Your project memory should not belong to one AI tool.
ShardStitch turns local project state into continuation packets for 31 AI handoff targets across native integrations and web-chat handoff, so work can survive lockout, tool switches, stale chats, and model changes.
Why cross-tool handoff matters.
AI coding work increasingly moves between tools. You might plan in Claude, implement in Cursor, debug in Codex, and ask Gemini or another model to inspect. The project needs a memory layer that is not owned by one model vendor.
What cross-tool memory is not.
It is not generic AI memory. It is not a hosted chat wrapper. It is project state that can be translated into the format each coding tool understands.
Check the receiving workspace.
A packet can name files absent from the next checkout. Confirm the repository, branch and commit, then compare uncommitted changes. For a remote agent, distinguish work pushed to the shared branch from edits still inside its workspace.
A native pickup file, MCP connection and pasted web-chat brief are different delivery paths. Ask the receiving agent to identify the goal, one unresolved check and the next bounded action before editing. If it repeats a rejected approach, correct the brief first.
The benefit.
You stop treating one chat transcript as the source of truth. The source of truth becomes the project: files, diffs, decisions, verification, and next action. Any supported AI tool can pick up from there.
Where it beats single-tool workflows.
Single-tool memory helps inside one ecosystem. Cross-tool handoff helps when the first tool is blocked, expensive, stale, or simply not the best tool for the next phase.
FAQ.
Is cross-tool memory the same as syncing rules?
No. Rules describe preferences and instructions. Cross-tool handoff carries live working state: what changed, what failed, what is true, and what to do next.
Why not stay in one AI coding tool?
Sometimes you can. But limits, stale context, model strengths, price, and outages often push real work across tools.