Connect your sources. An LLM builds an organized wiki. Your AI agents read it like a library. Not a RAG pipeline — a wiki.
The paradigm shift
Traditional knowledge bases make humans do the bookkeeping — organizing pages, writing summaries, keeping things consistent. The LLM Wiki flips this: AI does the tedious maintenance, humans focus on what matters.
“The tedious part of maintaining a knowledge base is not the reading or the thinking — it's the bookkeeping. The LLM handles the bookkeeping. You handle the thinking.”
Retrieves raw chunks at query time. No synthesis. Every query starts from scratch.
Humans write pages manually. Great structure, but impossible to keep current.
AI writes structured pages from sources. Always current. Agents read it like a library.
How it works
No RAG pipeline to build. No embeddings to manage. No infrastructure to operate.
Confluence, GitHub, Notion, Slack, monday.com, or plain uploads. Atlas4 reads the source and suggests a wiki structure.
The LLM reads everything, writes organized pages, adds cross-references. Watch the wiki grow in real-time.
Your agents read the wiki with MCP tools: wiki_cat, wiki_grep, wiki_ls. Like a library, not a search engine.
Features
Built for teams where some members are human and some are AI.
A SCHEMA.md file defines your wiki's DNA — folder structure, page templates, naming conventions. The LLM follows it on every ingestion.
Connect GitHub, Confluence, or Notion. When your docs change, the wiki updates automatically.
Agents contribute findings via wiki_contribute. Humans review and approve before merge into the wiki.
Detect contradictions, stale pages, coverage gaps, orphaned pages, and broken cross-references automatically.
Track which pages agents read, when, and how often. Identify unused content and coverage gaps.
Sections marked human-edited are never overwritten by the LLM. Human intent is sacred.
For agents
Add the MCP server config and your agent has organizational knowledge. No SDK, no API keys, no pipeline.
Claude Code
Claude Desktop
Cursor
Any MCP client
{
"mcpServers": {
"atlas4": {
"url": "https://atlas4.ai/mcp"
}
}
}Want your agent to sign up on its own? Share this page with it:
Agent onboarding guideNot just RAG
| Concern | Traditional RAG | Atlas4 |
|---|---|---|
| Storage | Vectors in a database | Markdown pages in folders |
| Processing | At query time (slow) | At write time (fast reads) |
| Output | Fragments scored by similarity | Complete, synthesized pages |
| Structure | Flat chunk store | Schema-driven wiki hierarchy |
| Cross-references | None | Automatic bidirectional links |
| Agent interface | Custom retrieval API | Standard MCP tools |
| Human readable? | Not really | Yes — it's a wiki |
| Cost model | Per-query embeddings + inference | Pay for ingestion. Reads are free. |
Pricing
Your agents read the wiki at zero cost. You only pay when the LLM writes.
Create your first wiki in 5 minutes. Connect sources. Watch the LLM build it. Let your agents read.
Get started free