By miantalha45
Cognee knowledge graph memory for Claude Code — session-aware storage, auto-routing recall, and persistent learning across sessions. Supports local mode and Cognee Cloud.
Store data permanently in the Cognee knowledge graph. Accepts a data category (user, project, or agent) to tag the data with the correct node_set for filtered retrieval.
Search Cognee memory. Session memory is automatically searched on every prompt via hooks. Use this skill explicitly for permanent knowledge graph search, filtered category search, or when you need more results than the automatic lookup provides.
Sync session cache entries into the permanent Cognee knowledge graph. Run this to make session memory searchable, or it runs automatically at session end.
Executes bash commands
Hook triggers when Bash tool is used
Modifies files
Hook triggers on file write and edit operations
Uses power tools
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Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Uses Bash, Write, or Edit tools
Uses Bash, Write, or Edit tools
Cognee Integrations - AI Memory for Your Agent Framework
Demo . Docs . Learn More · Join Discord · Join r/AIMemory . Core Repo
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: a permanent knowledge graph plus a fast session cache.
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 |
| 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/<name>/ with the full tool
reference and runnable examples. The table above is generated from
integrations/inventory.yml — see it for ownership,
versions, and compatible cognee ranges.
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 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
in the shell that launches Claude Code:
export LLM_API_KEY="sk-..."
To target Cognee Cloud or a remote server instead, set COGNEE_BASE_URL and
COGNEE_API_KEY. 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.
Using an agent framework instead? The Python SDK integrations (Strands, CrewAI, LangGraph, Google ADK, Claude Agent SDK) follow a
pip install→ setLLM_API_KEY→ attachcognee_tools()pattern. See each integration's README underintegrations/<name>/for a runnable example.
npx claudepluginhub miantalha45/cognee-integrations --plugin cognee-memoryUltra-compressed communication mode. Cuts 65% of output tokens (measured) while keeping full technical accuracy by speaking like a caveman.
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