Atlaso: Shared AI memory across Claude Code, Cursor, and ChatGPT
Atlaso launched on August 4, 2026, ranking fourth on Product Hunt’s daily leaderboard with 242 upvotes. The product addresses a friction point that has emerged as product teams start using multiple AI tools in parallel: every tool has its own memory, which means switching from Claude Code to Cursor to ChatGPT requires re-explaining the same context about the project, the relevant decisions, and the team’s working preferences.
Atlaso creates one persistent memory layer that integrates with all three tools simultaneously, plus OpenCode and Anthropic’s desktop app. When a session ends, the system extracts and stores relevant decisions, open questions, and watch-outs. When the next session begins — in any connected tool — Atlaso surfaces what is relevant before the first prompt arrives.
What it does
The memory architecture operates at two levels. Global memory holds personal preferences and working styles that carry across all projects — the kinds of constraints and preferences that would otherwise get re-typed into every new tool session. Project memory stays scoped to a specific repository, which prevents context from bleeding between different codebases or products.
A confidence-scoring system tracks whether stored memories are settled (stable facts about the project), contested (things that were true and may have changed), or preliminary (working assumptions not yet confirmed). This is a meaningful design decision: rather than treating all stored context as equally reliable, Atlaso flags uncertainty, so the model can weight information appropriately rather than presenting outdated context as current fact.
The tool captures memories automatically during sessions, with secrets redacted before storage. A paid tier adds Ambient Memory, a background enrichment process that distills captures into structured, tagged memories rather than leaving them as raw session notes. Data is encrypted in transit and at rest, with no model training on user data.
Pricing starts with a free tier limited to one device and one connected tool. The Pro tier at $10 per month adds unlimited devices, multiple tool connections, and Ambient Memory. A Build tier at $25 per month adds a developer-facing memory API.
Why it matters for product managers
The problem Atlaso addresses is not only a developer workflow issue. Product managers who move between AI tools for research, document generation, and feature analysis face the same context re-entry cost. Teams where multiple people use AI tools against the same product also benefit from a shared memory layer that doesn’t require manual coordination — a PM who records a key decision in one session makes that decision available to their next AI interaction without a handoff document.
The product is early, with a small initial review set, and the integration surface (MCP server) currently favors technical users who configure tools themselves. Teams evaluating it should account for setup overhead alongside the workflow benefits.