Beever Atlas is an innovative open-source solution that transforms your team's chat conversations from platforms like Slack, Discord, Microsoft Teams, and Mattermost into a structured, self-maintaining wiki. It automatically extracts, deduplicates, and organizes atomic facts into topic pages, creating a comprehensive, searchable knowledge base. This platform is ideal for teams combating knowledge fragmentation, improving information retrieval, and streamlining onboarding by leveraging existing communication data.
Key Features:
Multi-Platform Connect: Integrates seamlessly with Slack, Discord, Teams, Mattermost, or allows file imports.
LLM Wiki: Auto-generates and maintains a structured wiki per channel with topics, entities, decisions, and citations.
QA Agent: Provides natural language question-answering, streaming cited answers from the knowledge base.
MCP Server: Enables external AI agents like Claude Code and Cursor to query the knowledge base.
Wiki-First RAG: Distills conversations into clean, deduplicated knowledge *before* queries for superior answer quality.
Dual-Memory Architecture: Combines semantic and graph stores for fast hybrid search and entity relationships.
Use Cases:
Beever Atlas excels where valuable information is buried in chat logs. New team members can quickly onboard by browsing the auto-generated wiki, understanding past decisions. Development teams can document architectural decisions, making knowledge easily accessible. Support teams can rapidly find answers to customer queries, improving response times.
Pricing Information:
Beever Atlas is an open-source project, making the core software free to use and self-host. While the platform itself is free, users will need to obtain and configure API keys for external services like Gemini and Jina, which may have their own free tiers and usage-based costs.
User Experience and Support:
The platform features a user-friendly web dashboard. Comprehensive documentation is available, alongside community support via Discord, GitHub Discussions, and X/Twitter.
Technical Details:
Designed as a Docker Compose stack, Beever Atlas comprises backend (FastAPI), bot, and frontend (React) services. It leverages Weaviate for semantic memory, Neo4j for graph memory, MongoDB for state, and Redis for sessions. The project is primarily built with Python and TypeScript.
Pros and Cons:
Pros: Transforms chat into a structured, self-maintaining wiki; "Wiki-First RAG" ensures superior answer quality; Dual-memory architecture for precise retrieval; Open-source and self-hostable; Integrates with major chat platforms and external AI agents.
Cons: Requires self-hosting via Docker Compose; Dependency on external API keys (Gemini, Jina) for full functionality; API stability is currently "UNSTABLE" (v0.1.0).
Conclusion:
Beever Atlas offers a powerful solution for transforming team communications into a valuable, accessible, and continuously updated knowledge base. By operationalizing the "wiki-first" approach, it ensures higher quality answers and a more useful knowledge artifact. Explore Beever Atlas to unlock the hidden knowledge within your team's conversations.
For Enterprise Solution, contact: hello@votee.ai
