Mesh-LLM/agents-plugin

최근 기록된 소스 활동: 2026년 5월 28일 08:30

SkillsMP 카탈로그 업데이트: 2026년 11월 5일 19:57

수집된 skills: 2

GitHub 스타: 3
GitHub 포크: 1

AI 어시스턴트로 설치

이 프롬프트를 복사해 사용 중인 AI 어시스턴트에 입력하세요.

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/Mesh-LLM/agents-plugin.

저장소 README

README 원본 파일: README.md

README 원본 보기

README 전체 펼치기

# Mesh Agents Mesh Agents lets a [mesh-llm](https://github.com/Mesh-LLM/mesh-llm) node host and advertise useful [A2A](https://a2a-protocol.org/latest/specification/) agents. A mesh node is not itself an agent, and the whole mesh is not one giant agent. Instead, any node can publish one or more Agent Cards for agents it is willing to run. That makes the mesh a shared agent cloud. Instead of every developer hand-wiring the same local helpers, people can contribute agents to a private team mesh, discover agents contributed by others, and run them with the inference, tools, and runtime capacity available across the mesh. Codex, OpenCode, Goose, Claude, and other MCP-capable clients get one local MCP endpoint for finding agents, inspecting their native [A2A Agent Cards](https://a2a-protocol.org/latest/specification/#8-agent-discovery-the-agent-card), sending tasks, and reading artifacts. The agent might run on your node, another node in a private team mesh, or eventually a public mesh. The client does not need to know where the agent lives. Agents are defined once on the node that owns them and surfaced through Mesh's MCP endpoint. That makes them easy to share, easy to reuse, and independent of any one coding client. This README uses a pull request reviewer named `pr-review` as the running example. It is a realistic coding agent: a developer asks for a GitHub PR review, the coding client discovers `pr-review` through Mesh MCP, Mesh sends the work to the A2A agent, and the agent uses an ACP harness to inspect code and return findings. ## What It Is Mesh Agents provides: - a directory-backed A2A agent registry - A2A Agent Card loading from native JSON - MCP tools for listing agents, sending messages, reading tasks, and reading artifacts - persistent A2A task state backed by SQLite - an ACP bridge for local coding-agent harnesses such as OpenCode - CLI tools for authoring and validating local agent definitions - packaged Agent Skills for using and authoring Mesh agents from AI clients The local foundation is implemented today. Mesh-wide discovery and remote routing are the next layer. ## FAQ ### Is every mesh node an A2A agent? No. A mesh node can host zero, one, or many A2A agents. The node runs the Mesh Agents plugin, and the plugin advertises whichever local agent definitions are enabled by policy. ### Is the whole mesh exposed as one A2A agent? No. The mesh is the discovery and routing fabric. Individual agents are exposed through their own A2A Agent Cards. Your client talks to the local Mesh MCP endpoint, Mesh finds the right agent, and A2A carries the task to that agent. ### What does "defined once and surfaced through MCP" mean? You define an agent once under `~/.mesh-llm/agents/<agent-id>/` on the node that should run it. MCP-capable clients such as Codex, OpenCode, Goose, Claude, and Pi connect to Mesh's local MCP endpoint instead of each maintaining their own copy of the agent configuration. ### Can I keep an agent private? Yes. Local agent definitions start private. `visibility = "private"` keeps the agent off public surfaces, `policy.advertise_on_mesh = false` prevents gossip to other nodes, and `policy.public_mesh = false` protects against accidental publication on a public mesh. Publish agents only when you intentionally want other mesh peers to discover and use them. ### What if the agent has secret MCP connections? Keep that agent private unless you intentionally want other mesh peers to send it work. Secrets and private tool credentials stay on the node that owns the agent, but a published agent runs with that node's configured tools. Treat publishing as permission for other allowed peers to ask that agent to perform work. ### How do MCP, A2A, and ACP fit together? - MCP is how the coding client talks to Mesh. - A2A is how Mesh describes and sends tasks to agents. - ACP is how a local A2A agent drives a coding harness such as OpenCode, Codex, Goose, or Pi. ## Architecture ```mermaid flowchart LR User["Developer in chat"]:::user CodingAgent["Coding agent Codex, OpenCode, Goose, Claude"]:::client MeshMcp["Mesh node HTTP MCP endpoint 127.0.0.1:3131/mcp"]:::mcp MeshAgents["Mesh Agents plugin registry, routing, task state"]:::plugin subgraph Cloud["Mesh agent cloud"] direction TB Discovery["Agent discovery advertisements + ownership"]:::mesh PrReview["pr-review A2A Agent Card"]:::agent Docs["docs-maintainer A2A Agent Card"]:::agent Tests["test-runner A2A Agent Card"]:::agent Remote["remote node agents A2A Agent Cards"]:::agent end ACPBridge["ACP bridge"]:::acp Harness["Agent harness OpenCode, Codex, etc."]:::harness User -->|"asks for work"| CodingAgent CodingAgent -->|"MCP find agents + send task"| MeshMcp MeshMcp -->|"MCP tools"| MeshAgents MeshAgents -->|"discover + route"| Discovery Discovery -.-> PrReview Discovery -.-> Docs Discovery -.-> Tests Discovery -.-> Remote MeshAgents -->|"A2A message, task, artifacts"| PrReview PrReview -->|"ACP start session + stream events"| ACPBridge ACPBridge -->|"perform work"| Harness Harness -->|"results"| ACPBridge ACPBridge -->|"ACP events"| PrReview PrReview -->|"A2A task updates"| MeshAgents MeshAgents -->|"MCP artifacts + answer"| CodingAgent CodingAgent -->|"response"| User classDef user fill:#f8fafc,stroke:#64748b,color:#0f172a classDef client fill:#eef2ff,stroke:#4f46e5,color:#111827 classDef mcp fill:#ecfeff,stroke:#0891b2,color:#083344 classDef plugin fill:#f0fdf4,stroke:#16a34a,color:#052e16 classDef mesh fill:#fff7ed,stroke:#f97316,color:#431407 classDef agent fill:#fffbeb,stroke:#d97706,color:#451a03 classDef acp fill:#fdf2f8,stroke:#db2777,color:#500724 classDef harness fill:#f5f3ff,stroke:#7c3aed,color:#2e1065 ``` The coding agent only needs one integration point: Mesh's MCP endpoint. Through that endpoint it can discover available agents, inspect their A2A Agent Cards, send work, and read the resulting task state or artifacts. This keeps Codex, OpenCode, Goose, Claude, and similar clients out of the business of knowing how agents are hosted or routed. Mesh Agents runs inside the mesh node as the plugin that owns the agent control plane. It loads local Agent Cards from `~/.mesh-llm/agents/`, advertises available agents into the mesh, tracks which node owns each agent, and persists A2A task state. If an agent is local, the task can run on the current node. If the best agent is remote, Mesh routes the request to the owning node and returns the same task/artifact shape to the caller. A2A is the contract between Mesh and the agent. The Agent Card describes what the agent can do, and A2A tasks carry the work, status updates, and artifacts. For agents that need an interactive coding runtime, Mesh Agents bridges from A2A into ACP. ACP is the harness-facing protocol: it starts a session, streams work into OpenCode, Codex, or another compatible harness, and sends events back to the A2A task. The result flows back to the coding agent as normal MCP tool output. ## What It Is Used For Use Mesh Agents when you want a mesh node to expose useful task-oriented agents to AI clients. Examples: - a `pr-review` agent that reviews a GitHub pull request and returns prioritized findings - a `docs-maintainer` agent that updates docs from a change summary - a `release-notes` agent that turns merged PRs into release notes - a `test-runner` agent that runs project-specific checks and summarizes failures The caller talks to a local MCP tool. Mesh Agents owns the agent registry, task state, and runtime bridge. The rest of this README walks through `pr-review` as the example agent. ## Installation Install the agents plugin through [mesh-llm](https://github.com/Mesh-LLM/mesh-llm): ```bash mesh-llm plugins install Mesh-LLM/agents ``` After installation, run the plugin's user-facing CLI through `mesh-llm`. Released plugin archives include Agent Skills under `skills/`. Install the plugin skills into detected AI clients: ```bash mesh-llm skills install ``` Agent launch commands such as `mesh-llm goose`, `mesh-llm pi`, `mesh-llm opencode`, and `mesh-llm claude` also install available plugin skills for that agent before starting the session. The normal MCP endpoint is hosted by the running mesh node. Launch a supported client through mesh-llm and the client is wired to the mesh MCP endpoint, including the Mesh Agents tools: ```bash mesh-llm opencode mesh-llm goose mesh-llm pi mesh-llm claude ``` Or register Mesh's MCP endpoint in any AI tool that can speak MCP over HTTP: ```bash http://127.0.0.1:3131/mcp ``` ## Defining Agents Local agent definitions live under `~/.mesh-llm/agents/`. Each directory describes one agent that Mesh can advertise, route to, and execute. If you have not used A2A before, the key idea is that an agent has a public contract and a private runtime. The public contract is the A2A Agent Card: it answers "what is this agent, what can it do, and how do A2A clients talk to it?" The private runtime config answers "how does this machine actually run that agent?" Create the `pr-review` local agent definition: ```bash mesh-llm agents init pr-review --runtime opencode ``` That creates the files for the example pull request reviewer: ```text ~/.mesh-llm/ agents/ pr-review/ agent-card.json runtime.toml instructions.md ``` This repository also includes a ready-to-use demo definition under `examples/pr-review/`. During plugin development, copy that directory into your local agent registry and validate it: ```bash mkdir -p ~/.mesh-llm/agents cp -R examples/pr-review ~/.mesh-llm/agents/pr-review mesh-llm agents validate pr-review ``` The generated files and the example files use the same shape. `agents init` is the quickest way to start a new local agent; `examples/pr-review` is the richer demo version used throughout this README. ### Agent Card `agent-card.json` is the native [A2A Agent Card](https://a2a-protocol.org/latest/specification/#8-agent-discovery-the-agent-card). Think of it as the agent's business card. Mesh and MCP clients use it to decide whether an agent is appropriate for a task before sending work. A card describes: - the agent name, description, and version - the A2A interface Mesh exposes for that agent - supported input and output modes - whether streaming is supported - the agent's skills, such as `pr-review` or `release-notes` For the `pr-review` example, the starter card created by `mesh-llm agents init` is valid JSON and can be edited directly: ```json { "name": "Pr Review", "description": "Reviews pull requests and returns prioritized findings with file/line references.", "version": "0.1.0", "supportedInterfaces": [ { "url": "http://127.0.0.1:3131/a2a/agents/pr-review", "protocolBinding": "JSONRPC", "protocolVersion": "1.0" } ], "capabilities": { "streaming": true }, "defaultInputModes": ["text/plain"], "defaultOutputModes": ["text/markdown"], "skills": [ { "id": "pr-review", "name": "Pull request review", "description": "Inspect a GitHub pull request and report correctness, regression, and test risks.", "tags": ["github", "code-review", "pull-request"] } ] } ``` For mesh-local agents like `pr-review`, Mesh owns the served A2A URL. You normally edit the name, description, modes, capabilities, and skills; Mesh handles where the agent is actually reachable. ### Runtime Policy `runtime.toml` is Mesh Agents' private execution policy. It is not part of A2A and is not advertised as the agent's public contract. It tells the local mesh node whether to expose the agent, which harness to use, how much concurrency is allowed, and what workspace policy to apply when tasks run. Example `runtime.toml` for `pr-review`: ```toml enabled = true visibility = "private" [runtime] type = "opencode" max_concurrent_tasks = 1 [runtime.workspace] mode = "temp_per_task" keep = "on_failure" [instructions] file = "instructions.md" delivery = "first_prompt" [tools.mesh] enabled = true [policy] advertise_on_mesh = false public_mesh = false ``` Important fields: - `enabled`: controls whether Mesh loads the agent. - `visibility`: starts as `private`; use this to keep local agents off public surfaces. - `runtime.type`: selects how the agent runs. `opencode`, `goose`, and `pi` are named ACP presets. `acp` lets you provide an explicit ACP command. `remote` describes an externally hosted A2A agent. - `runtime.max_concurrent_tasks`: caps simultaneous work for this agent. The default is `1`, which is safest for coding agents that mutate workspaces. - `runtime.workspace.mode`: controls where work runs. `temp_per_task` creates a fresh temporary workspace per task; path-based workspace modes can pin an agent to a specific checkout. - `runtime.workspace.keep`: controls cleanup. `on_failure` keeps failed task workspaces for debugging. - `instructions.file`: points at the local instruction file delivered to the harness. - `instructions.delivery`: `first_prompt` prepends the instructions to the first task prompt sent into the harness. - `tools.mesh.enabled`: allows the harnessed agent to receive Mesh's own MCP tools. - `policy.advertise_on_mesh`: controls whether this node gossips the agent to other mesh nodes. - `policy.public_mesh`: protects against accidentally advertising private local agents on a public mesh. Named runtimes are just convenience presets over ACP. Use `type = "acp"` when you want full control over the command line: ```toml [runtime] type = "acp" command = "$HOME/bin/my-agent-harness" args = [ "acp", "--cwd", "{{ task.workspace }}", "--mcp", "{{ mesh.mcp_url }}", "--agent", "{{ agent.id }}", "--instructions", "{{ instructions.file }}" ] max_concurrent_tasks = 1 [runtime.env] MESH_LLM_MCP_URL = "{{ mesh.mcp_url }}" MESH_AGENT_ID = "{{ agent.id }}" TASK_ARTIFACTS_DIR = "{{ task.artifacts_dir }}" ``` Substitutions are expanded when a task starts, because task-specific values do not exist until then. Expansion applies to `runtime.command`, every `runtime.args` item, and `runtime.env` values. Mesh template variables use `{{ ... }}`. Environment variables use `$HOME`, `${HOME}`, or `{{ env.HOME }}`. Missing variables are configuration errors. Useful substitutions: | Variable | Example value | Meaning | |---|---|---| | `{{ agent.id }}` | `pr-review` | Agent directory id. | | `{{ agent.name }}` | `Pr Review` | Human-readable Agent Card name. | | `{{ agent.dir }}` | `/Users/alice/.mesh-llm/agents/pr-review` | Agent definition directory. | | `{{ agent.card_path }}` | `/Users/alice/.mesh-llm/agents/pr-review/agent-card.json` | Native A2A Agent Card path. | | `{{ agent.runtime_path }}` | `/Users/alice/.mesh-llm/agents/pr-review/runtime.toml` | Local runtime policy path. | | `{{ task.id }}` | `task-01J8R2` | A2A task id. | | `{{ task.workspace }}` | `/var/folders/.../mesh-a2a-pr-review-task-01J8R2` | Workspace assigned to this task. | | `{{ task.prompt_path }}` | `/Users/alice/.mesh-llm/a2a/agents/pr-review/runtime/task-01J8R2/prompt.txt` | File containing the task prompt. | | `{{ task.artifacts_dir }}` | `/Users/alice/.mesh-llm/a2a/agents/pr-review/runtime/task-01J8R2/artifacts` | Directory for harness-written outputs. | | `{{ task.logs_dir }}` | `/Users/alice/.mesh-llm/a2a/agents/pr-review/runtime/task-01J8R2/logs` | Directory for harness logs. | | `{{ instructions.file }}` | `/Users/alice/.mesh-llm/agents/pr-review/instructions.md` | Resolved instruction file path. | | `{{ instructions.dir }}` | `/Users/alice/.mesh-llm/agents/pr-review` | Directory containing the instruction file. | | `{{ mesh.mcp_url }}` | `http://127.0.0.1:3131/mcp` | Mesh-hosted MCP endpoint. | | `{{ mesh.api_url }}` | `http://127.0.0.1:3131` | Mesh node API and console base URL. | | `{{ mesh.openai_url }}` | `http://127.0.0.1:9337/v1` | Mesh OpenAI-compatible inference endpoint. | | `{{ mesh.model }}` | `auto` | Runtime model value, or `auto` if unset. | | `{{ mesh.data_dir }}` | `/Users/alice/.mesh-llm` | Mesh data directory. | | `{{ env.HOME }}` | `/Users/alice` | Explicit environment lookup. | | `$HOME`, `${HOME}` | `/Users/alice` | Shell-style environment expansion. | For example, this config: ```toml [runtime] type = "acp" command = "opencode" args = [ "acp", "--cwd", "{{ task.workspace }}", "--mcp", "{{ mesh.mcp_url }}", "--instructions", "{{ instructions.file }}" ] ``` might start the harness like this for a `pr-review` task: ```bash opencode acp \ --cwd /var/folders/.../mesh-a2a-pr-review-task-01J8R2 \ --mcp http://127.0.0.1:3131/mcp \ --instructions /Users/alice/.mesh-llm/agents/pr-review/instructions.md ``` ### Instructions `instructions.md` is the agent's operating brief. For `pr-review`, this is where you say what to prioritize, how to format findings, which checks to run, and what not to do. Mesh Agents sends those instructions into the ACP harness according to `runtime.toml`. Useful authoring commands: ```bash mesh-llm agents list mesh-llm agents init <agent-id> --runtime opencode mesh-llm agents validate [agent-id] mesh-llm agents show <agent-id> mesh-llm agents enable <agent-id> mesh-llm agents disable <agent-id> ``` `mesh-llm agents init` is the starter authoring tool for local Agent Cards. For `pr-review`, it creates a valid card skeleton, runtime policy, and instructions file. Edit `agent-card.json` against the official A2A Agent Card docs, then run `mesh-llm agents validate pr-review`. ## Discovering Agents Clients discover agents through MCP. In normal use, the MCP endpoint is `http://127.0.0.1:3131/mcp`. Register that URL in any AI tool that can speak MCP over HTTP, or use client launchers such as `mesh-llm opencode`, `mesh-llm goose`, `mesh-llm pi`, and `mesh-llm claude` to configure it for you. Mesh's hosted MCP endpoint is an aggregate endpoint, so tool names are prefixed by plugin name to avoid collisions with other plugins. The agents plugin exposes these tools through that endpoint: | Tool | Purpose | Example | |---|---|---| | `agents.get_agents` | List available agents. | "What mesh agents are available?" | | `agents.get_agent` | Read one agent's native Agent Card and runtime summary. | "Show me what the `pr-review` agent can do." | | `agents.send_message` | Send a message to an agent through A2A. | "Ask `pr-review` to review Mesh-LLM/mesh-llm#708." | | `agents.get_task` | Fetch persisted task state. | "Check whether the PR review task has finished." | | `agents.view_text_artifact` | Read text parts from a task artifact. | "Open the review findings artifact." | | `agents.view_data_artifact` | Read a structured task artifact. | "Show the structured findings returned by the agent." | When running as a mesh-llm plugin, these tools are surfaced through mesh-llm's hosted MCP endpoint alongside the other mesh-provided tools. The plugin's internal A2A bridge still owns the official tool semantics; mesh prefixes the visible names because it is aggregating multiple plugins into one MCP namespace. ## Using Agents Use agents by asking for the work you want done. You should not have to know that agents exist, name an agent, or ask for discovery first. The client uses Mesh's MCP tools behind the scenes to find the right A2A Agent Card, delegate the task, poll task state, and read artifacts. Example prompts using the `pr-review` agent: ```text Code review Mesh-LLM/mesh-llm#708. ``` ```text Review the current branch for correctness regressions and missing tests. ``` ```text Review this PR and return only actionable findings with file and line references: https://github.com/Mesh-LLM/mesh-llm/pull/708 ``` ```text Show me the findings artifact from that review. ``` ```text Check whether the previous pull request review task has completed. ``` The same pattern works for other agents such as `docs-maintainer`, `release-notes`, or `test-runner`. The client sees a normal local MCP tool flow. Mesh Agents handles A2A task state, runtime execution, and artifact persistence. ## Autonomous Agents Autonomous agents should still use the same Mesh MCP and A2A surfaces as user-invoked agents. The difference is the trigger: instead of a developer typing "Code review this PR", a local loop notices work, prompts a coding harness, and lets that harness discover and call Mesh agents. A practical starting point is `opencode-loopd`. Run it from the project that should be monitored: ```bash opencode-loopd --project . --every 5m --prompt-file loop-prompt.md ``` `--project .` scopes the loop to the current checkout, `--every 5m` controls the polling interval, and `--prompt-file loop-prompt.md` gives the loop its standing instructions. The OpenCode environment should already have Mesh MCP configured, either by launching through `mesh-llm opencode` or by registering Mesh's MCP endpoint directly: ```text http://127.0.0.1:3131/mcp ``` For example, `loop-prompt.md` for the `pr-review` demo might say: ```markdown Check this repository for pull requests that are ready for review. When a PR needs review, use Mesh MCP to find a suitable pull request review agent, send the review task, wait for completion, and summarize the findings artifact. Do not create duplicate reviews for the same PR. ``` This keeps autonomy out of the Mesh Agents admin surface. Mesh does not need a separate scheduler to make the first demo useful: the loop is just another MCP client. It discovers agents, sends A2A tasks, polls task state, and reads artifacts using the same tools a human-driven coding client would use. ## Developing Build the CLI: ```bash cargo build -p mesh-agents ``` Run checks: ```bash cargo fmt --all -- --check cargo clippy --workspace --all-targets --locked -- -D warnings cargo test --workspace --locked cargo build --workspace --locked ``` The live OpenCode ACP tests are ignored by default because they require an installed and configured OpenCode ACP agent. Run the private two-node PR review smoke before a demo: ```bash MESH_LLM_BIN=/path/to/mesh-llm ./scripts/e2e-private-mesh-pr-review.sh ``` The script starts two isolated private mesh nodes, advertises `examples/pr-review` from node A, discovers it from node B through `http://127.0.0.1:<console>/mcp`, sends a remote A2A task, and verifies `summary.md` plus `findings.json` artifacts. ### Adding Runtime Support Runtime support is deliberately split between the public Agent Card and the private harness bridge. A2A remains the public contract. ACP is how Mesh starts local coding harnesses. Most new coding runtimes should start with the generic ACP runtime. If the runtime can serve an ACP-compatible stdio process, users can configure it without a Mesh code change: ```toml [runtime] type = "acp" command = "my-agent-harness" args = [ "acp", "--cwd", "{{ task.workspace }}", "--mcp", "{{ mesh.mcp_url }}" ] max_concurrent_tasks = 1 ``` Add a named runtime only when Mesh should provide a first-class preset, for example `runtime.type = "my_harness"` instead of asking every user to remember the command and ACP arguments. Named runtimes such as `opencode`, `goose`, and `pi` should still resolve to ACP command templates. The usual code path is: 1. Add a `RuntimeKind` variant in `crates/mesh-agents-a2a/src/registry.rs`. 2. Add the matching `AgentRuntimeArg` value in `crates/mesh-agents-cli/src/main.rs`. 3. Teach `crates/mesh-agents-cli/src/agents.rs` how `mesh-llm agents init --runtime <name>` writes the starter `runtime.toml`. 4. Add command resolution in `crates/mesh-agents-acp-bridge/src/lib.rs`, mapping the new runtime to an ACP stdio command and default args. 5. Make sure the preset still runs through the same substitution path as `type = "acp"`. 6. Add tests for default command selection, explicit `runtime.command` override behavior, substitutions, and generated agent config. Runtimes that are already full remote A2A services should not go through ACP. Use `runtime.type = "remote"` and let Mesh route to the external A2A endpoint described by the Agent Card. Workspace layout: ```text crates/mesh-agents-a2a/ A2A Agent Card loading, local registry, local service, and SQLite task store. crates/mesh-agents-acp-bridge/ ACP harness bridge, OpenCode command expansion, workspace setup, and task execution. crates/mesh-agents-cli/ User-facing CLI and MCP stdio server. skills/ Agent Skills installed by mesh-llm for clients such as Goose, Pi, OpenCode, Claude, and Codex. ``` ## Releasing Releases are built by GitHub Actions. Push a version tag: ```bash git tag v0.1.0 git push origin v0.1.0 ``` Or run the `Release` workflow manually with a `v*` version. The workflow builds and uploads: - `agents-x86_64-unknown-linux-gnu.tar.gz` - `agents-aarch64-apple-darwin.tar.gz` - `agents-x86_64-pc-windows-msvc.zip` Each archive is rooted under `agents/` and includes `plugin.toml`, the native `agents` executable, packaged `skills/`, `README.md`, and `LICENSE`.

저장소 README를 불러오는 중…

이 저장소의 skills

분류 대기 중

skill

직업 분류

설명

업데이트

mesh-agents

미분류

Use when discovering, inspecting, or delegating work to mesh-llm A2A agents through Mesh's MCP tools.

원문 언어: 영어

2026-05-28

mesh-agent-authoring

미분류

Use when creating, editing, or validating mesh-llm agent definitions, A2A Agent Cards, runtime.toml files, or ACP runtime command substitutions.

원문 언어: 영어

2026-05-28

수집된 skill 2개 중 2개를 표시합니다.