openrouter-subagents
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@openrouter-subagentswhy is the sky blue?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
openrouter-subagents
An MCP server and CLI that exposes a model-agnostic "subagent" tool backed by
OpenRouter — one API key, every model. It defaults to OpenRouter
Fusion (openrouter/fusion), which runs a panel of models in parallel and has a judge model
synthesize them into a single answer. Sibling to
gpt-subagents-api (OpenAI API key) and
gpt-subagents-subscription (ChatGPT
subscription), and it ships the same orchestration patterns system.
Note: Uses an OpenRouter API key (
Authorization: Bearer …) against the OpenAI-compatible Chat Completions endpoint. Not affiliated with or endorsed by OpenRouter.
Tools
Tool | What it does |
| Ask any OpenRouter model. |
| Orchestration patterns for driving the model well (see below). |
Fusion cost: a Fusion call bills for every panel model plus the judge. Reach for it when multiple perspectives are worth the spend (research, critique, high cost-of-being-wrong); for quick tactical prompts pass a single model id instead.
Reasoning & sampling controls
Any model's reasoning level can be set — the server exposes OpenRouter's full unified reasoning
object, and OpenRouter translates it into whatever the target model natively speaks (OpenAI/Grok
effort levels, Anthropic thinking budgets, Gemini thinkingLevel, Qwen thinking budgets, on/off
flags for models like DeepSeek/GLM). A level a model doesn't support is mapped to the nearest one
it offers.
Param | Meaning |
| Named level, lowest → highest: |
| Exact reasoning token budget (Anthropic/Gemini/Qwen-style) for fine-grained control. Mutually exclusive with |
| Turn default-strength reasoning on/off without picking a level or budget. |
| Model still reasons, but the reasoning tokens aren't returned in the response. |
| Sampling temperature, 0–2 (lower = more deterministic). Applied when the model supports it; OpenRouter drops it for models that don't. |
Note for agents (MCP or CLI): if a call fails or any error occurs — timeout, rate limit, model rejection, provider outage — retry the exact same call first (waiting briefly for transient errors). If it keeps failing, report the error and ask; do not downgrade or change the configuration the user set (model, reasoning level/budget, temperature, Fusion panel/judge) without their direct say-so. This rule is also baked into the server's MCP
instructionsand the CLI--help.
Related MCP server: agentloop
CLI
Everything the MCP server does is also available as a plain shell command — same client, same patterns library, but the answer comes back as raw text on stdout with zero JSON-RPC framing. For agents that can run shell commands, this is the token-cheap way to delegate: no MCP envelope in either direction, and piped stdin means large inputs (diffs, logs, files) never have to be echoed through the model's context at all.
npm run build # compiles dist/cli.js
npm link # optional: puts `openrouter-subagents` on your PATH
# ask (the subcommand is optional); raw answer on stdout
openrouter-subagents "why is the sky blue?"
openrouter-subagents ask -m anthropic/claude-haiku-4.5 -e xhigh "prove sqrt(2) is irrational"
# piped stdin becomes the prompt — or the context when a prompt is given
git diff | openrouter-subagents ask -p "review this diff for bugs" -e high
openrouter-subagents ask -p "summarize" --context-file big-report.md -m openai/gpt-5-mini
# patterns
openrouter-subagents patterns
openrouter-subagents pattern two-layer-cross-model-expertFlags mirror the MCP tool: -m/--model, -i/--instructions (defaults to a terse general-purpose prompt),
-p/--prompt, -c/--context (each with a --*-file variant), -e/--effort
(none…max), --reasoning-tokens <n>, --reasoning on|off, --hide-reasoning,
-t/--temperature, and Fusion's --analysis-models / --judge. --help shows the full
reference. Exit codes: 0 success, 2 usage error, 1 API/network error.
Orchestration patterns
Patterns are reusable playbooks (Markdown in patterns/) that describe how to drive
the expert tool — splitting work, bundling context, calling the expert, verifying its output against
ground truth, and aggregating. They're exposed via list_patterns (catalog) and
get_pattern("<name>") (full text), read from disk at call time (no rebuild to add one), and the
server's instructions nudge the agent to consult them before non-trivial expert work.
name | what it does |
Wrap the OpenRouter expert in verifying Claude subagents so the orchestrator only ever sees parallel, context-cheap, ground-truth-checked conclusions. (Fusion makes the "cross-model" premise even stronger — the expert is a whole panel of model families.) | |
Fan concrete work out to the OpenRouter worker ( |
Both patterns ship a rendered diagram under patterns/html/. See
patterns/README.md to add your own.
Setup
Requires Node 18+ (uses the global fetch) and an OpenRouter API key.
npm install
npm run build
cp .env.example .env # then put your key in .envGet a key at https://openrouter.ai/keys and set OPENROUTER_API_KEY in .env. .env is gitignored
and must never be committed — only .env.example is tracked.
Configuring a default Fusion panel + judge (optional)
By default, openrouter/fusion uses OpenRouter's built-in "Quality" preset. You can override that
default for every Fusion call from your .env:
# 1–8 panel models that answer in parallel:
OPENROUTER_FUSION_ANALYSIS_MODELS=anthropic/claude-opus-latest,openai/gpt-latest,google/gemini-pro-latest
# the judge that synthesizes them:
OPENROUTER_FUSION_JUDGE_MODEL=anthropic/claude-opus-latestPrecedence is per-call arg > .env default > OpenRouter preset, resolved independently for the panel
and the judge: a per-call analysis_models / judge_model on ask_openrouter overrides the matching
.env default, and these defaults apply only to openrouter/fusion (they're ignored for any other model).
Register with Claude Code
claude mcp add -s user openrouter-subagents -- node /absolute/path/to/openrouter-subagents/dist/server.js(Claude Code reads MCP registrations at startup, so a newly added server appears after a full restart.)
How it works
ask_openrouterbuilds an OpenAI-style Chat Completions request (system+usermessages).For
openrouter/fusion, the panel (analysis_models) and judge (judge_model) are resolved with precedence per-call arg >.envdefault > OpenRouter's preset, then sent as aplugins: [{ id: "fusion", … }]entry. With none of them set, OpenRouter's built-in Quality preset is used.The request is POSTed to
https://openrouter.ai/api/v1/chat/completionswithAuthorization: Bearer $OPENROUTER_API_KEY; the answer ischoices[0].message.content.Fusion is slow (parallel panel + synthesis), so the client uses a generous request timeout (~280s).
Security
The API key lives in
.env(gitignored everywhere); only.env.example(a placeholder) is tracked.Outbound
instructions/prompt/contextare run through a best-effort secret redactor (API keys, tokens, private keys) before they leave your machine — not a guarantee; don't paste highly sensitive data.Data boundary: with the default
openrouter/fusion, a single call fans your input out to several third-party providers at once (e.g. Anthropic, OpenAI, Google) via OpenRouter.Local agent/editor state (
.mempalace/,.claude/,CLAUDE.local.md, IDE folders) is gitignored.
License
MIT
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