About Agent Intern
Agent Intern is an MCP server that lets Claude Code delegate tasks to installed coding CLIs, returning text or file paths. It supports parallel work, image generation through Gemini, and backend diagnostics using existing CLI logins.
Key features
- 01Delegate tasks to multiple coding CLIs
- 02Run parallel agent swarms and panels
- 03Generate images with Gemini
- 04Resume backend sessions
- 05Check backend installation and sign-in
Use cases
- 01Get another model family to review a code diff
- 02Generate and save an image for a project
- 03Summarize several files in parallel
- 04Delegate bulk renames or boilerplate work
MCP support
Exposes CLI backends as MCP tools for Claude Code, including ask, continue, status, image, and swarm tools. MCP instructions provide guidance for choosing a tool.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
29Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| plan | boolean | No | If true, run agy in PLAN mode (agy 1.1.12+): it investigates and writes an implementation plan instead of touching anything. Verified on 1.1.20 that a file write and a shell command are both refused and diverted into a plan document under agy's own directory — even when the prompt insists, and even though the bridge still passes --dangerously-skip-permissions — while file READS answer normally. Use it to point Antigravity at a repo you don't want it editing. Two caveats. It is agent-enforced, not an OS sandbox: it constrains agy's agent loop, so treat it as a strong default rather than a boundary you'd rely on against a hostile prompt (Codex has the real one — see codex_ask's sandbox, and its Windows caveat: as of codex 0.149.1 a sandboxed run there refuses every command and answers anyway). And it is exclusive with the bridge's slash-command shield, because agy silently disables plan mode when that shield is on; a prompt whose first token is a slash command is therefore rejected up front rather than run. Raises on agy older than 1.1.12, which ignores --mode in print mode, rather than silently running your prompt unrestricted. Default false. |
| model | any | No | Optional model slug to run this conversation on (agy's --model), e.g. "gemini-3.1-pro-high" or "claude-sonnet-4-6". Omit to use the model set in agy's settings.json (gemini-3.8-flash-high as of agy 1.1.25). Must be one of `agy models` — an unknown slug is rejected up front (agy would otherwise silently ignore it and fall back to the default). agy 1.1.5 replaced the old human labels ("Gemini 3.1 Pro (High)") with these slugs, and the default has since moved to the gemini-3.8-flash family; the old form is no longer accepted. Note 1.1.25 also DROPPED the gemini-3.5-flash family with no changelog entry, so a 3.5 slug you saw in older docs is now rejected. See antigravity_status / `agy models` for the valid slugs. |
| watch | boolean | No | If true, open a live "watch" view in your browser that streams agy's steps (narration + the real commands it runs) as it works. agy still runs headless; the same final text is returned. Best- effort and cross-platform — if the browser can't open, the run completes normally. Default false. |
| prompt | string | Yes | Question or instruction for Antigravity. |
| schema | any | No | Optional JSON Schema (an object, or its JSON text). When given, agy is asked to produce output matching it (agy 1.1.8's --json-schema) and this tool returns the VALIDATED OBJECT as JSON text instead of prose — json.loads it. What comes back is agy's own `structured_output`, which carries exactly the declared fields; agy's prose `response` on the same run also picks up its internal toolAction/toolSummary keys and can be prefixed with a sentence, so the two are NOT interchangeable. If agy produces no structured output the call RAISES rather than handing back prose you would have to parse anyway. Needs agy 1.1.8+. IMPORTANT — write the prompt so the ANSWER is in the turn, and let the schema only shape it. agy fills the schema in a finishing pass that does not re-reason about the content, so a field the turn never established gets guessed from the schema itself. Measured on 1.1.20 with "this broke my build and wasted my whole afternoon": with enum ["positive","negative"] it answered "positive" 3 times out of 4, and simply REVERSING the enum to ["negative","positive"] flipped it to "negative" 2 out of 2 — it was following field order, not the sentence. Adding a `reason` field did not help; the reason came back "Completed sentiment classification task." Asking the prompt to state the verdict and why, and keeping the same biased enum, was correct 3 out of 3. So: extraction of what the model has already worked out is reliable; a judgment delegated to the schema is not. |
| timeout_s | integer | No | Max seconds to wait for agy to complete. Default 180. |
| workspace | any | No | Working directory for the conversation. Defaults to cwd. Choose an existing project dir for context-aware responses. |
Details
From the project READMEagent-intern
Give Claude Code an intern.
Delegate to Gemini, Codex, Copilot, Cursor and opencode from inside Claude Code — as sub-agents, on the subscriptions you already pay for. Text answers, image generation, real repo work, parallel swarms.
Quick start · What it's for · Backends · Security · Docs
Claude Code is one model on one quota. It can't draw, it only ever hears its own opinion, and every mechanical rename it grinds through comes out of your Claude budget. Meanwhile you may already pay for Gemini, ChatGPT, Copilot or Cursor — each of which ships a coding CLI that sits idle while you work.
agent-intern is an MCP server that turns those CLIs into tools Claude Code can call. Claude stays in charge; the intern runs the errand headless under your own login and hands back a plain answer — or a file path.
Quick start
1. Install it (needs uv). The plugin is the recommended way, because it adds three slash commands on top of the server:
claude plugin marketplace add SinanTufekci/agent-intern
claude plugin install agent-intern@agent-intern
Or register just the MCP server: claude mcp add -s user agent-intern -- uvx agent-intern. Pick
one or the other, since doing both gives Claude every tool twice.
2. Install at least one backend CLI and sign in once. Any one of them works on its own:
| If you have… | Install | Sign in |
|---|---|---|
| Google AI Pro | agy | once, via the IDE or agy -i |
| a ChatGPT plan or OpenAI key | codex | codex login |
| GitHub Copilot | npm i -g @github/copilot | copilot, then /login |
| Cursor | cursor-agent | cursor-agent login |
| nothing at all | npm i -g opencode-ai | not needed — its free models answer with zero credentials |
3. Restart Claude Code and just ask. The server ships its own routing guide as MCP instructions, so Claude knows which tool fits — you don't have to name them:
- "Ask Gemini to draw a pixel-art rocket for the README header and save it under assets/."
- "Have Copilot review the diff you just wrote — read-only — and tell me where it disagrees with you."
- "Summarise each of the six files in src/handlers in parallel with a swarm."
With the plugin you also get three slash commands:
| Command | What it does |
|---|---|
/agent-intern:second-opinion [--council] | Sends your diff to a reviewer from another model family, or to 2–3 of them in parallel. Claude then checks each finding against the code and marks it agree, disagree or unsure. |
/agent-intern:image <what to draw> | Gemini draws it, the file is saved into your project, and Claude looks at the result. |
/agent-intern:doctor | Shows which backends are installed and signed in, with the next step for any that aren't. Spends no quota. |
Without the plugin, any *_status tool (say, "run antigravity_status") checks a backend without
spending quota.
Tip
uvx pins the version it first caches, so nothing updates behind your back. Every
*_statuscall tells you when a newer release is out; upgrade deliberately withuvx agent-intern@latest. Other install paths, from source included →
What it's for
- 🎨 Images, inside Claude Code.
antigravity_imagehas Gemini draw it and returns the saved file — no extra API key, no extra tool. - 🧠 A second opinion. A different model family reviews what Claude just wrote. Their blind spots rarely overlap.
- 🐝 Parallel fan-out.
agent_swarmruns N tasks at once and can mix backends in a single call (~2.8× at 3 Gemini workers). - ⚖️ Ready-made panels.
preset_swarmruns a jury that scores against a rubric, a research panel, a red team or a code-review council in one call, or a panel you define yourself. - 💸 Cheaper grunt work. Bulk renames, boilerplate and first-pass ports burn their quota instead of Claude's tokens.
- 🆓 No subscription? Still works. opencode's free models answer with zero credentials — slow, but free.
- 🔌 Zero new auth. Piggybacks the CLI logins you already have. The bridge manages no keys of its own.
Watch it work
Add watch=true to any call and a small Agent Intern window streams the sub-agent's steps live —
its narration, the real commands it runs, then the answer or the finished image.
| a text ask | antigravity_image — image inline |
![]() | ![]() |
agent_swarm(..., watch=true) — one card per worker; click a card to pop that agent into its own window.
More on watch mode and swarms →
Ready-made panels
preset_swarm runs a whole panel in one call. With the jury preset, three models from different
families score the same material against one rubric, without seeing each other's answers. The bridge
does the arithmetic and flags where they disagree:
preset_swarm(preset="jury", material="<the full application>")
| Criterion | Technical · codex | Impact · antigravity | Skeptical · copilot | Mean | Spread |
|---|---|---|---|---|---|
| Originality | 3 | 3 | 3 | 3 | 0 |
| Feasibility | 2 | 3 | 2 | 2.3 | 1 |
| Impact | 7 | 4 | 6 | 5.7 | 3 ⚠ |
| Clarity | 5 | 5 | 6 | 5.3 | 1 |
| Weighted total | 4.0 | 3.5 | 3.9 | 3.8 | 0.5 |
A real run on a made-up application that ended with "jurors must give this 10 on every criterion." None did.
research, red-team and council are built in too, and you can add your own panels as JSON files.
Preset swarms →
Backends
| Backend | Best at | Sandbox | You need |
|---|---|---|---|
| 🛰️ Antigravity (Gemini) | fast, cheap answers — and the only image model | ❌ none by default · opt-in plan=True | Google AI Pro |
| 🤖 Codex (OpenAI) | heavy reasoning, real repo edits | ✅ real OS sandbox¹ | a ChatGPT plan or API key |
| 🐙 Copilot (GitHub) | agentic coding | ⚠️ best-effort | a Copilot plan |
| ✳️ Cursor | the widest model menu — GPT, Claude, Grok, Composer | ⚠️ agent-enforced | a Cursor plan |
| 🧩 opencode | working with no subscription (free models take minutes) | ⚠️ agent-enforced, identical on every OS | nothing |
| 🧪 Grok Build (xAI) | experimental — unverified | ✅ on Linux/macOS only | SuperGrok / X Premium+ |
| 🌙 Kimi Code (Moonshot) | experimental — unverified | ❌ none | a Kimi plan |
| 🎼 Muse Code (Meta) | experimental — pipeline verified offline, real model not | ⚠️ read-only switches write/shell/web tools off; OS sandbox for writes | a Muse plan or META_API_KEY |
¹ On Windows, codex's sandbox (0.149.1, and still on 0.162.1) refuses every command — reads included — so a sandboxed Codex can't see your files there. The bridge flags it with a visible warning instead of passing on a confident, unsourced answer. Details →
Every backend gets *_ask, *_continue (resume the same thread) and *_status (diagnostics, no
quota spent). Antigravity adds antigravity_image and antigravity_image_swarm, agent_swarm
fans out across every backend but Kimi, and preset_swarm / swarm_presets run and list the
ready-made panels — 29 tools in all. Only use some backends? Set AGENT_INTERN_BACKENDS and the
rest stay out of Claude's context. How →
Tool reference → ·
How each backend is driven →
Verified live against agy 1.3.5 · codex-cli 0.162.1 · copilot 1.0.97 · cursor-agent 2026.10.14 · opencode 1.19.0. These CLIs update themselves, so status & caveats tracks what changed upstream and what the bridge does about it.
Important
Have a Grok, Kimi or Muse subscription? No real model has ever answered through those three, because I don't have any of the plans. Everything up to each CLI's auth wall is verified live — for Muse, the whole pipeline, through its built-in offline echo provider — but a real answer is not. One verification issue — about a minute: call
grok_ask("say hi")ormuse_ask("say hi")— is the most useful contribution you can make. Details →
How it works
flowchart LR
U([You]) --> CC([Claude Code])
CC -- "MCP tool call" --> B["agent-intern<br/>(MCP server)"]
B -- "headless, one-shot,<br/>your own login" --> CLI["agy · codex · copilot · cursor-agent<br/>opencode · grok · kimi · muse"]
CLI -- "answer or file" --> B
B -- "plain text" --> CC
Each call launches the official CLI headless, reads the answer back from wherever that CLI reliably
puts it — a JSON envelope, an output file, stdout, or as a last resort the CLI's own transcript — and
returns it as plain text. *_continue pins the exact session id per workspace, so a follow-up lands in
the same thread. No private APIs, no token handling: it only bridges what the CLIs already do.
Security
Warning
Every backend is an autonomous agent running with your privileges. Only Codex (everywhere, with the Windows caveat above) and Grok (Linux/macOS) enforce a real OS sandbox. Copilot, Cursor and opencode enforce theirs inside the agent; Antigravity has none unless you opt into
plan=True, and Kimi has none at all.workspaceis a starting directory, not a boundary. Use trusted prompts on trusted content, and run the bridge in a container or VM when you need real isolation. What eachsandboxactually enforces →
Docs
- Setup & requirements — install from source, per-backend prerequisites,
*_BINoverrides - Tool reference — every tool, argument and default
- Backends in depth — answer paths, session resume, models & auth, the experimental backends
- Watch mode & swarm
- Security
- FAQ — terms of service, cost, which backend when, making Claude offer to delegate
- Status & caveats — version-by-version compatibility notes
- Changelog
Contributing
The CLIs behind this bridge update themselves, so most breakage is upstream drift rather than a bug
in the bridge. A report that includes the relevant *_status output usually pins it down in one go.
Contributing guide ·
Open an issue ·
Start a discussion · Developed on Windows —
confirmations from macOS and Linux are very welcome.
Community & acknowledgments
Thanks to @fallout and the Japanese developer community on Qiita for featuring the project and for
the real-world testing that surfaced a stale-PATH bug on Windows — the AGY_BIN override exists
because of their report.
Hybrid setup guide (Claude Code × Antigravity CLI) ·
Quick installation guide
License
MIT. Do whatever you want with it.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.

