README

Cognee - The Free Open-Source AI Memory Platform for Agents

  
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  Cognee is a free open-source AI memory platform that gives AI agents persistent long-term memory across sessions. Turn documents, code, and conversations into a self-hosted knowledge graph your agents can search and reuse.

  Runs locally for free — no API key required.
  We rely on free small models that use your CPU.

  
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📄 Read the research paper: Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning — Markovic et al., 2025

 When to use Cognee

- Build a Company Brain. Bring documentation, conversations, tickets, code, and agent work into shared memory. Help your team and agents connect a decision to the discussion and implementation behind it. Explore Company Brain.
- Give agents memory across runs. Retain project context, past decisions, fixes, and learned rules. Distill useful session lessons into durable knowledge that another session can retrieve. Connect your agent.
- Ground agents in your domain. Structure memory around the entities and relationships your application needs, with custom data models and ontologies. Explore ontologies.

 Choose your starting point

| I want to… | Start here |
| --- | --- |
| Build memory without an LLM | Local Python quickstart |
| Explore a prebuilt graph without downloading models | Bundled demo |
| Generate answers with a local or hosted LLM | Optional LLM setup |
| Give an existing agent memory | Plugins and MCP |
| Run Cognee on my infrastructure | Deployment options |
| Use a managed service | Cognee Cloud |

 Quickstart

Requires Python 3.10–3.14.

1. Install Cognee with pip, uv, or your preferred Python package manager. The gliner extra brings the local extraction model used when no LLM key is configured:

bash
uv pip install "cognee[gliner]"


 Run locally without an LLM

2. Build and query memory. With no LLM key configured, Cognee extracts the graph with the local GLiNER model and embeds with a local embedding model; both download on first use.

Save this as quickstart.py and run python quickstart.py if you are feeling old school, or tell your LLM to do it:

python
import asyncio

import cognee


async def main():
     Extract a knowledge graph and embed the text with local models.
    await cognee.remember(
        "Marie Curie was born in Warsaw and worked at the University of Paris.",
        dataset_name="local_quickstart",
    )

     Retrieve the matching source text; no LLM generates an answer.
    results = await cognee.recall(
        "Where was Marie Curie born?",
        datasets=["local_quickstart"],
    )
    for result in results:
        print(result)


if __name__ == "__main__":
    asyncio.run(mai