Cognee

📄 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

#Quickstart

Requires Python 3.10–3.14.

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

uv pip install "cognee[gliner]"

#Optional: Configure the LLM

import os

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

Alternatively, create a .env file using our template.

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.

#Run locally without an LLM

In step 1, you did "cognee[gliner]" install.

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:

cognee-cli remember "Marie Curie was born in Warsaw." -d local_quickstart
cognee-cli recall "Where was Marie Curie born?" -d local_quickstart

Text ingestion, retrieval, and session storage work without an LLM. LLM-dependent improvement stages skip automatically.

Generated answers and media processing that requires a vision or transcription model need additional LLM configuration.

#Explore the bundled demo

To explore a prebuilt graph without downloading extraction or embedding models:

cognee-cli demo

This command works with the base pip install cognee package. It loads bundled sample data and runs keyword search without an API key. Use the local quickstart above to build a graph from your own text.

#How Cognee works

Cognee builds connected memory from different sources. Text becomes entities, relationships, and searchable chunks; code becomes a graph of symbols and dependencies. Session distillation curates accepted lessons into permanent memory.

At query time, retrieval selects relevant graph, vector, or code context. Your application can inspect the retrieved evidence and use it to answer a question or continue an agent task.

Explore the architecture and session lifecycle.

#Connect your agent

Install the Claude Code plugin:

claude plugin marketplace add topoteretes/cognee-integrations
claude plugin install cognee-memory@cognee

or Codex plugin

Make sure to enable hooks:

# ~/.codex/config.toml
[features]
hooks = true

codex plugin marketplace add topoteretes/cognee-integrations --ref main
codex plugin add cognee@cognee

Follow the plugin setup guide to configure local or remote memory.

Browse the integrations repository for agent frameworks, plugins, and source connectors. Each guide describes its setup and memory capture behavior.

To inspect a local installation in the UI:

cognee-cli -ui

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

#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.

#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.

The default Docker image does not include GLiNER. To ingest text without an LLM in Docker, add the gliner extra to your image; the local quickstart installs it explicitly.

#Run the Whole Memory Layer on Postgres

Graph memory traditionally means operating a stack — a graph database for relationships, a vector database for embeddings, Redis for sessions, and a relational database for metadata — all deployed, secured, and paid for before an agent remembers anything. In cognee 1.0 you can run the entire memory layer on a single Postgres instance.

⚠️ Warning: Using Postgres as a graph store is currently a released as a demo feature. The production ready feature is available as a licenced product. Use it to demo keeping relational metadata, PGVector, and graph working together

#Benchmarks and research

The BEAM evaluation measures conversational memory using synthetic long-context conversations and an LLM judge. The reported runs use Cognee's memory components with benchmark-specific data formatting, prompts, and retrieval configuration.

The two settings use different conversations, ingestion models, and retrieval-selection procedures. Read the methodology, models, limitations, and reproduction instructions before comparing these scores with other systems. The report also documents the remaining reproduction gap for the distributed 10M ingestion.

For the research behind Cognee's graph/LLM interface, see Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning (Markovic et al., 2025).

#Latest News

• v1.6.0 — Keyless workflows & pipeline reliability (September 18, 2026): build and search text memory with local models and no cloud LLM key.
• Local model downloads are announced on first use, and LLM-dependent improvement stages skip when no LLM is configured.
• Pipeline recovery preserves completed documents after crashes, and datasets track their embedding model to prevent mismatches.

#Community & Support

#Contributing

We welcome contributions from the community! Your input helps make Cognee better for everyone. See CONTRIBUTING.md to get started.

#Code of Conduct

We're committed to fostering an inclusive and respectful community. Read our Code of Conduct for guidelines.

#Research & Citation

We recently published a research paper on optimizing knowledge graphs for LLM reasoning:

@misc{markovic2025optimizinginterfaceknowledgegraphs,
      title={Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning},
      author={Vasilije Markovic and Lazar Obradovic and Laszlo Hajdu and Jovan Pavlovic},
      year={2025},
      eprint={2505.24478},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2505.24478},
}