智能代理网格

npm / pnpm / yarn, requires Node.js 20+

npm install -g agentsmesh # or: pnpm add -g agentsmesh / yarn global add agentsmesh npm install -D agentsmesh # pin per repo as a dev dependency, then run with npx ```

Binaries with SHA-256 checksums are on GitHub Releases.

Install

Every method ships the same CLI (agentsmesh, alias amsh). The npm package also exposes the TypeScript API.

```bash

60-second quickstart

agentsmesh init       # detect and import existing tool configs, or scaffold fresh
agentsmesh generate   # write each tool's native config from the one source
agentsmesh check      # CI drift gate against .agentsmesh/.lock

On a terminal, init runs a short wizard: pick targets, import detected configs (.cursor/, .claude/, .github/copilot-instructions.md, and more), enable lessons, and optionally generate right away. Nothing is written until you finish. Use --yes for the non-interactive default, --global for your user-level config under ~/.agentsmesh/, and npx agentsmesh if you installed it as a dev dependency.

[!TIP] Commit both .agentsmesh/ and the generated tool files, like a lockfile: the tools read the generated files directly, and agentsmesh check keeps the two from drifting.

One config for every AI coding tool, with a shared memory that learns from your repo.

<img src="https://raw.githubusercontent.com/sampleXbro/agentsmesh/master/assets/agentsmesh-banner.jpeg" alt="AgentsMesh: one source, every AI coding tool, agents that learn" width="100%" />

</div>

Every AI coding assistant reads its own config: CLAUDE.md, AGENTS.md, .cursor/rules/*.mdc, .github/copilot-instructions.md, .gemini/settings.json, and more. Keeping rules, prompts, MCP servers, hooks, and permissions the same across Claude Code, Cursor, GitHub Copilot, Gemini CLI, Codex CLI, and the rest by hand does not scale, and the copies drift.

AgentsMesh makes .agentsmesh/ the one source. Write rules, commands, agents, skills, MCP servers, hooks, ignore patterns, and permissions once. agentsmesh generate writes every tool's native files, agentsmesh import pulls existing configs back in without loss, and agentsmesh check fails CI when anything drifts.

Your agents learn from your repo. Lessons give every agent, in every tool, one git-tracked memory: capture a rule after a failure, recall it before the next edit. Same mistake, never twice.

[!NOTE] Full documentation, guides, and the per-tool reference: samplexbro.github.io/agentsmesh

-> "Normalize CLI display paths to forward slashes"

```

agentsmesh init --lessons wires the loop once: an always-on rule in _root.md, a lessons skill where supported, recall and capture hooks on hook-capable tools, and matching MCP tools (lessons_query, lessons_add) for agents without shell access. Rules can be scoped by file, command, or keyword, or always-on with --scope always.

Full walkthrough: Teach your AI agents with lessons · agentsmesh lessons reference

How AgentsMesh compares

Most sync tools stop at copying rule files into each assistant's folder. AgentsMesh also round-trips losslessly, rebases cross-tool links, lints before a tool would silently drop content, and lets anyone add a new tool as a plugin.

_{Reflects the public projects as of June 2026. Sources and detail in the alternatives guide; corrections welcome.}

Built to be depended on: 12,000+ tests on Linux, macOS, and Windows CI, JSON-Schema-validated configs, npm releases with provenance, and a per-file coverage floor in the test gate.