cognee

<div align="center"> <a href="https://github.com/topoteretes/cognee"> <img src="https://raw.githubusercontent.com/topoteretes/cognee/refs/heads/dev/assets/cognee-logo-transparent.png" alt="Cognee Logo" height="60"> </a> <br /> Cognee - The Open-Source AI Memory Platform for Agents <p align="center"> <a href="https://www.youtube.com/watch?v=8hmqS2Y5RVQ&t=13s">Demo</a> . <a href="https://docs.cognee.ai/">Docs</a> . <a href="https://cognee.ai">Learn More</a> · <a href="https://discord.gg/NQPKmU5CCg">Join Discord</a> · <a href="https://www.reddit.com/r/AIMemory/">Join r/AIMemory</a> . <a href="https://github.com/topoteretes/cognee-community">Community Plugins & Add-ons</a> </p> [](https://GitHub.com/topoteretes/cognee/network/) [](https://GitHub.com/topoteretes/cognee/stargazers/) [](https://GitHub.com/topoteretes/cognee/commit/) [](https://github.com/topoteretes/cognee/tags/) [](https://pepy.tech/project/cognee) [](https://github.com/topoteretes/cognee/blob/main/LICENSE) [](https://github.com/topoteretes/cognee/graphs/contributors) <a href="https://github.com/sponsors/topoteretes"><img src="https://img.shields.io/badge/Sponsor-❤️-ff69b4.svg" alt="Sponsor"></a> <p> <a href="https://trendshift.io/repositories/13955" target="_blank" style="display:inline-block;"> <img src="https://trendshift.io/api/badge/repositories/13955" alt="topoteretes%2Fcognee | Trendshift" width="250" height="55" /> </a> </p> 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 <p align="center"> 🌐 This README is also available in: : <!-- Keep these links. Translations will automatically update with the README. --> <a href="https://www.readme-i18n.com/topoteretes/cognee?lang=de">Deutsch</a> | <a href="https://www.readme-i18n.com/topoteretes/cognee?lang=es">Español</a> | <a href="https://www.readme-i18n.com/topoteretes/cognee?lang=fr">Français</a> | <a href="https://www.readme-i18n.com/topoteretes/cognee?lang=ja">日本語</a> | <a href="README_ko.md">한국어</a> | <a href="https://www.readme-i18n.com/topoteretes/cognee?lang=pt">Português</a> | <a href="https://www.readme-i18n.com/topoteretes/cognee?lang=ru">Русский</a> | <a href="https://www.readme-i18n.com/topoteretes/cognee?lang=zh">中文</a> </p> <div style="text-align: center"> <img src="https://raw.githubusercontent.com/topoteretes/cognee/refs/heads/main/assets/cognee_benefits.png" alt="Why cognee?" width="80%" /> </div> </div> 📄 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. [](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 ``` ## 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 [](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}, } ```