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  <br />

  Cognee - The Open-Source AI Memory Platform for Agents

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Cognee is the open-source AI memory platform that gives AI agents persistent long-term memory across sessions. Ingest data in any format, build a self-hosted knowledge graph, and let every agent recall, connect, and act with full context

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📄 Read the research paper: [Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning](https://arxiv.org/abs/2505.24478) — Markovic et al., 2025


## About Cognee

Cognee is an open-source AI memory platform for AI Agents. Ingest data in any format, and Cognee continuously builds a self-hosted knowledge graph that gives your agents persistent long-term memory across sessions. Cognee combines vector embeddings, graph reasoning, and cognitive-science-grounded ontology generation to make documents both searchable by meaning and connected by relationships that evolve as your knowledge does.

:star: _Help us reach more developers and grow the cognee community. Star this repo!_

:books: _Check our detailed [documentation](https://docs.cognee.ai/getting-started/installation#environment-configuration) for setup and configuration._

:crab: _Available as a plugin for your OpenClaw — [cognee-openclaw](https://www.npmjs.com/package/@cognee/cognee-openclaw)_

✴️ _Available as a plugin for your Claude Code — [claude-code-plugin](https://github.com/topoteretes/cognee-integrations/tree/main/integrations/claude-code)_



### Why use Cognee:

- Easily Build Company Brain - unify data from various sources in one place and enable Agents with your domain knowledge
- Knowledge infrastructure — unified ingestion, graph/vector search, runs locally, ontology grounding, multimodal
- Persistent and Learning Agents - learn from feedback, context management, cross-agent knowledge sharing
- Reliable and Trustworthy Agents - agentic user/tenant isolation, traceability, OTEL collector, audit traits

### Product Features

<p align="center">
  <img src="assets/cognee_products.png" alt="Cognee Products" width="80%" />
</p>

## Basic Usage & Feature Guide

To learn more, [check out this short, end-to-end Colab walkthrough](https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing) of Cognee's core features.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing)

## Quickstart

Let’s try Cognee in just a few lines of code.

### Prerequisites

- Python 3.10 to 3.14

### Step 1: Install Cognee

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

```bash
uv pip install cognee
```

### Step 2: Configure the LLM
```python
import os
os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"
```
Alternatively, create a `.env` file using our [template](https://github.com/topoteretes/cognee/blob/main/.env.template).

To integrate other LLM providers, see our [LLM Provider Documentation](https://docs.cognee.ai/setup-configuration/llm-providers).

### Step 3: Run the Pipeline

Cognee's API gives you four operations — `remember`, `recall`, `forget`, and `improve`:

```python
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())

```

### Use the Cognee CLI

```bash
cognee-cli remember "Cognee turns documents into AI memory."

cognee-cli recall "What does Cognee do?"

cognee-cli forget --all
```

To open the local UI, run:
```bash
cognee-cli -ui
```

> **Note:** The MCP server launched by `cognee-cli -ui` runs inside a Docker container.
> Docker Desktop, Colima, or any OCI-compatible runtime with a working `docker` CLI is
> required. See [Docker & Colima Setup](docs/docker-colima-setup.md) for details.

## Use with AI Agents

### Claude Code

Install the [Cognee memory plugin](https://github.com/topoteretes/cognee-integrations/tree/main/integrations/claude-code) to give Claude Code persistent memory across sessions. The plugin automatically captures tool calls into session memory via hooks and syncs to the permanent knowledge graph at session end.

**Setup:**

```bash
# Install cognee
pip install cognee

# Configure
export LLM_API_KEY="your-openai-key"

# Clone the plugin
git clone https://github.com/topoteretes/cognee-integrations.git

# Enable it (add to ~/.zshrc for permanent use)
claude --plugin-dir ./cognee-integrations/integrations/claude-code
```

Or connect to Cognee Cloud instead of running locally:

```bash
export COGNEE_SERVICE_URL="https://your-instance.cognee.ai"
export COGNEE_API_KEY="ck_..."
```

The plugin hooks into Claude Code's lifecycle — `SessionStart` initializes memory, `PostToolUse` captures actions, `UserPromptSubmit` injects relevant context, `PreCompact` preserves memory across context resets, and `SessionEnd` bridges session data into the permanent graph.

### Connect to Cognee Cloud

Point any Python agent at a managed Cognee instance — all SDK calls route to the cloud:

```python
import cognee

await cognee.serve(url="https://your-instance.cognee.ai", api_key="ck_...")

await cognee.remember("important context")
results = await cognee.recall("what happened?")

await cognee.disconnect()
```

## Examples

Browse more examples in the [`examples/`](examples/) folder — demos, guides, custom pipelines, and database configurations.

**Use Case 1 — Customer Support Agent**

```python
Goal: Resolve customer issues using their personal data across finance, support, and product history.

User: "My invoice looks wrong and the issue is still not resolved."

Cognee tracks: past interactions, failed actions, resolved cases, product history

# Agent response:
Agent: "I found 2 similar billing cases resolved last month.
        The issue was caused by a sync delay between payment
        and invoice systems — a fix was applied on your account."

# What happens under the hood:
- Unifies data sources from various company channels
- Reconstructs the interaction timeline and tracks outcomes
- Retrieves similar resolved cases
- Maps to the best resolution strategy
- Updates memory after execution so the agent never repeats the same mistake
```

**Use Case 2 — Expert Knowledge Distillation (SQL Copilot)**

```python
Goal: Help junior analysts solve tasks by reusing expert-level queries, patterns, and reasoning.

User: "How do I calculate customer retention for this dataset?"

Cognee tracks: expert SQL queries, workflow patterns, schema structures, successful implementations

# Agent response:
Agent: "Here's how senior analysts solved a similar retention query.
        Cognee matched your schema to a known structure and adapted
        the expert's logic to fit your dataset."

# What happens under the hood:
- Extracts and stores patterns from expert SQL queries and workflows
- Maps the current schema to previously seen structures
- Retrieves similar tasks and their successful implementations
- Adapts expert reasoning to the current context
- Updates memory with new successful patterns so junior analysts perform at near-expert level
```

## Deploy Cognee

Use [Cognee Cloud](https://www.cognee.ai) for a fully managed experience, or self-host with one of the 1-click deployment configurations below.

| Platform | Best For | Command |
|----------|----------|---------|
| **Cognee Cloud** | Managed service, no infrastructure to maintain | [Sign up](https://www.cognee.ai) or `await cognee.serve()` |
| **Modal** | Serverless, auto-scaling, GPU workloads | `bash distributed/deploy/modal-deploy.sh` |
| **Railway** | Simplest PaaS, native Postgres | `railway init && railway up` |
| **Fly.io** | Edge deployment, persistent volumes | `bash distributed/deploy/fly-deploy.sh` |
| **Render** | Simple PaaS with managed Postgres | Deploy to Render button |
| **Daytona** | Cloud sandboxes (SDK or CLI) | See `distributed/deploy/daytona_sandbox.py` |

See the [`distributed/`](distributed/) folder for deploy scripts, worker configurations, and additional details.

## Latest News

[![Watch Demo](https://img.youtube.com/vi/8hmqS2Y5RVQ/maxresdefault.jpg)](https://www.youtube.com/watch?v=8hmqS2Y5RVQ&t=13s)


## Community & Support

### Contributing
We welcome contributions from the community! Your input helps make Cognee better for everyone. See [`CONTRIBUTING.md`](CONTRIBUTING.md) to get started.

### Code of Conduct

We're committed to fostering an inclusive and respectful community. Read our [Code of Conduct](https://github.com/topoteretes/cognee/blob/main/CODE_OF_CONDUCT.md) for guidelines.

## Research & Citation

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

```bibtex
@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},
}
```
