Beever Atlas
Overview
Description from the maker (original English)
Open-source LLM knowledge base: team chat into a typed knowledge graph and auto-generated wiki.
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Search & AI memory
Install
Pick the AI app you use, then copy the settings.
Passed 2/2 checks · Runs on your computer
First: which AI do you use?
Pick your app and the steps appear below.
Works with Claude, with some setup first
Install Docker on your computer first (one time) · Only the Claude desktop app (Windows or Mac), not the web or phone
Install Docker
One time only; it works for every server that needs Docker
Open the Claude desktop app’s settings file
SettingsDeveloperEdit Config
Paste these settings and save the file
{ "mcpServers": { "beever-atlas": { "command": "docker", "args": [ "run", "-i", "--rm", "ghcr.io/beever-ai/beever-atlas:0.3.0", "python", "-m", "beever_atlas.api.mcp_server" ] } } }Quit Claude and open it again
Try it
Use Beever Atlas and tell me what you can do for me, then show me one thing✓ If Claude says it is using Beever Atlas or shows a Beever Atlas tool in the answer, you are connected
Menu names checked against Anthropic’s help page on Nov 11, 2026 · your screen may differ slightly by app version
ChatGPT cannot connect Beever Atlas yet
Beever Atlas runs on your computer, but ChatGPT can only connect servers that run on the web
What you can do instead
A · Use an app that can connect Beever Atlas
- Install Docker on your computer first (one time) · Only the Claude desktop app (Windows or Mac), not the web or phone
- Install Docker on your computer first (one time)
- Install Docker on your computer first (one time)
- Install Docker on your computer first (one time)
B · Others in Search & AI memory that work with ChatGPT
Picked from the most used in the same category, only ones that connect by address with no key
- AIHOT✓ ChatGPT
- Sui Knowledge Base (kapa.ai)✓ ChatGPT
- Exa✓ ChatGPT
Works with Claude Code, with some setup first
Install Docker on your computer first (one time)
Install Docker
One time only; it works for every server that needs Docker
Open Terminal
Mac: press Command+Space and type Terminal · Windows: open PowerShell
Paste this command and press Enter
claude mcp add beever-atlas -- docker run -i --rm ghcr.io/beever-ai/beever-atlas:0.3.0 python -m beever_atlas.api.mcp_serverType /mcp in Claude Code
You should see Beever Atlas listed as connected
Try it
Use Beever Atlas and tell me what you can do for me, then show me one thing✓ If Claude Code says it is using Beever Atlas or shows a Beever Atlas tool in the answer, you are connected
Menu names checked against Anthropic’s help page on Nov 11, 2026 · your screen may differ slightly by app version
Works with Cursor, with some setup first
Install Docker on your computer first (one time)
Install Docker
One time only; it works for every server that needs Docker
Open the AI chat in Cursor in Agent mode
Try it
Use Beever Atlas and tell me what you can do for me, then show me one thing✓ If Cursor says it is using Beever Atlas or shows a Beever Atlas tool in the answer, you are connected
Menu names checked against Cursor’s help page on Nov 11, 2026 · your screen may differ slightly by app version
Works with VS Code, with some setup first
Install Docker on your computer first (one time)
Install Docker
One time only; it works for every server that needs Docker
Open the AI chat in VS Code in Agent mode
Try it
Use Beever Atlas and tell me what you can do for me, then show me one thing✓ If VS Code says it is using Beever Atlas or shows a Beever Atlas tool in the answer, you are connected
Menu names checked against VS Code’s help page on Nov 11, 2026 · your screen may differ slightly by app version
For developers: Terminal commands and JSON settings
Installed on your computer
Claude Code
claude mcp add beever-atlas -- docker run -i --rm ghcr.io/beever-ai/beever-atlas:0.3.0 python -m beever_atlas.api.mcp_serverClaude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"beever-atlas": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/beever-ai/beever-atlas:0.3.0",
"python",
"-m",
"beever_atlas.api.mcp_server"
]
}
}
}Cursor (~/.cursor/mcp.json)
{
"mcpServers": {
"beever-atlas": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/beever-ai/beever-atlas:0.3.0",
"python",
"-m",
"beever_atlas.api.mcp_server"
]
}
}
}VS Code (mcp.json)
{
"servers": {
"beever-atlas": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/beever-ai/beever-atlas:0.3.0",
"python",
"-m",
"beever_atlas.api.mcp_server"
]
}
}
}These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
At a glance
Which apps can use it
Based on how this server connects and on each app’s own docs, checked Nov 10, 2026.
Run on your computer in
ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
Related guides
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 11, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account Beever-AI, which owns the repo
- ✓ Pass
Still maintained
Code last changed 2 months ago
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Discussion
Ask, share a tip or report that it does not work. Sorted by what people marked helpful.
No posts yet. Be the first to ask or share.
Posts here are written by users, not the PopNx team.
User reviews
No reviews yet
Compared with others in Search & AI memoryi
- More stars than 99% of the other 5884 servers in this category
- Published to the registry more recently than 17% of the other 5884 servers in this category
- Basic checks equal to or better than 100% of the other 5884 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Beever Atlas safe?
It passed 2 of 2 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed 2 months ago, and the latest version (0.3.0) reached the registry 5 months ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.
What can I use instead?
In the same category: Ruflo, Context7, Metabase
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
Beever Atlas
Turn your team's Slack, Discord, Teams & Mattermost chats
into a self-maintaining wiki — automatically.
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.
✨ 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.
🏗️ 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.
💡 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.
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.gitcd beever-atlas
2. Try the demo first (optional, no keys needed for seeding)
make demomake 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 | Where to get it |
|---|---|---|
GOOGLE_API_KEY | Gemini — extraction, entity graph, answers | aistudio.google.com/apikey |
JINA_API_KEY | Jina v4 embeddings (2048-dim) for semantic search | jina.ai/api-dashboard |
Optional (skip unless you know you need them):
| Key | What it enables |
|---|---|
TAVILY_API_KEY | External web search when QA retrieval confidence is low — tavily.com |
OLOSTEP_API_KEY | Olostep web search (alternative to Tavily). Set WEB_SEARCH_PROVIDER=olostep in your .env — olostep.com/dashboard |
| Slack / Discord / Teams bot tokens | Configured via the web UI after setup, not .env — the bot stores platform credentials encrypted in MongoDB |
Tip: Keep the two required keys handy before you start. Option 1 prompts for them interactively; Options 2 and 3 need them pasted into
.env.
4. Choose a deployment option
| Option | When to use | Time to "up" |
|---|---|---|
| 1. One-line install (recommended) | You want the fastest path to a running stack. | ~2 min first run |
| 2. Manual Docker | CI/CD, ops environments, or when you want explicit control over every step. | ~3 min first run |
| 3. Local development | Active contributors who need hot-reload on backend and frontend. | varies |
Option 1 — One-line install (recommended)
./atlasThe atlas installer walks you through a guided 5-step checklist:
- **Embedding model*