Beever Atlas
Turn team conversations into an AI-searchable knowledge base, open source.
If your team lives in Slack or Discord and you're tired of losing context in chat history, Atlas offers a remarkably polished open-source solution. It's early (v0.1) and requires Docker, but the dual-memory architecture and MCP support make it a standout for AI-assisted knowledge retrieval.
- Engineering teams wanting to capture institutional knowledge from chats
- Open-source enthusiasts who prefer self-hosted solutions
- Teams using AI assistants like Claude and needing a knowledge backend
- Organizations with strict data privacy requirements
- Teams seeking a fully managed SaaS solution with no setup
- Non-technical users uncomfortable with Docker and self-hosting
- Teams that don't use Slack, Discord, Teams, or Telegram
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In short
Beever Atlas — Turn team conversations into an AI-searchable knowledge base, open source. Best for Engineering teams wanting to capture institutional knowledge from chats, Open-source enthusiasts who prefer self-hosted solutions, Teams using AI assistants like Claude and needing a knowledge backend. Free to use.
What's new in Beever Atlas
Checked 14 days agoAcross the latest 1 update: 1 launch.
Viability Score
How likely is Beever Atlas to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Dual-memory architecture (semantic vector search + knowledge graph)
- 6-stage AI pipeline: sync, extract, validate, store, cluster, wiki
- AI natural language Q&A with grounded answers
- Automated wiki generation with citations and diagrams
- Multi-platform ingestion: Slack, Discord, Microsoft Teams, Telegram
- File import: PDFs, Markdown, documents
- MCP (Model Context Protocol) server for AI assistants
- REST API for programmatic access
- Docker deployment for self-hosting
- Entity extraction and relationship mapping
- Multi-hop reasoning over knowledge graph
- Temporal chain tracking for conversation history
- Apache 2.0 open source license
- Real-time sync from messaging platforms
- Quality gate validation (≥0.5 confidence threshold)
About Beever Atlas
Beever Atlas is an open-source, self-hostable knowledge base that automatically captures and structures information from your team's messaging platforms. Inspired by Karpathy's LLM Wiki, it uses a 6-stage AI pipeline to sync messages from Slack, Discord, Teams, and Telegram, extract facts and relationships, validate them with quality gates, store them in a dual-memory architecture (Weaviate for semantic search, Neo4j for knowledge graph), cluster related facts, and generate structured wiki pages with citations and diagrams. Designed for engineering teams and knowledge workers, Atlas enables natural language Q&A over your team's history, retrieving grounded answers in under 200ms for ~80% of queries. It supports file import (PDFs, Markdown, documents) alongside conversations, and provides a REST API and MCP server for integration with tools like Claude Desktop. What sets Atlas apart is its dual-memory approach: semantic vector search handles meaning-based queries, while a knowledge graph enables multi-hop reasoning over entity relationships. The result is a system that not only finds what was said but traces how decisions evolved over time, all while being fully open source (Apache 2.0) and self-hostable for data privacy.
Behind the Verdict
Beever Atlas enters the LLM wiki space with a clear focus: turning scattered team chats into a structured, queryable knowledge base. Its biggest strengths are the dual-memory architecture (Weaviate for semantic search, Neo4j for graph relationships) and the 6-stage pipeline that automatically generates wiki pages. For teams deeply embedded in Slack, Discord, Teams, or Telegram, this is a game changer—no more digging through endless threads. That said, Atlas is v0.1. You'll need Docker proficiency and comfort with self-hosting. There's no SaaS option yet, and the integration list currently ends at four messaging platforms (Gmail, GitHub, Notion are 'coming soon'). If you're non-technical or want a plug-and-play solution, this isn't it. Compared to Khoj or Obsidian+LLM, Atlas wins on team-oriented features like multi-platform chat ingestion and auto wiki generation. Mem0 might be simpler to set up, but Atlas's graph-based reasoning gives it an edge for tracing decision evolution. In short: pick Atlas if you're an engineering team that values data ownership and already uses Docker; pass if you need a managed service or your comms aren't on the supported platforms.
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Use Cases
- Capture and search through team Slack conversations to find past decisions and discussions
- Automatically generate structured wiki pages from Discord channel history
- Trace how a technical decision evolved over time using knowledge graph multi-hop queries
- Ask natural language questions about project context and get answers with source citations
- Ingest PDFs and documents alongside chat messages for a unified knowledge base
Limitations
- Atlas is in early v0.1 release; features and stability may evolve.
- It requires self-hosting (Docker) and infrastructure for Weaviate and Neo4j.
- The quality of extracted knowledge depends on LLM accuracy, with a confidence threshold of ≥0.5 for validations.
- No information on rate limits or concurrency scaling is provided.
12-month cost
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