# 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

- [AI Agent Memory Solutions by Lifecycle, Not Label | Cognee](https://www.cognee.ai/ai-agent-memory-solutions-by-lifecycle): AI agent memory now spans runtimes, independent platforms, and app-owned stacks. Compare them by what they do to memory over time, not by product label.
- [Open-Source Pinecone Alternatives: What Are You Trying to Replace? | Cognee](https://www.cognee.ai/open-source-pinecone-alternatives): Qdrant, Weaviate, Milvus, pgvector, Chroma, and LanceDB compared against Pinecone by deployment, retrieval features, and cost, so you know which one actually fits your workload.
- [AI Agent Memory: The Definitive Guide | Cognee](https://www.cognee.ai/agent-memory): The complete guide to AI agent memory: long-term memory for LLM agents, memory architectures from vector stores to knowledge graphs, and how to build one.
- [AI Memory Platforms for Sales Intelligence in 2026 | Cognee](https://www.cognee.ai/ai-memory-platforms-for-sales-intelligence): Sales AI already knows what's happening now. This 2026 guide compares the memory platforms trying to help it remember what happened before.
- [Agent Workflow Memory: How AI Agents Plan, Act, and Remember | Cognee](https://www.cognee.ai/agent-workflow-memory): Learn what agent workflow memory is, how it differs from workflow state, and how to add memory to AI agent workflows with cognee, an open-source memory tool.
- [Coding Agents Don't Need Bigger Context Windows — They Need Better Memory | Cognee](https://www.cognee.ai/coding-agents-need-memory-not-context): Throwing a million tokens at an agent makes it slower and more expensive. Graph-based memory decides what to retrieve, not window size.
- [Why AI Agents Forget: How to Fix Agent Memory Loss | Cognee](https://www.cognee.ai/why-ai-agents-forget): Learn why AI agents lose context and how to fix agent memory with better state, retrieval, long-term storage, and cognee's memory lifecycle.
- [What Is Agentic RAG? How It Works and When to Use It | Cognee](https://www.cognee.ai/what-is-agentic-rag): Agentic RAG puts an agent in control of retrieval — planning, choosing tools, and retrieving again when evidence is missing. Learn how it works and when it's worth it.
- [Give Claude Code Persistent Memory With cognee | Cognee](https://www.cognee.ai/give-claude-code-persistent-memory-with-cognee): Learn what Claude Code already remembers between sessions, where the gaps are, and how to set up and verify cognee's plugin for persistent, searchable memory.
- [GraphRAG vs RAG: Key Differences and How to Choose | Cognee](https://www.cognee.ai/graphrag-vs-rag): Compare GraphRAG vs RAG across retrieval, data structure, cost, use cases, and limitations. Learn when vector RAG is enough and when graph retrieval is worth the added complexity.