Modular AI agents.
Built to scale.
Quilt to ship.
A Git-native framework for AI agent instructions as small, ordered fragments. Edit structured source, then compile deterministic outputs — .claude/agents, AGENTS.md, Agent Skills — with a lock file that catches drift in CI.
Why AgentQuilt
One big agent file. A whole team editing it. Constant conflicts.
Large agent instruction files are hard to maintain in distributed teams. Two people editing the same prompt hit merge conflicts that are painful to resolve — agent instructions are semantic, not purely syntactic. And the same rules, copied by hand into Claude’s CLAUDE.md, OpenAI’s AGENTS.md, and more, quietly drift apart.
source fragment · _shared/040-review-policy.md
“All PRs require two approvals and a green CI run before merge.”
copied into 4 platform files ↓
“Require two approvals before merge.”
partial · 2d ago“One approval is fine — move fast.”
contradicts · 3w ago“Two approvals, plus a security pass.”
reworded · 5w ago“(review policy not mentioned)”
omitted · 2mo agoHow it works
Quilt once.
Run everywhere.
Each agent is a small agent.yaml manifest plus ordered instruction blocks. agentquilt build validates them and compiles deterministic outputs for every configured platform — writing a lock file so CI can catch drift.
See the source model →source fragments · .agentquilt/agents/reviewer
compiled outputs · never hand-edited
The source model
A manifest, some blocks, a generated agent.
Agent = Manifest + Instruction Blocks + Generated Prompt
Quickstart
Author → build → check.
In under a minute.
Install once, scaffold in your repo, then commit sources and generated outputs together. Node ≥ 18 · open source · MIT.