Beever-AI/beever-atlas

Turn your team's Slack, Discord, Teams & Mattermost chats
into a self-maintaining wiki — automatically.
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Beever Atlas pulls the conversations your team already has on Slack, Discord, Microsoft Teams, and Mattermost, extracts atomic facts, deduplicates them, and clusters them into topic pages with citations. A graph store links the people, decisions, and projects mentioned across channels. Ask questions in natural language and get answers cited back to the source messages — through the dashboard, or through MCP into Claude Code and Cursor.
If you want a knowledge base that grows on its own from the chats your team already has, this is it.
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✨ Features in action
Six short clips — connect a workspace, sync history, watch memory build, browse the auto-generated wiki, ask questions, plug external AI agents in via MCP.
Multi-Platform
Connect Slack, Discord, Teams, Mattermost, or file imports. One bot, every workspace.
Message Sync
Pull channel history on demand or on a schedule. Resumable and rate-limit aware.
Memory Ingestion
6-stage ADK pipeline distils messages into atomic facts, entities, and relationships.
LLM Wiki
Auto-maintained wiki per channel — overview, topics, people, decisions, citations.
QA Agent
Streams cited answers over SSE. Smart router picks semantic or graph per question.
MCP Server
Plug Claude Code / Cursor into your knowledge base — 28 tools, per-agent auth.
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🏗️ Architecture
Conversations from any supported platform flow into a unified ingestion pipeline that produces two complementary memory systems — a **3-tier semantic store** (channel / topic / atomic fact) for fast hybrid search, and a **graph store** that extracts entities and their relationships. Those memories fuel two consumer surfaces: the **LLM Wiki** (distilled, auto-maintained) and **QA Agents** (served through the dashboard directly, or through **MCP** into Claude Code / Cursor).
From chat platforms to MCP agents — one ingestion path, two memory systems, two delivery surfaces.
Under the hood, three services (backend, bot, frontend) are backed by four data stores (Weaviate, Neo4j, MongoDB, Redis). See the architecture overview on the documentation site for the full design — component responsibilities, dual-memory internals, and the smart query router.
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💡 Why Wiki-First RAG?
Most RAG systems answer questions by retrieving raw message snippets and feeding them straight to an LLM. Beever Atlas takes a different approach: it continuously distils conversations into a structured, auto-maintained wiki — with topic pages, entity graphs, decisions, and citations — before any query is issued. When you ask a question, the retrieval layer works against clean, deduplicated knowledge rather than noisy chat history. This means answers are more consistent, citations are traceable to source messages, and the wiki itself becomes a useful artifact your team can browse independently of the Q&A interface. The dual-memory architecture (semantic + graph) lets the query router pick the right retrieval strategy per question, keeping latency low and context precise.
A live auto-generated channel wiki: overview, concept map, topics, FAQ, glossary — distilled from 246 Slack messages, not hand-written.
The inspiration: LLMs read wikis, not chat logs
The per-channel wiki concept is directly inspired by Andrej Karpathy's observation that LLMs are far better at reasoning over curated, encyclopedic content (books, docs, wikis) than over raw conversational transcripts. Chat history is noisy, redundant, temporally scattered, and full of implicit context that only humans resolve. A wiki, by contrast, is the *already-distilled* form of that knowledge — deduplicated, structured, citation-bearing, and organised by topic rather than by timestamp.
Beever Atlas operationalises this insight: every synced channel gets its own **auto-generated, continuously-updated wiki** — sections for topics, entities, decisions, open questions, and timelines — rebuilt incrementally as new messages arrive. The QA agent retrieves against this wiki first, falling back to raw messages only when a fact hasn't been distilled yet.