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<div align="center"> <a href="https://github.com/topoteretes/cognee"> <img src="assets/cognee-logo.svg" alt="Cognee Logo" width="260"> </a> Cognee - The Free Open-Source AI Memory Platform for Agents <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> <a href="https://GitHub.com/topoteretes/cognee/network/"><img src="https://img.shields.io/github/forks/topoteretes/cognee.svg?style=social&label=Fork&maxAge=2592000" alt="GitHub forks"></a> <a href="https://github.com/topoteretes/cognee"><img src="https://img.shields.io/github/stars/topoteretes/cognee.svg?style=social&label=Star&maxAge=2592000" alt="GitHub stars"></a> <a href="https://GitHub.com/topoteretes/cognee/commit/"><img src="https://badgen.net/github/commits/topoteretes/cognee" alt="GitHub commits"></a> <a href="https://github.com/topoteretes/cognee/tags/"><img src="https://badgen.net/github/tag/topoteretes/cognee" alt="GitHub tag"></a> <a href="https://pepy.tech/project/cognee"><img src="https://static.pepy.tech/badge/cognee" alt="Downloads"></a> <a href="https://github.com/topoteretes/cognee/blob/main/LICENSE"><img src="https://img.shields.io/github/license/topoteretes/cognee?colorA=00C586&colorB=000000" alt="License"></a> <a href="https://github.com/topoteretes/cognee/graphs/contributors"><img src="https://img.shields.io/github/contributors/topoteretes/cognee?colorA=00C586&colorB=000000" alt="Contributors"></a> <a href="https://github.com/sponsors/topoteretes"><img src="https://img.shields.io/badge/Sponsor-❤️-ff69b4.svg" alt="Sponsor"></a> <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> 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:  <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> <img src="assets/cognee-demo.gif" alt="Cognee Demo" width="80%" /> </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 ## 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](https://www.cognee.ai/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](#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](https://docs.cognee.ai/guides/ontology-support). ## Choose your starting point | I want to… | Start here | | --- | --- | | Build memory without an LLM | [Local Python quickstart](#run-locally-without-an-llm) | | Explore a prebuilt graph without downloading models | [Bundled demo](#explore-the-bundled-demo) | | Generate answers with a local or hosted LLM | [Optional LLM setup](#optional-configure-the-llm) | | Give an existing agent memory | [Plugins and MCP](#connect-your-agent) | | Run Cognee on my infrastructure | [Deployment options](#deploy-cognee) | | Use a managed service | [Cognee Cloud](https://docs.cognee.ai/cognee-cloud/overview) | ## 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(main()) ``` The same workflow is available from the CLI: ```bash 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. The bundled GLiNER extractor is a demo of Cognee's small-model pipeline. For a production-ready version with higher accuracy and broader label coverage, [reach out to us](mailto:social@cognee.ai). ### Optional: Configure the LLM **3. Add an LLM** to get generated answers instead of retrieved passages: ```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). Once a key is set, Cognee uses OpenAI for language models and embeddings, and processing and generated answers make provider calls. See [installation](https://docs.cognee.ai/getting-started/installation), [other providers](https://docs.cognee.ai/setup-configuration/llm-providers), or [local Ollama models](https://docs.cognee.ai/guides/local-ollama) for other setups. ### Explore the bundled demo To explore a prebuilt graph without downloading extraction or embedding models: ```bash 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. <img src="assets/remember.svg" alt="Text, code, and session guidance follow their ingestion paths into persistent Cognee memory" width="100%"> 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. <img src="assets/recall.svg" alt="Recall retrieves a document fact, a code symbol, and a learned release rule for an agent's next task" width="100%"> | Operation | What it does | Learn more | | --- | --- | --- | | `remember` | Store content or code in permanent memory, or in a session when a session ID is supplied. | [Store memory](https://docs.cognee.ai/core-concepts/main-operations/remember) | | `recall` | Retrieve context and answers, using automatic routing or a chosen search strategy. | [Query memory](https://docs.cognee.ai/core-concepts/main-operations/recall) | | `improve` | Enrich memory, apply feedback, and bridge session knowledge into the graph. | [Improve memory](https://docs.cognee.ai/core-concepts/main-operations/improve) | | `forget` | Remove a specific item or dataset. | [Delete memory](https://docs.cognee.ai/core-concepts/main-operations/forget) | Explore the [architecture](https://docs.cognee.ai/core-concepts/architecture) and [session lifecycle](https://docs.cognee.ai/core-concepts/sessions-and-caching). ## Connect your agent Install the Claude Code plugin: ```bash claude plugin marketplace add topoteretes/cognee-integrations claude plugin install cognee-memory@cognee ``` Or install the Codex plugin. Enable hooks first, either with the CLI: ```bash codex features enable hooks ``` or in `~/.codex/config.toml`: ```toml [features] hooks = true ``` Then add the marketplace and the plugin: ```bash codex plugin marketplace add topoteretes/cognee-integrations --ref main codex plugin add cognee@cognee ``` Follow the [plugin setup guide](https://github.com/topoteretes/cognee-integrations/tree/main/integrations/claude-code) to configure local or remote memory. | Interface | Start here | | --- | --- | | Claude Code memory plugin | [Install and configure the plugin](https://github.com/topoteretes/cognee-integrations/tree/main/integrations/claude-code) | | OpenClaw memory plugin | [Install `@cognee/cognee-openclaw`](https://www.npmjs.com/package/@cognee/cognee-openclaw) | | Cursor, Cline, and other MCP clients | [Cognee MCP guide](https://docs.cognee.ai/cognee-mcp/mcp-overview) and [server README](cognee-mcp/README.md) | | Python applications | [Python API reference](https://docs.cognee.ai/python-api) | | TypeScript applications | [TypeScript SDK](https://docs.cognee.ai/typescript/getting-started) | | Rust applications | [Cognee-RS](https://github.com/topoteretes/cognee-rs) | | Applications using HTTP | [REST API reference](https://docs.cognee.ai/api-reference/introduction) | Browse the [integrations repository](https://github.com/topoteretes/cognee-integrations) for agent frameworks, plugins, and source connectors. Each guide describes its setup and memory capture behavior. To inspect a local installation in the UI: ```bash cognee-cli -ui ``` The UI launcher requires Node.js/npm; Docker is needed for its MCP service. See [local UI setup](https://docs.cognee.ai/cognee-cli/overview). ## Explore examples - [Build a small Company Brain from text, code, and session lessons](examples/cookbooks/company_brain/docs_code_conversations/company_brain_demo.py). - [Build a Company Brain from your database, ticket export, and docs](examples/cookbooks/company_brain/company_qa/) (or a sample company), then browse it in the UI and query it from Claude Code or Codex. - [Import memory from Mem0, Letta, Zep, or Graphiti](https://docs.cognee.ai/examples/migrate-memory-systems) using the COGX exchange format. - [Run with local Ollama models](https://docs.cognee.ai/guides/local-ollama), including a local embedding model. - [Visualize your knowledge graph](https://docs.cognee.ai/guides/graph-visualization) and inspect its connections. - [Browse runnable examples](examples/README.md) for ingestion, sessions, feedback, and custom pipelines. - [Run the prebuilt API with Docker Compose](docs/minimal-docker-compose.md) or use the [deployment templates](distributed/deploy/README.md). - [Explore community adapters and add-ons](https://github.com/topoteretes/cognee-community). <a id="run-with-docker"></a> ## Deploy Cognee For a local API demo using a prebuilt image, follow the [minimal Docker Compose guide](docs/minimal-docker-compose.md). It includes a persistent-volume configuration and explains the single-user demo settings. Or run the image directly: ```bash docker run --rm -it -p 8000:8000 \ -e LLM_API_KEY="sk-..." \ -e ENABLE_BACKEND_ACCESS_CONTROL=false \ -v cognee_storage:/cognee-storage \ cognee/cognee:main ``` `ENABLE_BACKEND_ACCESS_CONTROL=false` is the single-user/local posture — without it the API defaults to multi-tenant mode and every `/api/v1` call requires an authenticated user. To keep authentication on instead, set `DEFAULT_USER_PASSWORD` to make the default account loginable (see the [compose guide](docs/minimal-docker-compose.md)). Note that `--rm` discards container-local data on exit; the `-v cognee_storage:/cognee-storage` named volume keeps your memory across runs. To run the API, UI, and MCP server from a source checkout, clone this repository, enter its directory, copy [`.env.template`](.env.template) to `.env`, and configure your providers. Then run: ```bash 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](https://docs.cognee.ai/setup-configuration/permissions) and [deployment templates](distributed/deploy/README.md). [Cognee Cloud](https://docs.cognee.ai/cognee-cloud/overview) provides the managed option. The `cognee/cognee` image ships with the GLiNER runtime baked in, so text ingestion and retrieval work in Docker without an LLM key; the local extraction and embedding models download on first use. Set `LLM_API_KEY` (as above) when you want generated answers. ## 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. Since cognee 1.0 you can run the entire memory layer on a single Postgres instance. > **⚠️ Warning:** Using Postgres as a graph store is currently released as a demo feature. The production-ready version is available as a licensed product. Use the demo to keep relational metadata, PGVector, and graph state working together in one Postgres service. <a id="benchmarks"></a> ## Benchmarks and research The [BEAM evaluation](cognee/eval_framework/beam/REPORT.md) 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. | BEAM context | Reported score (0–1) | Scope | | --- | --- | --- | | 100K tokens | **0.79** | Fixed hybrid retrieval; four evaluation rounds over 20 questions from one held-out conversation. | | 10M tokens | **0.67** | Exploratory result; question-type routing selected and scored on the same question set, averaged over five rounds. | The two settings use different conversations, ingestion models, and retrieval-selection procedures. Read the [methodology, models, limitations, and reproduction instructions](cognee/eval_framework/beam/REPORT.md) 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](https://arxiv.org/abs/2505.24478) (Markovic et al., 2025). ## Latest News [![Watch Demo](https://img.youtube.com/vi/8hmqS2Y5RVQ/maxresdefault.jpg)](https://www.youtube.com/watch?v=8hmqS2Y5RVQ&t=13s) - **[v1.6.1 — Google Sync & Visualization](https://github.com/topoteretes/cognee/releases/tag/v1.6.1)** (September 24, 2026): Google Drive and Gmail OAuth connectors bundled in the SDK, `/visualize/json` streamed in chunks for large graphs, and the GLiNER installer moved off the event loop with CPU torch installed on first use. - Document `external_metadata` is stamped on every chunk and surfaced in hybrid retrieval. **Breaking:** `dlt` is now a core dependency — make sure it is installed if you manage dependencies by hand. - **[v1.6.0 — Keyless workflows & pipeline reliability](https://github.com/topoteretes/cognee/releases/tag/v1.6.0)** (September 18, 2026): build and search text memory with local models and no cloud LLM key; LLM-dependent improvement stages skip when no LLM is configured, and pipeline recovery preserves completed documents after crashes. ## 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}, } ```

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