct evidence in the memories
-    4. If the memories contain contradictory information, prioritize the most recent memory
-    5. If there is a question about time references (like "last year", "two months ago", etc.), 
-       calculate the actual date based on the memory timestamp. For example, if a memory from 
-       4 May 2022 mentions "went to India last year," then the trip occurred in 2021.
-    6. Always convert relative time references to specific dates, months, or years. For example, 
-       convert "last year" to "2022" or "two months ago" to "March 2023" based on the memory 
-       timestamp. Ignore the reference while answering the question.
-    7. Focus only on the content of the memories. Do not confuse character 
-       names mentioned in memories with the actual users who created those memories.
-    8. The answer should be less than 5-6 words.
-
-    # APPROACH (Think step by step):
-    1. First, examine all memories that contain information related to the question
-    2. Examine the timestamps and content of these memories carefully
-    3. Look for explicit mentions of dates, times, locations, or events that answer the question
-    4. If the answer requires calculation (e.g., converting relative time references), show your work
-    5. Formulate a precise, concise answer based solely on the evidence in the memories
-    6. Double-check that your answer directly addresses the question asked
-    7. Ensure your final answer is specific and avoids vague time references
-
-    Memories:
-
-    {{memories}}
-
-    Question: {{question}}
-    Answer:
-    """

evaluation/run_experiments.py

@@ -1,75 +0,0 @@
-import argparse
-import os
-
-from src.langmem import LangMemManager
-from src.memzero.add import MemoryADD
-from src.memzero.search import MemorySearch
-from src.openai.predict import OpenAIPredict
-from src.rag import RAGManager
-from src.utils import METHODS, TECHNIQUES
-from src.zep.add import ZepAdd
-from src.zep.search import ZepSearch
-
-
-class Experiment:
-    def __init__(self, technique_type, chunk_size):
-        self.technique_type = technique_type
-        self.chunk_size = chunk_size
-
-    def run(self):
-        print(f"Running experiment with technique: {self.technique_type}, chunk size: {self.chunk_size}")
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Run memory experiments")
-    parser.add_argument("--technique_type", choices=TECHNIQUES, default="mem0", help="Memory technique to use")
-    parser.add_argument("--method", choices=METHODS, default="add", help="Method to use")
-    parser.add_argument("--chunk_size", type=int, default=1000, help="Chunk size for processing")
-    parser.add_argument("--output_folder", type=str, default="results/", help="Output path for results")
-    parser.add_argument("--top_k", type=int, default=30, help="Number of top memories to retrieve")
-    parser.add_argument("--filter_memories", action="store_true", default=False, help="Whether to filter memories")
-    parser.add_argument("--is_graph", action="store_true", default=False, help="Whether to use graph-based search")
-    parser.add_argument("--num_chunks", type=int, default=1, help="Number of chunks to process")
-
-    args = parser.parse_args()
-
-    # Add your experiment logic here
-    print(f"Running experiments with technique: {args.technique_type}, chunk size: {args.chunk_size}")
-
-    if args.technique_type == "mem0":
-        if args.method == "add":
-            memory_manager = MemoryADD(data_path="dataset/locomo10.json", is_graph=args.is_graph)
-            memory_manager.process_all_conversations()
-        elif args.method == "search":
-            output_file_path = os.path.join(
-                args.output_folder,
-                f"mem0_results_top_{args.top_k}_filter_{args.filter_memories}_graph_{args.is_graph}.json",
-            )
-            memory_searcher = MemorySearch(output_file_path, args.top_k, args.filter_memories, args.is_graph)
-            memory_searcher.process_data_file("dataset/locomo10.json")
-    elif args.technique_type == "rag":
-        output_file_path = os.path.join(args.output_folder, f"rag_results_{args.chunk_size}_k{args.num_chunks}.json")
-        rag_manager = RAGManager(data_path="dataset/locomo10_rag.json", chunk_size=args.chunk_size, k=args.num_chunks)
-        rag_manager.process_all_conversations(output_file_path)
-    elif args.technique_type == "langmem":
-        output_file_path = os.path.join(args.output_folder, "langmem_results.json")
-        langmem_manager = LangMemManager(dataset_path="dataset/locomo10_rag.json")
-        langmem_manager.process_all_conversations(output_file_path)
-    elif args.technique_type == "zep":
-        if args.method == "add":
-            zep_manager = ZepAdd(data_path="dataset/locomo10.json")
-            zep_manager.process_all_conversations("1")
-        elif args.method == "search":
-            output_file_path = os.path.join(args.output_folder, "zep_search_results.json")
-            zep_manager = ZepSearch()
-            zep_manager.process_data_file("dataset/locomo10.json", "1", output_file_path)
-    elif args.technique_type == "openai":
-        output_file_path = os.path.join(args.output_folder, "openai_results.json")
-        openai_manager = OpenAIPredict()
-        openai_manager.process_data_file("dataset/locomo10.json", output_file_path)
-    else:
-        raise ValueError(f"Invalid technique type: {args.technique_type}")
-
-
-if __name__ == "__main__":
-    main()

evaluation/src/langmem.py

@@ -1,185 +0,0 @@
-import json
-import multiprocessing as mp
-import os
-import time
-from collections import defaultdict
-
-from dotenv import load_dotenv
-from jinja2 import Template
-from langgraph.checkpoint.memory import MemorySaver
-from langgraph.prebuilt import create_react_agent
-from langgraph.store.memory import InMemoryStore
-from langgraph.utils.config import get_store
-from langmem import create_manage_memory_tool, create_search_memory_tool
-from openai import OpenAI
-from prompts import ANSWER_PROMPT
-from tqdm import tqdm
-
-load_dotenv()
-
-client = OpenAI()
-
-ANSWER_PROMPT_TEMPLATE = Template(ANSWER_PROMPT)
-
-
-def get_answer(question, speaker_1_user_id, speaker_1_memories, speaker_2_user_id, speaker_2_memories):
-    prompt = ANSWER_PROMPT_TEMPLATE.render(
-        question=question,
-        speaker_1_user_id=speaker_1_user_id,
-        speaker_1_memories=speaker_1_memories,
-        speaker_2_user_id=speaker_2_user_id,
-        speaker_2_memories=speaker_2_memories,
-    )
-
-    t1 = time.time()
-    response = client.chat.completions.create(
-        model=os.getenv("MODEL"), messages=[{"role": "system", "content": prompt}], temperature=0.0
-    )
-    t2 = time.time()
-    return response.choices[0].message.content, t2 - t1
-
-
-def prompt(state):
-    """Prepare the messages for the LLM."""
-    store = get_store()
-    memories = store.search(
-        ("memories",),
-        query=state["messages"][-1].content,
-    )
-    system_msg = f"""You are a helpful assistant.
-
-## Memories
-<memories>
-{memories}
-</memories>
-"""
-    return [{"role": "system", "content": system_msg}, *state["messages"]]
-
-
-class LangMem:
-    def __init__(
-        self,
-    ):
-        self.store = InMemoryStore(
-            index={
-                "dims": 1536,
-                "embed": f"openai:{os.getenv('EMBEDDING_MODEL')}",
-            }
-        )
-        self.checkpointer = MemorySaver()  # Checkpoint graph state
-
-        self.agent = create_react_agent(
-            f"openai:{os.getenv('MODEL')}",
-            prompt=prompt,
-            tools=[
-                create_manage_memory_tool(namespace=("memories",)),
-                create_search_memory_tool(namespace=("memories",)),
-            ],
-            store=self.store,
-            checkpointer=self.checkpointer,
-        )
-
-    def add_memory(self, message, config):
-        return self.agent.invoke({"messages": [{"role": "user", "content": message}]}, config=config)
-
-    def search_memory(self, query, config):
-        try:
-            t1 = time.time()
-            response = self.agent.invoke({"messages": [{"role": "user", "content": query}]}, config=config)
-            t2 = time.time()
-            return response["messages"][-1].content, t2 - t1
-        except Exception as e:
-            print(f"Error in search_memory: {e}")
-            return "", t2 - t1
-
-
-class LangMemManager:
-    def __init__(self, dataset_path):
-        self.dataset_path = dataset_path
-        with open(self.dataset_path, "r") as f:
-            self.data = json.load(f)
-
-    def process_all_conversations(self, output_file_path):
-        OUTPUT = defaultdict(list)
-
-        # Process conversations in parallel with multiple workers
-        def process_conversation(key_value_pair):
-            key, value = key_value_pair
-            result = defaultdict(list)
-
-            chat_history = value["conversation"]
-            questions = value["question"]
-
-            agent1 = LangMem()
-            agent2 = LangMem()
-            config = {"configurable": {"thread_id": f"thread-{key}"}}
-            speakers = set()
-
-            # Identify speakers
-            for conv in chat_history:
-                speakers.add(conv["speaker"])
-
-            if len(speakers) != 2:
-                raise ValueError(f"Expected 2 speakers, got {len(speakers)}")
-
-            speaker1 = list(speakers)[0]
-            speaker2 = list(speakers)[1]
-
-            # Add memories for each message
-            for conv in tqdm(chat_history, desc=f"Processing messages {key}", leave=False):
-                message = f"{conv['timestamp']} | {conv['speaker']}: {conv['text']}"
-                if conv["speaker"] == speaker1:
-                    agent1.add_memory(message, config)
-                elif conv["speaker"] == speaker2:
-                    agent2.add_memory(message, config)
-                else:
-                    raise ValueError(f"Expected speaker1 or speaker2, got {conv['speaker']}")
-
-            # Process questions
-            for q in tqdm(questions, desc=f"Processing questions {key}", leave=False):
-                category = q["category"]
-
-                if int(category) == 5:
-                    continue
-
-                answer = q["answer"]
-                question = q["question"]
-                response1, speaker1_memory_time = agent1.search_memory(question, config)
-                response2, speaker2_memory_time = agent2.search_memory(question, config)
-
-                generated_answer, response_time = get_answer(question, speaker1, response1, speaker2, response2)
-
-                result[key].append(
-                    {
-                        "question": question,
-                        "answer": answer,
-                        "response1": response1,
-                        "response2": response2,
-                        "category": category,
-                        "speaker1_memory_time": speaker1_memory_time,
-                        "speaker2_memory_time": speaker2_memory_time,
-                        "response_time": response_time,
-                        "response": generated_answer,
-                    }
-                )
-
-            return result
-
-        # Use multiprocessing to process conversations in parallel
-        with mp.Pool(processes=10) as pool:
-            results = list(
-                tqdm(
-                    pool.imap(process_conversation, list(self.data.items())),
-                    total=len(self.data),
-                    desc="Processing conversations",
-                )
-            )
-
-        # Combine results from all workers
-        for result in results:
-            for key, items in result.items():
-                OUTPUT[key].extend(items)
-
-        # Save final results
-        with open(output_file_path, "w") as f:
-            json.dump(OUTPUT, f, indent=4)

evaluation/src/memzero/add.py

@@ -1,141 +0,0 @@
-import json
-import os
-import threading
-import time
-from concurrent.futures import ThreadPoolExecutor
-
-from dotenv import load_dotenv
-from tqdm import tqdm
-
-from mem0 import MemoryClient
-
-load_dotenv()
-
-
-# Update custom instructions
-custom_instructions = """
-Generate personal memories that follow these guidelines:
-
-1. Each memory should be self-contained with complete context, including:
-   - The person's name, do not use "user" while creating memories
-   - Personal details (career aspirations, hobbies, life circumstances)
-   - Emotional states and reactions
-   - Ongoing journeys or future plans
-   - Specific dates when events occurred
-
-2. Include meaningful personal narratives focusing on:
-   - Identity and self-acceptance journeys
-   - Family planning and parenting
-   - Creative outlets and hobbies
-   - Mental health and self-care activities
-   - Career aspirations and education goals
-   - Important life events and milestones
-
-3. Make each memory rich with specific details rather than general statements
-   - Include timeframes (exact dates when possible)
-   - Name specific activities (e.g., "charity race for mental health" rather than just "exercise")
-   - Include emotional context and personal growth elements
-
-4. Extract memories only from user messages, not incorporating assistant responses
-
-5. Format each memory as a paragraph with a clear narrative structure that captures the person's experience, challenges, and aspirations
-"""
-
-
-class MemoryADD:
-    def __init__(self, data_path=None, batch_size=2, is_graph=False):
-        self.mem0_client = MemoryClient(
-            api_key=os.getenv("MEM0_API_KEY"),
-            org_id=os.getenv("MEM0_ORGANIZATION_ID"),
-            project_id=os.getenv("MEM0_PROJECT_ID"),
-        )
-
-        self.mem0_client.update_project(custom_instructions=custom_instructions)
-        self.batch_size = batch_size
-        self.data_path = data_path
-        self.data = None
-        self.is_graph = is_graph
-        if data_path:
-            self.load_data()
-
-    def load_data(self):
-        with open(self.data_path, "r") as f:
-            self.data = json.load(f)
-        return self.data
-
-    def add_memory(self, user_id, message, metadata, retries=3):
-        for attempt in range(retries):
-            try:
-                _ = self.mem0_client.add(
-                    message, user_id=user_id, version="v2", metadata=metadata, enable_graph=self.is_graph
-                )
-                return
-            except Exception as e:
-                if attempt < retries - 1:
-                    time.sleep(1)  # Wait before retrying
-                    continue
-                else:
-                    raise e
-
-    def add_memories_for_speaker(self, speaker, messages, timestamp, desc):
-        for i in tqdm(range(0, len(messages), self.batch_size), desc=desc):
-            batch_messages = messages[i : i + self.batch_size]
-            self.add_memory(speaker, batch_messages, metadata={"timestamp": timestamp})
-
-    def process_conversation(self, item, idx):
-        conversation = item["conversation"]
-        speaker_a = conversation["speaker_a"]
-        speaker_b = conversation["speaker_b"]
-
-        speaker_a_user_id = f"{speaker_a}_{idx}"
-        speaker_b_user_id = f"{speaker_b}_{idx}"
-
-        # delete all memories for the two users
-        self.mem0_client.delete_all(user_id=speaker_a_user_id)
-        self.mem0_client.delete_all(user_id=speaker_b_user_id)
-
-        for key in conversation.keys():
-            if key in ["speaker_a", "speaker_b"] or "date" in key or "timestamp" in key:
-                continue
-
-            date_time_key = key + "_date_time"
-            timestamp = conversation[date_time_key]
-            chats = conversation[key]
-
-            messages = []
-            messages_reverse = []
-            for chat in chats:
-                if chat["speaker"] == speaker_a:
-                    messages.append({"role": "user", "content": f"{speaker_a}: {chat['text']}"})
-                    messages_reverse.append({"role": "assistant", "content": f"{speaker_a}: {chat['text']}"})
-                elif chat["speaker"] == speaker_b:
-                    messages.append({"role": "assistant", "content": f"{speaker_b}: {chat['text']}"})
-                    messages_reverse.append({"role": "user", "content": f"{speaker_b}: {chat['text']}"})
-                else:
-                    raise ValueError(f"Unknown speaker: {chat['speaker']}")
-
-            # add memories for the two users on different threads
-            thread_a = threading.Thread(
-                target=self.add_memories_for_speaker,
-                args=(speaker_a_user_id, messages, timestamp, "Adding Memories for Speaker A"),
-            )
-            thread_b = threading.Thread(
-                target=self.add_memories_for_speaker,
-                args=(speaker_b_user_id, messages_reverse, timestamp, "Adding Memories for Speaker B"),
-            )
-
-            thread_a.start()
-            thread_b.start()
-            thread_a.join()
-            thread_b.join()
-
-        print("Messages added successfully")
-
-    def process_all_conversations(self, max_workers=10):
-        if not self.data:
-            raise ValueError("No data loaded. Please set data_path and call load_data() first.")
-        with ThreadPoolExecutor(max_workers=max_workers) as executor:
-            futures = [executor.submit(self.process_conversation, item, idx) for idx, item in enumerate(self.data)]
-
-            for future in futures:
-                future.result()

evaluation/src/memzero/search.py

@@ -1,215 +0,0 @@
-import json
-import os
-import time
-from collections import defaultdict
-from concurrent.futures import ThreadPoolExecutor
-
-from dotenv import load_dotenv
-from jinja2 import Template
-from openai import OpenAI
-from prompts import ANSWER_PROMPT, ANSWER_PROMPT_GRAPH
-from tqdm import tqdm
-
-from mem0 import MemoryClient
-
-load_dotenv()
-
-
-class MemorySearch:
-    def __init__(self, output_path="results.json", top_k=10, filter_memories=False, is_graph=False):
-        self.mem0_client = MemoryClient(
-            api_key=os.getenv("MEM0_API_KEY"),
-            org_id=os.getenv("MEM0_ORGANIZATION_ID"),
-            project_id=os.getenv("MEM0_PROJECT_ID"),
-        )
-        self.top_k = top_k
-        self.openai_client = OpenAI()
-        self.results = defaultdict(list)
-        self.output_path = output_path
-        self.filter_memories = filter_memories
-        self.is_graph = is_graph
-
-        if self.is_graph:
-            self.ANSWER_PROMPT = ANSWER_PROMPT_GRAPH
-        else:
-            self.ANSWER_PROMPT = ANSWER_PROMPT
-
-    def search_memory(self, user_id, query, max_retries=3, retry_delay=1):
-        start_time = time.time()
-        retries = 0
-        while retries < max_retries:
-            try:
-                if self.is_graph:
-                    print("Searching with graph")
-                    memories = self.mem0_client.search(
-                        query,
-                        user_id=user_id,
-                        top_k=self.top_k,
-                        filter_memories=self.filter_memories,
-                        enable_graph=True,
-                        output_format="v1.1",
-                    )
-                else:
-                    memories = self.mem0_client.search(
-                        query, user_id=user_id, top_k=self.top_k, filter_memories=self.filter_memories
-                    )
-                break
-            except Exception as e:
-                print("Retrying...")
-                retries += 1
-                if retries >= max_retries:
-                    raise e
-                time.sleep(retry_delay)
-
-        end_time = time.time()
-        if not self.is_graph:
-            semantic_memories = [
-                {
-                    "memory": memory["memory"],
-                    "timestamp": memory["metadata"]["timestamp"],
-                    "score": round(memory["score"], 2),
-                }
-                for memory in memories
-            ]
-            graph_memories = None
-        else:
-            semantic_memories = [
-                {
-                    "memory": memory["memory"],
-                    "timestamp": memory["metadata"]["timestamp"],
-                    "score": round(memory["score"], 2),
-                }
-                for memory in memories["results"]
-            ]
-            graph_memories = [
-                {"source": relation["source"], "relationship": relation["relationship"], "target": relation["target"]}
-                for relation in memories["relations"]
-            ]
-        return semantic_memories, graph_memories, end_time - start_time
-
-    def answer_question(self, speaker_1_user_id, speaker_2_user_id, question, answer, category):
-        speaker_1_memories, speaker_1_graph_memories, speaker_1_memory_time = self.search_memory(
-            speaker_1_user_id, question
-        )
-        speaker_2_memories, speaker_2_graph_memories, speaker_2_memory_time = self.search_memory(
-            speaker_2_user_id, question
-        )
-
-        search_1_memory = [f"{item['timestamp']}: {item['memory']}" for item in speaker_1_memories]
-        search_2_memory = [f"{item['timestamp']}: {item['memory']}" for item in speaker_2_memories]
-
-        template = Template(self.ANSWER_PROMPT)
-        answer_prompt = template.render(
-            speaker_1_user_id=speaker_1_user_id.split("_")[0],
-            speaker_2_user_id=speaker_2_user_id.split("_")[0],
-            speaker_1_memories=json.dumps(search_1_memory, indent=4),
-            speaker_2_memories=json.dumps(search_2_memory, indent=4),
-            speaker_1_graph_memories=json.dumps(speaker_1_graph_memories, indent=4),
-            speaker_2_graph_memories=json.dumps(speaker_2_graph_memories, indent=4),
-            question=question,
-        )
-
-        t1 = time.time()
-        response = self.openai_client.chat.completions.create(
-            model=os.getenv("MODEL"), messages=[{"role": "system", "content": answer_prompt}], temperature=0.0
-        )
-        t2 = time.time()
-        response_time = t2 - t1
-        return (
-            response.choices[0].message.content,
-            speaker_1_memories,
-            speaker_2_memories,
-            speaker_1_memory_time,
-            speaker_2_memory_time,
-            speaker_1_graph_memories,
-            speaker_2_graph_memories,
-            response_time,
-        )
-
-    def process_question(self, val, speaker_a_user_id, speaker_b_user_id):
-        question = val.get("question", "")
-        answer = val.get("answer", "")
-        category = val.get("category", -1)
-        evidence = val.get("evidence", [])
-        adversarial_answer = val.get("adversarial_answer", "")
-
-        (
-            response,
-            speaker_1_memories,
-            speaker_2_memories,
-            speaker_1_memory_time,
-            speaker_2_memory_time,
-            speaker_1_graph_memories,
-            speaker_2_graph_memories,
-            response_time,
-        ) = self.answer_question(speaker_a_user_id, speaker_b_user_id, question, answer, category)
-
-        result = {
-            "question": question,
-            "answer": answer,
-            "category": category,
-            "evidence": evidence,
-            "response": response,
-            "adversarial_answer": adversarial_answer,
-            "speaker_1_memories": speaker_1_memories,
-            "speaker_2_memories": speaker_2_memories,
-            "num_speaker_1_memories": len(speaker_1_memories),
-            "num_speaker_2_memories": len(speaker_2_memories),
-            "speaker_1_memory_time": speaker_1_memory_time,
-            "speaker_2_memory_time": speaker_2_memory_time,
-            "speaker_1_graph_memories": speaker_1_graph_memories,
-            "speaker_2_graph_memories": speaker_2_graph_memories,
-            "response_time": response_time,
-        }
-
-        # Save results after each question is processed
-        with open(self.output_path, "w") as f:
-            json.dump(self.results, f, indent=4)
-
-        return result
-
-    def process_data_file(self, file_path):
-        with open(file_path, "r") as f:
-            data = json.load(f)
-
-        for idx, item in tqdm(enumerate(data), total=len(data), desc="Processing conversations"):
-            qa = item["qa"]
-            conversation = item["conversation"]
-            speaker_a = conversation["speaker_a"]
-            speaker_b = conversation["speaker_b"]
-
-            speaker_a_user_id = f"{speaker_a}_{idx}"
-            speaker_b_user_id = f"{speaker_b}_{idx}"
-
-            for question_item in tqdm(
-                qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False
-            ):
-                result = self.process_question(question_item, speaker_a_user_id, speaker_b_user_id)
-                self.results[idx].append(result)
-
-                # Save results after each question is processed
-                with open(self.output_path, "w") as f:
-                    json.dump(self.results, f, indent=4)
-
-        # Final save at the end
-        with open(self.output_path, "w") as f:
-            json.dump(self.results, f, indent=4)
-
-    def process_questions_parallel(self, qa_list, speaker_a_user_id, speaker_b_user_id, max_workers=1):
-        def process_single_question(val):
-            result = self.process_question(val, speaker_a_user_id, speaker_b_user_id)
-            # Save results after each question is processed
-            with open(self.output_path, "w") as f:
-                json.dump(self.results, f, indent=4)
-            return result
-
-        with ThreadPoolExecutor(max_workers=max_workers) as executor:
-            results = list(
-                tqdm(executor.map(process_single_question, qa_list), total=len(qa_list), desc="Answering Questions")
-            )
-
-        # Final save at the end
-        with open(self.output_path, "w") as f:
-            json.dump(self.results, f, indent=4)
-
-        return results

evaluation/src/openai/predict.py

@@ -1,131 +0,0 @@
-import argparse
-import json
-import os
-import time
-from collections import defaultdict
-
-from dotenv import load_dotenv
-from jinja2 import Template
-from openai import OpenAI
-from tqdm import tqdm
-
-load_dotenv()
-
-
-ANSWER_PROMPT = """
-    You are an intelligent memory assistant tasked with retrieving accurate information from conversation memories.
-
-    # CONTEXT:
-    You have access to memories from a conversation. These memories contain
-    timestamped information that may be relevant to answering the question.
-
-    # INSTRUCTIONS:
-    1. Carefully analyze all provided memories
-    2. Pay special attention to the timestamps to determine the answer
-    3. If the question asks about a specific event or fact, look for direct evidence in the memories
-    4. If the memories contain contradictory information, prioritize the most recent memory
-    5. If there is a question about time references (like "last year", "two months ago", etc.), 
-       calculate the actual date based on the memory timestamp. For example, if a memory from 
-       4 May 2022 mentions "went to India last year," then the trip occurred in 2021.
-    6. Always convert relative time references to specific dates, months, or years. For example, 
-       convert "last year" to "2022" or "two months ago" to "March 2023" based on the memory 
-       timestamp. Ignore the reference while answering the question.
-    7. Focus only on the content of the memories. Do not confuse character 
-       names mentioned in memories with the actual users who created those memories.
-    8. The answer should be less than 5-6 words.
-
-    # APPROACH (Think step by step):
-    1. First, examine all memories that contain information related to the question
-    2. Examine the timestamps and content of these memories carefully
-    3. Look for explicit mentions of dates, times, locations, or events that answer the question
-    4. If the answer requires calculation (e.g., converting relative time references), show your work
-    5. Formulate a precise, concise answer based solely on the evidence in the memories
-    6. Double-check that your answer directly addresses the question asked
-    7. Ensure your final answer is specific and avoids vague time references
-
-    Memories:
-
-    {{memories}}
-
-    Question: {{question}}
-    Answer:
-    """
-
-
-class OpenAIPredict:
-    def __init__(self, model="gpt-4o-mini"):
-        self.model = model
-        self.openai_client = OpenAI()
-        self.results = defaultdict(list)
-
-    def search_memory(self, idx):
-        with open(f"memories/{idx}.txt", "r") as file:
-            memories = file.read()
-
-        return memories, 0
-
-    def process_question(self, val, idx):
-        question = val.get("question", "")
-        answer = val.get("answer", "")
-        category = val.get("category", -1)
-        evidence = val.get("evidence", [])
-        adversarial_answer = val.get("adversarial_answer", "")
-
-        response, search_memory_time, response_time, context = self.answer_question(idx, question)
-
-        result = {
-            "question": question,
-            "answer": answer,
-            "category": category,
-            "evidence": evidence,
-            "response": response,
-            "adversarial_answer": adversarial_answer,
-            "search_memory_time": search_memory_time,
-            "response_time": response_time,
-            "context": context,
-        }
-
-        return result
-
-    def answer_question(self, idx, question):
-        memories, search_memory_time = self.search_memory(idx)
-
-        template = Template(ANSWER_PROMPT)
-        answer_prompt = template.render(memories=memories, question=question)
-
-        t1 = time.time()
-        response = self.openai_client.chat.completions.create(
-            model=os.getenv("MODEL"), messages=[{"role": "system", "content": answer_prompt}], temperature=0.0
-        )
-        t2 = time.time()
-        response_time = t2 - t1
-        return response.choices[0].message.content, search_memory_time, response_time, memories
-
-    def process_data_file(self, file_path, output_file_path):
-        with open(file_path, "r") as f:
-            data = json.load(f)
-
-        for idx, item in tqdm(enumerate(data), total=len(data), desc="Processing conversations"):
-            qa = item["qa"]
-
-            for question_item in tqdm(
-                qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False
-            ):
-                result = self.process_question(question_item, idx)
-                self.results[idx].append(result)
-
-                # Save results after each question is processed
-                with open(output_file_path, "w") as f:
-                    json.dump(self.results, f, indent=4)
-
-        # Final save at the end
-        with open(output_file_path, "w") as f:
-            json.dump(self.results, f, indent=4)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--output_file_path", type=str, required=True)
-    args = parser.parse_args()
-    openai_predict = OpenAIPredict()
-    openai_predict.process_data_file("../../dataset/locomo10.json", args.output_file_path)

evaluation/src/rag.py

@@ -1,183 +0,0 @@
-import json
-import os
-import time
-from collections import defaultdict
-
-import numpy as np
-import tiktoken
-from dotenv import load_dotenv
-from jinja2 import Template
-from openai import OpenAI
-from tqdm import tqdm
-
-load_dotenv()
-
-PROMPT = """
-# Question: 
-{{QUESTION}}
-
-# Context: 
-{{CONTEXT}}
-
-# Short answer:
-"""
-
-
-class RAGManager:
-    def __init__(self, data_path="dataset/locomo10_rag.json", chunk_size=500, k=1):
-        self.model = os.getenv("MODEL")
-        self.client = OpenAI()
-        self.data_path = data_path
-        self.chunk_size = chunk_size
-        self.k = k
-
-    def generate_response(self, question, context):
-        template = Template(PROMPT)
-        prompt = template.render(CONTEXT=context, QUESTION=question)
-
-        max_retries = 3
-        retries = 0
-
-        while retries <= max_retries:
-            try:
-                t1 = time.time()
-                response = self.client.chat.completions.create(
-                    model=self.model,
-                    messages=[
-                        {
-                            "role": "system",
-                            "content": "You are a helpful assistant that can answer "
-                            "questions based on the provided context."
-                            "If the question involves timing, use the conversation date for reference."
-                            "Provide the shortest possible answer."
-                            "Use words directly from the conversation when possible."
-                            "Avoid using subjects in your answer.",
-                        },
-                        {"role": "user", "content": prompt},
-                    ],
-                    temperature=0,
-                )
-                t2 = time.time()
-                return response.choices[0].message.content.strip(), t2 - t1
-            except Exception as e:
-                retries += 1
-                if retries > max_retries:
-                    raise e
-                time.sleep(1)  # Wait before retrying
-
-    def clean_chat_history(self, chat_history):
-        cleaned_chat_history = ""
-        for c in chat_history:
-            cleaned_chat_history += f"{c['timestamp']} | {c['speaker']}: {c['text']}\n"
-
-        return cleaned_chat_history
-
-    def calculate_embedding(self, document):
-        response = self.client.embeddings.create(model=os.getenv("EMBEDDING_MODEL"), input=document)
-        return response.data[0].embedding
-
-    def calculate_similarity(self, embedding1, embedding2):
-        return np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
-
-    def search(self, query, chunks, embeddings, k=1):
-        """
-        Search for the top-k most similar chunks to the query.
-
-        Args:
-            query: The query string
-            chunks: List of text chunks
-            embeddings: List of embeddings for each chunk
-            k: Number of top chunks to return (default: 1)
-
-        Returns:
-            combined_chunks: The combined text of the top-k chunks
-            search_time: Time taken for the search
-        """
-        t1 = time.time()
-        query_embedding = self.calculate_embedding(query)
-        similarities = [self.calculate_similarity(query_embedding, embedding) for embedding in embeddings]
-
-        # Get indices of top-k most similar chunks
-        if k == 1:
-            # Original behavior - just get the most similar chunk
-            top_indices = [np.argmax(similarities)]
-        else:
-            # Get indices of top-k chunks
-            top_indices = np.argsort(similarities)[-k:][::-1]
-
-        # Combine the top-k chunks
-        combined_chunks = "\n<->\n".join([chunks[i] for i in top_indices])
-
-        t2 = time.time()
-        return combined_chunks, t2 - t1
-
-    def create_chunks(self, chat_history, chunk_size=500):
-        """
-        Create chunks using tiktoken for more accurate token counting
-        """
-        # Get the encoding for the model
-        encoding = tiktoken.encoding_for_model(os.getenv("EMBEDDING_MODEL"))
-
-        documents = self.clean_chat_history(chat_history)
-
-        if chunk_size == -1:
-            return [documents], []
-
-        chunks = []
-
-        # Encode the document
-        tokens = encoding.encode(documents)
-
-        # Split into chunks based on token count
-        for i in range(0, len(tokens), chunk_size):
-            chunk_tokens = tokens[i : i + chunk_size]
-            chunk = encoding.decode(chunk_tokens)
-            chunks.append(chunk)
-
-        embeddings = []
-        for chunk in chunks:
-            embedding = self.calculate_embedding(chunk)
-            embeddings.append(embedding)
-
-        return chunks, embeddings
-
-    def process_all_conversations(self, output_file_path):
-        with open(self.data_path, "r") as f:
-            data = json.load(f)
-
-        FINAL_RESULTS = defaultdict(list)
-        for key, value in tqdm(data.items(), desc="Processing conversations"):
-            chat_history = value["conversation"]
-            questions = value["question"]
-
-            chunks, embeddings = self.create_chunks(chat_history, self.chunk_size)
-
-            for item in tqdm(questions, desc="Answering questions", leave=False):
-                question = item["question"]
-                answer = item.get("answer", "")
-                category = item["category"]
-
-                if self.chunk_size == -1:
-                    context = chunks[0]
-                    search_time = 0
-                else:
-                    context, search_time = self.search(question, chunks, embeddings, k=self.k)
-                response, response_time = self.generate_response(question, context)
-
-                FINAL_RESULTS[key].append(
-                    {
-                        "question": question,
-                        "answer": answer,
-                        "category": category,
-                        "context": context,
-                        "response": response,
-                        "search_time": search_time,
-                        "response_time": response_time,
-                    }
-                )
-                with open(output_file_path, "w+") as f:
-                    json.dump(FINAL_RESULTS, f, indent=4)
-
-        # Save results
-        with open(output_file_path, "w+") as f:
-            json.dump(FINAL_RESULTS, f, indent=4)

evaluation/src/utils.py

@@ -1,3 +0,0 @@
-TECHNIQUES = ["mem0", "rag", "langmem", "zep", "openai"]
-
-METHODS = ["add", "search"]

evaluation/src/zep/add.py

@@ -1,76 +0,0 @@
-import argparse
-import json
-import os
-
-from dotenv import load_dotenv
-from tqdm import tqdm
-from zep_cloud import Message
-from zep_cloud.client import Zep
-
-load_dotenv()
-
-
-class ZepAdd:
-    def __init__(self, data_path=None):
-        self.zep_client = Zep(api_key=os.getenv("ZEP_API_KEY"))
-        self.data_path = data_path
-        self.data = None
-        if data_path:
-            self.load_data()
-
-    def load_data(self):
-        with open(self.data_path, "r") as f:
-            self.data = json.load(f)
-        return self.data
-
-    def process_conversation(self, run_id, item, idx):
-        conversation = item["conversation"]
-
-        user_id = f"run_id_{run_id}_experiment_user_{idx}"
-        session_id = f"run_id_{run_id}_experiment_session_{idx}"
-
-        # # delete all memories for the two users
-        # self.zep_client.user.delete(user_id=user_id)
-        # self.zep_client.memory.delete(session_id=session_id)
-
-        self.zep_client.user.add(user_id=user_id)
-        self.zep_client.memory.add_session(
-            user_id=user_id,
-            session_id=session_id,
-        )
-
-        print("Starting to add memories... for user", user_id)
-        for key in tqdm(conversation.keys(), desc=f"Processing user {user_id}"):
-            if key in ["speaker_a", "speaker_b"] or "date" in key:
-                continue
-
-            date_time_key = key + "_date_time"
-            timestamp = conversation[date_time_key]
-            chats = conversation[key]
-
-            for chat in tqdm(chats, desc=f"Adding chats for {key}", leave=False):
-                self.zep_client.memory.add(
-                    session_id=session_id,
-                    messages=[
-                        Message(
-                            role=chat["speaker"],
-                            role_type="user",
-                            content=f"{timestamp}: {chat['text']}",
-                        )
-                    ],
-                )
-
-    def process_all_conversations(self, run_id):
-        if not self.data:
-            raise ValueError("No data loaded. Please set data_path and call load_data() first.")
-        for idx, item in tqdm(enumerate(self.data)):
-            if idx == 0:
-                self.process_conversation(run_id, item, idx)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--run_id", type=str, required=True)
-    args = parser.parse_args()
-    zep_add = ZepAdd(data_path="../../dataset/locomo10.json")
-    zep_add.process_all_conversations(args.run_id)

evaluation/src/zep/search.py

@@ -1,140 +0,0 @@
-import argparse
-import json
-import os
-import time
-from collections import defaultdict
-
-from dotenv import load_dotenv
-from jinja2 import Template
-from openai import OpenAI
-from prompts import ANSWER_PROMPT_ZEP
-from tqdm import tqdm
-from zep_cloud import EntityEdge, EntityNode
-from zep_cloud.client import Zep
-
-load_dotenv()
-
-TEMPLATE = """
-FACTS and ENTITIES represent relevant context to the current conversation.
-
-# These are the most relevant facts and their valid date ranges
-# format: FACT (Date range: from - to)
-
-{facts}
-
-
-# These are the most relevant entities
-# ENTITY_NAME: entity summary
-
-{entities}
-
-"""
-
-
-class ZepSearch:
-    def __init__(self):
-        self.zep_client = Zep(api_key=os.getenv("ZEP_API_KEY"))
-        self.results = defaultdict(list)
-        self.openai_client = OpenAI()
-
-    def format_edge_date_range(self, edge: EntityEdge) -> str:
-        # return f"{datetime(edge.valid_at).strftime('%Y-%m-%d %H:%M:%S') if edge.valid_at else 'date unknown'} - {(edge.invalid_at.strftime('%Y-%m-%d %H:%M:%S') if edge.invalid_at else 'present')}"
-        return f"{edge.valid_at if edge.valid_at else 'date unknown'} - {(edge.invalid_at if edge.invalid_at else 'present')}"
-
-    def compose_search_context(self, edges: list[EntityEdge], nodes: list[EntityNode]) -> str:
-        facts = [f"  - {edge.fact} ({self.format_edge_date_range(edge)})" for edge in edges]
-        entities = [f"  - {node.name}: {node.summary}" for node in nodes]
-        return TEMPLATE.format(facts="\n".join(facts), entities="\n".join(entities))
-
-    def search_memory(self, run_id, idx, query, max_retries=3, retry_delay=1):
-        start_time = time.time()
-        retries = 0
-        while retries < max_retries:
-            try:
-                user_id = f"run_id_{run_id}_experiment_user_{idx}"
-                edges_results = (
-                    self.zep_client.graph.search(
-                        user_id=user_id, reranker="cross_encoder", query=query, scope="edges", limit=20
-                    )
-                ).edges
-                node_results = (
-                    self.zep_client.graph.search(user_id=user_id, reranker="rrf", query=query, scope="nodes", limit=20)
-                ).nodes
-                context = self.compose_search_context(edges_results, node_results)
-                break
-            except Exception as e:
-                print("Retrying...")
-                retries += 1
-                if retries >= max_retries:
-                    raise e
-                time.sleep(retry_delay)
-
-        end_time = time.time()
-
-        return context, end_time - start_time
-
-    def process_question(self, run_id, val, idx):
-        question = val.get("question", "")
-        answer = val.get("answer", "")
-        category = val.get("category", -1)
-        evidence = val.get("evidence", [])
-        adversarial_answer = val.get("adversarial_answer", "")
-
-        response, search_memory_time, response_time, context = self.answer_question(run_id, idx, question)
-
-        result = {
-            "question": question,
-            "answer": answer,
-            "category": category,
-            "evidence": evidence,
-            "response": response,
-            "adversarial_answer": adversarial_answer,
-            "search_memory_time": search_memory_time,
-            "response_time": response_time,
-            "context": context,
-        }
-
-        return result
-
-    def answer_question(self, run_id, idx, question):
-        context, search_memory_time = self.search_memory(run_id, idx, question)
-
-        template = Template(ANSWER_PROMPT_ZEP)
-        answer_prompt = template.render(memories=context, question=question)
-
-        t1 = time.time()
-        response = self.openai_client.chat.completions.create(
-            model=os.getenv("MODEL"), messages=[{"role": "system", "content": answer_prompt}], temperature=0.0
-        )
-        t2 = time.time()
-        response_time = t2 - t1
-        return response.choices[0].message.content, search_memory_time, response_time, context
-
-    def process_data_file(self, file_path, run_id, output_file_path):
-        with open(file_path, "r") as f:
-            data = json.load(f)
-
-        for idx, item in tqdm(enumerate(data), total=len(data), desc="Processing conversations"):
-            qa = item["qa"]
-
-            for question_item in tqdm(
-                qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False
-            ):
-                result = self.process_question(run_id, question_item, idx)
-                self.results[idx].append(result)
-
-                # Save results after each question is processed
-                with open(output_file_path, "w") as f:
-                    json.dump(self.results, f, indent=4)
-
-        # Final save at the end
-        with open(output_file_path, "w") as f:
-            json.dump(self.results, f, indent=4)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--run_id", type=str, required=True)
-    args = parser.parse_args()
-    zep_search = ZepSearch()
-    zep_search.process_data_file("../../dataset/locomo10.json", args.run_id, "results/zep_search_results.json")

examples/mem0-demo/components/assistant-ui/memory-ui.tsx

@@ -16,10 +16,10 @@ type RetrievedMemory = {
 
 type NewMemory = {
   id: string;
-  data: {
+  data?: {
     memory: string;
   };
-  event: "ADD" | "DELETE";
+  event: "ADD" | "UPDATE" | "DELETE" | "GET";
 };
 
 type NewMemoryAnnotation = {
@@ -47,14 +47,16 @@ const useMemories = (): Memory[] => {
     () =>
       annotations?.filter(isMemoryAnnotation).flatMap((a) => {
         if (a.type === "mem0-update") {
-          return a.memories.map(
-            (m): Memory => ({
-              event: m.event,
-              id: m.id,
-              memory: m.data.memory,
-              score: 1,
-            })
-          );
+          return a.memories
+            .filter((m): m is NewMemory & { data: { memory: string } } => m.data != null)
+            .map(
+              (m): Memory => ({
+                event: m.event,
+                id: m.id,
+                memory: m.data.memory,
+                score: 1,
+              })
+            );
         } else if (a.type === "mem0-get") {
           return a.memories.map((m) => ({
             event: "GET",

examples/notebooks/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 cookbooks.helper.mem0_teachability import Mem0Teachability\n",
+    "from helper.mem0_teachability import Mem0Teachability\n",
     "\n",
     "teachability = Mem0Teachability(\n",
     "    verbosity=2,  # for visibility of what's happening\n",

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

@@ -1,6 +1,6 @@
 {
   "name": "mem0",
-  "version": "0.2.9",
+  "version": "0.2.10",
   "description": "Persistent memory for Claude Code. Remembers decisions, patterns, and preferences across sessions.",
   "author": {
     "name": "Mem0",

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

@@ -1,6 +1,6 @@
 {
   "name": "mem0",
-  "version": "0.2.9",
+  "version": "0.2.10",
   "description": "Persistent memory for Codex. Remembers decisions, patterns, and preferences across sessions.",
   "author": {
     "name": "Mem0",

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

@@ -1,6 +1,6 @@
 {
   "name": "mem0",
-  "version": "0.2.9",
+  "version": "0.2.10",
   "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search using the Mem0 Platform MCP server.",
   "author": {
     "name": "Mem0",

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

@@ -0,0 +1,78 @@
+# Changelog
+
+All notable changes to the `@mem0/opencode-plugin` will be documented in this file.
+
+## 0.2.0 — Native SDK tools, MCP-free, leaner skill set
+
+### Changed (breaking)
+
+- **Memory tools are now native OpenCode tools** registered via the `@opencode-ai/plugin` `tool()` helper and backed by the `mem0ai` SDK directly. The plugin no longer registers or depends on the remote MCP server (`mcp.mem0.ai`); the bundled `opencode.json` and the regex-based MCP call interception have been removed. Tools: `add_memory`, `search_memories`, `get_memories`, `get_memory`, `update_memory`, `delete_memory`, `delete_all_memories`, `delete_entities`, `list_entities`, plus a `get_event_status` helper for async-write status.
+- **Skills load via the `config` hook (`skills.paths`)** instead of being copied into the project's `.opencode/` directory on startup. The `installSkills()` filesystem copy and the `cli.ts` installer (`mem0-opencode` bin) have been removed — install with `opencode plugin @mem0/opencode-plugin`.
+- **Trimmed to 9 focused skills** (`context-loader`, `dream`, `forget`, `status`, `search`, `scope`, `pin`, `remember`, `tour`). Removed `import`, `export`, `memory-reviewer`, `mem0` (SDK reference), `list-projects`, `stats`, and `onboard`. The old stateful `switch-project` skill is superseded by the project/session/global scope model and the new `/mem0-scope` skill.
+
+### Added
+
+- **Expanded telemetry to the full shared `plugin.*` schema.** In addition to `plugin.session_start` and `plugin.tool_use`, the plugin now emits `plugin.user_prompt`, `plugin.bash_error`, `plugin.pre_compact`, and `plugin.session_stop`. `tool_use` now fires from inside each native tool. Every event also carries `project_hash` (anonymized `sha256(app_id)`) and `os_version`, matching the editor plugin's `telemetry.py`.
+- **Auto-dream — gated automatic memory consolidation** (ported from the pi-agent plugin). When the time (`minHours`, default 24), session-count (`minSessions`, default 5), and memory-count (`minMemories`, default 20) gates all pass, the plugin injects a consolidation protocol so the agent merges duplicates, drops stale/sensitive entries, and rewrites vague ones before answering. A filesystem lock (`~/.mem0/mem0-dream.lock`) prevents concurrent sessions from dreaming at once, and completion resets the gates. Tune via the `dream` block in `~/.mem0/settings.json`; disable with `MEM0_DREAM=false`. Emits `plugin.dream_triggered` / `plugin.dream_completed`.
+- **Memory `scope` — per-call parameter and a persistent default.** `search_memories`, `get_memories`, `add_memory`, and `delete_all_memories` accept an optional `scope`: `"project"` (this repo, default), `"session"` (this run, adds `run_id`), or `"global"` (across all the user's projects — `app_id: "*"` for reads, user-wide for writes). The new **`/mem0-scope` skill** views and changes the *default* scope (used when no scope is passed), persisted to `~/.mem0/settings.json` (`default_scope`) and read **fresh on each memory operation** so changes apply immediately — no restart. `add_memory` / `search_memories` / `get_memories` honor the default (an explicit `scope`, `filters`, or `agent_id` still wins; a `project` default preserves prior behavior, including `global_search`).
+
+### Changed
+
+- **`/mem0-status` now reports the active default scope and auto-dream readiness.** It reads `default_scope` from `~/.mem0/settings.json` (falling back to `project`) and shows the auto-dream gate progress (sessions / memories / time vs. thresholds) so it's clear *why* a consolidation hasn't run yet.
+
+### Fixed
+
+- **Skills load in place via `skills.paths` — no copying.** The `config` hook adds the plugin's own `opencode-skills/` directory to OpenCode's `skills.paths`, so OpenCode discovers the skills directly from the linked/installed plugin package (recursive `**/SKILL.md` scan). The `installSkills()` step that copied skills into `~/.config/opencode/skills/` (and the legacy `~/.opencode/skills/`) and its version-marker gating are removed — the plugin no longer writes into those directories or creates `~/.opencode`. The `config` hook still registers the `/mem0-*` slash commands via `config.command`: OpenCode's TUI slash menu is built from `config.command`, and skills on `skills.paths` are available to the agent's skill tool but do not appear as slash commands on their own. Skill dir names are `mem0-<skill>` (matching `^[a-z0-9]+(-[a-z0-9]+)*$`); commands are `/mem0-<skill>`.
+- **Robust project-id (`app_id`) detection.** Parsed from the git remote's `owner/repo` — handling https, scp-style ssh, and **custom ssh host aliases** like `git@github.com-work:owner/repo.git` — falling back to the git repo's **root directory name** (not the cwd, which may be a sub-directory or your home dir), then the cwd. Fixes the project showing as your username/home when OpenCode was launched outside the repo root.
+- **Auto-dream visibility + robustness.** When auto-dream doesn't fire, the plugin logs the blocking gate (e.g. `auto-dream waiting — memories: 3 < 20`), and `/mem0-status` surfaces the same gate progress. The session-start memory count is parsed defensively (handles both paginated `{count}` and bare-array SDK responses) so the memory gate evaluates correctly.
+- **Error-pattern lookup** in `tool.execute.after` no longer issues two identical `mem0.search()` calls; it now performs a single `topK: 6` search.
+- Corrected the documented system-prompt hook name from `experimental.chat.system.transform` to the actual `experimental.chat.messages.transform`.
+
+### Safety
+
+- **`delete_all_memories` deliberately ignores the default scope.** Deleting user-wide always requires an explicit `scope="global"`, so raising the default to `global` can never turn a routine cleanup into a cross-project wipe.
+
+## 0.1.3 — File-context injection, session summaries & activity timeline, anonymous telemetry
+
+### Added
+
+- **File-context injection (`tool.execute.before` / Read):** Before the agent reads a file, the plugin searches mem0 for memories referencing that file path and injects prior work as system context. Gates on file size (>= 1,500 bytes). Gives the agent "I've worked on this file before" awareness automatically.
+- **Stop hook session summary (`experimental.session.compacting`):** Enhanced session compaction to store a structured `session_summary` memory with `infer=True`, letting the mem0 backend AI extract key facts (request, decisions, learnings, next steps). Previously only stored a raw stats string.
+- **SessionStart activity timeline:** The initial memory loading now formats recent memories with type icons (⚖️ decision, 🔴 bug_fix, 🔵 task_learning, etc.) and relative age indicators (2h ago, 1d ago) instead of bare text. Provides a visual "Recent Activity" timeline on first message.
+- **PostHog telemetry (`telemetry.ts`):** Anonymous, fire-and-forget usage events. Opt out with `MEM0_TELEMETRY=false`. Only fires when an API key is present; never sends memory content, prompts, or the API key — only an anonymized `sha256(apiKey)[:32]` identity plus event type, platform, and plugin version. Emits the same schema as the Mem0 editor plugin (`plugin.*` events, `source: "plugin"`, `platform: "opencode"`) so OpenCode appears as a `platform` in the shared plugin dashboard. Events: `plugin.session_start` (with memory count) and `plugin.tool_use` (`add` / `search` / `update` / `delete`).
+
+### Changed
+
+- **`experimental.session.compacting` handler:** Now stores `metadata.type=session_summary` with `metadata.source=opencode-stop` instead of `metadata.type=session_state` with `metadata.source=pre-compaction`. Includes a structured prompt that instructs mem0's AI to extract request, decisions, learnings, and next steps.
+- **Initial context formatting:** Memories shown on first message now include type icons and age labels for quick scanning.
+
+## 0.1.2 — Automatic coding categories & global search
+
+### Added
+
+- **Auto-configured coding categories:** The plugin now automatically sets up 17 coding categories (e.g. `architecture_decisions`, `api_design`, `security`, `debugging_notes`) on the Mem0 project at startup. Runs in the background on every session start via `autoSetupCategories()`, is fully idempotent, and never blocks initialization. Uses SHA-256 fingerprints of the category list and API key — stored in `~/.mem0/categories_setup.json` — to skip redundant API calls on subsequent sessions.
+- **Global search mode (`global_search` setting):** New `global_search` toggle in `~/.mem0/settings.json` (default: `false`). When enabled, all `search_memories` and `get_memories` calls use `{"OR": [{"user_id": "*"}]}` instead of the per-user per-project `AND` filter — returning all memories across all users and all `app_id` scopes. Writes (`add_memory`) still tag with the current `user_id` and `app_id`. Applies to all plugin search paths: initial load, per-message recall, resume detection, error-pattern lookup, and compaction context.
+- **`/mem0:switch-project --global` / `--no-global`:** Enables or disables global search via the switch-project skill. Persists to `~/.mem0/settings.json`. No manual config editing needed.
+- **`MEM0_GLOBAL_SEARCH` environment variable:** Exported to child shells via the `shell.env` hook (`"true"` or `"false"`).
+
+### Changed
+
+- **Search filters are now dynamic:** All search paths throughout the plugin construct filters based on the `global_search` setting instead of always using `AND [user_id, app_id]`.
+- **Resume-context searches broadened:** Resume and error-pattern searches no longer include `metadata.type` sub-filters (`session_state`, `decision`, `anti_pattern`, `bug_fix`), broadening recall.
+- **System context message updated:** Informs the model when global search is active (`"Global search is ON — searches return all memories across all users and projects. Writes still use user_id=..., app_id=..."`).
+- **`/mem0:onboard` Step 5 is no longer interactive:** Removed the manual category installation prompt. Categories now configure automatically in the background; the onboarding step only verifies status and stores a fallback `project_profile` memory if the background run hasn't finished yet.
+- **`/mem0:switch-project` skill expanded:** Description and execution updated to document the `--global` and `--no-global` flags alongside the existing project-name argument.
+
+## 0.1.1
+
+- CI/CD publish flow test (`#5288`).
+- Fixed tsconfig, added `publishConfig` and bun lockfile (`#5273`).
+- Renamed package to `@mem0/opencode-plugin` (`#5272`).
+- Added plugin array to bundled `opencode.json` (`#5271`).
+
+## 0.1.0 — Initial release
+
+- **OpenCode plugin** (`@mem0/opencode-plugin` on npm): Pure TypeScript plugin using the `mem0ai` TS SDK — no Python, no shell scripts. Hooks into all 6 OpenCode events (`chat.message`, `tool.execute.before`, `tool.execute.after`, `experimental.chat.system.transform`, `experimental.session.compacting`, `shell.env`). Features: session start memory loading, per-prompt semantic search, error pattern detection with memory lookup, resume/remember intent detection, auto-capture every 3rd message, periodic save nudges, full metadata defaults injection (confidence, source, type, session_id, files, branch), identity injection for search/get/delete filters, type-filtered error pre-fetch (anti_pattern + bug_fix), pre-compaction memory capture, MEMORY.md write blocking, and secret redaction.
+- **16 OpenCode-native skills** bundled in `opencode-skills/`: `context-loader`, `dream`, `export`, `forget`, `health`, `import`, `list-projects`, `mem0` (SDK reference), `memory-reviewer`, `onboard`, `peek`, `pin`, `remember`, `stats`, `switch-project`, `tour`. All skills are pure MCP-tool-based — no Python scripts, no shell scripts, no Claude Code dependencies.
+- **Auto-install skills and commands (`installSkills()`):** On plugin load, copies all 16 skills to `.opencode/skills/` and creates command wrapper files in `.opencode/commands/` so they appear in the OpenCode `/` palette.
+- **CLI installer (`cli.ts`):** `bunx @mem0/opencode-plugin install` auto-configures plugin and MCP server in `~/.config/opencode/opencode.json`.

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

@@ -0,0 +1,101 @@
+# @mem0/opencode-plugin
+
+Persistent memory for [OpenCode](https://opencode.ai). Your agent remembers decisions, preferences, and learnings across sessions automatically.
+
+## Install
+
+```bash
+opencode plugin @mem0/opencode-plugin
+```
+
+This adds the plugin to your `~/.config/opencode/opencode.json`. The plugin registers its memory tools and skills itself — there is no MCP server to configure.
+
+**Or let your agent do it** — paste this into OpenCode:
+
+```
+Install @mem0/opencode-plugin by following https://raw.githubusercontent.com/mem0ai/mem0/main/integrations/mem0-plugin/.opencode-plugin/README.md
+```
+
+Get your API key (free): [app.mem0.ai/dashboard/api-keys](https://app.mem0.ai/dashboard/api-keys)
+
+```bash
+echo 'export MEM0_API_KEY="m0-your-key"' >> ~/.zshrc && source ~/.zshrc
+```
+
+Restart OpenCode.
+
+## What's included
+
+| Component | Description |
+|-----------|-------------|
+| **9 Native Memory Tools** | `add_memory`, `search_memories`, `get_memories`, `update_memory`, `delete_memory`, and more — registered as OpenCode tools, backed by the `mem0ai` SDK (no MCP server required) |
+| **Lifecycle Hooks** | Auto-search on session start and every prompt, error memory lookup, compaction context, secret redaction |
+| **9 Skills** | `/mem0-remember`, `/mem0-tour`, `/mem0-search`, `/mem0-status`, `/mem0-scope`, `/mem0-dream`, `/mem0-forget`, `/mem0-pin`, `/mem0-context-loader` — discovered in place from the plugin via OpenCode's `skills.paths` |
+
+## Hooks
+
+Pure TypeScript — no Python, no shell scripts. Memory operations are native OpenCode tools backed by the [mem0ai](https://www.npmjs.com/package/mem0ai) SDK directly.
+
+| Hook | Event | What it does |
+|------|-------|-------------|
+| **Config** | `config` | Registers the `/mem0-*` slash commands (via `config.command`) and adds the plugin's own `opencode-skills/` dir to OpenCode's `skills.paths` for in-place skill discovery — no copying into `~/.config/opencode/skills` |
+| **Chat message** | `chat.message` | Loads prior memories on session start, searches relevant memories before each prompt, auto-captures learnings periodically |
+| **Pre-tool** | `tool.execute.before` | Blocks MEMORY.md writes, steering them to the `add_memory` tool |
+| **Post-tool** | `tool.execute.after` | Scans bash errors and pre-fetches related memories |
+| **Messages transform** | `experimental.chat.messages.transform` | Injects memory context (session memories, search results, error lookups) into the prompt |
+| **Compaction** | `experimental.session.compacting` | Stores session state memory, then injects prior memories into compaction context so nothing is lost |
+| **Shell env** | `shell.env` | Exports `MEM0_USER_ID`, `MEM0_APP_ID`, `MEM0_SESSION_ID`, and `MEM0_BRANCH` to shell |
+
+## Memory Tools
+
+| Tool | Description |
+|------|-------------|
+| `add_memory` | Save text or conversation history |
+| `search_memories` | Semantic search across memories |
+| `get_memories` | List memories with filters and pagination |
+| `get_memory` | Retrieve a specific memory by ID |
+| `update_memory` | Overwrite a memory's text by ID |
+| `delete_memory` | Delete a single memory by ID |
+| `delete_all_memories` | Bulk delete all memories in scope |
+| `delete_entities` | Delete an entity and its memories |
+| `list_entities` | List users/agents/apps stored in Mem0 |
+
+## Memory scope
+
+Every memory tool accepts an optional `scope`, and you can set the **default**
+scope (used when none is passed) with the `/mem0-scope` skill:
+
+| Scope | Reads | Writes |
+|-------|-------|--------|
+| `project` (default) | this repo (`user_id` + `app_id`) | this repo |
+| `session` | this run (adds `run_id`) | this run |
+| `global` | all your projects (`app_id="*"`) | user-wide (drops `app_id`) |
+
+```
+/mem0-scope            # show the current default scope
+/mem0-scope global     # save & search across all your projects by default
+/mem0-scope project    # back to repo-only (default)
+```
+
+The default persists in `~/.mem0/settings.json` (`default_scope`) and is read
+fresh on each memory operation, so a change applies immediately — no restart.
+`delete_all_memories` always requires an explicit `scope="global"` to delete
+user-wide, so changing the default can't trigger a cross-project wipe.
+
+## Verify
+
+Start OpenCode and ask: *"Search my memories for recent decisions"*
+
+If the `mem0` tools respond, you're all set.
+
+## Troubleshooting
+
+| Problem | Fix |
+|---------|-----|
+| No tools appearing | Restart OpenCode after installing |
+| 401 Unauthorized | `echo $MEM0_API_KEY` must print your `m0-` key |
+| Plugin not loading | Run `opencode plugin @mem0/opencode-plugin` again |
+
+## License
+
+Apache-2.0

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

@@ -6,7 +6,7 @@
       "name": "@mem0/opencode-plugin",
       "dependencies": {
         "@opencode-ai/plugin": "^1.0.162",
-        "mem0ai": "^3.0.5",
+        "mem0ai": "^3.0.8",
       },
       "devDependencies": {
         "bun-types": ">=1.3.14",
@@ -462,7 +462,7 @@
 
     "md5": ["md5@2.3.0", "", { "dependencies": { "charenc": "0.0.2", "crypt": "0.0.2", "is-buffer": "~1.1.6" } }, "sha512-T1GITYmFaKuO91vxyoQMFETst+O71VUPEU3ze5GNzDm0OWdP8v1ziTaAEPUr/3kLsY3Sftgz242A1SetQiDL7g=="],
 
-    "mem0ai": ["mem0ai@3.0.5", "", { "dependencies": { "axios": "^1.15.2", "openai": "^4.93.0", "uuid": "9.0.1", "zod": "^3.24.1" }, "peerDependencies": { "@anthropic-ai/sdk": "^0.40.1", "@azure/identity": "^4.0.0", "@azure/search-documents": "^12.0.0", "@cloudflare/workers-types": "^4.20250504.0", "@google/genai": "^1.2.0", "@langchain/core": "^1.1.47", "@mistralai/mistralai": "^1.5.2", "@qdrant/js-client-rest": "1.13.0", "@supabase/supabase-js": "^2.49.1", "@types/jest": "29.5.14", "@types/pg": "8.11.0", "better-sqlite3": "^12.6.2", "cloudflare": "^4.2.0", "compromise": "^14.0.0", "groq-sdk": "0.3.0", "natural": "^8.0.1", "ollama": "^0.5.14", "pg": "8.11.3", "redis": "^4.6.13" } }, "sha512-W/R59d5fMpUGHhPEnyoo36GSz5NFJbAs+vS4BxoIvE+t19mIJfoz/2FJSKIw80mT8AkeNYpDfzc/DYRu2IzYIw=="],
+    "mem0ai": ["mem0ai@3.0.8", "", { "dependencies": { "axios": "^1.16.0", "openai": "^4.93.0", "uuid": "^11.1.1", "zod": "^3.24.1" }, "peerDependencies": { "@anthropic-ai/sdk": "^0.40.1", "@azure/identity": "^4.0.0", "@azure/search-documents": "^12.0.0", "@cloudflare/workers-types": "^4.20250504.0", "@google/genai": "^1.40.0", "@langchain/core": "^1.1.47", "@mistralai/mistralai": "^1.5.2", "@qdrant/js-client-rest": "^1.18.0", "@supabase/supabase-js": "^2.49.1", "@types/jest": "29.5.14", "@types/pg": "8.11.0", "better-sqlite3": "^12.6.2", "cloudflare": "^4.2.0", "compromise": "^14.0.0", "groq-sdk": "0.3.0", "natural": "^8.0.1", "ollama": "^0.5.14", "pg": "8.11.3", "redis": "^4.6.13" } }, "sha512-6lvHGOYc/Z2r0JEulS559MVPOOiOtfAqG02VESDY7b0WAZwsmgLWAjQkbmWnD9fbXQQ3QvSpSaVx1q6Ex01eLQ=="],
 
     "memjs": ["memjs@1.3.2", "", {}, "sha512-qUEg2g8vxPe+zPn09KidjIStHPtoBO8Cttm8bgJFWWabbsjQ9Av9Ky+6UcvKx6ue0LLb/LEhtcyQpRyKfzeXcg=="],
 
@@ -652,7 +652,7 @@
 
     "util-deprecate": ["util-deprecate@1.0.2", "", {}, "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw=="],
 
-    "uuid": ["uuid@9.0.1", "", { "bin": { "uuid": "dist/bin/uuid" } }, "sha512-b+1eJOlsR9K8HJpow9Ok3fiWOWSIcIzXodvv0rQjVoOVNpWMpxf1wZNpt4y9h10odCNrqnYp1OBzRktckBe3sA=="],
+    "uuid": ["uuid@11.1.1", "", { "bin": { "uuid": "dist/esm/bin/uuid" } }, "sha512-vIYxrBCC/N/K+Js3qSN88go7kIfNPssr/hHCesKCQNAjmgvYS2oqr69kIufEG+O4+PfezOH4EbIeHCfFov8ZgQ=="],
 
     "web-streams-polyfill": ["web-streams-polyfill@3.3.3", "", {}, "sha512-d2JWLCivmZYTSIoge9MsgFCZrt571BikcWGYkjC1khllbTeDlGqZ2D8vD8E/lJa8WGWbb7Plm8/XJYV7IJHZZw=="],
 

integrations/mem0-plugin/.opencode-plugin/dream.test.ts

@@ -0,0 +1,100 @@
+import { afterEach, beforeEach, describe, expect, test } from "bun:test";
+import { mkdtempSync, rmSync, writeFileSync } from "node:fs";
+import { tmpdir } from "node:os";
+import { join } from "node:path";
+import {
+  loadDreamConfig,
+  incrementSessionCount,
+  checkCheapGates,
+  checkMemoryGate,
+  acquireDreamLock,
+  releaseDreamLock,
+  recordDreamCompletion,
+  DREAM_DEFAULTS,
+  DREAM_PROTOCOL,
+} from "./dream";
+
+let dir: string;
+
+beforeEach(() => {
+  dir = mkdtempSync(join(tmpdir(), "mem0-dream-"));
+});
+
+afterEach(() => {
+  try {
+    rmSync(dir, { recursive: true, force: true });
+  } catch {
+    /* ignore */
+  }
+  delete process.env.MEM0_DREAM;
+});
+
+describe("auto-dream gates", () => {
+  test("memory gate passes at >= minMemories, fails below", () => {
+    expect(checkMemoryGate(DREAM_DEFAULTS.minMemories, {}).pass).toBe(true);
+    expect(checkMemoryGate(DREAM_DEFAULTS.minMemories - 1, {}).pass).toBe(false);
+  });
+
+  test("cheap gates: fresh state blocks on session count, passes after enough sessions", () => {
+    // Fresh state: time gate passes (lastConsolidatedAt=0), but 0 sessions blocks.
+    expect(checkCheapGates(dir, {}).proceed).toBe(false);
+    for (let i = 0; i < DREAM_DEFAULTS.minSessions; i++) {
+      incrementSessionCount(dir, `ses_${i}`);
+    }
+    expect(checkCheapGates(dir, {}).proceed).toBe(true);
+  });
+
+  test("incrementSessionCount only counts distinct session ids", () => {
+    incrementSessionCount(dir, "ses_a");
+    incrementSessionCount(dir, "ses_a");
+    incrementSessionCount(dir, "ses_a");
+    expect(checkCheapGates(dir, { minHours: 0 }).reason).toContain("sessions: 1");
+  });
+
+  test("recordDreamCompletion resets gates (recent time blocks again)", () => {
+    for (let i = 0; i < 6; i++) incrementSessionCount(dir, `ses_${i}`);
+    expect(checkCheapGates(dir, {}).proceed).toBe(true);
+    recordDreamCompletion(dir);
+    const r = checkCheapGates(dir, {});
+    expect(r.proceed).toBe(false);
+    expect(r.reason).toContain("time");
+  });
+
+  test("dream lock is exclusive and reclaimable after release", () => {
+    expect(acquireDreamLock(dir)).toBe(true);
+    expect(acquireDreamLock(dir)).toBe(false);
+    releaseDreamLock(dir);
+    expect(acquireDreamLock(dir)).toBe(true);
+  });
+});
+
+describe("dream config", () => {
+  test("defaults when no settings file", () => {
+    const cfg = loadDreamConfig(dir);
+    expect(cfg.enabled).toBe(true);
+    expect(cfg.auto).toBe(true);
+    expect(cfg.minMemories).toBe(DREAM_DEFAULTS.minMemories);
+  });
+
+  test("MEM0_DREAM=false force-disables", () => {
+    process.env.MEM0_DREAM = "false";
+    expect(loadDreamConfig(dir).enabled).toBe(false);
+  });
+
+  test("settings.json dream block overrides defaults", () => {
+    writeFileSync(
+      join(dir, "settings.json"),
+      JSON.stringify({ dream: { minMemories: 99, auto: false } }),
+    );
+    const cfg = loadDreamConfig(dir);
+    expect(cfg.minMemories).toBe(99);
+    expect(cfg.auto).toBe(false);
+    expect(cfg.enabled).toBe(true);
+  });
+
+  test("protocol uses native tools, not the MCP tool", () => {
+    expect(DREAM_PROTOCOL).toContain("get_memories");
+    expect(DREAM_PROTOCOL).toContain("add_memory");
+    expect(DREAM_PROTOCOL).not.toContain("mem0_memory");
+  });
+});

integrations/mem0-plugin/.opencode-plugin/dream.ts

@@ -0,0 +1,225 @@
+/**
+ * Auto-dream: gated automatic memory consolidation for the Mem0 OpenCode plugin.
+ *
+ * Ported from the (stable) pi-agent plugin's dream module and adapted to
+ * OpenCode's hook model. When the cheap gates (time since last consolidation +
+ * sessions since) and the memory-count gate all pass, the plugin injects the
+ * DREAM_PROTOCOL into the agent's context so it consolidates memories (merge
+ * duplicates, drop stale/sensitive entries, rewrite vague ones) before
+ * answering. A filesystem lock prevents concurrent sessions from dreaming at
+ * once, and completion is recorded so it won't re-trigger until the next cycle.
+ *
+ * State + lock live in ~/.mem0/ alongside settings.json. Opt out with
+ * MEM0_DREAM=false, or tune via the `dream` block in ~/.mem0/settings.json.
+ */
+
+import { existsSync, mkdirSync, readFileSync, writeFileSync, unlinkSync } from "node:fs";
+import { join } from "node:path";
+
+export interface DreamConfig {
+  enabled: boolean;
+  auto: boolean;
+  minHours: number;
+  minSessions: number;
+  minMemories: number;
+}
+
+interface DreamState {
+  lastConsolidatedAt: number;
+  sessionsSince: number;
+  lastSessionId: string | null;
+}
+
+interface DreamLock {
+  pid: number;
+  startedAt: number;
+}
+
+const LOCK_STALE_MS = 60 * 60 * 1000;
+
+export const DREAM_DEFAULTS: DreamConfig = {
+  enabled: true,
+  auto: true,
+  minHours: 24,
+  minSessions: 5,
+  minMemories: 20,
+};
+
+function statePath(stateDir: string): string {
+  return join(stateDir, "mem0-dream-state.json");
+}
+
+function lockPath(stateDir: string): string {
+  return join(stateDir, "mem0-dream.lock");
+}
+
+function ensureDir(dir: string): void {
+  try {
+    mkdirSync(dir, { recursive: true });
+  } catch {
+    /* exists */
+  }
+}
+
+function readState(stateDir: string): DreamState {
+  try {
+    return JSON.parse(readFileSync(statePath(stateDir), "utf-8")) as DreamState;
+  } catch {
+    return { lastConsolidatedAt: 0, sessionsSince: 0, lastSessionId: null };
+  }
+}
+
+function writeState(stateDir: string, state: DreamState): void {
+  ensureDir(stateDir);
+  writeFileSync(statePath(stateDir), JSON.stringify(state, null, 2));
+}
+
+/**
+ * Load dream config from ~/.mem0/settings.json (`dream` block), applying
+ * defaults. MEM0_DREAM=false (or 0/no/off) force-disables regardless.
+ */
+export function loadDreamConfig(settingsDir: string): DreamConfig {
+  let envEnabled: boolean | undefined;
+  const env = process.env.MEM0_DREAM;
+  if (env !== undefined) {
+    const s = env.toLowerCase();
+    envEnabled = s !== "false" && s !== "0" && s !== "no" && s !== "off";
+  }
+
+  let cfg: DreamConfig = { ...DREAM_DEFAULTS };
+  try {
+    const sp = join(settingsDir, "settings.json");
+    if (existsSync(sp)) {
+      const settings = JSON.parse(readFileSync(sp, "utf-8"));
+      const d = settings?.dream;
+      if (d && typeof d === "object") {
+        cfg = {
+          enabled: typeof d.enabled === "boolean" ? d.enabled : cfg.enabled,
+          auto: typeof d.auto === "boolean" ? d.auto : cfg.auto,
+          minHours: typeof d.minHours === "number" ? d.minHours : cfg.minHours,
+          minSessions: typeof d.minSessions === "number" ? d.minSessions : cfg.minSessions,
+          minMemories: typeof d.minMemories === "number" ? d.minMemories : cfg.minMemories,
+        };
+      }
+    }
+  } catch {
+    /* defaults */
+  }
+
+  if (envEnabled !== undefined) cfg.enabled = envEnabled;
+  return cfg;
+}
+
+/** Count a new session toward the dream gate (once per distinct sessionId). */
+export function incrementSessionCount(stateDir: string, sessionId: string): void {
+  const state = readState(stateDir);
+  if (state.lastSessionId !== sessionId) {
+    state.sessionsSince++;
+    state.lastSessionId = sessionId;
+    writeState(stateDir, state);
+  }
+}
+
+/** Cheap gates that don't need an API call: time since last + sessions since. */
+export function checkCheapGates(
+  stateDir: string,
+  config: Partial<DreamConfig>,
+): { proceed: boolean; reason?: string } {
+  const minHours = config.minHours ?? DREAM_DEFAULTS.minHours;
+  const minSessions = config.minSessions ?? DREAM_DEFAULTS.minSessions;
+  const state = readState(stateDir);
+
+  const hoursSince = (Date.now() - state.lastConsolidatedAt) / 3_600_000;
+  if (hoursSince < minHours) {
+    return { proceed: false, reason: `time: ${hoursSince.toFixed(1)}h < ${minHours}h` };
+  }
+  if (state.sessionsSince < minSessions) {
+    return { proceed: false, reason: `sessions: ${state.sessionsSince} < ${minSessions}` };
+  }
+  return { proceed: true };
+}
+
+/** Memory-count gate (uses the count already fetched at session init). */
+export function checkMemoryGate(
+  memoryCount: number,
+  config: Partial<DreamConfig>,
+): { pass: boolean; reason?: string } {
+  const minMemories = config.minMemories ?? DREAM_DEFAULTS.minMemories;
+  if (memoryCount < minMemories) {
+    return { pass: false, reason: `memories: ${memoryCount} < ${minMemories}` };
+  }
+  return { pass: true };
+}
+
+/** Acquire an exclusive dream lock (stale locks > 1h are reclaimed). */
+export function acquireDreamLock(stateDir: string): boolean {
+  ensureDir(stateDir);
+  const lp = lockPath(stateDir);
+
+  try {
+    const lock = JSON.parse(readFileSync(lp, "utf-8")) as DreamLock;
+    if (Date.now() - lock.startedAt < LOCK_STALE_MS) {
+      return false;
+    }
+    try {
+      unlinkSync(lp);
+    } catch {
+      /* race ok */
+    }
+  } catch {
+    /* no lock file */
+  }
+
+  const lock: DreamLock = { pid: process.pid, startedAt: Date.now() };
+  try {
+    writeFileSync(lp, JSON.stringify(lock), { flag: "wx" });
+    return true;
+  } catch {
+    return false;
+  }
+}
+
+export function releaseDreamLock(stateDir: string): void {
+  try {
+    unlinkSync(lockPath(stateDir));
+  } catch {
+    /* already gone */
+  }
+}
+
+/** Reset the gates after a successful consolidation. */
+export function recordDreamCompletion(stateDir: string): void {
+  const state = readState(stateDir);
+  state.lastConsolidatedAt = Date.now();
+  state.sessionsSince = 0;
+  state.lastSessionId = null;
+  writeState(stateDir, state);
+}
+
+/**
+ * Consolidation protocol injected into the agent context when a dream is
+ * triggered. Uses the plugin's native OpenCode memory tools (get_memories /
+ * add_memory / delete_memory) rather than an MCP tool.
+ */
+export const DREAM_PROTOCOL = `<mem0-dream>
+You are running memory consolidation. Complete these steps using the mem0 memory tools (get_memories, add_memory, delete_memory):
+
+1. ORIENT — Call get_memories to list all memories. Count by category. Note oldest/newest.
+
+2. GATHER TARGETS — Review each memory. Classify as:
+   - DELETE: sensitive information (API keys, passwords, tokens), expired/stale entries, noise, redundant operational details
+   - MERGE: near-duplicates (same fact stated differently). Keep the better-worded one, delete the other.
+   - REWRITE: vague, first-person, or poorly-categorized entries. add_memory with improved text, then delete_memory the old one.
+   - KEEP: everything else.
+   Skip any memory starting with "[PINNED]".
+
+3. CONSOLIDATE — Execute the changes:
+   - Delete stale/duplicate entries with delete_memory
+   - For merges: add_memory the merged text, delete_memory both originals
+   - For rewrites: add_memory the improved version, delete_memory the original
+
+4. REPORT — Summarize: how many reviewed, deleted, merged, rewritten, final count.
+
+Quality targets: zero sensitive data stored, zero duplicates, all entries are atomic (one fact each), 15-50 words each.
+After consolidation, respond to the user's message normally.
+</mem0-dream>`;

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

@@ -0,0 +1,999 @@
+// Mem0 memory plugin for OpenCode: captures and recalls memories across sessions
+// (add / search / manage) via the Mem0 platform, wired through OpenCode plugin hooks.
+// Memory operations are exposed as native OpenCode tools backed by the mem0ai SDK
+// (no MCP server required).
+import type {Plugin} from "@opencode-ai/plugin";
+import {tool} from "@opencode-ai/plugin";
+import {MemoryClient} from "mem0ai";
+import {userInfo} from "os";
+import {basename, resolve, dirname} from "path";
+import {randomBytes} from "crypto";
+import {existsSync, mkdirSync, readFileSync, writeFileSync, readdirSync} from "fs";
+import {homedir} from "os";
+import {join} from "path";
+import {createHash} from "crypto";
+import {captureEvent} from "./telemetry";
+import {
+  loadDreamConfig,
+  incrementSessionCount,
+  checkCheapGates,
+  checkMemoryGate,
+  acquireDreamLock,
+  releaseDreamLock,
+  recordDreamCompletion,
+  DREAM_PROTOCOL,
+} from "./dream";
+import {asScope, scopeSearchFilters, scopeWriteParams, resolveDefaultScope, SCOPE_GUIDANCE, type Scope} from "./scope";
+import {parseProjectFromRemote} from "./project";
+
+async function getUserId(): Promise<string> {
+  if (process.env.MEM0_USER_ID) return process.env.MEM0_USER_ID;
+  try {
+    return userInfo().username;
+  } catch {
+  }
+  return process.env.USER || process.env.USERNAME || "unknown";
+}
+
+async function getProjectId($: any): Promise<string> {
+  if (process.env.MEM0_APP_ID) return process.env.MEM0_APP_ID;
+  // Prefer the git remote's owner/repo — stable across clones, worktrees, and
+  // sub-directories (handles https + ssh, incl. custom host aliases).
+  try {
+    const r = await $`git remote get-url origin`.quiet();
+    const project = parseProjectFromRemote(r.stdout.toString());
+    if (project) return project;
+  } catch {
+  }
+  // No usable remote: use the git repo ROOT dir name, not cwd (which may be a
+  // sub-directory, or your home dir if OpenCode was launched outside a repo).
+  try {
+    const r = await $`git rev-parse --show-toplevel`.quiet();
+    const top = r.stdout.toString().trim();
+    if (top) return basename(top);
+  } catch {
+  }
+  return basename(process.cwd());
+}
+
+async function getBranch($: any): Promise<string> {
+  try {
+    const r = await $`git branch --show-current`.quiet();
+    return r.stdout.toString().trim() || "main";
+  } catch {
+  }
+  return "main";
+}
+
+function extractMemories(res: any): Array<{ memory: string; id: string }> {
+  const arr = res?.results ?? res;
+  if (!Array.isArray(arr)) return [];
+  return arr.map((m: any) => ({memory: m.memory ?? "", id: m.id ?? ""}));
+}
+
+function generateSessionId(): string {
+  const ts = Math.floor(Date.now() / 1000);
+  const rnd = randomBytes(3).toString("hex");
+  return `ses_${ts}_${rnd}`;
+}
+
+const SECRET_PATTERNS = [
+  /sk-[A-Za-z0-9]{20,}/g,
+  /m0-[A-Za-z0-9]{20,}/g,
+  /AKIA[0-9A-Z]{16}/g,
+  /xox[baprs]-[A-Za-z0-9-]{20,}/g,
+  /ghp_[A-Za-z0-9]{36,}/g,
+  /gho_[A-Za-z0-9]{36,}/g,
+];
+
+function redact(text: string): string {
+  let out = text;
+  for (const re of SECRET_PATTERNS) {
+    out = out.replace(re, "[REDACTED]");
+  }
+  return out;
+}
+
+/** Read & parse `~/.mem0/settings.json`, returning {} when missing/invalid. */
+function loadSettings(): Record<string, unknown> {
+  try {
+    const settingsPath = join(homedir(), ".mem0", "settings.json");
+    if (!existsSync(settingsPath)) return {};
+    return JSON.parse(readFileSync(settingsPath, "utf8"));
+  } catch {
+  }
+  return {};
+}
+
+function loadGlobalSearch(): boolean {
+  return loadSettings().global_search === true;
+}
+
+/**
+ * The user's persisted default memory scope (set via the `mem0-scope` skill).
+ * Read fresh so a scope change takes effect on the next memory operation without
+ * restarting OpenCode. Defaults to "project".
+ */
+function loadDefaultScope(): Scope {
+  return resolveDefaultScope(loadSettings());
+}
+
+const CODING_CATEGORIES = [
+  "architecture_decisions", "api_design", "data_models", "algorithms",
+  "dependencies", "environment_setup", "testing_strategy", "debugging_notes",
+  "performance", "security", "deployment", "code_conventions",
+  "error_handling", "refactoring_history", "integrations", "onboarding",
+  "project_meta",
+];
+
+function categoriesFingerprint(): string {
+  const sorted = [...CODING_CATEGORIES].sort();
+  return createHash("sha256").update(sorted.join("\n")).digest("hex").slice(0, 16);
+}
+
+function apiKeyFingerprint(apiKey: string): string {
+  return createHash("sha256").update(apiKey).digest("hex").slice(0, 16);
+}
+
+async function autoSetupCategories(mem0: MemoryClient, apiKey: string): Promise<void> {
+  const stateDir = join(homedir(), ".mem0");
+  const stateFile = join(stateDir, "categories_setup.json");
+  const keyFp = apiKeyFingerprint(apiKey);
+  const catFp = categoriesFingerprint();
+
+  let state: Record<string, string> = {};
+  try {
+    if (existsSync(stateFile)) {
+      state = JSON.parse(readFileSync(stateFile, "utf8"));
+    }
+  } catch {}
+
+  if (state[keyFp] === catFp) return;
+
+  try {
+    const project = await mem0.getProject({fields: ["customCategories"]});
+    const existing: string[] = (project as any)?.custom_categories ?? (project as any)?.customCategories ?? [];
+    const sortedExisting = [...existing].sort();
+    const sortedTarget = [...CODING_CATEGORIES].sort();
+    if (JSON.stringify(sortedExisting) === JSON.stringify(sortedTarget)) {
+      state[keyFp] = catFp;
+      mkdirSync(stateDir, {recursive: true});
+      writeFileSync(stateFile, JSON.stringify(state, null, 2) + "\n");
+      return;
+    }
+
+    await mem0.updateProject({customCategories: CODING_CATEGORIES as any});
+
+    state[keyFp] = catFp;
+    mkdirSync(stateDir, {recursive: true});
+    writeFileSync(stateFile, JSON.stringify(state, null, 2) + "\n");
+  } catch {
+  }
+}
+
+const NUDGE_RE =
+  /\b(remember\s+(this|that)|memorize|save\s+this|note\s+(this|that)|don'?t\s+forget|always\s+remember|never\s+forget|keep\s+(this|that)\s+in\s+(mind|memory)|store\s+(this|that))\b/i;
+
+const RESUME_RE =
+  /where\s+(did\s+)?(we|I)\s+(leave|left)\s+off|continue\s+(from\s+)?(where|last)|what\s+were\s+we\s+(working|doing)|pick\s+up\s+where|resume\s+(from\s+|where\s+)|what.s\s+the\s+(current|latest)\s+(state|status)|catch\s+me\s+up|where\s+are\s+we/i;
+
+const ERROR_STRONG_RE =
+  /Traceback \(most recent call last\)|panic: |FATAL:|error\[E\d+\]/;
+const ERROR_MULTI_RE = /(Error:|Exception:)/g;
+const WRITE_TOOLS = new Set(["Write", "Edit", "MultiEdit", "write", "edit", "multiEdit"]);
+
+function resolveFilters(args: any, globalSearch: boolean, userId: string, appId: string): any {
+  if (args.filters) {
+    const existingFilters = args.filters;
+    if (typeof existingFilters === "object" && existingFilters !== null) {
+      const andClauses: any[] = existingFilters.AND;
+      if (Array.isArray(andClauses)) {
+        const hasUid = andClauses.some(
+          (c: any) => c && typeof c === "object" && "user_id" in c,
+        );
+        const hasAid = andClauses.some(
+          (c: any) => c && typeof c === "object" && "app_id" in c,
+        );
+        const hasAgentId = andClauses.some(
+          (c: any) => c && typeof c === "object" && "agent_id" in c,
+        );
+        const newClauses = [...andClauses];
+        if (args.agent_id || hasAgentId) {
+          if (!hasAgentId) newClauses.push({ agent_id: args.agent_id });
+        } else {
+          if (!hasUid) newClauses.push({ user_id: args.user_id ?? userId });
+        }
+        if (!hasAid) newClauses.push({ app_id: args.app_id ?? appId });
+        return { AND: newClauses };
+      } else if (andClauses === undefined) {
+        const hasUid = "user_id" in existingFilters;
+        const hasAid = "app_id" in existingFilters;
+        const hasAgentId = "agent_id" in existingFilters;
+        if (!hasAid || (!hasUid && !hasAgentId)) {
+          const existing = Object.entries(existingFilters).map(
+            ([k, v]) => ({ [k]: v }),
+          );
+          if (args.agent_id || hasAgentId) {
+            if (!hasAgentId) existing.push({ agent_id: args.agent_id });
+          } else {
+            if (!hasUid) existing.push({ user_id: args.user_id ?? userId });
+          }
+          if (!hasAid) existing.push({ app_id: args.app_id ?? appId });
+          return { AND: existing };
+        }
+      }
+    }
+    return args.filters;
+  }
+
+  if (globalSearch) {
+    return { OR: [{ user_id: "*" }] };
+  }
+
+  if (args.agent_id) {
+    return {
+      AND: [{ agent_id: args.agent_id }, { app_id: args.app_id ?? appId }],
+    };
+  }
+
+  return {
+    AND: [{ user_id: args.user_id ?? userId }, { app_id: args.app_id ?? appId }],
+  };
+}
+
+function extractUserText(input: any, output: any): string {
+  const parts: any[] = output?.parts;
+  if (Array.isArray(parts)) {
+    return parts
+      .filter((p: any) => p.type === "text" && !p.synthetic)
+      .map((p: any) => p.text ?? "")
+      .join("\n");
+  }
+  const msg = output?.message ?? input?.message;
+  if (typeof msg?.content === "string") return msg.content;
+  if (typeof msg?.text === "string") return msg.text;
+  return "";
+}
+
+const Mem0Plugin: Plugin = async (ctx) => {
+  const {$, client} = ctx;
+
+  const apiKey = process.env.MEM0_API_KEY;
+
+  if (!apiKey) {
+    try {
+      await client.app.log({
+        body: {
+          service: "mem0",
+          level: "error",
+          message:
+            "MEM0_API_KEY environment variable not set. Get one at https://app.mem0.ai/dashboard/api-keys",
+        },
+      });
+    } catch {
+    }
+    return {};
+  }
+
+  const mem0 = new MemoryClient({apiKey});
+  const userId = await getUserId();
+  const appId = await getProjectId($);
+  const branch = await getBranch($);
+  const stats = {adds: 0, searches: 0, messages: 0};
+  const sessionId = generateSessionId();
+  const globalSearch = loadGlobalSearch();
+
+  let initialized = false;
+  let memoryCount = 0;
+  let msgCount = 0;
+
+  const systemContext: string[] = [];
+
+  // Auto-dream: gated memory-consolidation state (ported from the pi-agent plugin).
+  const mem0StateDir = join(homedir(), ".mem0");
+  const dreamConfig = loadDreamConfig(mem0StateDir);
+  let dreamTriggered = false;
+  let dreamWriteSeen = false;
+
+  // Emit a session_stop telemetry event once when the process winds down.
+  let sessionStopSent = false;
+  const emitSessionStop = () => {
+    if (sessionStopSent) return;
+    sessionStopSent = true;
+    captureEvent(
+      "session_stop",
+      {adds: stats.adds, searches: stats.searches, messages: stats.messages},
+      apiKey,
+      appId,
+    );
+    // Finish an in-flight auto-dream: record completion if the agent consolidated,
+    // and always release the lock so the next eligible session can dream.
+    if (dreamTriggered) {
+      if (dreamWriteSeen) {
+        recordDreamCompletion(mem0StateDir);
+        captureEvent("dream_completed", {}, apiKey, appId);
+      }
+      releaseDreamLock(mem0StateDir);
+      dreamTriggered = false;
+    }
+  };
+  try {
+    process.on("beforeExit", emitSessionStop);
+  } catch {
+  }
+
+  // Auto-configure coding categories in background (idempotent, never blocks)
+  Promise.resolve().then(() => autoSetupCategories(mem0, apiKey)).catch(() => {
+  });
+
+  // Register a `/mem0-<skill>` slash command per bundled skill. OpenCode's TUI
+  // slash menu is populated from `config.command` entries (skills discovered via
+  // `skills.paths` are available to the agent's skill tool but do NOT appear as
+  // slash commands), so this is what makes `/mem0-scope` etc. typeable.
+  function registerCommands(skillsDir: string, opencodeConfig: any) {
+    for (const entry of readdirSync(skillsDir, {withFileTypes: true})) {
+      if (!entry.isDirectory()) continue;
+      const skillMd = resolve(skillsDir, entry.name, "SKILL.md");
+      if (!existsSync(skillMd)) continue;
+
+      let desc = `Mem0 ${entry.name} skill`;
+      try {
+        const content = readFileSync(skillMd, "utf8");
+        const m = content.match(/^description:\s*(.+)$/m);
+        if (m) desc = m[1].trim();
+      } catch {
+      }
+
+      opencodeConfig.command ??= {};
+      opencodeConfig.command[entry.name] = {
+        template: `Load and execute the \`${entry.name}\` skill.
+
+Use the mem0 memory tools (add_memory, search_memories, get_memories, get_memory, update_memory, delete_memory, delete_all_memories, delete_entities, list_entities, get_event_status) as instructed by the skill.
+
+Identity context (resolved at plugin startup):
+- user_id: ${userId}
+- app_id: ${appId}
+- session_id: ${sessionId}
+- branch: ${branch}`,
+        description: desc,
+      };
+    }
+  }
+
+  // Resolve read filters for the memory tools. Precedence: an explicit `scope`
+  // arg wins; then explicit `filters`/`agent_id`; otherwise fall back to the
+  // user's persisted default scope (read fresh so /mem0-scope applies at once).
+  // A "project" default preserves the existing behavior, including global_search.
+  function readScopeFilters(args: any): any {
+    if (args.scope) return scopeSearchFilters(asScope(args.scope), userId, appId, sessionId);
+    if (args.filters || args.agent_id) return resolveFilters(args, globalSearch, userId, appId);
+    const ds = loadDefaultScope();
+    return ds === "project"
+      ? resolveFilters(args, globalSearch, userId, appId)
+      : scopeSearchFilters(ds, userId, appId, sessionId);
+  }
+
+  return {
+    "chat.message": chatMessageHook,
+    "experimental.chat.messages.transform": chatMessagesTransformHook,
+    "tool.execute.before": toolExecuteBeforeHook,
+    "tool.execute.after": toolExecuteAfterHook,
+    "experimental.session.compacting": compactionHook,
+
+    "shell.env": async (
+      _input: { cwd: string; sessionID?: string },
+      output: { env: Record<string, string> },
+    ) => {
+      if (output?.env) {
+        output.env.MEM0_USER_ID = userId;
+        output.env.MEM0_APP_ID = appId;
+        output.env.MEM0_SESSION_ID = sessionId;
+        output.env.MEM0_BRANCH = branch;
+        output.env.MEM0_GLOBAL_SEARCH = globalSearch ? "true" : "false";
+      }
+    },
+
+    config: async (opencodeConfig: any) => {
+      // Point OpenCode at the plugin's OWN skills directory via `skills.paths`
+      const here = import.meta.filename;
+      const skillsDir = [
+        resolve(dirname(dirname(here)), "opencode-skills"),
+        resolve(dirname(here), "opencode-skills"),
+      ].find(existsSync);
+      if (!skillsDir) return;
+
+      opencodeConfig.skills ??= {};
+      opencodeConfig.skills.paths ??= [];
+      if (!opencodeConfig.skills.paths.includes(skillsDir)) {
+        opencodeConfig.skills.paths.push(skillsDir);
+      }
+
+      // Register the /mem0-* slash commands (the TUI slash menu reads these from
+      // config.command; skills.paths alone does not create slash commands).
+      registerCommands(skillsDir, opencodeConfig);
+    },
+
+    tool: {
+      add_memory: tool({
+        description: "Add a new memory. This method is called everytime the user informs anything about themselves, their preferences, or anything that has any relevant information which can be useful in the future conversation. This can also be called when the user asks you to remember something. Set infer to false to store the memory verbatim without LLM fact extraction.",
+        args: {
+          text: tool.schema.string().describe("Memory text content"),
+          user_id: tool.schema.string().optional().describe("User ID"),
+          app_id: tool.schema.string().optional().describe("App/Project ID"),
+          agent_id: tool.schema.string().optional().describe("Agent ID"),
+          metadata: tool.schema.record(tool.schema.string(), tool.schema.any()).optional().describe("Metadata key-value pairs"),
+          infer: tool.schema.boolean().optional().describe("Set to false to store memory verbatim without LLM fact extraction"),
+          scope: tool.schema.string().optional().describe('Write scope: "project" (this repo, default), "session" (this run), or "global" (user-wide, all projects). Use "global" only when explicitly asked.')
+        },
+        async execute(args) {
+          stats.adds++;
+          if (dreamTriggered) dreamWriteSeen = true;
+          captureEvent("tool_use", {tool: "add_memory"}, apiKey, appId);
+          const effScope: Scope = args.scope ? asScope(args.scope) : loadDefaultScope();
+          const sp = scopeWriteParams(effScope, userId, appId, sessionId);
+          const finalUserId = args.agent_id ? args.user_id : (args.user_id ?? sp.user_id);
+          const finalAppId = args.app_id ?? sp.app_id;
+
+          const meta = args.metadata ?? {};
+          if (meta.confidence === undefined) meta.confidence = 0.7;
+          if (!meta.source) meta.source = "opencode";
+          if (!meta.type) meta.type = "task_learning";
+          if (!meta.session_id) meta.session_id = sessionId;
+          if (!meta.files) meta.files = ["*"];
+          if (!meta.branch) meta.branch = branch;
+
+          let infer = args.infer;
+          if (meta.confidence >= 1.0 && infer === undefined) {
+            infer = false;
+          }
+
+          const res = await mem0.add(
+            [{ role: "user", content: args.text }],
+            {
+              user_id: finalUserId,
+              app_id: finalAppId,
+              run_id: sp.run_id,
+              agent_id: args.agent_id,
+              metadata: meta,
+              infer
+            } as any
+          );
+          return JSON.stringify(res);
+        }
+      }),
+
+      search_memories: tool({
+        description: "Search through stored memories.",
+        args: {
+          query: tool.schema.string().describe("Search query"),
+          user_id: tool.schema.string().optional().describe("User ID"),
+          app_id: tool.schema.string().optional().describe("App/Project ID"),
+          agent_id: tool.schema.string().optional().describe("Agent ID"),
+          filters: tool.schema.record(tool.schema.string(), tool.schema.any()).optional().describe("Key-value filters (e.g. metadata or user/app filters)"),
+          limit: tool.schema.number().optional().describe("Maximum number of results to return (top_k)"),
+          top_k: tool.schema.number().optional().describe("Maximum number of results to return (alternative parameter)"),
+          scope: tool.schema.string().optional().describe('Search scope: "project" (this repo, default), "session" (this run only), or "global" (across ALL your projects). Only use "global" when the user explicitly asks to search across projects.'),
+        },
+        async execute(args) {
+          stats.searches++;
+          captureEvent("tool_use", {tool: "search_memories"}, apiKey, appId);
+          const topK = args.limit ?? args.top_k ?? 10;
+          const filters = readScopeFilters(args);
+
+          const res = await mem0.search(args.query, {
+            filters,
+            topK,
+          });
+          return JSON.stringify(res);
+        }
+      }),
+
+      get_memories: tool({
+        description: "List all memories in the memory store, optionally filtered.",
+        args: {
+          user_id: tool.schema.string().optional().describe("User ID"),
+          app_id: tool.schema.string().optional().describe("App/Project ID"),
+          agent_id: tool.schema.string().optional().describe("Agent ID"),
+          filters: tool.schema.record(tool.schema.string(), tool.schema.any()).optional().describe("Metadata/identity filters"),
+          page: tool.schema.number().optional().describe("Page number"),
+          page_size: tool.schema.number().optional().describe("Page size"),
+          scope: tool.schema.string().optional().describe('Scope: "project" (default), "session", or "global" (across ALL your projects). Use "global" only when explicitly asked.'),
+        },
+        async execute(args) {
+          captureEvent("tool_use", {tool: "get_memories"}, apiKey, appId);
+          const filters = readScopeFilters(args);
+
+          const res = await mem0.getAll({
+            page: args.page,
+            pageSize: args.page_size,
+            filters,
+          });
+          return JSON.stringify(res);
+        }
+      }),
+
+      get_memory: tool({
+        description: "Retrieve a specific memory by its ID.",
+        args: {
+          id: tool.schema.string().describe("The ID of the memory to retrieve"),
+        },
+        async execute(args) {
+          captureEvent("tool_use", {tool: "get_memory"}, apiKey, appId);
+          const res = await mem0.get(args.id);
+          return JSON.stringify(res);
+        }
+      }),
+
+      update_memory: tool({
+        description: "Update the content or metadata of a specific memory.",
+        args: {
+          id: tool.schema.string().describe("The ID of the memory to update"),
+          text: tool.schema.string().optional().describe("New text content for the memory"),
+          metadata: tool.schema.record(tool.schema.string(), tool.schema.any()).optional().describe("New metadata key-value pairs"),
+        },
+        async execute(args) {
+          captureEvent("tool_use", {tool: "update_memory"}, apiKey, appId);
+          const res = await mem0.update(args.id, {
+            text: args.text,
+            metadata: args.metadata,
+          });
+          return JSON.stringify(res);
+        }
+      }),
+
+      delete_memory: tool({
+        description: "Delete specific memories by their ID.",
+        args: {
+          id: tool.schema.string().describe("The ID of the memory to delete"),
+        },
+        async execute(args) {
+          if (dreamTriggered) dreamWriteSeen = true;
+          captureEvent("tool_use", {tool: "delete_memory"}, apiKey, appId);
+          const res = await mem0.delete(args.id);
+          return JSON.stringify(res);
+        }
+      }),
+
+      delete_all_memories: tool({
+        description: "Delete all memories.",
+        args: {
+          user_id: tool.schema.string().optional().describe("User ID whose memories to delete"),
+          app_id: tool.schema.string().optional().describe("App ID whose memories to delete"),
+          agent_id: tool.schema.string().optional().describe("Agent ID whose memories to delete"),
+          scope: tool.schema.string().optional().describe('Scope to delete: "project" (default), "session", or "global" (user-wide). Use "global" only when explicitly asked.'),
+        },
+        async execute(args) {
+          if (dreamTriggered) dreamWriteSeen = true;
+          captureEvent("tool_use", {tool: "delete_all_memories"}, apiKey, appId);
+          const sp = args.scope ? scopeWriteParams(asScope(args.scope), userId, appId, sessionId) : null;
+          const res = await mem0.deleteAll({
+            user_id: sp ? sp.user_id : (args.agent_id ? args.user_id : (args.user_id ?? userId)),
+            app_id: sp ? sp.app_id : (args.app_id ?? appId),
+            run_id: sp?.run_id,
+            agent_id: args.agent_id,
+          } as any);
+          return JSON.stringify(res);
+        }
+      }),
+
+      delete_entities: tool({
+        description: "Delete user/agent/app/run entities and all their associated memories.",
+        args: {
+          user_id: tool.schema.string().optional().describe("User ID of the entity to delete"),
+          agent_id: tool.schema.string().optional().describe("Agent ID of the entity to delete"),
+          app_id: tool.schema.string().optional().describe("App/Project ID of the entity to delete"),
+          run_id: tool.schema.string().optional().describe("Run ID of the entity to delete"),
+        },
+        async execute(args) {
+          captureEvent("tool_use", {tool: "delete_entities"}, apiKey, appId);
+          const res = await mem0.deleteUsers({
+            userId: args.user_id,
+            agentId: args.agent_id,
+            appId: args.app_id,
+            runId: args.run_id,
+          });
+          return JSON.stringify(res);
+        }
+      }),
+
+      list_entities: tool({
+        description: "List all user/agent/app/run entities.",
+        args: {
+          page: tool.schema.number().optional().describe("Page number"),
+          page_size: tool.schema.number().optional().describe("Page size"),
+        },
+        async execute(args) {
+          captureEvent("tool_use", {tool: "list_entities"}, apiKey, appId);
+          const res = await mem0.users({
+            page: args.page,
+            pageSize: args.page_size,
+          });
+          return JSON.stringify(res);
+        }
+      }),
+
+      get_event_status: tool({
+        description: "Check the status of an asynchronous memory operation by event_id.",
+        args: {
+          event_id: tool.schema.string().describe("The ID of the event/async operation to check"),
+        },
+        async execute(args) {
+          captureEvent("tool_use", {tool: "get_event_status"}, apiKey, appId);
+          const response = await mem0.client.get(`/v1/event/${args.event_id}/`);
+          return JSON.stringify(response.data);
+        }
+      }),
+    },
+  };
+
+  async function chatMessageHook(input: any, output: any) {
+    const userText = extractUserText(input, output);
+    if (!userText || userText.length < 10) return;
+
+    const safeText = redact(userText);
+    msgCount++;
+    stats.messages++;
+
+    if (!initialized) {
+      initialized = true;
+
+      if (dreamConfig.enabled) {
+        incrementSessionCount(mem0StateDir, sessionId);
+      }
+
+      const searchFilters = globalSearch
+        ? {OR: [{user_id: "*"}]}
+        : {AND: [{user_id: userId}, {app_id: appId}]};
+
+      try {
+        const all = await mem0.getAll({
+          filters: searchFilters,
+          page: 1,
+          pageSize: 1,
+        });
+        const a: any = all;
+        memoryCount =
+          typeof a?.count === "number"
+            ? a.count
+            : Array.isArray(a)
+              ? a.length
+              : Array.isArray(a?.results)
+                ? a.results.length
+                : 0;
+
+        if (globalSearch) {
+          systemContext.push(
+            `Global search is ON — searches return all memories across all users and projects. Writes still use user_id="${userId}", app_id="${appId}".`,
+          );
+        } else {
+          systemContext.push(
+            `Always include user_id="${userId}" and app_id="${appId}" in every search_memories filter and add_memory call.`,
+          );
+        }
+
+        if (memoryCount === 0) {
+          systemContext.push(
+            "New project with 0 memories. Capture decisions, conventions, and learnings as you work via the add_memory tool or the remember skill.",
+          );
+        }
+
+        if (memoryCount > 0) {
+          systemContext.push(
+            "Search mem0 for recent decisions and task learnings before responding. Run 2 parallel searches: one for decision type, one for task_learning type.",
+          );
+          try {
+            const res = await mem0.search(
+              "recent session state decisions and learnings",
+              {
+                filters: searchFilters,
+                topK: 5,
+              },
+            );
+            stats.searches++;
+            const memories = extractMemories(res);
+            if (memories.length > 0) {
+              const memLines = memories
+                .map((m) => `- ${m.memory}`)
+                .join("\n");
+              systemContext.push(`Prior context from mem0:\n${memLines}`);
+            }
+          } catch {
+          }
+        }
+
+        systemContext.push(
+          "Mem0 searches apply when user references past work, decision questions, errors, or non-trivial tasks. Queries use noun-phrases, 2-4 parallel calls with different metadata.type filters, and include user_id + app_id.",
+        );
+        systemContext.push(SCOPE_GUIDANCE);
+        const activeScope = loadDefaultScope();
+        if (activeScope !== "project") {
+          systemContext.push(
+            `Active default memory scope is "${activeScope}" (set via /mem0-scope). Memory tools use this when no explicit scope is given: "session" limits to this run (run_id="${sessionId}"); "global" spans all your projects (app_id="*"). Pass an explicit scope to override per call. delete_all_memories still requires an explicit scope="global" to delete user-wide.`,
+          );
+        }
+      } catch (err: any) {
+        try {
+          await client.app.log({
+            body: {
+              service: "mem0",
+              level: "error",
+              message: `Session init error: ${err?.message}`,
+            },
+          });
+        } catch {
+        }
+      }
+
+      captureEvent("session_start", {memory_count: memoryCount}, apiKey, appId);
+
+      // Auto-dream: when the time/session/memory gates pass, inject the
+      // consolidation protocol so the agent tidies memories before answering.
+      if (dreamConfig.enabled && dreamConfig.auto && !dreamTriggered) {
+        const gates = checkCheapGates(mem0StateDir, dreamConfig);
+        const memGate = checkMemoryGate(memoryCount, dreamConfig);
+        if (gates.proceed && memGate.pass && acquireDreamLock(mem0StateDir)) {
+          dreamTriggered = true;
+          systemContext.push(DREAM_PROTOCOL);
+          captureEvent("dream_triggered", {memory_count: memoryCount}, apiKey, appId);
+        } else {
+          // Make "why didn't auto-dream run?" answerable from the logs.
+          const waiting = [gates.reason, memGate.reason].filter(Boolean).join("; ");
+          if (waiting) {
+            try {
+              await client.app.log({
+                body: {service: "mem0", level: "info", message: `auto-dream waiting — ${waiting}`},
+              });
+            } catch {
+            }
+          }
+        }
+      }
+    }
+
+    const hasRemember = NUDGE_RE.test(safeText);
+    if (hasRemember) {
+      systemContext.push(
+        "[MEMORY TRIGGER] User asked to remember something. Call add_memory with the user's statement, confidence=1.0, infer=false.",
+      );
+    }
+
+    const hasResume = RESUME_RE.test(safeText);
+    if (hasResume) {
+      try {
+        const resumeFilters = globalSearch
+          ? {OR: [{user_id: "*"}]}
+          : {
+            AND: [
+              {user_id: userId},
+              {app_id: appId},
+            ],
+          };
+        const [stateRes, decisionsRes] = await Promise.all([
+          mem0.search("session state current task", {
+            filters: resumeFilters,
+            topK: 3,
+          }),
+          mem0.search("recent decisions and learnings", {
+            filters: resumeFilters,
+            topK: 3,
+          }),
+        ]);
+        stats.searches += 2;
+        const all = [
+          ...extractMemories(stateRes),
+          ...extractMemories(decisionsRes),
+        ];
+        const seen = new Set<string>();
+        const unique = all.filter((m) => {
+          if (seen.has(m.id)) return false;
+          seen.add(m.id);
+          return true;
+        });
+        if (unique.length > 0) {
+          const memLines = unique.map((m) => `- ${m.memory}`).join("\n");
+          systemContext.push(
+            `Session resume context:\n${memLines}\n\nThese memories provide context for resuming work.`,
+          );
+        }
+      } catch {
+      }
+    }
+
+    if (!hasResume && memoryCount > 0) {
+      try {
+        const msgFilters = globalSearch
+          ? {OR: [{user_id: "*"}]}
+          : {AND: [{user_id: userId}, {app_id: appId}]};
+        const res = await mem0.search(safeText, {
+          filters: msgFilters,
+          topK: 5,
+        });
+        stats.searches++;
+        const memories = extractMemories(res);
+        if (memories.length > 0) {
+          const memLines = memories.map((m) => `- ${m.memory}`).join("\n");
+          systemContext.push(`Relevant memories:\n${memLines}`);
+        }
+      } catch {
+      }
+    }
+
+    if (msgCount % 3 === 0) {
+      Promise.resolve().then(async () => {
+        try {
+          await mem0.add([{role: "user", content: safeText}], {
+            user_id: userId,
+            app_id: appId,
+            metadata: {
+              type: "auto_capture",
+              source: "opencode",
+              confidence: 0.7,
+              session_id: sessionId,
+              branch,
+            },
+            infer: true,
+          } as any);
+          stats.adds++;
+        } catch {
+        }
+      });
+    }
+
+    if (msgCount % 5 === 0 && stats.adds < Math.floor(msgCount / 3)) {
+      systemContext.push(
+        "After responding, store any new decisions, learnings, or preferences from this exchange via add_memory. Keep it to 1 sentence per memory.",
+      );
+    }
+
+    captureEvent(
+      "user_prompt",
+      {remember_detected: hasRemember, resume_detected: hasResume},
+      apiKey,
+      appId,
+    );
+  }
+
+  async function toolExecuteBeforeHook(input: any, output: any) {
+    const toolName: string = input?.tool ?? "";
+
+    if (WRITE_TOOLS.has(toolName)) {
+      const fp = String(
+        output?.args?.file_path ?? output?.args?.filePath ?? "",
+      );
+      if (/MEMORY\.md|\.claude\/memory/i.test(fp)) {
+        throw new Error(
+          "Use the add_memory tool instead of writing to MEMORY.md",
+        );
+      }
+    }
+  }
+
+  async function chatMessagesTransformHook(_input: any, output: { messages: { info: any; parts: any[] }[] }) {
+    if (systemContext.length === 0 || !output?.messages?.length) return;
+
+    const firstUser = output.messages.find(
+      (m) => m.info.role === "user",
+    );
+    if (!firstUser || !firstUser.parts.length) return;
+
+    const marker = "## Mem0 Memory Context";
+    if (firstUser.parts.some((p: any) => p.type === "text" && p.text?.includes(marker))) return;
+
+    const block = `${marker}\n\n${systemContext.join("\n\n")}`;
+    const ref = firstUser.parts[0];
+    firstUser.parts.unshift({...ref, type: "text", text: block});
+  }
+
+  async function toolExecuteAfterHook(input: any, _output: any) {
+    const toolName: string = input?.tool ?? "";
+    const toolOutput: string = input?.output ?? _output?.output ?? "";
+
+    if (toolName === "bash" && toolOutput.length >= 50) {
+      const command: string = input?.args?.command ?? "";
+      if (/git\s+(commit|merge|rebase)/.test(command)) return;
+
+      const hasStrongError = ERROR_STRONG_RE.test(toolOutput);
+      const multiErrors = (toolOutput.match(ERROR_MULTI_RE) ?? []).length;
+      if (!hasStrongError && multiErrors < 2) return;
+
+      try {
+        const errorLine =
+          toolOutput
+            .split("\n")
+            .find((l: string) =>
+              /Error:|Exception:|panic:|FAIL:|fatal:/i.test(l),
+            )
+            ?.replace(/^\s+/, "")
+            .slice(0, 120) ?? "";
+
+        const traceFiles = [
+          ...new Set(
+            toolOutput.match(
+              /[a-zA-Z0-9_./-]+\.(py|ts|tsx|js|jsx|rs|go|rb|java|sh)(:\d+)?/g,
+            ) ?? [],
+          ),
+        ].slice(0, 5);
+
+        const errorQuery = errorLine.slice(0, 80);
+        if (errorQuery.length < 10) return;
+
+        captureEvent("bash_error", {error_detected: true}, apiKey, appId);
+
+        const errorFilters = globalSearch
+          ? {OR: [{user_id: "*"}]}
+          : {
+            AND: [
+              {user_id: userId},
+              {app_id: appId},
+            ],
+          };
+        const res = await mem0.search(`error: ${errorQuery}`, {
+          filters: errorFilters,
+          topK: 6,
+        });
+        stats.searches++;
+        const unique = extractMemories(res);
+
+        let ctx = `Error detected: \`${command.slice(0, 100)}\` produced:\n> ${errorLine}`;
+        if (traceFiles.length > 0) {
+          ctx += `\nFiles in stack trace: ${traceFiles.join(", ")}`;
+        }
+        if (unique.length > 0) {
+          const lines = unique.map((m) => `- ${m.memory}`).join("\n");
+          ctx += `\nPrior error memories:\n${lines}`;
+        }
+        ctx +=
+          "\nStore resolved errors as anti_pattern or bug_fix memories for future reference.";
+        systemContext.push(ctx);
+      } catch {
+      }
+    }
+  }
+
+  async function compactionHook(input: { sessionID?: string }, output: { context: string[]; prompt?: string }) {
+    try {
+      const compactSessionId = input?.sessionID ?? sessionId;
+      captureEvent(
+        "pre_compact",
+        {adds: stats.adds, searches: stats.searches, messages: stats.messages},
+        apiKey,
+        appId,
+      );
+      const summaryContent = `Session compacting. Project: ${appId}. Branch: ${branch}. Session: ${compactSessionId}. Stats: ${stats.adds} memories stored, ${stats.searches} searches, ${stats.messages} messages.`;
+      Promise.resolve().then(async () => {
+        try {
+          await mem0.add([{role: "user", content: summaryContent}], {
+            user_id: userId,
+            app_id: appId,
+            metadata: {
+              type: "session_state",
+              source: "pre-compaction",
+              session_id: compactSessionId,
+              branch,
+            },
+            infer: true,
+          } as any);
+        } catch {
+        }
+      });
+
+      const compactFilters = globalSearch
+        ? {OR: [{user_id: "*"}]}
+        : {AND: [{user_id: userId}, {app_id: appId}]};
+      const res = await mem0.search("session state decisions learnings", {
+        filters: compactFilters,
+        topK: 10,
+      });
+      const memories = extractMemories(res);
+      if (memories.length > 0 && output?.context) {
+        const lines = memories.map((m) => `- ${m.memory}`).join("\n");
+        output.context.push(
+          `## Mem0 Memories (preserve across compaction)\n\n${lines}\n\nIMPORTANT: After compaction, store any key decisions or learnings using the add_memory tool.`,
+        );
+      }
+    } catch {
+    }
+  }
+};
+
+export default Mem0Plugin;

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

@@ -1,5 +1,5 @@
 ---
-name: context-loader
+name: mem0-context-loader
 description: Searches and injects relevant memories into context before starting work on a task. Use when beginning a new task, switching context, or when project history, past decisions, or coding conventions need to be loaded.
 ---
 

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

@@ -1,5 +1,5 @@
 ---
-name: dream
+name: mem0-dream
 description: Consolidates stored memories by merging duplicates, resolving contradictions, and pruning stale entries. Use when memory count is high, search results feel noisy or repetitive, or periodic cleanup is needed to maintain memory quality.
 ---
 
@@ -192,7 +192,7 @@ Dream complete — merged: <N>, pruned: <N>, conflicts resolved: <N>, skipped: <
 
 ## Auto mode
 
-When invoked with `--auto` (e.g., `/mem0:dream --auto`), run non-interactively:
+When invoked with `--auto` (e.g., `/mem0-dream --auto`), run non-interactively:
 
 - **Merges**: applied automatically (no contradiction, both are compatible).
 - **Prunes**: applied automatically (age/confidence-based, no ambiguity).
@@ -219,7 +219,7 @@ In auto mode:
    - If no match, store the reminder:
    ```python
    add_memory(
-       text="mem0-dream detected <N> contradiction(s) requiring manual review. Run /mem0:dream to resolve them interactively.",
+       text="mem0-dream detected <N> contradiction(s) requiring manual review. Run /mem0-dream to resolve them interactively.",
        user_id="<active_user_id>",
        app_id="<active_project_id>",
        metadata={"type": "task_learning", "source": "mem0-dream-auto", "branch": "<active_branch>"},
@@ -229,8 +229,8 @@ In auto mode:
 
 ## See also
 
-- `/mem0:forget` — targeted deletion of specific memories (search + confirm + delete)
-- `/mem0:health --deep` — quick quality scan without applying changes
+- `/mem0-forget` — targeted deletion of specific memories (search + confirm + delete)
+- `/mem0-status --deep` — quick quality scan without applying changes
 
 ## Output formatting
 

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

@@ -1,5 +1,5 @@
 ---
-name: forget
+name: mem0-forget
 description: Deletes memories by search query or memory ID with confirmation before removal. Use when removing outdated decisions, incorrect memories, sensitive data, or cleaning up after experiments. Also handles undo of recent additions.
 ---
 
@@ -12,8 +12,8 @@ Delete specific memories from mem0.
 ### Step 1: Parse input
 
 The user provides either:
-- A search query: `/mem0:forget auth module decisions`
-- A memory ID: `/mem0:forget <memory_id>`
+- A search query: `/mem0-forget auth module decisions`
+- A memory ID: `/mem0-forget <memory_id>`
 
 If no argument, ask: "What should I forget? Provide a search query or memory ID."
 
@@ -67,7 +67,7 @@ If the user says "undo last N memories" or "undo last write":
 3. Sort results by creation time descending and show the last N entries (default 1). Ask for confirmation.
 4. Delete confirmed entries via `delete_memory`.
 
-If `MEM0_SESSION_ID` is not set or the search returns no results, tell the user: "No recent memory IDs tracked this session. Try `/mem0:tour` to browse recent memories, or `/mem0:forget <search query>` to find specific ones."
+If `MEM0_SESSION_ID` is not set or the search returns no results, tell the user: "No recent memory IDs tracked this session. Try `/mem0-tour` to browse recent memories, or `/mem0-forget <search query>` to find specific ones."
 
 ## Output formatting
 

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

@@ -1,5 +1,5 @@
 ---
-name: pin
+name: mem0-pin
 description: Pins or unpins a memory to protect it from pruning during dream consolidation. Use when a memory is critical and must never be removed, such as architecture decisions, security constraints, or immutable team conventions.
 ---
 
@@ -29,12 +29,12 @@ Call `get_memory` with the selected memory ID. Store:
 
 ### Step 3: Pin it
 
-The MCP `update_memory` tool only accepts `memory_id`, `text`, and `source` — it
-does not accept a `metadata` parameter. To pin, append a pin marker to the text:
+The `update_memory` tool updates a memory by `id`. To pin durably, append a pin
+marker to the text so it travels with the memory:
 
 ```python
 pinned_text = "[PINNED] " + original_text if not original_text.startswith("[PINNED]") else original_text
-update_memory(memory_id=<selected_id>, text=pinned_text)
+update_memory(id=<selected_id>, text=pinned_text)
 ```
 
 **For new memories** (user wants to pin text that isn't stored yet):

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

@@ -1,5 +1,5 @@
 ---
-name: remember
+name: mem0-remember
 description: Stores a memory verbatim from user input with appropriate type classification and metadata. Use when the user says remember this, save this, store this, note that, or explicitly asks to record a decision, preference, convention, or learning.
 ---
 
@@ -11,7 +11,7 @@ Store a fact or learning directly into mem0.
 
 ### Step 1: Extract the content
 
-The user provides the content as an argument: `/mem0:remember <text>`
+The user provides the content as an argument: `/mem0-remember <text>`
 
 If no text was provided, ask: "What should I remember?"
 

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

@@ -0,0 +1,119 @@
+---
+name: mem0-scope
+description: Views or changes the default memory scope (project, session, or global) used when saving and searching memories. Use when the user wants to control whether memories are scoped to this repo, this run, or shared across all their projects.
+---
+
+# Mem0 Scope
+
+View or change the **default memory scope** — the scope the memory tools use when
+no explicit `scope` is given. The setting persists in `~/.mem0/settings.json`
+(`default_scope`) and the plugin reads it fresh on each memory operation, so a
+change takes effect immediately in the current session.
+
+The three scopes:
+
+- `project` (default) — this repo only. Filters by `user_id` + `app_id`.
+- `session` — this run only. Adds `run_id` (the current session) so memories are
+  isolated to this conversation.
+- `global` — across ALL your projects. Reads use `app_id="*"`; writes drop
+  `app_id` so the memory is user-wide.
+
+## Execution
+
+### Step 1: Determine intent
+
+Look at the user's message for a target scope word: `project`, `session`, or
+`global` (also accept "repo"→project, "run"→session, "all"/"everywhere"→global).
+
+- No target word present → **View mode** (Step 2).
+- A target word present → **Change mode** (Step 3).
+
+### Step 2: View mode — show the current scope
+
+1. Read the current default scope from settings:
+
+   ```bash
+   _S="$HOME/.mem0/settings.json"
+   [ -f "$_S" ] && grep -o '"default_scope"[[:space:]]*:[[:space:]]*"[a-z]*"' "$_S" | grep -o '[a-z]*"$' | tr -d '"' || echo "project"
+   ```
+
+   If the command prints nothing, the scope is `project` (the default).
+
+2. (Optional) Show how many memories live in the current scope by calling
+   `get_memories` with `scope="<current>"`, `page_size=1`, and reading the
+   `count` (or result length) from the response.
+
+3. Display using the identity the plugin exported (do NOT re-shell git):
+
+   ```
+   Mem0 memory scope
+
+   Current default scope: <current>
+
+     project  - this repo only (user + app_id)        <marker if active>
+     session  - this run only (adds run_id)           <marker if active>
+     global   - all your projects (app_id = *)         <marker if active>
+
+   User:    ${MEM0_USER_ID}
+   Project: ${MEM0_APP_ID}
+   Session: ${MEM0_SESSION_ID}
+
+   To change: /mem0-scope session    (or project / global)
+   ```
+
+   Put `[active]` next to the current scope. If you fetched a count in step 2,
+   add a `Memories in scope: <N>` line.
+
+### Step 3: Change mode — set a new scope
+
+1. Validate the target is one of `project`, `session`, `global`. If not, show the
+   three options and stop.
+
+2. Read the existing settings so you preserve every other key. Use the Read tool
+   on `~/.mem0/settings.json` (it may not exist yet — treat a missing file as
+   `{}`).
+
+3. Write the file back with the Write tool, keeping ALL existing keys and only
+   setting `"default_scope"` to the target. Pretty-print with 2-space indent and
+   a trailing newline. Do not drop `global_search`, `dream`, `auto_save`, or any
+   other field that was present.
+
+   Example resulting file (when other keys already existed):
+
+   ```json
+   {
+     "auto_save": true,
+     "search_limit": 10,
+     "default_scope": "global"
+   }
+   ```
+
+4. Confirm:
+
+   ```
+   Default memory scope changed: <old> -> <new>
+
+   <one line describing the effect — see below>
+   Applies immediately to memory tools in this session.
+
+   To revert: /mem0-scope <old>
+   ```
+
+   Effect lines:
+   - project → "New memories and searches are limited to this repo."
+   - session → "New memories and searches are limited to this run (this conversation)."
+   - global  → "New memories and searches span all your projects. delete_all_memories still needs an explicit scope=global to delete user-wide."
+
+### Notes
+
+- This only changes the **default**. Any memory tool call can still pass an
+  explicit `scope` to override it for that one call.
+- `delete_all_memories` deliberately ignores the default scope: deleting
+  user-wide always requires an explicit `scope="global"`, so changing the
+  default can never turn a routine cleanup into a cross-project wipe.
+- `global` scope (this user, all their projects) is distinct from the separate
+  `global_search` setting (all users). Leave `global_search` untouched here.
+
+## Output formatting
+
+IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like **bold**, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.

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

@@ -1,17 +1,17 @@
 ---
-name: peek
+name: mem0-search
 description: Searches memories and displays compact one-liner results, or looks up a specific memory by ID. Use for quick memory lookups, checking if a decision was recorded, resolving [mem0:id] citations, or browsing memories without full category detail.
 ---
 
-# Mem0 Peek
+# Mem0 Search
 
-Quick search with compact output. Lighter than `/mem0:tour`.
+Quick search with compact output. Lighter than `/mem0-tour`.
 
 ## Execution
 
 ### Step 1: Parse query
 
-The user provides a search query: `/mem0:peek auth middleware`
+The user provides a search query: `/mem0-search auth middleware`
 
 If no query provided, ask: "What should I search for?"
 
@@ -37,7 +37,7 @@ Run 2 parallel `search_memories` calls:
 Deduplicate by ID, then show compact results:
 
 ```
-## mem0 peek: "<query>" (<N> results)
+## mem0 search: "<query>" (<N> results)
 
 1. [decision] Auth module uses JWT with RS256 keys (2025-05-15) [mem0:a3f8b2c1]
 2. [anti_pattern] Don't use symmetric HS256 — leaked in env (2025-05-10) [mem0:7e2d9f4a]

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

@@ -1,9 +1,9 @@
 ---
-name: health
-description: Diagnoses mem0 connectivity, API key validity, and memory read/write functionality. Use when memory operations fail, searches return empty, add_memory errors occur, MCP connection drops, or to verify the plugin is working correctly.
+name: mem0-status
+description: Diagnoses mem0 connectivity, API key validity, and memory read/write functionality. Use when memory operations fail, searches return empty, add_memory errors occur, or to verify the plugin is working correctly.
 ---
 
-# Mem0 Health Check
+# Mem0 Status
 
 Run a diagnostic check on the mem0 plugin. Useful for troubleshooting.
 
@@ -23,21 +23,25 @@ _KEY="${MEM0_API_KEY:-}"
 
 ### Check 2: Identity resolution
 
-Resolve identity from environment variables set by the plugin's `shell.env` hook:
+Resolve identity from the `MEM0_*` environment variables set by the plugin's `shell.env` hook. These are the exact values the plugin uses to scope memories, so report them directly. Do NOT re-run `git` here: the plugin already resolved branch and project from git at session start, and re-shelling git can disagree with it — e.g. it prints an empty branch that renders as `(not a git repo)` while the Session check below shows `branch=main`. One source of truth keeps the two lines consistent.
 
 ```bash
-echo "user_id=${MEM0_USER_ID:-${USER:-}}"
+echo "user_id=${MEM0_USER_ID:-${USER:-default}}"
 echo "project_id=${MEM0_APP_ID:-}"
-echo "branch=$(git branch --show-current 2>/dev/null || echo '')"
+echo "branch=${MEM0_BRANCH:-main}"
+_S="$HOME/.mem0/settings.json"
+_SCOPE="$(grep -o '"default_scope"[[:space:]]*:[[:space:]]*"[a-z]*"' "$_S" 2>/dev/null | grep -o '[a-z]*"$' | tr -d '"')"
+echo "default_scope=${_SCOPE:-project}"
 ```
 
 - `user_id`: from `MEM0_USER_ID`, falling back to `$USER`
 - `project_id`: from `MEM0_APP_ID`
-- `branch`: from `git branch --show-current`
+- `branch`: from `MEM0_BRANCH` (the plugin's resolved value; falls back to `main` outside a git repo)
+- `default_scope`: from `~/.mem0/settings.json` (`default_scope`), falling back to `project`. This is the scope memory tools use when none is given; change it with `/mem0-scope`.
 
-PASS if all three are non-empty. WARN if any falls back to defaults.
+PASS if `user_id` and `project_id` are non-empty. WARN if `project_id` is empty — the `shell.env` hook may not have fired (restart OpenCode). Report the branch verbatim from `MEM0_BRANCH`; never invent a string like `(not a git repo)`.
 
-### Check 3: MCP server connectivity
+### Check 3: Memory tool connectivity
 
 Call `search_memories` with:
 - `query="health check"`
@@ -75,25 +79,59 @@ echo "branch=${MEM0_BRANCH:-}"
 - If all three are non-empty: PASS — "Session active"
 - If any are missing: WARN — "Plugin env vars not set; shell.env hook may not have fired"
 
+### Check 6: Auto-dream readiness
+
+Explain whether auto-dream (memory consolidation) is eligible to run, and if not, exactly which gate is blocking. Auto-dream runs at most once per session and only when **all** gates pass: time since last consolidation ≥ `minHours`, sessions since ≥ `minSessions`, and project memory count ≥ `minMemories`.
+
+Read the gate state and thresholds:
+
+```bash
+_ST="$HOME/.mem0/mem0-dream-state.json"
+_SET="$HOME/.mem0/settings.json"
+echo "sessions_since=$(grep -o '"sessionsSince"[[:space:]]*:[[:space:]]*[0-9]*' "$_ST" 2>/dev/null | grep -o '[0-9]*$' || echo 0)"
+echo "last_consolidated_ms=$(grep -o '"lastConsolidatedAt"[[:space:]]*:[[:space:]]*[0-9]*' "$_ST" 2>/dev/null | grep -o '[0-9]*$' || echo 0)"
+echo "min_hours=$(grep -o '"minHours"[[:space:]]*:[[:space:]]*[0-9]*' "$_SET" 2>/dev/null | grep -o '[0-9]*$' || echo 24)"
+echo "min_sessions=$(grep -o '"minSessions"[[:space:]]*:[[:space:]]*[0-9]*' "$_SET" 2>/dev/null | grep -o '[0-9]*$' || echo 5)"
+echo "min_memories=$(grep -o '"minMemories"[[:space:]]*:[[:space:]]*[0-9]*' "$_SET" 2>/dev/null | grep -o '[0-9]*$' || echo 20)"
+echo "now_s=$(date +%s)"
+echo "dream_env=${MEM0_DREAM:-unset}"
+```
+
+For the memory count, reuse the project memory count from Check 3/4 (or call `get_memories` with the project filter, `page_size=1`, and read `count`).
+
+Compute each gate:
+- **time**: `hours_since = (now_s - last_consolidated_ms/1000) / 3600`. Passes when `≥ min_hours`. If `last_consolidated_ms` is 0 it has never run → time gate passes.
+- **sessions**: passes when `sessions_since ≥ min_sessions`.
+- **memories**: passes when project memory count `≥ min_memories`.
+
+Report:
+- If `dream_env` is `false`/`0`/`no`/`off`, or `dream.enabled` is false in settings: WARN — "Auto-dream disabled".
+- If all three gates pass: PASS — "eligible (runs at next session start)".
+- Otherwise: WARN — list the blocking gate(s), e.g. `sessions 2/5, memories 3/20`. This is expected, not an error — auto-dream is just waiting. Note the user can run `/mem0-dream` to consolidate now, or lower the thresholds via the `dream` block in `~/.mem0/settings.json`.
+
 ### Display
 
 ```
-## mem0 health
+## mem0 status
 
 PASS  API Key         m0-dVe...
 PASS  Identity        user=kartik, project=mem0, branch=main
-PASS  MCP Connection  142ms
+PASS  Default scope   project
+PASS  Memory Tools    142ms
 PASS  Write/Read      write + delete OK
 PASS  Session         session_id=abc123, app_id=mem0, branch=main
+WARN  Auto-dream      waiting — sessions 2/5, memories 3/20 (/mem0-dream to run now)
 
 All checks passed.
 ```
 
+The Auto-dream line is informational: WARN here means "waiting on gates", not a failure. Show PASS when eligible, or "disabled" when turned off.
+
 If any check fails, add a `## Troubleshooting` section with specific fix steps for each failure.
 
 ## Extended mode: Memory Quality Analysis
 
-When invoked with `--deep` (e.g., `/mem0:health --deep`), run the standard 5 checks above **plus** a memory quality scan.
+When invoked with `--deep` (e.g., `/mem0-status --deep`), run the standard 6 checks above **plus** a memory quality scan.
 
 ### Quality Check 1: Duplicates
 
@@ -150,9 +188,9 @@ Duplicates: <N> · Stale: <N> · Contradictions: <N> · Orphans: <N>
 ```
 
 If all counts are 0: `Memory quality: clean.`
-If any non-zero: append `Run /mem0:dream to fix.`
+If any non-zero: append `Run /mem0-dream to fix.`
 
-To fix issues found by `--deep`, run `/mem0:dream` for automated consolidation (merges, prunes, conflict resolution).
+To fix issues found by `--deep`, run `/mem0-dream` for automated consolidation (merges, prunes, conflict resolution).
 
 ## Output formatting
 

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

@@ -1,5 +1,5 @@
 ---
-name: tour
+name: mem0-tour
 description: Browses all stored memories grouped by category with full content display. Use when reviewing all project memories, exploring stored knowledge, onboarding to a project, or getting an overview of captured decisions, conventions, and learnings.
 ---
 
@@ -9,8 +9,8 @@ Show the user what mem0 has stored for the current project.
 
 ## Cross-project mode
 
-When invoked with `--all-projects` (e.g., `/mem0:tour --all-projects` or
-`/mem0:tour --all-projects auth middleware`), search across ALL projects:
+When invoked with `--all-projects` (e.g., `/mem0-tour --all-projects` or
+`/mem0-tour --all-projects auth middleware`), search across ALL projects:
 
 1. Call `get_memories` with `filters={"AND": [{"user_id": "<active_user_id>"}]}`, `page_size=200` — **no `app_id` filter**.
 2. If a search query was also provided, run `search_memories` with `query=<query>`,
@@ -33,7 +33,7 @@ If `--all-projects` is NOT present, use the standard single-project flow below.
 
 ## Peek mode (compact search)
 
-When `/mem0:tour` receives a search query argument (e.g., `/mem0:tour auth middleware`)
+When `/mem0-tour` receives a search query argument (e.g., `/mem0-tour auth middleware`)
 WITHOUT `--all-projects`, run in **peek mode** — compact one-liner results:
 
 1. Run 2 parallel `search_memories` calls:
@@ -148,7 +148,7 @@ Identity - user: <user_id>  project: <project_id>  branch: <branch>
 If zero memories found for this project, print:
 ```
 No memories stored yet for project <project_id>.
-Run /mem0-onboard to import project files, or start working - mem0 captures learnings automatically.
+Start working - mem0 captures learnings automatically, or use /mem0-remember to save something now.
 ```
 
 ## Output formatting

integrations/mem0-plugin/.opencode-plugin/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mem0/opencode-plugin",
-  "version": "0.1.3",
+  "version": "0.2.0",
   "type": "module",
   "description": "Mem0 persistent memory plugin for OpenCode — add, search, and manage memories across sessions",
   "main": "dist/index.js",
@@ -11,9 +11,6 @@
       "import": "./dist/index.js"
     }
   },
-  "bin": {
-    "mem0-opencode": "./cli.ts"
-  },
   "publishConfig": {
     "access": "public"
   },
@@ -23,19 +20,16 @@
     "opencode-plugin",
     "mem0",
     "memory",
-    "mcp",
     "ai-memory",
     "persistent-memory"
   ],
   "repository": {
     "type": "git",
     "url": "https://github.com/mem0ai/mem0",
-    "directory": "mem0-plugin/.opencode-plugin"
+    "directory": "integrations/mem0-plugin/.opencode-plugin"
   },
   "files": [
     "dist",
-    "cli.ts",
-    "opencode.json",
     "LICENSE",
     "opencode-skills"
   ],
@@ -49,6 +43,7 @@
   "opencode": {
     "type": "plugin",
     "hooks": [
+      "config",
       "chat.message",
       "tool.execute.before",
       "tool.execute.after",
@@ -59,7 +54,7 @@
   },
   "dependencies": {
     "@opencode-ai/plugin": "^1.0.162",
-    "mem0ai": "^3.0.5"
+    "mem0ai": "^3.0.8"
   },
   "devDependencies": {
     "bun-types": ">=1.3.14",

integrations/mem0-plugin/.opencode-plugin/project.test.ts

@@ -0,0 +1,29 @@
+import { describe, expect, test } from "bun:test";
+import { parseProjectFromRemote } from "./project";
+
+describe("parseProjectFromRemote", () => {
+  test("ssh remote with a custom host alias (github.com-work)", () => {
+    expect(parseProjectFromRemote("git@github.com-mem0:mem0ai/mem0.git")).toBe("mem0ai-mem0");
+  });
+
+  test("standard scp-style ssh remote", () => {
+    expect(parseProjectFromRemote("git@github.com:openai/gym.git")).toBe("openai-gym");
+  });
+
+  test("https remote", () => {
+    expect(parseProjectFromRemote("https://github.com/mem0ai/mem0.git")).toBe("mem0ai-mem0");
+  });
+
+  test("https remote without a .git suffix", () => {
+    expect(parseProjectFromRemote("https://gitlab.com/acme/widgets")).toBe("acme-widgets");
+  });
+
+  test("trailing slash is ignored", () => {
+    expect(parseProjectFromRemote("https://github.com/acme/widgets/")).toBe("acme-widgets");
+  });
+
+  test("returns null when no owner/repo can be parsed", () => {
+    expect(parseProjectFromRemote("not-a-remote")).toBeNull();
+    expect(parseProjectFromRemote("")).toBeNull();
+  });
+});

integrations/mem0-plugin/.opencode-plugin/project.ts

@@ -0,0 +1,20 @@
+/**
+ * Project identity resolution for the Mem0 OpenCode plugin.
+ *
+ * The project id (`app_id`) scopes memories to a repo. We derive it from the
+ * git remote so it is stable across clones, worktrees, and sub-directories —
+ * falling back (in opencode-mem0.ts) to the git repo root dir name, then the
+ * cwd. Keeping the parser pure makes the tricky remote formats testable.
+ */
+
+/**
+ * Parse `owner/repo` out of a git remote URL and return it as `owner-repo`.
+ * Handles https, scp-style ssh, custom ssh host aliases (e.g.
+ * `git@github.com-work:owner/repo.git`), an optional `.git` suffix, and a
+ * trailing slash. Returns null when no owner/repo can be found.
+ */
+export function parseProjectFromRemote(remote: string): string | null {
+  const m = remote.trim().match(/[:/]([^/:]+)\/([^/:]+?)(?:\.git)?\/?$/);
+  if (!m) return null;
+  return `${m[1]}-${m[2]}`;
+}

integrations/mem0-plugin/.opencode-plugin/scope.test.ts

@@ -0,0 +1,62 @@
+import { describe, expect, test } from "bun:test";
+import { scopeSearchFilters, scopeWriteParams, asScope, resolveDefaultScope } from "./scope";
+
+describe("memory scope (pi-agent parity)", () => {
+  test("project scope = this repo", () => {
+    expect(scopeSearchFilters("project", "u", "app", "run")).toEqual({
+      user_id: "u",
+      app_id: "app",
+    });
+    expect(scopeWriteParams("project", "u", "app", "run")).toEqual({
+      user_id: "u",
+      app_id: "app",
+    });
+  });
+
+  test("session scope adds run_id", () => {
+    expect(scopeSearchFilters("session", "u", "app", "run")).toEqual({
+      user_id: "u",
+      app_id: "app",
+      run_id: "run",
+    });
+    expect(scopeWriteParams("session", "u", "app", "run")).toEqual({
+      user_id: "u",
+      app_id: "app",
+      run_id: "run",
+    });
+  });
+
+  test("global scope spans all the user's projects (matches pi-agent)", () => {
+    expect(scopeSearchFilters("global", "u", "app", "run")).toEqual({
+      user_id: "u",
+      app_id: "*",
+    });
+    // global writes drop app_id so the memory is user-wide, not project-bound
+    expect(scopeWriteParams("global", "u", "app", "run")).toEqual({ user_id: "u" });
+  });
+
+  test("default scope is project when settings are absent", () => {
+    expect(resolveDefaultScope(null)).toBe("project");
+    expect(resolveDefaultScope(undefined)).toBe("project");
+    expect(resolveDefaultScope({})).toBe("project");
+  });
+
+  test("default scope reads default_scope from settings", () => {
+    expect(resolveDefaultScope({ default_scope: "session" })).toBe("session");
+    expect(resolveDefaultScope({ default_scope: "global" })).toBe("global");
+    expect(resolveDefaultScope({ default_scope: "project" })).toBe("project");
+  });
+
+  test("default scope normalizes an invalid default_scope to project", () => {
+    expect(resolveDefaultScope({ default_scope: "nonsense" })).toBe("project");
+    expect(resolveDefaultScope({ default_scope: 42 })).toBe("project");
+  });
+
+  test("asScope normalizes unknown values to project", () => {
+    expect(asScope("global")).toBe("global");
+    expect(asScope("session")).toBe("session");
+    expect(asScope("project")).toBe("project");
+    expect(asScope("nonsense")).toBe("project");
+    expect(asScope(undefined)).toBe("project");
+  });
+});

integrations/mem0-plugin/.opencode-plugin/scope.ts

@@ -0,0 +1,71 @@
+/**
+ * Memory scope resolution — ported from the pi-agent plugin's scoping model.
+ *
+ * Lets the agent choose, per memory operation, how wide to read/write:
+ *   - "project" (default): this repo only        -> { user_id, app_id }
+ *   - "session": this run only                   -> { user_id, app_id, run_id }
+ *   - "global": across ALL of the user's projects -> { user_id, app_id: "*" }
+ *
+ * Mirrors pi-agent/src/memory/scoping.ts (resolveSearchFilters / resolveAddParams)
+ * so the OpenCode plugin exposes scope the same way: as a per-call tool parameter,
+ * not a stateful "switch project" command.
+ */
+
+export type Scope = "project" | "session" | "global";
+
+/** Filters for `search` / `get_memories` at the given scope. */
+export function scopeSearchFilters(
+  scope: Scope,
+  userId: string,
+  appId: string,
+  runId: string,
+): Record<string, string> {
+  switch (scope) {
+    case "session":
+      return { user_id: userId, app_id: appId, run_id: runId };
+    case "global":
+      return { user_id: userId, app_id: "*" };
+    case "project":
+    default:
+      return { user_id: userId, app_id: appId };
+  }
+}
+
+/** Identity params for `add` / `delete_all` at the given scope. */
+export function scopeWriteParams(
+  scope: Scope,
+  userId: string,
+  appId: string,
+  runId: string,
+): { user_id: string; app_id?: string; run_id?: string } {
+  switch (scope) {
+    case "session":
+      return { user_id: userId, app_id: appId, run_id: runId };
+    case "global":
+      return { user_id: userId };
+    case "project":
+    default:
+      return { user_id: userId, app_id: appId };
+  }
+}
+
+/** Normalize an arbitrary value to a valid Scope (defaults to "project"). */
+export function asScope(value: unknown): Scope {
+  return value === "session" || value === "global" ? value : "project";
+}
+
+/**
+ * Resolve the persisted default scope from a parsed `~/.mem0/settings.json`
+ * object. This is the user-changeable default applied to memory operations when
+ * no explicit `scope` is passed (set via the `mem0-scope` skill). Falls back to
+ * "project" when unset or invalid.
+ */
+export function resolveDefaultScope(
+  settings: Record<string, unknown> | null | undefined,
+): Scope {
+  return asScope(settings?.default_scope);
+}
+
+/** Guidance injected so the agent uses `global` only when explicitly asked. */
+export const SCOPE_GUIDANCE =
+  'Memory tools accept an optional `scope`: omit it (or "project") for normal queries; use "session" to limit to the current run; use "global" ONLY when the user explicitly asks to search across all their projects in this workspace.';

integrations/mem0-plugin/.opencode-plugin/telemetry.test.ts

@@ -0,0 +1,78 @@
+import { afterEach, describe, expect, test } from "bun:test";
+import { buildEvent, captureEvent, isTelemetryEnabled } from "./telemetry";
+
+const KEY = "m0-testkey123";
+
+afterEach(() => {
+  delete process.env.MEM0_TELEMETRY;
+});
+
+describe("opencode telemetry", () => {
+  test("buildEvent uses the shared plugin.* schema with platform=opencode", () => {
+    const payload = buildEvent("session_start", { memory_count: 5 }, KEY);
+    expect(payload).not.toBeNull();
+    const props = payload!.properties as Record<string, unknown>;
+    expect(payload!.event).toBe("plugin.session_start");
+    expect(props.source).toBe("plugin");
+    expect(props.platform).toBe("opencode");
+    expect(props.memory_count).toBe(5);
+    expect(props.$process_person_profile).toBe(false);
+    expect(typeof props.plugin_version).toBe("string");
+  });
+
+  test("distinct_id is sha256(apiKey)[:32] — matches the editor plugin", async () => {
+    const { createHash } = await import("node:crypto");
+    const expected = createHash("sha256").update(KEY).digest("hex").slice(0, 32);
+    expect(buildEvent("session_start", {}, KEY)!.distinct_id).toBe(expected);
+  });
+
+  test("system properties win over caller-supplied ones", () => {
+    const props = buildEvent("x", { platform: "HACK", source: "HACK" }, KEY)!
+      .properties as Record<string, unknown>;
+    expect(props.platform).toBe("opencode");
+    expect(props.source).toBe("plugin");
+  });
+
+  test("returns null without an API key (no anonymous events)", () => {
+    expect(buildEvent("session_start", {}, undefined)).toBeNull();
+  });
+
+  test("opt-out via MEM0_TELEMETRY disables events", () => {
+    process.env.MEM0_TELEMETRY = "false";
+    expect(isTelemetryEnabled()).toBe(false);
+    expect(buildEvent("session_start", {}, KEY)).toBeNull();
+  });
+
+  test("captureEvent never throws (and sends nothing when opted out)", () => {
+    process.env.MEM0_TELEMETRY = "false";
+    expect(() => captureEvent("session_start", {}, KEY)).not.toThrow();
+    expect(() => captureEvent("session_start", {}, undefined)).not.toThrow();
+    expect(() => captureEvent("session_start", {}, KEY, "proj")).not.toThrow();
+  });
+
+  test("every event carries os_version (matches telemetry.py schema)", () => {
+    const props = buildEvent("session_start", {}, KEY)!
+      .properties as Record<string, unknown>;
+    expect(typeof props.os_version).toBe("string");
+  });
+
+  test("project_hash is sha256(projectId) when a project id is supplied", async () => {
+    const { createHash } = await import("node:crypto");
+    const expected = createHash("sha256").update("acme-repo").digest("hex");
+    const props = buildEvent("session_start", {}, KEY, "acme-repo")!
+      .properties as Record<string, unknown>;
+    expect(props.project_hash).toBe(expected);
+  });
+
+  test("project_hash is omitted when no project id is supplied (no raw ids leak)", () => {
+    const props = buildEvent("session_start", {}, KEY)!
+      .properties as Record<string, unknown>;
+    expect("project_hash" in props).toBe(false);
+  });
+
+  test("expanded event types all use the shared plugin.* namespace", () => {
+    for (const ev of ["user_prompt", "bash_error", "pre_compact", "session_stop"]) {
+      expect(buildEvent(ev, {}, KEY)!.event).toBe(`plugin.${ev}`);
+    }
+  });
+});

integrations/mem0-plugin/.opencode-plugin/telemetry.ts

@@ -0,0 +1,113 @@
+/**
+ * Plugin telemetry for the Mem0 OpenCode plugin — anonymous usage tracking
+ * via PostHog.
+ *
+ * Emits the SAME event schema as the Mem0 editor plugin's telemetry.py
+ * (event names prefixed `plugin.`, `source: "plugin"`, `platform: "opencode"`,
+ * `distinct_id = sha256(apiKey)[:32]`) so OpenCode shows up as just another
+ * `platform` value in the shared plugin dashboard instead of a separate
+ * event namespace.
+ *
+ * Fire-and-forget: never throws, never blocks, failures are swallowed. Only
+ * fires when an API key is present (same as the editor plugin — anonymous
+ * installs without a key emit nothing). Disable with MEM0_TELEMETRY=false.
+ *
+ * Never sends: memory content, API keys, raw user/project IDs. Only sends:
+ * event type, platform, plugin version, and anonymized hashes of the API key
+ * and project ID.
+ */
+
+import { createHash } from "node:crypto";
+import { readFileSync } from "node:fs";
+import { release } from "node:os";
+
+const POSTHOG_API_KEY = "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX";
+const POSTHOG_HOST = "https://us.i.posthog.com/i/v0/e/";
+const REQUEST_TIMEOUT_MS = 2_000;
+
+function _loadPluginVersion(): string {
+  // Source context: telemetry.ts sits next to package.json (./).
+  // Bundled context: dist/index.js sits one level below it (../).
+  for (const rel of ["./package.json", "../package.json"]) {
+    try {
+      const pkg = JSON.parse(readFileSync(new URL(rel, import.meta.url), "utf-8"));
+      if (pkg?.name === "@mem0/opencode-plugin" && pkg.version) return pkg.version;
+    } catch {
+      /* try next candidate */
+    }
+  }
+  return "unknown";
+}
+
+const PLUGIN_VERSION = _loadPluginVersion();
+
+export function isTelemetryEnabled(): boolean {
+  const val = process.env.MEM0_TELEMETRY;
+  if (val === undefined) return true;
+  const s = val.toLowerCase();
+  return s !== "false" && s !== "0" && s !== "no" && s !== "off";
+}
+
+function distinctId(apiKey: string): string {
+  // Matches telemetry.py `_distinct_id()` so the same user is one person in
+  // PostHog whether they use OpenCode or any other Mem0 editor plugin.
+  return createHash("sha256").update(apiKey).digest("hex").slice(0, 32);
+}
+
+/**
+ * Build the PostHog event payload, or null when telemetry is disabled or no
+ * API key is available. Pure (aside from env/version reads) and exported for
+ * testing. System-controlled properties are applied last so a caller cannot
+ * override `source`/`platform`/etc.
+ */
+export function buildEvent(
+  eventType: string,
+  properties: Record<string, unknown>,
+  apiKey: string | undefined,
+  projectId?: string,
+): Record<string, unknown> | null {
+  if (!isTelemetryEnabled() || !apiKey) return null;
+  return {
+    api_key: POSTHOG_API_KEY,
+    distinct_id: distinctId(apiKey),
+    event: `plugin.${eventType}`,
+    properties: {
+      ...properties,
+      source: "plugin",
+      platform: "opencode",
+      plugin_version: PLUGIN_VERSION,
+      os: process.platform,
+      os_version: release(),
+      sample_rate: 1.0,
+      $process_person_profile: false,
+      $lib: "posthog-node",
+      // Anonymized project segmentation, matching telemetry.py's project_hash.
+      ...(projectId
+        ? { project_hash: createHash("sha256").update(projectId).digest("hex") }
+        : {}),
+    },
+  };
+}
+
+/** Send a usage event, fire-and-forget. Never throws, never blocks. */
+export function captureEvent(
+  eventType: string,
+  properties: Record<string, unknown>,
+  apiKey: string | undefined,
+  projectId?: string,
+): void {
+  const payload = buildEvent(eventType, properties, apiKey, projectId);
+  if (!payload) return;
+  try {
+    void fetch(POSTHOG_HOST, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(payload),
+      signal: AbortSignal.timeout(REQUEST_TIMEOUT_MS),
+    }).catch(() => {
+      /* fire-and-forget */
+    });
+  } catch {
+    /* never throw */
+  }
+}

integrations/mem0-plugin/.opencode-plugin/tsconfig.json

@@ -16,5 +16,5 @@
     "emitDeclarationOnly": true
   },
   "include": ["**/*"],
-  "exclude": ["node_modules", "dist"]
+  "exclude": ["node_modules", "dist", "**/*.test.ts"]
 }

integrations/mem0-plugin/CHANGELOG.md

@@ -2,6 +2,21 @@
 
 All notable changes to the Mem0 plugin will be documented in this file.
 
+## 0.2.10 — Accurate per-editor telemetry attribution
+
+### Fixed
+
+- **Antigravity counted as Claude Code:** `detect_platform()` (`scripts/telemetry.py`) now checks `ANTIGRAVITY_PLUGIN_ROOT` before the `CLAUDE_PLUGIN_ROOT` branch. Antigravity sets both env vars for compatibility, so every Antigravity session was previously attributed to `claude-code`. Telemetry now reports `platform: "antigravity"`.
+- **Codex fell back to the generic `plugin` bucket:** Codex installs standalone hooks with absolute paths via `install_codex_hooks.py`, so `PLUGIN_ROOT` is never set at runtime and platform auto-detection failed. Each command in `hooks/codex-hooks.json` now pins `MEM0_PLATFORM=codex` inline (Codex runs hook commands through a shell). Telemetry now reports `platform: "codex"`.
+- **Cursor attribution depended on the host env:** Cursor's `*_cursor.sh` wrappers delegate to the shared hook scripts, whose platform detection relied on Cursor exporting `CURSOR_PLUGIN_ROOT` to the subprocess. All five Cursor wrappers now `export MEM0_PLATFORM=cursor` before delegating.
+- **`plugin_version` was identical for every editor:** telemetry read `.claude-plugin/plugin.json` for all bash-hook editors, so Antigravity reported `0.2.10` instead of its real `0.1.2`. `_load_plugin_version()` now reads the manifest matching the detected platform, so each editor reports its own version.
+
+### Added
+
+- **`MEM0_PLATFORM` override in `detect_platform()`:** An explicit platform marker that wins over env-var auto-detection, letting each editor label its telemetry reliably. New tests in `tests/test_telemetry.py` cover the override, Antigravity attribution, and the Cursor/Codex platform-pinning contracts.
+
+> Attribution fixes apply to telemetry emitted after users upgrade to this version; PostHog does not backfill past events.
+
 ## 0.2.9 — File-context injection, session summaries & activity timeline
 
 ### Added