gpt-subagents
Integrates OpenAI's GPT models (Codex and Architect) as subagents that can be called for coding, debugging, testing, reasoning, and architecture tasks.
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., "@gpt-subagentsUse the two-layer pattern for cross-model verification of my refactoring plan."
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.
gpt-subagents-api
An MCP server that lets Claude Code delegate to OpenAI "expert" models as subagents — and ships a small, extensible library of orchestration patterns that teach the calling agent how to use those experts well.
Claude orchestrates; GPT gives a second opinion from a different model family (different blind spots). The patterns make that second opinion parallel, context-cheap, and verified instead of blindly trusted.
The subagent tools
Tool | Model | Use it for |
| Caller-selected (e.g. gpt-5.3-codex) | Routine coding — patches, debugging, tests, repo inspection, concrete edits. Cheaper and faster. |
| Caller-selected (e.g. gpt-5.5, high reasoning) | Hard reasoning, architecture decisions, security / threat modeling, review of large or high-risk changes. |
Both tools require a model parameter — pass any valid OpenAI model id. Suggested defaults are
shown above, but the server does not hardcode any model.
⚠️ Expert reasoning models can be confidently wrong. Treat
ask_gpt_architectoutput as a hypothesis and verify claims against real files, docs, and tests before acting. The same caution applies to any review or audit — use the orchestration patterns below to make verification automatic.
Both tools take a task/question plus optional context. Inbound context is run through a
sanitizeContext pass that redacts obvious secrets (OpenAI/Anthropic keys) before it leaves your
machine — but don't rely on it as your only safeguard; avoid pasting secrets.
Related MCP server: Task Agents
Orchestration patterns
Patterns are reusable playbooks (Markdown files in patterns/) that describe how to
drive the expert tools — splitting work, bundling context, calling the expert, verifying its
output against ground truth, and aggregating results.
Two tools expose them to the agent:
list_patterns— catalog of every pattern (name, title, summary, when to use).get_pattern("<name>")— the full text of one pattern.
Patterns are read from disk at call time, so adding or editing one needs no rebuild. The
server's startup instructions nudge the agent to consult patterns before any non-trivial
ask_gpt_architect work — or any review, audit, or large-document analysis.
Shipped patterns
name | what it does |
Wrap the GPT expert in verifying Claude subagents so the orchestrator only ever sees parallel, context-cheap, ground-truth-checked conclusions. |
The two-layer pattern also ships a rendered, styled diagram at
patterns/html/two-layer-cross-model-expert.html — open it in a
browser for the visual walkthrough.
See patterns/README.md to add your own.
Setup
Requirements: Node 18+ and an OpenAI API key.
# 1. Install dependencies
npm install
# 2. Add your key (this file is gitignored and must never be committed)
cp .env.example .env
# then edit .env and set OPENAI_API_KEY=sk-...
# 3. Build
npm run buildThis compiles to dist/. The server loads .env from the project root (one level up from
dist/server.js), or falls back to an inherited OPENAI_API_KEY in the environment.
Register with Claude Code
claude mcp add gpt-subagents-api -- node /absolute/path/to/gpt-subagents-api/dist/server.jsOr add it to your MCP client config manually:
{
"mcpServers": {
"gpt-subagents-api": {
"command": "node",
"args": ["/absolute/path/to/gpt-subagents-api/dist/server.js"]
}
}
}Once connected, the server advertises four tools: ask_gpt_worker, ask_gpt_architect,
list_patterns, and get_pattern.
Project layout
gpt-subagents-api/
├── server.ts # MCP server: tool defs + server instructions
├── gptAgents.ts # OpenAI calls (worker + architect) and secret sanitization
├── patterns.ts # Loads/parses pattern Markdown from patterns/
├── patterns/ # Orchestration patterns (one Markdown file each)
│ ├── README.md
│ └── two-layer-cross-model-expert.md
├── .env.example # Placeholder; copy to .env (gitignored)
└── dist/ # Build output (gitignored)Security notes
.envis gitignored and never tracked — only the.env.exampleplaceholder is committed. Local agent/editor state (.mempalace/,.claude/,CLAUDE.local.md, IDE folders) is gitignored too, so dev-environment data doesn't leak into the repo.sanitizeContextredactssk-…keys andOPENAI_API_KEY=/ANTHROPIC_API_KEY=assignments from outbound context. It's a backstop, not a guarantee — keep secrets out of prompts.Verify expert output. Expert reasoning models are powerful but can be confidently wrong; the
two-layer-cross-model-expertpattern is the recommended way to act onask_gpt_architectoutput safely.
License
ISC
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