Cognee is an open-source AI memory platform that enables agents and applications to build, store, and query persistent knowledge graphs from documents, code, and conversations. The system provides a Python SDK, a FastAPI REST API, a CLI, a Next.js frontend, and an MCP server for agent integration. Core operations include ingesting data (remember), extracting entities and relationships via LLMs or GLiNER (cognify), retrieving context (recall), and self-improvement loops (improve). The architecture is modular with adapter/registry patterns for graph databases (Ladybug, Neo4j, Turso), vector stores (LanceDB, PGVector), LLM providers (LiteLLM, OpenAI, Anthropic, Ollama), and integrations (Slack, GitHub, Google Drive). The frontend provides a dashboard for dataset management, knowledge graph visualization, and business analytics. The project also includes an evaluation framework for benchmarking retrieval strategies against standard datasets like HotpotQA and BEAM. While production-grade in many areas, some components are marked as demo-quality or have cloud-only stubs.
This repository is an 'awesome list' curated by the TeleAI-UAGI team, aggregating open-source and closed-source products, papers, benchmarks, tutorials, and surveys on memory systems for LLM/MLLM agents. It does not contain application business logic or runnable services; its primary output is a meticulously structured README.md that serves as a reference catalog. The accompanying Python scripts automate two maintenance tasks: (1) checking that open-source entries are ordered by GitHub star count and respecting a 100-star boundary for emerging projects, and (2) generating self-hosted star-history SVG charts. The scripts are well-crafted, with robust parsing, GitHub API integration, and careful edge-case handling (grace bands, orphan detection), making them reusable for other awesome-list maintainers. The repository's value lies in its curated content rather than its code, but the supporting infrastructure demonstrates solid engineering principles.