topoteretes/cognee

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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.
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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:
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:
The same workflow is available from the CLI:
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:
Alternatively, create a .env file using our template.
Once a key is set, Cognee uses OpenAI for language models and embeddings, and processing and generated answers make provider calls. See installation, other providers, or local Ollama models for other setups.
Explore the bundled demo
To explore a prebuilt graph without downloading extraction or embedding models:
This command works with the base pip install cognee package. It loads bundled sample data and runs keyword search without an API key. Use the local quickstart above to build a graph from your own text.
How Cognee works
Cognee builds connected memory from different sources. Text becomes entities, relationships, and searchable chunks; code becomes a graph of symbols and dependencies. Session distillation curates accepted lessons into permanent memory.
At query time, retrieval selects relevant graph, vector, or code context. Your application can inspect the retrieved evidence and use it to answer a question or continue an agent task.