Cognee

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.

🌐 This README is also available in:

Deutsch | Español | Français | 日本語 | 한국어 | Português | Русский | 中文

📄 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:

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:

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(main())

The same workflow is available from the CLI: