Beever-AI

Beever-AI / beever-atlas

Public

Your First LLM-Wiki Conversation Knowledge Base

docs.beever.aiatlas discord-bot fastapi gemini google-adk knowledge-base
14
0
100% credibility
Found Apr 22, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Beever Atlas automatically converts team conversations from Slack, Discord, Teams, and Mattermost into a self-maintaining wiki with cited answers and entity graphs.

How It Works

1
🔍 Discover Beever Atlas

You hear about a tool that turns your team's Slack or Discord chats into an automatic wiki and check it out.

2
🚀 Try the demo

Run a quick demo to see sample chats turn into a wiki with facts, topics, and answers — no setup needed.

3
🧠 Get free AI helpers

Sign up for two free AI services (Gemini and embeddings) that make your wiki smart.

4
⚙️ Launch with one click

Click to start everything on your computer — dashboard, bot, and storage all ready.

5
🔗 Connect your team's chats

Link your Slack, Discord, or Teams workspace through a simple setup screen.

6
⏳ Sync channels

Pick channels to pull messages from — it grabs history and builds your wiki automatically.

7
💬 Ask questions

Chat with your wiki for answers with links back to original messages, or browse topics and people.

📚 Your wiki grows

New chats keep updating the wiki — onboard teammates read summaries instead of scrolling history.

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AI-Generated Review

What is beever-atlas?

Beever Atlas is a Python tool that ingests conversations from Slack, Discord, Teams, and Mattermost, distilling them into a self-maintaining LLM-wiki knowledge base per channel. It extracts facts, entities, and relationships into topic pages, overviews, and graphs with source citations, letting you ask natural language questions via dashboard or MCP integration for Claude and Cursor. Setup is Docker Compose with a one-line `./atlas` installer and seeded demo for quick testing.

Why is it gaining traction?

Its wiki-first RAG beats dumping raw chat logs into LLMs—answers cite clean, deduplicated summaries, not noisy threads, with a browsable artifact teams actually use. Dual semantic search and graph traversal handle both facts and relations like "who decided X?", plus resumable syncs respect rate limits. Multi-platform bot and API stability warnings keep it pragmatic for real workflows.

Who should use this?

Slack-heavy engineering teams turning async chats into queryable decisions; PMs tracking project timelines without scrolling history; new hires onboarding via auto-generated channel wikis. Devs integrating chat knowledge into Cursor or Claude via MCP for context-aware coding.

Verdict

Worth a spin for conversation-to-knowledge pipelines—strong docs, demo, and Apache 2.0 make first GitHub steps easy despite 14 stars and 1.0% credibility signaling early maturity (v0.1 APIs unstable). Test the seeded demo before production syncs.

(198 words)

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