# cognee

> cognee is an open-source AI memory engine that turns unstructured data into a queryable knowledge graph for agents and LLM applications.

This file follows the llms.txt convention (https://llmstxt.org). It points language models at the most useful entry points on cognee.ai.

## Main pages

- [Home](https://www.cognee.ai/): cognee — open-source AI memory engine for agents.
- [Product](https://www.cognee.ai/product): How cognee turns unstructured data into a queryable memory graph for AI agents.
- [Pricing](https://www.cognee.ai/pricing): Plans and pricing for cognee Cloud and self-hosted deployments.
- [Consulting](https://www.cognee.ai/consulting): Hands-on engineering help to design, build, and ship AI memory systems with cognee.
- [Research & evaluation results](https://www.cognee.ai/research-and-evaluation-results): Benchmarks and evaluation results for cognee against other memory and retrieval systems.
- [About us](https://www.cognee.ai/about-us): The team and mission behind cognee.
- [Blog](https://www.cognee.ai/blog): Deep dives, tutorials, and product news from the cognee team.
- [Academy](https://www.cognee.ai/academy): Educational content on AI memory, knowledge graphs, and agentic systems.
- [Wiki](https://www.cognee.ai/wiki): Reference material and concepts behind cognee.

## Developer resources

- [Documentation (developer portal)](https://docs.cognee.ai): Full developer documentation, guides, and reference for building with cognee.
- [Quickstart](https://docs.cognee.ai/getting-started/quickstart): Install cognee and build your first memory pipeline in minutes.
- [REST API reference](https://docs.cognee.ai/api-reference/introduction): REST API endpoints, request/response schemas, and authentication (API keys and Bearer tokens).
- [OpenAPI / Swagger spec](https://api.aws.cognee.ai/docs): Interactive OpenAPI (Swagger) explorer auto-generated from the cognee Cloud API specification.
- [Python SDK reference](https://docs.cognee.ai/python-api): Reference for the cognee Python SDK.
- [MCP server](https://docs.cognee.ai/cognee-mcp/mcp-overview): cognee MCP server: expose cognee memory to MCP-compatible agents, IDEs, and tools.
- [cognee Cloud](https://docs.cognee.ai/cognee-cloud/overview): Managed cloud developer portal: API keys, dashboards, and hosted memory infrastructure.
- [GitHub](https://github.com/topoteretes/cognee): Source code, issues, and examples for the open-source memory engine.

## Latest 10 blog posts

- [ChatGPT Memory: How It Works, Its Limits & How to Build Your Own | Cognee](https://www.cognee.ai/chatgpt-memory): How ChatGPT memory works (saved memories and chat history reference), what it cannot do, why the API has no memory, and how to add persistent memory to your own app with cognee.
- [Knowledge Graphs and LLMs: How They Work Together | Cognee](https://www.cognee.ai/knowledge-graph-llm): How knowledge graphs and LLMs complement each other: LLMs build the graph, the graph grounds the LLM, GraphRAG connects multi-hop context, and the graph becomes agent memory.
- [Temporal Knowledge Graphs: What They Are & Why AI Memory Needs Them | Cognee](https://www.cognee.ai/temporal-knowledge-graph): What a temporal knowledge graph is, valid time vs transaction time, how contradictions are resolved with history kept, and why AI agent memory depends on it.
- [Local AI Memory: Keeping Agent Memory Off the Cloud | Cognee](https://www.cognee.ai/local-ai-memory): What has to run on your own hardware for agent memory to count as local: four levels of locality, where cloud calls hide in extraction, embeddings, and reranking, how much hardware each stage needs, and a disconnect test for fully offline memory.
- [AI Memory Tools vs. Databases: 5 Compared (2026) | Cognee](https://www.cognee.ai/ai-memory-tools-vs-databases): Compare LangMem, Mem0, cognee, Zep, and Letta by how much of the stack each AI memory tool takes over — from memory primitives over your own store to managed temporal graphs and memory built into the agent runtime.
- [How to Evaluate AI Memory in 2026: 5 Tools Compared | Cognee](https://www.cognee.ai/how-to-evaluate-ai-memory-tools): Compare how Mem0, Zep, cognee, Letta, and LangMem let you evaluate AI memory — open benchmark harnesses, retrieval-versus-answer scoring, published results, and what you still have to test yourself.
- [5 AI Memory Tools for Small Models and Local Agents | Cognee](https://www.cognee.ai/ai-memory-tools-for-small-models): Compare Graphiti, Mem0, cognee, Basic Memory, and Letta for small models and local agents — which AI memory tools need generative inference, what runs offline, and how much work lands on the agent’s own model.
- [5 Open-Source LLM & Agent Evaluation Tools in 2026 | Cognee](https://www.cognee.ai/llm-agent-evaluation-tools): Compare DeepEval, Ragas, Promptfoo, Inspect AI, and Opik across output, retrieval, trajectory, and task-outcome evaluation layers — what each tool actually tests and how far it goes beyond scoring a single model response.
- [5 New Databases for AI Workloads in 2026 | Cognee](https://www.cognee.ai/new-databases-for-ai-workloads): Compare HelixDB, turbopuffer, LanceDB, SurrealDB, and Turso across architecture, retrieval, and primary AI use — from graph-and-vector engines to object-storage-backed search and a database provisioned per agent.
- [Keenable + cognee: Persistent Memory for Web Retrieval](https://www.cognee.ai/keenable-cognee-web-retrieval-memory): Keenable returns clean Markdown for any URL, and cognee turns it into persistent, graph-backed memory an agent can recall later — how the fetch backend, config, and pipeline fit together.