topoteretes / cognee

Cognee - The Free Open-Source AI Memory Platform for Agents 
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 Cognee is a free open-source AI memory platform that gives AI agents persistent long-term memory across sessions. Turn documents, code, and conversations into a self-hosted knowledge graph your agents can search and reuse. 
 Runs locally for free — no API key required. 
 We rely on free small models that use your CPU. 
 🌐 This README is also available in: 
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📄 Read the research paper: Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning — Markovic et al., 2025
## When to use Cognee
- **Build a Company Brain.** Bring documentation, conversations, tickets, code, and agent work into shared memory. Help your team and agents connect a decision to the discussion and implementation behind it. Explore Company Brain.
- **Give agents memory across runs.** Retain project context, past decisions, fixes, and learned rules. Distill useful session lessons into durable knowledge that another session can retrieve. Connect your agent.
- **Ground agents in your domain.** Structure memory around the entities and relationships your application needs, with custom data models and ontologies. Explore ontologies.
## Choose your starting point
| I want to… | Start here |
| --- | --- |
| Build memory without an LLM | Local Python quickstart |
| Explore a prebuilt graph without downloading models | Bundled demo |
| Generate answers with a local or hosted LLM | Optional LLM setup |
| Give an existing agent memory | Plugins and MCP |
| Run Cognee on my infrastructure | Deployment options |
| Use a managed service | Cognee Cloud |
## Quickstart
Requires **Python 3.10–3.14**.
**1. Install Cognee** with **pip**, **uv**, or your preferred Python package manager. The `gliner` extra brings the local extraction model used when no LLM key is configured:
 [code] 
### Run locally without an LLM
**2. Build and query memory.** With no LLM key configured, Cognee extracts the graph with the local GLiNER model and embeds with a local embedding model; both download on first use.
Save this as `quickstart.py` and run `python quickstart.py` if you are feeling old school, or tell your LLM to do it:
 [code] 
The same workflow is available from the CLI:
 [code] 
Text ingestion, retrieval, and session storage work without an LLM. LLM-dependent improvement stages skip automatically.
Generated answers and media processing that requires a vision or transcription model need additional LLM configuration.
The bundled GLiNER extractor is a demo of Cognee's small-model pipeline. For a production-ready version with higher accuracy and broader label coverage, reach out to us.
### Optional: Configure the LLM
**3. Add an LLM** to get generated answers instead of retrieved passages:
 [code] 
Alternatively, create a `.env` file using our template.