---
id: cognee
version: "1.4.2"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# cognee — Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning.

License: permissive · Maintenance: active · Downloads: 195.2K/mo

## What it is and what it does

Cognee is an open-source AI memory platform that ingests data in any format and builds a self-hosted knowledge graph to give AI agents persistent long-term memory across sessions. It combines vector embeddings, graph reasoning, and cognitive-science-grounded ontology generation so documents are searchable by meaning and connected by relationships that evolve over time. The platform exposes four core operations—remember (store in graph), recall (query with auto-routing), forget (delete), and improve (refine)—plus a CLI and Docker deployment option. It integrates with LLM providers via LiteLLM and supports multimodal ingestion, session-scoped memory caching, and cross-agent knowledge sharing.

The package is designed for building company knowledge bases, enabling agents with domain expertise, and supporting reliable, trustworthy agent deployments with user/tenant isolation and audit trails. It runs locally by default but can be deployed as a containerized API server. The 44 runtime dependencies include FastAPI for the server, LanceDB for vector storage, Pydantic for configuration, and instructor for LLM-guided parsing; the dependency footprint is substantial but reflects a full-stack knowledge infrastructure rather than a lightweight library.

Use it for:

- Build a persistent knowledge base for a multi-turn chatbot or agent that recalls context across separate conversations.
- Ingest company documents (PDFs, emails, wikis) into a unified graph and let agents query domain knowledge with semantic search.
- Integrate Cognee memory into Claude Code or other AI coding assistants to preserve coding context and decisions across sessions.
- Deploy a self-hosted knowledge graph API for multiple agents or tenants with isolated memory and audit trails.
- Prototype agentic workflows that learn from feedback and improve reasoning by connecting new insights to existing knowledge.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Cognee builds a self-hosted knowledge graph from ingested data, giving AI agents persistent long-term memory across sessions with vector search, graph reasoning, and ontology grounding.

Yes. Cognee is actively maintained (30023 stars, last commit 2026-08-14), has low install friction, carries no known vulnerabilities, and offers a permissive Apache-2.0 license. It solves a real problem—persistent, searchable memory for AI agents—with a mature feature set (graph + vector search, multimodal ingestion, session memory, CLI, Docker support). The 44 dependencies are substantial but justified by the full-stack nature of the platform. Install if you need agent memory infrastructure; skip if you only need lightweight semantic search or simple vector storage.

## Install

pip install cognee
uv add cognee
poetry add cognee

## Installing cognee

Before you install: Low friction install with a pure-Python wheel. Active maintenance (last commit 2026-08-14) and strong community signal (30023 stars). Requires Python 3.10–3.14 and 44 runtime dependencies including FastAPI, LanceDB, LiteLLM, and OpenAI; no compiled dependencies block installation.

License in practice: Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for proprietary projects and commercial deployments.

Quickstart:

import cognee
import asyncio

async def main():
    await cognee.remember("Cognee turns documents into AI memory.")
    results = await cognee.recall("What does Cognee do?")
    for result in results:
        print(result)

asyncio.run(main())

Requires Python 3.10–3.14 and LLM_API_KEY environment variable (e.g., OpenAI API key) to function; Docker optional but recommended for the CLI UI.

Verify before relying:

- Whether the 44 runtime dependencies can be selectively installed or if all are required for basic usage.
- Performance characteristics and scalability limits for knowledge graph size and query latency.
- Whether session memory and permanent graph storage can use different backends (e.g., local cache vs. remote database).
- Exact ontology generation mechanism and how it evolves as knowledge changes.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 195.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ai agent memory system, knowledge graph builder, semantic search and retrieval, llm context enrichment, persistent agent memory, vector and graph database, document ingestion pipeline, knowledge-graph, agent-memory, rag

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