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Cognee

Open-source memory layer that turns docs and chats into agent-searchable knowledge graphs.

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About

Cognee is an open-source AI memory platform: feed it documents, code, tickets, and conversations and it builds a persistent knowledge graph your agents can search and reuse across sessions. It is designed to run locally for free — the default pipeline uses small models on CPU with no API key required — while letting you plug in stronger hosted models. The team frames the flagship use case as a 'Company Brain' shared between humans and agents, and published a research paper on the knowledge-graph/LLM interface in 2025.

What it can do

  • **Persistent memory**: documents, code, and conversations become a queryable knowledge graph
  • **Local-first**: free CPU pipeline with no API key; bring your own models when ready
  • **Agent integration**: designed as the memory backend for agent frameworks
  • **Research-backed**: arXiv paper on KG/LLM interfaces (Markovic et al., 2025)
  • **Community plugins**: cognee-community repo for add-ons

Setup

```bash

pip install cognee

```

```python

import cognee

# Add documents to memory

await cognee.add("The project deadline moved to Friday.")

await cognee.cognify()

# Ask across everything remembered

answer = await cognee.search("When is the deadline?")

```

Runs locally by default (no API key). Configure hosted LLMs/embeddings via env vars for higher quality. Docs: docs.cognee.ai.

Prerequisites

Python 3.10+. Optional: LLM/embedding provider API keys for higher-quality pipelines (local CPU models work out of the box).

Use cases

agent memoryknowledge graphsRAGlong-term context

Works with

Claude CodelangchainsmolagentscustomGeneric API

Trust score — 9/10

/10 trustVerified

Scored 9/10 (verified tier, metrics-v1). Evidence: 32,105 GitHub stars (log-scaled vs catalog peers); blended with ~86,048 monthly package downloads; last push 0d ago; 89 releases/12mo; 680 open issues. Manual review: 32k stars / 86k PyPI-mo; free local CPU pipeline, no API key; research paper published; graph quality depends on plugged-in models. License: Apache-2.0.

★ 32,105 GitHub stars

How the trust score is built

Full methodology →
Adoption · stars/downloads, log-scaled vs peers
8.1/10
Maintenance · recency, releases, issue load
9.2/10
Openness · license + docs
10.0/10
Manual review · sanity read + red-flag check
9.0/10
  • · 32,105 GitHub stars (log-scaled vs catalog peers)
  • · blended with ~86,048 monthly package downloads
  • · last push 0d ago; 89 releases/12mo; 680 open issues
  • · manual review: 32k stars / 86k PyPI-mo; free local CPU pipeline, no API key; research paper published; graph quality depends on plugged-in models

Sources

Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.