beever-atlas

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.

What this unlocks in practice

• Better answers, fewer hallucinations — retrieval operates on fact-dense prose with explicit entity relationships, not on fragmented turn-by-turn chat.
• Traceable citations — every wiki claim links back to the source messages that produced it, so answers are auditable all the way down to the original Slack/Discord/Teams thread.
• A browsable artifact, not just a Q&A box — the wiki is useful on its own. New teammates onboarding to a channel can read the distilled wiki instead of scrolling three months of history.
• Cheaper inference at query time — the expensive distillation work happens once, at ingestion. Queries hit compact, pre-digested context instead of re-summarising raw logs on every request.
• Graph-aware reasoning — the entity graph built alongside the wiki lets the query router answer relational questions ("who worked on X with Y?") that pure vector RAG struggles with.

For a detailed comparison with other LLM knowledge tools, see the comparison page on the documentation site.

🚀 Quick Start

Beever Atlas ships as a Docker Compose stack (backend + bot + web + 4 datastores). You can try a seeded demo in 30 seconds with zero keys, then pick one of three deployment options to install it for real.

1. Get the code

git clone https://github.com/beever-ai/beever-atlas.git
cd beever-atlas

2. Try the demo first (optional, no keys needed for seeding)

make demo

make demo brings up the full stack pre-loaded with a public Wikipedia corpus (Ada Lovelace + Python history). Seeding uses pre-computed fixtures — no API keys required. Asking questions via /api/ask needs a free-tier GOOGLE_API_KEY because the QA agent calls Gemini. See demo/README.md for curl examples.

Skip this step if you're ready to install for real.

3. Before you start: get your API keys

Two free keys are required before installing. Both offer generous free tiers — enough to sync a small team's channels for testing.

| Key | Purpose | Whe