Practical overview
When it fits
Use Cognee when an existing agent or application needs durable, searchable context rather than only the context available during one conversation. The project also fits local experimentation and self-hosted knowledge-layer deployments.
Who it is for
- Developers building AI agents that need memory beyond a single session.
- Teams connecting documentation, conversations, tickets, code and agent work in a shared knowledge layer.
- Builders who want a local or self-hosted Python-based memory component.
Documented uses
- Persist useful agent context across runs.
- Index documents and code for retrieval.
- Store conversation history as reusable context.
- Organize domain knowledge around custom entities, relationships, data models and ontologies.
- Provide a local memory workflow using small models.
Requirements & limits
The repository describes Cognee as free and open-source. The documented local workflow runs without an API key and uses local models; those models download on first use. A separate managed Cognee Cloud option is listed in the documentation.
- Python 3.10–3.14 is required for the documented quickstart.
- The local no-LLM path uses GLiNER for extraction and a local embedding model.
- Recall returns matching source text in the documented example; generated answers require additional LLM configuration.
- An existing agent or application must connect to Cognee when using it as an agent-memory component.
- Custom ontologies and data models are available for domain-specific memory organization.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Create and query local agent memory
Store text in a dataset and retrieve matching source text without configuring an LLM.
Before you start
- Python 3.10–3.14
- Cognee installed with the GLiNER extra
- A local environment able to download the extraction and embedding models on first use
Steps
Install Cognee with the GLiNER extra using a supported Python package manager.
Call Cognee's remember workflow with text and a dataset name.
Call the recall workflow with a question and the same dataset.
Print the returned results.
Expected outcome
The local workflow stores the supplied text and returns matching source text for a query.
Guide limitations
- This workflow retrieves source text rather than generating an answer.
- LLM-dependent improvement stages are skipped without an LLM key.
- Vision- or transcription-dependent media processing requires additional model setup.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 2 October 2026
- Original website title
- topoteretes/cognee · Official homepage title · retained in its source language