Cognee Integrations - AI Memory for Your Agent Framework

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# Cognee Integrations Monorepo for all Cognee-owned integration packages. Each integration gives an agent framework (Strands, CrewAI, LangGraph, Google ADK, …) a persistent **memory layer** backed by [cognee](https://github.com/topoteretes/cognee): a permanent knowledge graph plus a fast session cache. ## Available Integrations Install these from their public registries — you do **not** need to clone this monorepo to use them. | Framework | Package | Install | |---|---|---| | Strands | `cognee-integration-strands` | `pip install cognee-integration-strands` | | CrewAI | `cognee-integration-crewai` | `pip install cognee-integration-crewai` | | LangGraph | `cognee-integration-langgraph` | `pip install cognee-integration-langgraph` | | Google ADK | `cognee-integration-google-adk` | `pip install cognee-integration-google-adk` | | Claude Agent SDK | `cognee-integration-claude` | `pip install cognee-integration-claude` | | Hermes Agent | `cognee-integration-hermes-agent` | `pip install cognee-integration-hermes-agent` | | OpenClaw | `@cognee/cognee-openclaw` | `npm install @cognee/cognee-openclaw` or install from clawhub.ai| | n8n | `n8n-nodes-cognee` | install via n8n community nodes | | Dify (Cloud) | `cognee` | install from the Dify marketplace | | Dify (self-hosted) | `cognee-sdk` | install from the Dify marketplace | Each integration has its own `README.md` under `integrations//` with the full tool reference and runnable examples. The table above is generated from [`integrations/inventory.yml`](integrations/inventory.yml) — see it for ownership, versions, and compatible cognee ranges. ### Chat bots & editor Memory apps and editor tooling that talk to a running **cognee server** (`COGNEE_BASE_URL`) over its HTTP API — no in-process cognee. Each lives under `integrations//` with a runnable example and its own `README.md`. | Integration | Package | What it does | |---|---|---| | Chat-memory core | `cognee-integration-chat-memory` | the shared `ChatMemoryAdapter` every cognee chat bot builds on | | Telegram | `cognee-integration-telegram` | each chat is a memory; `/ask` with cited message links | | Slack | `cognee-integration-slack` | per-channel memory; `@cognee` / `/recall` cited answers | | Web chat widget | `cognee-integration-web-widget` | one-script-tag embeddable widget + "ask our docs" | | Second brain | `cognee-integration-second-brain` | cross-transport personal memory (Telegram + web), `/link` identity merge | | VS Code | `cognee-vscode` | remember/recall + "ask my project memory" with source-file citations | ## Claude Code Quickstart The Claude Code integration is a **plugin** — it gives Claude Code persistent memory across sessions with no code to write. It auto-captures your prompts, tool traces, and responses, and auto-recalls relevant context on every prompt. **1. Install the Claude Code plugin** Run these slash commands directly in the Claude Code chat: ``` /plugin marketplace add topoteretes/cognee-integrations /plugin install cognee-memory@cognee ``` **2. Configure your LLM key** In local mode (the default), the plugin bootstraps a local Cognee API on `http://localhost:8011`. Cognee extracts knowledge with an LLM, so set `LLM_API_KEY` **once** in `~/.cognee/.env` (shared by the Claude Code and Codex plugins) — one paste, no editor needed: ```bash mkdir -p ~/.cognee cat >> ~/.cognee/.env <<'EOF' LLM_API_KEY="sk-..." EOF chmod 600 ~/.cognee/.env ``` A plain `export` in the launching shell also works and overrides the file. Re-pasting the block with a new value is safe — the last value wins. To target Cognee Cloud or a remote server instead, set `COGNEE_BASE_URL` and `COGNEE_API_KEY` there. On startup you should see a **"Cognee Memory Connected"** message. **3. Use Claude Code as usual** Memory is captured and recalled automatically — no extra steps. You can also invoke the skills explicitly: ``` /cognee-memory:cognee-remember # store something now /cognee-memory:cognee-search # query memory /cognee-memory:cognee-sync # persist the session into the graph ``` For full configuration (datasets, sessions, sync watchers, cloud mode), see [`integrations/claude-code/README.md`](integrations/claude-code/README.md). > **Using an agent framework instead?** The Python SDK integrations (Strands, CrewAI, > LangGraph, Google ADK, Claude Agent SDK) follow a `pip install` → > set `LLM_API_KEY` → attach `cognee_tools()` pattern. See each integration's README > under `integrations//` for a runnable example. ### Two memory tiers Built on cognee v1.0, the integrations share the same two tiers: - **Permanent knowledge graph** — durable memory that survives across sessions. - **Session cache** — a cheap per-session cache (no graph extraction up front) that is promoted into the permanent graph on sync (`/cognee-memory:cognee-sync`, or `cognee.improve(session_ids=[...])` in the SDK integrations). ## Using the Python Integrations Every Python integration installs from PyPI and follows the same shape: **install → set `LLM_API_KEY` → build the cognee tools → pass them to your agent.** The only thing that differs per framework is the import line and how you construct the agent. ```bash pip install cognee-integration-strands # or -crewai, -langgraph, -google-adk, -claude export LLM_API_KEY="sk-..." # cognee extracts knowledge with an LLM ``` The tools come in two styles depending on the integration's version: | Framework | Package | Build the tools with | Tools | |---|---|---|---| | Strands | `cognee-integration-strands` | `cognee_tools(session_id=None)` | `remember`, `recall` | | Claude Agent SDK | `cognee-integration-claude` | `cognee_tools(session_id=None)` | `remember`, `recall` | | CrewAI | `cognee-integration-crewai` | `from … import add_tool, search_tool` | `add_tool`, `search_tool` | | Google ADK | `cognee-integration-google-adk` | `from … import add_tool, search_tool` | `add_tool`, `search_tool` | | LangGraph | `cognee-integration-langgraph` | `get_sessionized_cognee_tools(user_id)` | `add_tool`, `search_tool` | **`cognee_tools()` style** (cognee v1.0 — Strands, Claude Agent SDK). Writes go to the permanent graph; pass `session_id=...` to use the session cache instead: ```python from cognee_integration_strands import cognee_tools from strands import Agent from strands.models.openai import OpenAIModel agent = Agent(model=OpenAIModel(...), tools=cognee_tools()) agent("Remember that we signed a contract with Meditech Solutions for £1.2M.") print(agent("What is the value of the Meditech Solutions contract?")) ``` **`add_tool` / `search_tool` style** (CrewAI, Google ADK, LangGraph). Here you also ingest source documents yourself with `cognee.add(...)` + `cognee.cognify()` before searching: ```python import cognee from cognee_integration_crewai import add_tool, search_tool # CrewAI / Google ADK from crewai import Agent await cognee.add("Meditech Solutions — healthcare industry, contract worth £1.2M.") await cognee.cognify() # build the knowledge graph agent = Agent(role="Analyst", goal="…", backstory="…", tools=[add_tool, search_tool]) print(agent.kickoff("Which contracts are in the healthcare industry?")) ``` LangGraph is the same style but builds its tools per user: `add_tool, search_tool = get_sessionized_cognee_tools("user-1")`. Each integration's `README.md` under `integrations//` has a complete runnable example (`examples/`) and the full tool reference. ## Structure Each integration lives under `integrations//` and is an independently publishable package. ``` integrations/ openclaw/ -> @openclaw/memory-cognee (npm) claude-code/ -> Cognee plugin for Claude Code codex/ -> Cognee plugin marketplace for Codex ``` ## Adding a New Integration ### Python integrations _(Template coming soon. For now, follow the TypeScript pattern below and adapt for Python with `pyproject.toml`.)_ ### TypeScript/Node integrations (e.g., OpenClaw plugins) 1. Create `integrations//` with `package.json`, entry file, and plugin manifest 2. Follow the target platform's plugin conventions 3. Add an entry to `integrations/inventory.yml` CI auto-detects new integrations by language (Python via `pyproject.toml`, TypeScript via `package.json`) — no workflow edits needed. ## Development Each integration is developed independently with its own toolchain: ```bash # Python integrations cd integrations/ uv sync --dev uv run pytest tests/ -v uv run ruff check . # TypeScript integrations cd integrations/ npm install npx tsc --noEmit ``` ## Version Pinning Policy Python integrations must pin the `cognee` dependency with a bounded range (e.g., `cognee>=0.5.1,<0.6.0`). This is enforced by CI via `scripts/check_version_pins.py`. TypeScript integrations that talk to Cognee via HTTP API are exempt from package pinning but should document compatible Cognee server versions. When a new `cognee` version is released: 1. Update the bounds in affected integrations 2. Run tests to verify compatibility 3. Bump the integration version 4. Publish the updated package ## Publishing Each integration is published independently via tag-per-package: ```bash # TypeScript: publishes to npm git tag openclaw-v2026.2.4 && git push --tags # Python (when added): publishes to PyPI # git tag -v && git push --tags ``` The `publish.yml` workflow parses the tag, runs tests, and publishes to the appropriate registry. ## CI - **Lint**: Ruff on every PR across all Python integrations - **Tests**: Auto-detects changed integrations and runs the right test suite (pytest for Python, tsc for TypeScript) - **Pin check**: Validates bounded `cognee` dependencies in Python integrations - **Publish**: Tag-triggered per-package publishing to PyPI or npm ## Inventory `integrations/inventory.yml` tracks all known integrations with ownership, migration status, package names, and version info. Update it when adding or migrating integrations.