Atlaso

Quick Take

Atlaso is built around one specific frustration: every AI coding tool starts cold. Switch from Claude Code to Cursor, or open a new Codex session, and you’re re-explaining your project, your preferences, and decisions you already made somewhere else. Atlaso connects once to each tool you use and gives them a shared memory, so what one tool learns, the others already know.

How It Works

Atlaso positions itself as one persistent, shared memory that Claude Code, Cursor, Codex, Claude Desktop, OpenCode, and Antigravity read from and write to automatically. ChatGPT and Meta AI aren’t natively supported yet, but any tool that supports MCP can still connect.

• Global and per-project memory. Some context (your general preferences, how you like code explained, your working style) travels with you everywhere. Other context should stay scoped to one project rather than leaking into unrelated work; Atlaso separates the two.
• Ambient Memory. Before you type anything, Atlaso surfaces a short orientation pulled from your own memory, so a session picks up roughly where you actually left off instead of starting from nothing.
• One connection per tool, not per session. You connect Atlaso to a tool once; after that, memory recall and capture happen automatically in the background.

Key Facts

• Supported tools today: Claude Code, Cursor, Codex, Claude Desktop, OpenCode, Antigravity, plus any tool that supports MCP
• Not yet supported natively: ChatGPT, Meta AI
• Pricing: Free (1 device, 1 tool) / Pro $10/month (unlimited devices and tools, Ambient Memory, background enrichment) / Build $25/month (adds a developer memory API)
• Privacy claims: Secrets scrubbed before storage, encryption in transit and at rest, no training on your memory, no selling your data
• Protocol: MCP (Model Context Protocol) for broad compatibility beyond the natively supported tools

Why Cross-Tool Memory Matters

This connects directly to a pattern showing up across serious AI-assisted work: portable context, the idea that your working knowledge shouldn’t live locked inside one vendor’s chat history. A context file like a project’s CLAUDE.md or AGENTS.md solves part of this by giving any tool that reads it the same starting information. Atlaso is aimed at a related but distinct gap: memory that updates itself from what actually happens in your sessions, and follows you automatically between tools rather than requiring you to maintain a file by hand. Where it differs from a full agent knowledge base is scope and audience: Atlaso is built around one person’s working context across their own tools, not a team-wide, versioned knowledge system.

Honest Limitations

• Free tier is genuinely limited. One device, one tool, no cross-tool syncing, which is the entire point of the product; most of the value sits behind the $10/month Pro plan.
• A commercial, hosted product. Your memory lives in Atlaso’s infrastructure, not something you self-host. If that’s a dealbreaker, a self-hosted MCP memory server is the alternative.
• Coverage is still expanding. ChatGPT and Meta AI aren’t natively supported yet; check current status before assuming full coverage of every tool you use.
• New enough to lack a long track record. As with any memory system handling real project context, evaluate its actual recall accuracy on your own workflows before trusting it heavily.

Alternatives Worth Knowing

• mcp-memory, a self-hosted, open-source MCP memory server for developers who want full control over their own infrastructure; see Atlaso vs mcp-memory
• Foundation by Chroma, a research-preview, team-oriented agent memory system built on a vector database rather than a lightweight cross-tool layer
• A hand-maintained context file like CLAUDE.md or AGENTS.md, a simpler, free, manual alternative to automatic memory capture

For the underlying concepts, see Portable Context, Agent Knowledge Base, Provenance-Aware Memory, and Persistent Agent State.