Cognee

Deploy Cognee

For a local API demo using a prebuilt image, follow the minimal Docker Compose guide. It includes a persistent-volume configuration and explains the single-user demo settings.

To run the API, UI, and MCP server from a source checkout, clone this repository, enter its directory, copy .env.template to .env, and configure your providers. Then run:

docker compose --profile ui --profile mcp up

The default ports are API 8000, UI 3000, and MCP 8001. For deployment beyond a local demo, configure authentication, persistent storage, and compatible backends using the permissions guide and deployment templates. Cognee Cloud provides the managed option.

Quickstart

Requires Python 3.10–3.14.

You can install Cognee with pip, uv, or your preferred Python package manager.

uv pip install cognee

Explore examples

• Build a small Company Brain from text, code, and session lessons.
• Import memory from Mem0, Letta, Zep, or Graphiti using the COGX exchange format.
• Run with local Ollama models, including a local embedding model.
• Visualize your knowledge graph and inspect its connections.
• Browse runnable examples for ingestion, sessions, feedback, and custom pipelines.
• Run the prebuilt API with Docker Compose or use the deployment templates.
• Explore community adapters and add-ons.

<a id="run-with-docker"></a>

Step 2: Configure the LLM

import os
os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"

The default uses OpenAI for language models and embeddings. Processing and generated answers make provider calls. See installation, other providers, or local Ollama models for other setups.

import cognee
import asyncio


async def main():
    # Store permanently in the knowledge graph (runs add + cognify + improve)
    await cognee.remember("Cognee turns documents into AI memory.")

    # Store in session memory (fast cache, syncs to graph in background)
    await cognee.remember("User prefers detailed explanations.", session_id="chat_1")

    # Query with auto-routing (picks best search strategy automatically)
    results = await cognee.recall("What does Cognee do?")
    for result in results:
        print(result)

    # Query session memory first, fall through to graph if needed
    results = await cognee.recall("What does the user prefer?", session_id="chat_1")
    for result in results:
        print(result)

    # Delete when done
    await cognee.forget(dataset="main_dataset")


if __name__ == '__main__':
    asyncio.run(main())

~/.codex/config.toml

[features] hooks = true

Follow the [plugin setup guide](https://github.com/topoteretes/cognee-integrations/tree/main/integrations/claude-code) to configure local or remote memory.

| Interface | Start here |
| --- | --- |
| Claude Code memory plugin | [Install and configure the plugin](https://github.com/topoteretes/cognee-integrations/tree/main/integrations/claude-code) |
| OpenClaw memory plugin | [Install `@cognee/cognee-openclaw`](https://www.npmjs.com/package/@cognee/cognee-openclaw) |
| Cursor, Cline, and other MCP clients | [Cognee MCP guide](https://docs.cognee.ai/cognee-mcp/mcp-overview) and [server README](cognee-mcp/README.md) |
| Python applications | [Python API reference](https://docs.cognee.ai/python-api) |
| TypeScript applications | [TypeScript SDK](https://docs.cognee.ai/typescript/getting-started) |
| Rust applications | [Cognee-RS](https://github.com/topoteretes/cognee-rs) |
| Applications using HTTP | [REST API reference](https://docs.cognee.ai/api-reference/introduction) |

Browse the [integrations repository](https://github.com/topoteretes/cognee-integrations) for agent frameworks, plugins, and source connectors. Each guide describes its setup and memory capture behavior.

To inspect a local installation in the UI:

The UI launcher requires Node.js/npm; Docker is needed for its MCP service. See local UI setup.

Try it without an API key

cognee-cli demo