Beever Atlas is an innovative LLM knowledge base implementation that improves accuracy and traceability by distilling chat data into structured wikis and knowledge graphs. It offers extensive MCP integration and is suitable for teams needing knowledge management. However, it requires self-hosting, configuration is complex, and API stability is not guaranteed, making it best for technically capable teams.
Beever Atlas is an open-source LLM knowledge base that connects Slack, Discord, Microsoft Teams, and Mattermost. It extracts atomic facts from messages, deduplicates them, and clusters them into topic pages with citations. Data is stored in a semantic store (Weaviate) and a graph store (Neo4j), enabling relational queries about people, decisions, and projects. Users can query the knowledge base via a dashboard or through MCP, allowing AI agents like Claude Code and Cursor to get cited answers in natural language.
git clone https://github.com/beever-ai/beever-atlas.git && cd beever-atlas.make demo to try the seeded demo (no API keys needed).GOOGLE_API_KEY (Gemini) and JINA_API_KEY (embeddings) are required; optionally TAVILY_API_KEY or OLOSTEP_API_KEY for web search../atlas interactive installer, or manually copy .env.example and fill in keys, then run docker compose up -d --build..mcp.json with streamable-http or stdio as per docs.{
"mcpServers": {
"beever-atlas": {
"url": "https://atlas.example.com/mcp",
"transport": "streamable-http",
"headers": {
"Authorization": "Bearer ${BEEVER_MCP_KEY}"
}
}
}
}