Introducing monday Atlas

The wiki that AI writes and agents read

Connect your sources. An LLM builds an organized wiki. Your AI agents read it like a library. Not a RAG pipeline — a wiki.

atlas-frontend.up.railway.app
entities/services/auth-service.md
Title: Auth ServiceTags: auth oauth
Auth Service
Handles authentication and authorization for all services. Implements OAuth 2.0 with PKCE and JWT-based session management.
Key Endpoints
POST /auth/login — user login, returns JWT
POST /auth/refresh — token refresh with rotation
GET /auth/verify — validate JWT

How it works

Three steps. Five minutes.Your agents have knowledge.

No RAG pipeline to build. No embeddings to manage. No infrastructure to operate.

Connect sources

Confluence, GitHub, Notion, Slack, monday.com, or plain uploads. Atlas reads the source and suggests a wiki structure.

LLM synthesizes

The LLM reads everything, writes organized pages, adds cross-references. Watch the wiki grow in real-time.

Agents read it

Your agents read the wiki with MCP tools: wiki_cat, wiki_grep, wiki_ls. Like a library, not a search engine.

Features

Everything a knowledge wiki needs.Nothing it doesn't.

Built for teams where some members are human and some are AI.

Schema-driven structure

A SCHEMA.md file defines your wiki's DNA — folder structure, page templates, naming conventions. The LLM follows it on every ingestion.

Auto-sync sources

Connect GitHub, Confluence, or Notion. When your docs change, the wiki updates automatically.

Agent contributions

Agents contribute findings via wiki_contribute. Humans review and approve before merge into the wiki.

Lint & health checks

Detect contradictions, stale pages, coverage gaps, orphaned pages, and broken cross-references automatically.

Usage analytics

Track which pages agents read, when, and how often. Identify unused content and coverage gaps.

Human edits protected

Sections marked human-edited are never overwritten by the LLM. Human intent is sacred.

For agents

Any agent. 30 seconds to set up.

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

.claude/settings.json
{
  "mcpServers": {
    "atlas-wiki": {
      "url": "https://your-api.up.railway.app/mcp/wiki-id"
    }
  }
}

Not just RAG

A wiki, not a retrieval pipeline

ConcernTraditional RAGmonday Atlas
StorageVectors in a databaseMarkdown pages in folders
ProcessingAt query time (slow)At write time (fast reads)
OutputFragments scored by similarityComplete, synthesized pages
StructureFlat chunk storeSchema-driven wiki hierarchy
Cross-referencesNoneAutomatic bidirectional links
Agent interfaceCustom retrieval APIStandard MCP tools
Human readable?Not reallyYes — it's a wiki
Cost modelPer-query embeddings + inferencePay for ingestion. Reads are free.

Pricing

Pay for ingestion. Reads are free.

Your agents read the wiki at zero cost. You only pay when the LLM writes.

Free

$0
  • 2 wikis
  • 50 pages/wiki
  • 10 ingestions/mo
  • MCP access
Get started

Starter

$19/mo
  • 5 wikis
  • 200 pages/wiki
  • 50 ingestions/mo
  • Source connectors
Get started
Most popular

Pro

$49/mo
  • 20 wikis
  • 500 pages/wiki
  • 200 ingestions/mo
  • Usage analytics
Get started

Business

$149/mo
  • Unlimited wikis
  • Unlimited pages
  • Unlimited ingestions
  • SSO + priority support
Get started

Your agents deserve a library,not a search engine.

Create your first wiki in 5 minutes. Connect sources. Watch the LLM build it. Let your agents read.

Get started free
monday Atlas

The LLM-Wiki for AI agents. Upload docs, get a wiki, connect any agent.

Integrations

  • Claude Code
  • Claude Desktop
  • Cursor
  • Any MCP client

Company

  • monday.com
  • GitHub
  • Documentation
monday.com · Atlas KnowledgeBase