connect-agents
Mesh-LLM/mesh-llm/.agents/skills/connect-agents/SKILL.md
Use this skill when connecting agent tools or OpenAI clients to mesh-llm — launching or configuring Goose, Claude Code, OpenCode, Pi, curl, or any OpenAI-compatible client against a local or remote mesh, picking a model, or validating tool-call reliability.
Skill3.5k starsChanged 45 days ago
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What's in it
- connect-agents
- Mental model
- Built-in launchers (preferred)
- Manual config (any OpenAI client)
- Validating agent behavior
- Blackboard (cross-mesh agent coordination)
- Gotchas
- Config-only Hermes and OpenClaw
---
name: connect-agents
description: Use this skill when connecting agent tools or OpenAI clients to mesh-llm — launching or configuring Goose, Claude Code, OpenCode, Pi, curl, or any OpenAI-compatible client against a local or remote mesh, picking a model, or validating tool-call reliability.
metadata:
short-description: Connect agents and OpenAI clients to mesh-llm
---
# connect-agents
Use this when pointing an agent harness or any OpenAI client at a running
mesh-llm node. Full reference: `mesh/docs/AGENTS.md`.
## Mental model
- Use `http://<host>:9337/v1` only for loopback hosts (`localhost`, `127.0.0.1`,
or `::1`). For non-loopback traffic, use `https://<host>:9337/v1`, an SSH
tunnel that terminates at a loopback endpoint, or trusted private-network
isolation; never send cleartext HTTP to an untrusted remote host.
- `GET /v1/models` lists everything reachable (local + mesh peers); requests
route by the `model` field.
- Special model ids: `auto` lets the mesh pick; `mesh` engages the
mixture-of-agents path. Otherwise use an exact id from `/v1/models`.
- For coding agents, pick a tool-capable model. If `--model` is omitted, the
built-in launchers pick the strongest tool-capable model available.
## Built-in launchers (preferred)
mesh-llm launches the major agent CLIs with config injected for you:
```bash
mesh-llm goose [--model <id>] # writes ~/.config/goose/custom_providers/mesh.json
mesh-llm claude [--model <id>]
mesh-llm opencode [--model <id>] [--host <h>] # injects OPENCODE_CONFIG_CONTENT (no file edits)
mesh-llm pi [--model <id>] [--host <h>] # writes ~/.pi/agent/models.json
```
- `goose`/`claude` reuse a local mesh on the chosen `--port`.
- `opencode`/`pi` target `--host` (default `127.0.0.1:9337`) and auto-start a
local client only for loopback targets; the auto-started node is cleaned up
when the harness exits.
- `mesh-llm pi --write` / `mesh-llm opencode --write` update config without
launching (use `--host` for remote meshes).
- Agent launch commands also install available plugin skills for that agent
(`mesh-llm skills install` does it standalone).
## Manual config (any OpenAI client)
For a loopback node use `http://127.0.0.1:9337/v1`; for a remote node use
`https://<host>:9337/v1`, an SSH tunnel, or trusted private-network isolation.
Keep the `/v1` path and use any non-empty API key:
```bash
export GOOSE_PROVIDER=openai GOOSE_MODEL="<id-from-v1-models>"
export OPENAI_HOST="http://127.0.0.1:9337" OPENAI_API_KEY="mesh"
```
```bash
curl -s http://localhost:9337/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{"model":"auto","messages":[{"role":"user","content":"hello"}]}'
```
Exact manual provider JSON for OpenCode and Pi is in `mesh/docs/AGENTS.md`.
## Validating agent behavior
Direct API contract probe (tool-call forcing, streaming reconstruction):
```bash
scripts/qa-agent-tool-call-reliability.py \
--base-url http://127.0.0.1:9337/v1 --models auto,mesh --attempts 3 \
--output target/agent-tool-call-reliability/results.jsonl
```
Broader harness (models, chat, streaming, plus optional Goose/OpenCode/Pi
smokes): `scripts/qa-nightly-stability.py` — see `mesh/docs/AGENTS.md`. Use
`--print-plan` on either script for a side-effect-free preview.
## Blackboard (cross-mesh agent coordination)
Agents can share status/questions across the mesh via the blackboard plugin —
even from a client-only node:
```bash
mesh-llm plugins install blackboard
mesh-llm blackboard "STATUS: [org/repo branch:main] refactoring billing module"
mesh-llm blackboard --search "QUESTION"
```
MCP access: the management endpoint `http://127.0.0.1:3131/mcp` exposes
`blackboard_post`, `blackboard_search`, `blackboard_feed`. Posts are visible to
every peer — never post secrets, credentials, private paths, or customer data.
## Gotchas
- Use a base URL ending in `/v1`; prefer chat-completions over the Responses
API unless the client documents Responses support.
- Model ids must match `/v1/models` exactly (they can contain spaces — quote
them).
- An empty `/v1/models` usually means the model is still loading or no mesh was
joined yet — check `/api/status` on `:3131` (see `mesh-join`).
- The response `"model"` field tells you which node/model actually answered.
## Config-only Hermes and OpenClaw
`mesh-llm hermes --write` and `mesh-llm openclaw --write` add a named Mesh
provider without changing the default or launching anything. They require a
running endpoint; use `--host`, `--model` (default `auto`), `--config-path` for
custom profiles, and `--context-length` to lower the serving-derived budget.
OpenClaw selects `mesh/auto`; Hermes selects provider `mesh`, model `auto`.
Existing files are backed up; formatting/comments normalize, and conflicts or
includes are refused. See `docs/CLI.md` for schema requirements and context caveats.
More agent context in Mesh-LLM/mesh-llm
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