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beever-atlas

Active·★ 360·Apache-2.0·Updated 2026-05-25
★ RAG / Knowledge Base★ API Integration

Your First LLM-Wiki Conversation Knowledge Base

Beever Atlas automatically pulls conversations from Slack, Discord, Microsoft Teams, and Mattermost, extracts atomic facts, deduplicates them, and clusters them into topic pages with citations. It provides an LLM-first wiki that grows on its own, and allows natural language querying with answers cited back to source messages. The system also offers an MCP server for integration with AI agents like Claude Code and Cursor.

#discord-bot#fastapi#gemini#google-adk#knowledge-base#llm#mattermost#mcp
$ Install
$ git clone https://github.com/beever-ai/beever-atlas.git && cd beever-atlas && ./atlas
↗ Visit site★ GitHub
01

Features

01Multi-Platform Integration (Slack, Discord, Teams, Mattermost)
02Message Sync (on-demand or scheduled, resumable, rate-limit aware)
03Memory Ingestion (6-stage ADK pipeline extracting facts, entities, relationships)
04LLM Wiki (auto-maintained per-channel wiki with topics, decisions, citations)
05QA Agent (streaming answers with semantic/graph hybrid retrieval)
02

Compatibility

Slack
Slack
Verified via docs
Discord
Discord
Verified via docs
Microsoft Teams
Teams
Verified via docs
Mattermost
Mattermost
Verified via docs
03

Quick start

1
$ git clone https://github.com/beever-ai/beever-atlas.git
2
$ cd beever-atlas
3
$ ./atlas
04

Use cases

↳Automatically build a browsable wiki from team chat history for onboarding and reference.
↳Query team knowledge in natural language with answers cited back to original messages.
↳Integrate with AI coding assistants via MCP to access team knowledge from within Claude Code or Cursor.
05

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Related searches

beever-atlas AlternativesBest RAG / Knowledge Base Tools 2026Open Source RAG / Knowledge Basebeever-atlas Tutorialbeever-atlas Vs Competitorsdiscord-botfastapigemini

Comments

Log in to leave a comment
  • Parker Rivera
    Parker RiveraApr 9, 2026

    Conversational wiki that learns from your interactions is a compelling concept.

  • E
    Ellis DavisMar 21, 2026

    The LLM-native approach means retrieval is semantic, not just keyword matching.

  • Reese Kim
    Reese KimMar 20, 2026

    Good for teams building shared institutional knowledge with AI assistance.

  • Dakota Chen
    Dakota ChenMar 6, 2026

    Knowledge accumulates naturally through use rather than manual data entry.

On this page
01Features02Compatibility03Quick start04Use cases05Alternatives
Stats
GitHub Stars★ 360
Last commit5d ago
StatusActive
LicenseApache-2.0
CategoryRAG / Knowledge Base
Trend (30d)
+14.4↑ 0.4%
Links
Documentation↗Discussion↗Issues↗Releases↗

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