# topoteretes/cognee

Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.

Repository: https://github.com/topoteretes/cognee
Canonical: https://ross.abutalabs.com/products/cognee
Homepage: https://www.cognee.ai
Language: Python
License: Apache-2.0
License Family: permissive
Topics: ai, cognitive-architecture, vector-database, ai-agents, graph-database, ai-memory, cognitive-memory, knowledge, knowledge-graph, graph-rag, contributions-welcome, good-first-issue, good-first-pr, help-wanted, open-source, context-engineering, agent-memory, agent-skills, memory-management
Last push: 2026-08-26T16:30:54+00:00

## Health v2 (maintenance only)
Score: 91/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 79
- inputs: {"age_days": 1113, "days_push": 7, "days_rel": 10, "gap_med": 4, "n_releases_24m": 104}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 30281, forks 2969 (observed 2026-08-28T04:11:54.353088+00:00)

## What it is
Cognee is an open-source Python library and platform that gives AI agents persistent long-term memory by ingesting data in any format and building a self-hosted knowledge graph combined with vector search. It ships SDKs, an HTTP API, MCP server, and plugins for tools like Claude Code, OpenAI Agents SDK, and Hermes, enabling agents to remember, recall, and reason across sessions.

## Use cases
- give my ai agent persistent memory across sessions
- build a self-hosted knowledge graph from documents
- add long-term memory to claude code or coding agents
- implement graph rag for llm context enrichment
- store and recall agent decisions and past work
- build a company brain from docs, chats, and tickets
- connect an mcp memory server to my ai assistant
- index markdown notes and recall relevant context automatically

## When to choose
- you need agents to remember facts, decisions, and context across sessions
- you want a self-hosted, open-source memory engine with graph + vector retrieval
- you use Claude Code, OpenAI Agents SDK, or MCP-compatible tools and want drop-in memory
- you need structured, ontology-aware memory rather than loose text embeddings
- you want a company knowledge base with citations and provenance

## When to avoid
- you only need simple key-value session state with no semantic retrieval
- you want a fully managed hosted service with zero infrastructure
- your project is not Python or Node-based and you cannot run the API server
- you need lightweight in-process memory without a graph database dependency

## Facets
- artifact type: library
- maturity: active
- function: rag, vector-database, agent-framework, mcp, search-engine, etl, llm-inference
- domain: artificial-intelligence, large-language-models, databases, developer-tools
- platform: python, self-hosted, cross-platform, cli
- tags: agent-memory, knowledge-graph, graph-rag, memory-management, context-engineering, cognitive-architecture, mcp-server, long-term-memory, self-hosted-ai, semantic-layer, ai-agents, retrieval-augmented-generation, knowledge-graphs, docker

## Member repositories
- topoteretes/cognee (main) score 91

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:54.353088+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:52:47.116698+00:00, confidence not recorded.
  - readme: https://github.com/topoteretes/cognee (fetched 2026-08-28T04:11:54.353088+00:00, sha ab619e9295d3)
  - homepage: https://www.cognee.ai (fetched 2026-08-29T07:49:52.431479+00:00, sha 90ac0b69aa57)
  - site_page: https://docs.cognee.ai (fetched 2026-08-29T07:49:52.434284+00:00, sha 7f9d0919ac17)
  - site_page: https://docs.cognee.ai/cognee-mcp/integrations/claude-code (fetched 2026-08-29T07:49:52.437847+00:00, sha 2249c1fa4b23)
  - site_page: https://docs.cognee.ai/integrations/openai-agents-sdk-integration (fetched 2026-08-29T07:49:52.439674+00:00, sha b1f93873c712)
  - site_page: https://docs.cognee.ai/getting-started/llm-quickstart-skill (fetched 2026-08-29T07:49:52.441873+00:00, sha ac1c2d69bbd0)
  - site_page: https://docs.cognee.ai/integrations/openclaw-integration (fetched 2026-08-29T07:49:52.443866+00:00, sha c905c6e6387f)
  - site_page: https://docs.cognee.ai/integrations/hermes-agent-integration (fetched 2026-08-29T07:49:52.445895+00:00, sha 3ff484c73783)
  - registry_pypi: https://pypi.org/pypi/cognee/json (fetched 2026-08-29T07:49:52.450102+00:00, sha 7a4ea68c6c46)
  - site_page: https://www.cognee.ai/pricing (fetched 2026-08-29T07:49:52.436034+00:00, sha 6ecfcdb68ec6)
- Data as of 2026-08-30T08:39:29.467469+00:00.