Claude Code × Antigravity + Codex + Copilot + Cursor — MCP Bridge

Drive four external coding CLIs — Google's Antigravity (Gemini 3.5 Flash), OpenAI Codex, the GitHub Copilot CLI, and Cursor — as sub-agents inside Claude Code. Text answers, image generation, real repo work, and parallel swarms, on quota you already pay for.

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One MCP server, four backends. It exposes Google Antigravity, OpenAI Codex, the GitHub Copilot CLI, and Cursor to Claude Code as clean MCP tools so you can delegate work to a different model family mid-task — without leaving your terminal, and on the subscriptions you already have. Each backend is independent: install one, two, three, or all four.

• 🛰️ Antigravity (agy, Gemini 3.5 Flash High). Fast, cheap tool-calling — and the only backend with an image model. Its headless print mode (agy -p) historically had a stdout bug: it wrote the answer to the controlling terminal instead of its stdout, so anything capturing stdout got nothing (and, under a TUI, agy's text leaked into the host's prompt). agy 1.0.15 fixed this on Windows — -p now writes the clean answer to stdout — so the bridge prefers stdout and falls back to reading agy's own transcript files only when stdout is empty (older agy, non-Windows, or --sandbox runs). It still detaches agy from the terminal so older versions can't leak.
• 🤖 Codex (codex exec, OpenAI). A strong reasoner for real code/repo work. It writes its final message straight to a file the bridge asks for (no scraping), supports model selection, and has a real, enforced sandbox.
• 🐙 Copilot (copilot -p, GitHub). GitHub's agentic coder. Stdout-native like Codex (-s prints just the answer), with model selection (--model), a best-effort tool/path permission knob, and a deterministic resume mechanism (the bridge sets each session's UUID itself).
• ✳️ Cursor (cursor-agent -p, Cursor). Cursor's agentic coder, with the widest model menu — GPT, Claude, Grok, and Composer via --model (validated against cursor-agent models). Stdout-native like Codex/Copilot (--output-format text prints just the answer), an agent-enforced sandbox (read-only via --mode ask), and a deterministic resume mechanism (the bridge mints each chat's id itself via create-chat). No image model.

All four share the same niceties: a *_continue to resume a thread, a live "watch" window to see the agent work, a unified agent_swarm that runs many tasks in parallel across all backends at once, and *_status diagnostics that spend no quota.

Warning

This runs unsandboxed code with your privileges. agy -p auto-executes its tools (read/write files, run shell commands, reach the network) with no usable approval gate — its --sandbox blocks only shell commands, leaving file writes and network egress wide open. codex exec also runs autonomously, but its sandbox flag (default read-only) is a real, enforced boundary. copilot -p runs headless with --allow-all-tools; its sandbox maps to best-effort tool/path permissions (read-only denies the local write/shell tools) — safer than agy, but not an OS sandbox like Codex's. cursor-agent -p runs headless with --trust (and --force for writes); its sandbox is agent-enforced (read-only = --mode ask, which makes the write/shell tools unavailable) — best-effort like Copilot, not an OS sandbox. In all four cases the workspace argument is a starting context, not a security boundary. Only use these with trusted prompts on trusted content; for real isolation, run the bridge inside a container or VM. Full details →

Why you'd want this

🧠 Second opinion	Ask a different model family — Gemini or GPT — mid-task without switching tools.
🎨 Image generation	Have Gemini draw an image and get the saved file back — no extra API key or image tool.
🛠️ Real coding sub-agent	Hand a focused repo task to Codex with a real workspace-write sandbox.
💸 Cheap delegation	Burn Antigravity / Codex quota on grunt work instead of Claude tokens.
🐝 Parallel fan-out	Run N tasks at once, mixing Gemini and Codex workers in a single swarm.
📁 Cross-repo reads	Point a worker at another project directory and let it read/answer there.
🔌 Zero new auth	Piggybacks the logins you already did — no keys for the bridge to manage.

The four backends at a glance

The bridge normalizes all four CLIs into the same shape, but they differ where it matters. Pick per task:

	🛰️ Antigravity (agy)	🤖 Codex (codex exec)	🐙 Copilot (copilot -p)	✳️ Cursor (cursor-agent -p)
Model	Selectable via model (agy's --model); Gemini 3.5 Flash (High) default (see Model & auth)	Selectable via model (codex's -m)	Selectable via model (--model)	Selectable via model (--model), validated against cursor-agent models
Best at	Fast, cheap tool-calling; quick answers	Heavier reasoning; real code/repo work	Agentic coding; real code/repo work	Agentic coding; wide model menu (GPT/Claude/Grok/Composer)
Image generation	✅ antigravity_image (+ antigravity_image_swarm)	❌ no image model	❌ no image model	❌ no image model
Sandbox	❌ no real boundary (--sandbox blocks only shell)	✅ real, enforced: read-only / workspace-write / danger-full-access	⚠️ best-effort: tool/path permissions (read-only denies write/shell) — not an OS sandbox	⚠️ agent-enforced: mode/force (read-only = --mode ask, write/shell tools unavailable) — not an OS sandbox
How the answer is read	stdout on agy 1.0.15+ (Windows); else scraped from transcript.jsonl	Written to a file via -o/--output-last-message	stdout (-s silent mode)	stdout (--output-format text)
Continue mechanism	Pins the workspace's conversation id (--conversation)	Resumes the session id (codex exec resume <id>)	Resumes a self-set session UUID (--session-id)	Mints a chat id (create-chat) and resumes it (--resume <id>)
Auth	OS credential store (AI Pro session)	codex login (ChatGPT account or API key)	OS credential store (copilot login) or a GitHub token env	cursor-agent login (OS credential store) or CURSOR_API_KEY
In a swarm	Runs with an isolated HOME to avoid state races	Fresh one-shot — needs no isolation	Fresh one-shot — needs no isolation	Fresh one-shot — needs no isolation

How it works

All four backends run headless and one-shot per call; the bridge's job is to get a clean answer out of each and hand it to Claude Code as a plain string.

flowchart LR
    A([Claude Code]) -- "MCP tool call" --> B["bridge<br/>(server.py)"]
    B -- "antigravity_*" --> C[agy -p]
    B -- "codex_*" --> D[codex exec]
    B -- "copilot_*" --> E[copilot -p]
    B -- "cursor_*" --> F[cursor-agent -p]
    C -- "stdout (1.0.15+)<br/>or transcript.jsonl / .db" --> B
    D -- "output-last-message file" --> B
    E -- "stdout (-s silent)" --> B
    F -- "stdout (--output-format text)" --> B
    B -- "plain text" --> A

Loading

Antigravity. On agy 1.0.15+ (Windows), agy -p writes its clean final answer straight to stdout, and the bridge returns that directly. On older agy — or non-Windows, or a --sandbox run — stdout stays empty and the bridge falls back to agy's own transcript at:

~/.gemini/antigravity-cli/brain/<conv-id>/.system_generated/logs/transcript.jsonl

For the fallback it locates the conversation via cache/last_conversations.json (falling back to the newest brain/ directory touched since launch), streams the transcript, and returns the final source=MODEL, status=DONE, type=PLANNER_RESPONSE entry — the answer, minus the intermediate tool-calling steps (or the SQLite .db agy dual-writes, when no JSONL exists). Either way, antigravity_continue pins the workspace's exact conversation id via --conversation, so it never resumes the wrong thread.

Codex. codex exec is well-behaved: the bridge passes -o/--output-last-message <file> and codex writes its final message straight there — no scraping. Continue works by capturing the session id from codex's own rollout files (~/.codex/sessions/.../rollout-*.jsonl) and resuming with codex exec resume <id>, falling back to the newest on-disk session for that cwd after a server restart.

Copilot. copilot -p "<prompt>" -s runs a prompt non-interactively and prints the clean final answer to stdout — the bridge reads it there, no scraping. It runs headless with --allow-all-tools --no-ask-user --no-auto-update (so it never blocks on a prompt), and disables copilot's flaky builtin GitHub-API MCP by default for predictable latency (COPILOT_GITHUB_MCP=1 re-enables it). Continue is deterministic: copilot's --session-id <uuid> both sets a new session's id and resumes an existing one, so the bridge generates the UUID itself, pins it to the workspace, and resumes that exact session — falling back after a restart to the newest on-disk session (~/.copilot/session-state/<id>/workspace.yaml) whose recorded cwd matches.

Cursor. cursor-agent -p --output-format text --trust "<prompt>" runs a prompt non-interactively and writes the clean final answer straight to stdout — the bridge reads it there, no scraping (--trust trusts the workspace so it never blocks on a prompt). Continue is deterministic and race-free: cursor-agent create-chat mints a fresh chat and prints its id, so the bridge mints the id itself, pins it to the workspace, and resumes that exact chat with -p --resume <chatId> — no rollout-scraping. After a restart it falls back to the newest on-disk chat under ~/.cursor/chats/<md5(workspace)>/<chat-id>/ whose meta.json cwd matches (the chat-dir hash is itself md5 of the workspace path).

Set up in 60 seconds

Prerequisites — install whichever backend(s) you want, and sign in once each:

• Antigravity: install agy and sign in to Antigravity once (via the IDE or agy -i).
• Codex: install codex and run codex login once (ChatGPT account or API key).
• Copilot: install copilot (npm i -g @github/copilot, or winget install GitHub.Copilot) and run copilot then /login once (or set a COPILOT_GITHUB_TOKEN/GH_TOKEN env var).
• Cursor: install cursor-agent (curl https://cursor.com/install -fsSL | bash) and run cursor-agent login once (or set a CURSOR_API_KEY env var).

You don't need all four — the tools for a missing CLI simply report "not found" via their *_status tool.

Recommended — no clone, you control updates

With uv installed, register the bridge straight from PyPI under mcpServers in ~/.claude.json — no path to hardcode, no git pull to remember:

"agent-intern": {
  "command": "uvx",
  "args": ["agent-intern"]
}

uvx pins to the version it first caches and does not auto-upgrade, so you never run an update you didn't choose — important, since the bridge runs unsandboxed code: a surprise (or compromised) release can't execute until you opt in. When the startup check warns that a newer release is out, upgrade deliberately and restart Claude Code:

uvx agent-intern@latest      # fetch + run the newest release (refreshes uv's cache)

Tip

Prefer hands-off auto-updates? Put "args": ["agent-intern@latest"] in the config instead — every launch runs the newest release. Convenient, but it pulls new code without asking each time.

From source

Clone it instead if you want to hack on the bridge or pin a local copy:

git clone https://github.com/SinanTufekci/agent-intern.git
cd agent-intern
pip install fastmcp
python test_smoke.py        # 4 real round-trips (ask, continue, image, swarm) — prints four PASS lines

Note

The smoke test costs a tiny bit of quota and takes ~30–60 s. It exercises the Antigravity path.

Then point Claude Code at the absolute path to server.py under mcpServers in ~/.claude.json:

Windows	macOS / Linux
"agent-intern": { "command": "python", "args": ["C:\\path\\to\\server.py"] }	"agent-intern": { "command": "python3", "args": ["/path/to/server.py"] }

Restart Claude Code. Fifteen tools appear, each prefixed mcp__agent-intern__:

• Antigravity (5): antigravity_ask, antigravity_continue, antigravity_image, antigravity_image_swarm, antigravity_status
• Codex (3): codex_ask, codex_continue, codex_status
• Copilot (3): copilot_ask, copilot_continue, copilot_status
• Cursor (3): cursor_ask, cursor_continue, cursor_status
• Shared (1): agent_swarm — fans a list of tasks out across all four backends in one run

The single-prompt tools — Antigravity, Codex, Copilot, and Cursor — take a watch=true flag for the live browser view (Watch mode).

Note

Your client learns how to use the bridge on its own. The server ships MCP instructions — a short routing guide (when to reach for each tool, which backend to pick, and to pass workspace so the sub-agent has repo context) that a client like Claude Code injects into the model's context on connect, as an "MCP Server Instructions" block. So the host model knows how and when to drive these tools without you explaining them — you can just ask for the result.

"Use antigravity_ask to summarize the README of this repo in three bullets." → Claude routes the prompt through the bridge, agy reads the file under the workspace root, and the answer comes back as a plain string. Swap in codex_ask, copilot_ask, or cursor_ask to have GPT, Copilot, or Cursor do the same.

Tools

🛰️ Antigravity

Tool	Purpose
antigravity_ask(prompt, workspace?, model?, timeout_s?=180, watch?=false)	Start a new Antigravity conversation. model selects the model (agy's --model, e.g. "Claude Sonnet 4.6 (Thinking)"); validated against agy models, defaults to your settings.json model. watch=true opens the live browser view (Watch mode).
antigravity_continue(prompt, workspace?, model?, timeout_s?=180, watch?=false)	Continue the conversation rooted at workspace (pinned by id). agy's model is per-invocation, so model can differ from the original ask. watch=true opens the live view.
antigravity_image(prompt, output_path?, workspace?, timeout_s?=240, watch?=false)	Generate an image; saves the file (extension corrected to the real bytes) and returns its path + format/size. watch=true streams progress and shows the image inline.
antigravity_image_swarm(prompts, output_paths?, workspaces?, max_concurrency?=4, timeout_s?=240, watch?=false)	Generate several images in parallel (one worker per prompt).
antigravity_status()	Setup diagnostics: the bridge's own version + whether a newer release is available, plus agy version/compat, state dirs, and newest-transcript readability. Spends no quota.

🤖 Codex

Tool	Purpose
codex_ask(prompt, workspace?, sandbox?="read-only", model?, timeout_s?=180, watch?=false)	Start a new Codex session. sandbox is a real boundary (see Codex bridge); model selects the model (-m). watch=true opens the live view, streaming codex's steps from its --json event stream.
codex_continue(prompt, workspace?, timeout_s?=180, watch?=false)	Continue the Codex session rooted at workspace — resumes the exact session id, falling back to the newest on-disk session for that cwd after a server restart. The resumed session keeps its original sandbox and model. watch=true opens the live view.
codex_status()	Setup diagnostics: codex version, login status (codex login status), sessions dir. Spends no quota.

🐙 Copilot

Tool	Purpose
copilot_ask(prompt, workspace?, sandbox?="read-only", model?, timeout_s?=180, watch?=false)	Start a new Copilot session. sandbox maps to copilot's tool/path permissions (best-effort, not an OS sandbox — see Copilot bridge); model selects the model (--model). watch=true opens the live view, streaming copilot's steps from its --output-format json event stream.
copilot_continue(prompt, workspace?, sandbox?="read-only", timeout_s?=180, watch?=false)	Continue the Copilot session rooted at workspace — resumes the exact self-set session id, falling back to the newest on-disk session for that cwd after a restart. Unlike Codex, sandbox applies here too (copilot re-applies permissions each turn). watch=true opens the live view.
copilot_status()	Setup diagnostics: copilot version, an auth hint (no login status command exists, so best-effort), session-state dir. Spends no quota.

✳️ Cursor

Tool	Purpose
cursor_ask(prompt, workspace?, sandbox?="read-only", model?, timeout_s?=180, watch?=false)	Start a new Cursor chat. sandbox maps to cursor's mode/force flags (agent-enforced, not an OS sandbox — see Cursor bridge); model selects the model (--model, validated against cursor-agent models). watch=true opens the live view, streaming cursor's steps from its --output-format stream-json event stream.
cursor_continue(prompt, workspace?, sandbox?="read-only", timeout_s?=180, watch?=false)	Continue the Cursor chat rooted at workspace — resumes the exact chat id the bridge minted (create-chat + --resume), falling back to the newest on-disk chat for that cwd after a restart. watch=true opens the live view.
cursor_status()	Setup diagnostics: the bridge's own version + whether a newer release is available, plus cursor version and login status (cursor-agent status). Spends no quota.

🐝 Shared

Tool	Purpose
agent_swarm(tasks, max_concurrency?=4, timeout_s?=180, watch?=false)	Run several tasks in parallel across all four backends — each task names its backend (antigravity, codex, copilot, or cursor) plus a prompt (an optional model for any backend, and sandbox for Codex/Copilot/Cursor). Every answer comes back in one block; watch=true opens the live dashboard (Swarm).

workspace defaults to the MCP server's current working directory. Point it at a real project dir for context-aware answers — every backend gives the model access to files under that root (Codex, Copilot, and Cursor honoring their sandbox).

antigravity_image forces agy to save to an explicit absolute path — without one, agy falls back to its own scratch dir (~/.gemini/antigravity-cli/scratch/). It then corrects the file extension to match the real bytes: agy's image model picks the format itself (JPEG for photo-like images, PNG for flat graphics), so a requested out.png may come back as out.jpg. The returned path always reflects the true format.

🤖 Codex bridge — the well-behaved sibling

codex exec writes its final message to a file the bridge asks for via -o/--output-last-message, so the answer comes back without any scraping (where agy needed a transcript workaround before 1.0.15 fixed its stdout). Three things make Codex worth reaching for over Antigravity:

• Real sandbox. sandbox accepts read-only (default — reads and answers, writes nothing), workspace-write (may edit files under the workspace), or danger-full-access (no sandbox — avoid). Unlike agy's no-op --sandbox, codex's -s actually enforces this. codex exec has no interactive approval gate, so this flag is your safety boundary — opt into write access deliberately.
• Model selection works. model maps to codex's -m. (agy's --model works in print mode too as of 1.0.16; all four backends now expose the same model knob.)
• Stronger reasoning. Codex is a coding agent, not an image model — there's no codex_image. Its strength is reasoning and real code/repo work; hand it the jobs that need a heavier model.

Auth. Uses your existing Codex login (ChatGPT account or API key). Run codex login once; check with codex_status. No new keys for the bridge to manage.

Warning

codex exec runs the model as an autonomous agent with no interactive approval gate. The sandbox flag (default read-only) is the real boundary, but workspace-write / danger-full-access let it modify files — and a swarm runs N agents at once. Only use it with trusted prompts on trusted content.

🐙 Copilot bridge — GitHub's agentic coder

The GitHub Copilot CLI (copilot, from @github/copilot) is stdout-native like Codex: copilot -p "<prompt>" -s runs a prompt non-interactively and prints just the final answer to stdout, so the bridge reads it there — no scraping. What makes it worth reaching for:

• Model selection. model maps to copilot's --model (e.g. gpt-5.3-codex, claude-sonnet-4.6, or auto). An unavailable model errors immediately with a clear message.
• Deterministic, race-free continue. copilot's --session-id <uuid> both sets a new session's id and resumes an existing one, so the bridge generates the UUID itself and pins it to the workspace — no rollout-scraping. After a restart it falls back to the newest on-disk session (~/.copilot/session-state/<id>/workspace.yaml) whose recorded cwd matches.
• Fast by default. Runs with --allow-all-tools --no-ask-user --no-auto-update, and disables copilot's builtin GitHub-API MCP (--disable-builtin-mcps) because its flaky HTTP connect can stall a call up to ~60 s. Set COPILOT_GITHUB_MCP=1 to keep it (for Copilot's issue/PR/repo tools).

Sandbox is best-effort, not enforced. Unlike Codex's OS sandbox, copilot's boundary is tool/path permissions. The sandbox knob maps to copilot flags for a uniform cross-backend field:

• read-only (default) — auto-approves tools so it runs headless, then denies the local write and shell tools (--deny-tool). Best-effort: it is not an OS sandbox, and network/MCP tools can still act. For a hard read-only boundary, use codex_ask instead.
• workspace-write — writes allowed, but file access stays confined to the workspace (no --allow-all-paths).
• danger-full-access — --allow-all (tools + all paths + all URLs). Avoid.

Auth. Uses your existing Copilot login — run copilot then /login once (stored in the OS credential store), or set COPILOT_GITHUB_TOKEN/GH_TOKEN/GITHUB_TOKEN for headless use. Check with copilot_status. If copilot isn't on PATH (the winget install can land off a stale PATH), set COPILOT_BIN to its full path — e.g. %LOCALAPPDATA%\Microsoft\WinGet\Packages\GitHub.Copilot_*\copilot.exe.

Warning

copilot -p runs the model as an autonomous agent with --allow-all-tools (required to run headless). Its sandbox is best-effort tool/path permissions, not an OS sandbox — safer than agy, weaker than Codex's read-only. Only use it with trusted prompts on trusted content.

✳️ Cursor bridge — the widest model menu

Cursor's agent CLI (cursor-agent, from cursor.com/cli) is stdout-native like Codex and Copilot: cursor-agent -p --output-format text --trust "<prompt>" runs a prompt non-interactively and writes just the final answer to stdout, so the bridge reads it there — no scraping (--trust trusts the workspace so it won't block on a prompt). What makes it worth reaching for:

• The widest model menu. model maps to cursor's --model (e.g. gpt-5.2, sonnet-4-thinking, auto, and parameterized ids like claude-opus-4-8[context=1m]) — GPT, Claude, Grok, and Composer in one place. The bridge validates the label against cursor-agent models and rejects a typo up front (like agy). Omit model to use your Cursor account default.
• Deterministic, race-free continue. cursor-agent create-chat mints a fresh chat and prints its id, and -p --resume <chatId> resumes that exact chat — so the bridge mints the id itself, pins it to the workspace, and resumes deterministically (no rollout-scraping, same idea as Copilot's self-set session id). After a restart it falls back to the newest on-disk chat under ~/.cursor/chats/<md5(workspace)>/<chat-id>/ whose meta.json cwd matches (the chat-dir hash is itself md5 of the workspace path).

Sandbox is agent-enforced, not an OS sandbox. Like Copilot, cursor's boundary is which tools the agent can reach, not an OS jail. The sandbox knob maps to cursor's mode/force flags for a uniform cross-backend field:

• read-only (default) — --mode ask: the write and shell tools are unavailable, so cursor analyzes and answers but makes no edits (verified: it refuses to write files). Agent-enforced and best-effort — it is not an OS sandbox. For a hard read-only boundary, use codex_ask instead.
• workspace-write — --force: edits and commands allowed, file access rooted at --workspace.
• danger-full-access — --force --sandbox disabled (OS sandbox off). Avoid.

(Cursor also exposes an OS-level --sandbox enabled/disabled; the bridge drives the uniform field via mode/force.)

Auth. Uses your existing Cursor login — run cursor-agent login once (OS credential store), or set CURSOR_API_KEY for headless use. Check with cursor_status. If cursor-agent isn't reliably on PATH (the installer drops a cursor-agent.CMD shim a bare name can't launch on Windows), set CURSOR_BIN to its full path — mirrors the AGY_BIN/CODEX_BIN/COPILOT_BIN overrides.

Warning

cursor-agent -p runs the model as an autonomous agent with --trust (and --force when writes are allowed). Its sandbox is agent-enforced (read-only makes the write/shell tools unavailable), not an OS sandbox — safer than agy, weaker than Codex's read-only. Only use it with trusted prompts on trusted content.

👁️ Watch mode — Agent Intern (experimental)

Pass watch=true to any single-prompt tool — antigravity_ask, antigravity_continue, antigravity_image, codex_ask, codex_continue, copilot_ask, copilot_continue, cursor_ask, or cursor_continue — to watch the agent work live in a little chat-style browser window called Agent Intern. The agent still runs headless; alongside it the bridge serves a tiny page on 127.0.0.1 and opens it in a small, chromeless app window that renders the exchange as a conversation: your prompt shows as a chat bubble, the agent's live steps stream in a collapsible "thinking" trace — its planner narration (▸), the real commands it runs ($), and completions (✓), read live (from agy's transcript, or codex's / copilot's JSON event stream, or cursor's --output-format stream-json) — and the final answer arrives as a Markdown card (and, for antigravity_image with watch=true, the generated image shown inline). A *_continue run opens with the prior turns of the conversation shown as history, so it reads as one ongoing thread rather than a blank new window. (A watched cursor_continue is the exception — Cursor stores its transcript in an opaque SQLite blob, so its window opens without visible prior-turn history.)

text ask / continue (agy, codex, copilot, or cursor)	antigravity_image — image inline

_{Real captures — the agent runs headless while the Agent Intern window renders the exchange as a chat conversation: your prompt as a CLAUDE bubble, live steps (▸ narration · $ commands · ✓ completions) in a collapsible trace, then the final Markdown answer or inline image.}

• Cross-platform & best-effort. Prefers a Chromium browser (--app mode) for the windowed look; falls back to a normal browser window. If nothing can open, the run still completes and returns normally.
• Window size. Set AGY_WATCH_WINDOW_SIZE (e.g. AGY_WATCH_WINDOW_SIZE=480,700) to resize the window; default is 560,760. Press Enter / Esc in the window to close it.
• One window, reused — but concurrent runs stay separate. Repeated sequential watch calls reuse the already-open window instead of stacking a new one (the open page resets itself for the new run; the swarm dashboard rebuilds for the new fan-out). A run that starts while another watched run is still working gets its own window instead — so two concurrent single-worker runs (e.g. a codex_ask and a copilot_ask at once) each stream into their own view and never clobber each other. If you closed the window, the next run opens a fresh one. Set AGY_WATCH_ALWAYS_NEW=1 to force a new window every time.
• Chat layout & history. Prompts render as chat bubbles (labelled CLAUDE, since the MCP client writes them) — long ones clamp to a few lines with a show more / show less toggle — and answers as Markdown cards tagged with the backend (AGY / CODEX / COPILOT / CURSOR). A *_continue run seeds the window with the conversation's prior turns, read from each backend's own session store (agy's transcript, codex's rollout, copilot's events.jsonl; Cursor's store is opaque, so a watched cursor_continue opens without visible history). The swarm's per-worker detail window uses the same chat design for its one task.
• Progress, keyboard & copy. Each panel shows a time progress bar (elapsed / timeout). The swarm dashboard adds an overall done/total bar and per-row time bars; use ↑/↓ to select a worker and ↵ to open its detail window. Answers render as Markdown with a copy button, and a "jump to latest" badge appears if you scroll up.
• Coarse, not token-level. The backends flush their step stream in chunks, so you get a handful of live steps, not character streaming. The returned value is identical to the non-watch call. Nothing is sent anywhere but your own machine.

🐝 Swarm — run agents in parallel

agent_swarm fans a list of tasks out to workers that run truly concurrently (capped at max_concurrency, default 4), then returns every worker's result in one block. Each task names its own backend, so a single swarm can mix Antigravity (Gemini), Codex, Copilot, and Cursor workers — hand the reasoning-heavy jobs to Codex, Copilot, or Cursor and the quick ones to Gemini, all at once. Good for independent sub-tasks: summarise N files, ask the same question about N repos, fix N bugs. (antigravity_image_swarm stays separate — it generates N images, and only agy has an image model.)

agent_swarm(tasks=[
  {"backend": "antigravity", "prompt": "Summarise src/auth.py in 2 bullets."},
  {"backend": "codex", "prompt": "Find and fix the failing test in tests/",
   "sandbox": "workspace-write", "workspace": "./repo"},
  {"backend": "copilot", "prompt": "Explain what src/api.py exposes.",
   "sandbox": "read-only", "workspace": "./repo"},
  {"backend": "cursor", "prompt": "Draft a docstring for src/utils.py.",
   "model": "auto", "workspace": "./repo"},
])

_{agent_swarm(..., watch=true) — one row per worker (with a backend badge); the done/total bar climbs as workers finish. Click a row (or ↑/↓ then ↵) to pop that agent into its own window.}

How it stays correct under concurrency. The single-agent agy tools serialize through a lock because agy rewrites last_conversations.json on every call, so concurrent runs sharing one state dir would race. The swarm sidesteps this: each agy worker runs with its own isolated HOME/USERPROFILE, so agy's brain/, cache/, and last_conversations.json never collide — no lock needed. Auth still works because agy reads it from the OS credential store, not from ~/.gemini (verified on agy 1.0.9). Codex, Copilot, and Cursor workers need no such isolation — each is a fresh one-shot (codex exec with its own -o file; copilot -p with its own self-set session id; cursor-agent -p with its own minted chat id). Each worker's cwd is its real workspace, so file access is unchanged. Measured ~2.8× speedup at 3 agy workers (the AI Pro backend does not serialize per-account); higher max_concurrency trades quota/rate-limit pressure for wall-clock.

• Per-task fields — backend (antigravity/codex/copilot/cursor) and prompt are required; workspace defaults to the server cwd; sandbox and model apply to Codex, Copilot, and Cursor (ignored for Antigravity). Swarm workers are one-shot — there is no *_continue for a swarm worker's session.
• Error isolation — a worker that fails is reported in place; the others still return.
• watch=true — opens a thin live Agent Swarm dashboard (one row per worker, with a backend badge, repo, prompt, and latest step). Click a row to pop that agent into its own window streaming its full step log.

Warning

A swarm launches N unsandboxed agents at once — N× the prompt-injection "lethal trifecta" surface of a single call (see Security). Only use it with trusted prompts on trusted content. Codex workers honor their enforced sandbox; Copilot and Cursor workers honor their best-effort sandbox; Antigravity workers have no real boundary.

Model & auth

	🛰️ Antigravity	🤖 Codex	🐙 Copilot	✳️ Cursor
Model	Selectable via the model argument (agy's --model, e.g. "Gemini 3.1 Pro (High)", "Claude Sonnet 4.6 (Thinking)"); omit to use the "model" field in agy's settings.json (Gemini 3.5 Flash (High) by default). Switching model in -p used to hang (through ~1.0.14) but is fixed as of 1.0.16. agy silently ignores an unknown label, so the bridge validates it against agy models and rejects a typo. Flash High is speed-optimized for cheap tool-calling; pick a bigger label for heavier work.	Selectable via the model argument (codex's -m). codex does not hang on a switch, so model choice is a first-class knob.	Selectable via the model argument (--model, e.g. gpt-5.3-codex, claude-sonnet-4.6, auto); omit for your account default. An unavailable model errors immediately.	Selectable via the model argument (--model, e.g. gpt-5.2, sonnet-4-thinking, auto, or parameterized ids like claude-opus-4-8[context=1m]); a wide GPT/Claude/Grok/Composer menu, validated against cursor-agent models (a typo is rejected up front). Omit for your Cursor account default.
Auth	Piggybacks whatever credential store agy uses on your OS (Windows Credential Manager, macOS Keychain, libsecret on Linux — the bridge never touches it directly). Log in once; every call silent-auths on the same AI Pro quota you already pay for.	Uses your existing Codex login — ChatGPT account or API key. Run codex login once; verify with codex_status.	Uses your existing Copilot login — run copilot then /login once (OS credential store), or set COPILOT_GITHUB_TOKEN/GH_TOKEN/GITHUB_TOKEN. Verify with copilot_status.	Uses your existing Cursor login — run cursor-agent login once (OS credential store), or set CURSOR_API_KEY. Verify with cursor_status.

⚠️ Security

All four backends run the model as an autonomous agent. The difference is whether you get a real boundary: Codex enforces one, Copilot and Cursor offer best-effort ones, Antigravity offers none.

Antigravity — no usable boundary

agy -p auto-executes its own tools — reading and writing files, running shell commands, reaching the network — with no approval gate and no opt-out. This isn't a choice the bridge makes; it's how agy's print mode works. Re-verified empirically on agy 1.0.9 / Windows (all three checks below still hold):

• Print mode runs out-of-workspace file writes and live network fetches even without --dangerously-skip-permissions — that flag is a no-op for -p. There is no agy flag that disables tool execution in print mode.
• agy 1.0.5 integrated a permission system (its logs show toolPermission=request-review), but it still does not gate print-mode execution — a fresh -p run created a file outside the workspace with no prompt. agy 1.0.12 reshuffled how that permission config merges (per-project files under ~/.gemini/config/projects/ now take precedence over ~/.gemini/antigravity-cli/settings.json), and 1.0.13 made "Always Approve" rule matching strict (non-regex) by default with a regex: opt-in and relaxed its redirection checks — but those are config/interactive-approval changes, they add no print-mode approval gate, and the bridge reads none of it.
• --sandbox is not a usable boundary. agy 1.0.6 fixed its propagation into -p (the 1.0.6/1.0.7 changelog calls this "sandbox isolation correctly enforced") and it now does block terminal/ shell command execution — but re-verified on 1.0.9 that it leaves the write_to_file tool and network wide open: under --sandbox the model still wrote a file outside its workspace. agy 1.0.9 hardened the sandbox's command path (stricter exact-match command checks; .git added to its dangerous-paths list), but none of that closes the out-of-workspace write_to_file hole. On top of that, a --sandbox run whose blocked terminal command halts it writes no JSONL transcript (only the SQLite .db, re-confirmed on 1.0.9). The bridge can now read that .db, but still never passes --sandbox — it's no boundary, with file writes and network left open.

Codex — a real sandbox you should use

codex exec also has no interactive approval gate, but its sandbox flag is a genuine boundary that codex enforces:

• read-only (default) — reads and answers; writes nothing. Safe for untrusted questions on trusted content.
• workspace-write — may edit files under the workspace. Opt in deliberately, per task.
• danger-full-access — no sandbox at all. Avoid.

Because there's no approval prompt, the flag you pass is the safety decision — choose it per call.

Copilot — best-effort, not an OS sandbox

copilot -p runs headless with --allow-all-tools (required — otherwise it blocks on per-tool permission prompts). Its sandbox maps to copilot's tool/path permission flags, which are a real-ish but not enforced boundary:

• read-only (default) — auto-approves tools to run headless, then denies the local write and shell tools (--deny-tool). Blocks local file edits and command execution, but it is not an OS sandbox: other tools (including network/MCP) can still act. Weaker than Codex's read-only.
• workspace-write — writes allowed, but file access stays confined to the workspace (no --allow-all-paths).
• danger-full-access — --allow-all (tools + all paths + all URLs). Avoid.

For a hard read-only boundary, prefer codex_ask.

Cursor — best-effort, agent-enforced

cursor-agent -p runs headless with --trust (and --force when writes are allowed). Its sandbox maps to cursor's mode/force flags — an agent-enforced, not OS-level, boundary:

• read-only (default) — --mode ask: the local write and shell tools are unavailable, so cursor analyzes and answers but makes no edits (verified: it refuses to write files). Like Copilot, this is agent-enforced and not an OS sandbox. Weaker than Codex's read-only.
• workspace-write — --force: edits and commands allowed, file access rooted at --workspace.
• danger-full-access — --force --sandbox disabled (OS sandbox off). Avoid.

For a hard read-only boundary, prefer codex_ask.

What that means for you

• The workspace argument is only a starting context, not a security boundary — Antigravity can and does act outside it; Codex is bounded by its enforced sandbox; Copilot by its best-effort tool/path permissions; Cursor by its agent-enforced mode/force.
• An Antigravity call effectively runs arbitrary code with your user privileges. A Copilot or Cursor call does too outside its best-effort denials; a Codex call does unless you keep it at read-only.
• Only invoke these with trusted prompts on trusted content. Untrusted input here is the classic prompt-injection lethal trifecta: private-data access + code execution + network egress.
• For real isolation, run the whole bridge inside a container or VM.

The bridge itself does only cross-platform filesystem reads under ~/.gemini/antigravity-cli/, ~/.codex/, ~/.copilot/, and ~/.cursor/ — no private APIs, no token theft. The risk above is entirely in what the sub-agents are allowed to do.

FAQ

Is this against Google's / OpenAI's / GitHub's / Cursor's Terms of Service?

It runs the official agy, codex, copilot, and cursor-agent CLIs under your own logins — no private APIs, no token theft, no quota abuse. It just bridges what the CLIs already do. That said, your AI Pro / Antigravity, OpenAI / Codex, GitHub Copilot, and Cursor ToS apply, and you're responsible for staying within them.

Do I need all four CLIs?

No. Each backend is independent — install only the CLI(s) you want. The tools for a missing backend report "not found" via their *_status tool (antigravity_status / codex_status / copilot_status / cursor_status) and never crash the server.

When should I use Antigravity vs Codex vs Copilot vs Cursor?

Use Antigravity for fast, cheap tool-calling, quick answers, and image generation (it's the only backend with an image model) — and it now lets you pick the model too (agy's --model). Use Codex for heavier reasoning, real code/repo work, or when you want a real, enforced workspace-write sandbox. Use Copilot for agentic coding on your GitHub Copilot plan, or as a second coding opinion alongside Codex — noting its sandbox is best-effort, not enforced. Use Cursor for agentic coding on a Cursor plan, or when you want the widest model menu — GPT, Claude, Grok, and Composer, all via model — noting its sandbox is agent-enforced, like Copilot's. All four let you choose a model; in a swarm you can mix all four. See The four backends at a glance.

Will it break when agy updates?

Less likely now. As of agy 1.0.15 the bridge prefers agy's stdout on the happy path (1.0.15 fixed the print-mode stdout bug on Windows — -p now writes the clean answer there), which removes its dependence on agy's undocumented transcript schema for normal runs. It still falls back to reading the JSONL transcript, or the SQLite .db agy dual-writes, when stdout is empty (older agy, non-Windows, or --sandbox runs) — so a schema change would only bite that fallback path. Re-verified working on 1.0.15 (stdout answer clean under tool use; transcript/.db fallback intact; live ask round-trip + antigravity_status diagnostics pass). Still, if you rely on the fallback, pin a known-good agy version.

Which model does Antigravity use — can I pick it?

Yes. Pass model to antigravity_ask/antigravity_continue (or per task in agent_swarm) — it maps to agy's --model, taking any label from agy models (e.g. "Gemini 3.1 Pro (High)", "Claude Sonnet 4.6 (Thinking)"). Omit it to use the "model" field in agy's settings.json, which defaults to Gemini 3.5 Flash (High) — speed-optimized for cheap tool-calling.

agy 1.0.5 added --model, but through ~1.0.14 switching to a different model in -p hung the call, so earlier bridge versions stayed single-model. Re-verified on agy 1.0.16 that the hang is fixed — a Claude label answers as Anthropic Claude, a Gemini label as Gemini, each in seconds. One caveat the bridge handles for you: agy silently ignores an unknown label (it falls back to the default with no error), so the bridge validates your label against agy models and rejects a typo up front.

Can it generate images?

Yes — that's the antigravity_image tool, on the Antigravity backend. agy's print mode generates real images on your AI Pro quota; antigravity_image drives it, saves the file to a path you choose (or a timestamped default in your workspace), fixes the extension to match the real bytes (agy picks JPEG or PNG itself), and returns the path. Verified on agy 1.0.9 / Windows. Codex has no image model — it's a coding agent.

Does it cost extra money?

No. It uses the same quota you already pay for — AI Pro for Antigravity, your Codex plan for Codex, your GitHub Copilot plan for Copilot, your Cursor plan for Cursor. The smoke test spends a negligible amount.

Does it stream responses?

The final answer is request/response — the CLIs return it all at once, so the tools return when the agent finishes (each call typically takes 10–30 s; Copilot's reasoning models can run longer). If you want to watch the agent work as it goes, pass watch=true to any single-prompt tool: it opens the Agent Intern browser window and live-streams the agent's steps — see Watch mode. It's coarse (a handful of steps, not token-by-token), and the returned value is identical to the non-watch call.

Can I run several calls at once?

The single-agent tools are serialized inside the server: agy rewrites last_conversations.json on every call, so concurrent runs sharing one state dir would race and could return the wrong conversation. A threading.Lock makes extra requests queue rather than race.

For real parallelism use agent_swarm — each agy worker runs in its own isolated state dir (and Codex/Copilot/Cursor workers need none), so they don't race and the lock isn't needed (~2.8× at 3 workers). That's the supported way to run many calls at once, across any backend.

Status & caveats

• ✅ Verified on agy 1.1.0 — base dir, last_conversations.json (still keyed by workspace path), the brain/.../transcript.jsonl path, the transcript schema, and the -p/-c/--print-timeout flags are all unchanged; a live antigravity_ask + conversation-pinned antigravity_continue round-trip returns clean over stdout and antigravity_status diagnostics pass. 1.1.0 added an agent execution-mode system — a --mode flag (accept-edits | plan) and a new interactive request-review default that pauses before file writes — but it does not touch the bridge: -p is spawned with DEVNULL stdin, so the approval gate never engages and print mode still auto-executes (a file-writing task completed in ~36 s, exit 0, with and without --mode accept-edits). --sandbox behavior is likewise unchanged (blocks the terminal, not file writes). The print-mode stdout path (fixed on 1.0.15, Windows) still applies; the transcript stays the fallback.
• ✅ Verified on codex-cli 0.141.0 — codex exec, -o/--output-last-message, codex exec resume, the --json event stream, and the ~/.codex/sessions/.../rollout-*.jsonl layout the continue path reads are all in place; a live codex_ask round-trip + codex_status pass.
• ✅ Verified on copilot 1.0.68 — copilot -p -s (clean stdout answer), --session-id set-then-resume, --model, --output-format json (watch stream), and the ~/.copilot/session-state/<id>/workspace.yaml layout the continue fallback reads are all in place; live copilot_ask / copilot_continue round-trips + a mixed agent_swarm pass.
• ✅ Verified on cursor-agent 2026.07.08 — cursor-agent -p --output-format text --trust (clean stdout answer), create-chat + -p --resume <id>, --model (validated against cursor-agent models), --output-format stream-json (watch stream), and the ~/.cursor/chats/<md5(workspace)>/<chat-id>/meta.json layout the continue fallback reads are all in place; live cursor_ask / cursor_continue round-trips + a mixed agent_swarm pass.
• 🖥️ Console-detach — before 1.0.15 agy -p wrote its answer to the controlling terminal, not stdout; under a TUI that text leaked into the host's prompt (seen on 1.0.9). 1.0.15 fixed this on Windows (stdout now carries the answer), but the bridge still spawns agy detached (CREATE_NO_WINDOW / a new POSIX session), which prevents the leak on older/other platforms and is harmless on 1.0.15+.
• 💾 SQLite migration — handled — agy still dual-writes a .db per conversation; on the fallback path, when the JSONL transcript is absent (already true for --sandbox runs, and the announced future default) _read_response falls back to reading the .db, verified to match across 100+ conversations. See the FAQ.
• 🐛 agy stdout bug — fixed on 1.0.15 — -p now prints the clean answer to stdout in a non-TTY subprocess (Windows), so the bridge prefers stdout and only scrapes the transcript when stdout is empty (older agy, non-Windows, or --sandbox). (Codex and Copilot never had this problem — both are stdout-native.)
• 👁️ Watch mode is experimental — pass watch=true to any single-prompt tool to open the Agent Intern window and watch the agent work live (coarse steps; image shown inline). Best-effort and cross-platform; see Watch mode.
• 🔒 Sandbox — agy's --sandbox blocks only shell commands, so it's no boundary and the bridge never passes it. Codex's sandbox is real and enforced — use it; default read-only. Copilot's sandbox is best-effort (tool/path denials, not an OS sandbox); default read-only. Cursor's sandbox is agent-enforced (mode/force; read-only = --mode ask makes write/shell unavailable, not an OS sandbox); default read-only. See Security.

Requirements

• Python 3.10+
• For the Antigravity tools: agy 1.0.0+ on PATH (state-file layout re-verified on 1.0.15) and an active Antigravity / AI Pro session
• For the Codex tools: codex on PATH and logged in (codex login) — verified on codex-cli 0.141.0
• For the Copilot tools: copilot on PATH and logged in (copilot → /login, or a COPILOT_GITHUB_TOKEN/GH_TOKEN env) — verified on copilot 1.0.68
• For the Cursor tools: cursor-agent on PATH and logged in (cursor-agent login, or a CURSOR_API_KEY env) — verified on cursor-agent 2026.07.08

Each backend is independent — install only the CLI(s) you plan to use; the other tools simply report "not found" via their *_status tool.

Tip

If agy isn't reliably on PATH (e.g. a new terminal or reboot drops it on Windows), set the AGY_BIN env var to its full path and the bridge will use that instead of "agy" — e.g. AGY_BIN=%LOCALAPPDATA%\agy\bin\agy.exe. Likewise, set CODEX_BIN if codex isn't reliably on PATH (the native Windows installer puts it under %LOCALAPPDATA%\Programs\OpenAI\Codex\bin\), and COPILOT_BIN if copilot isn't (the winget install lands under %LOCALAPPDATA%\Microsoft\WinGet\Packages\GitHub.Copilot_*\copilot.exe). Finally, set CURSOR_BIN if cursor-agent isn't reliably on PATH (the installer drops a cursor-agent.CMD shim a bare name can't launch on Windows).

The bridge uses only cross-platform Python (Path.home(), subprocess) and reads paths under ~/.gemini/antigravity-cli/, ~/.codex/, ~/.copilot/, and ~/.cursor/, which the CLIs write the same way on every OS. Developed and verified on Windows; macOS and Linux should work unmodified provided the CLIs run there. If you test it on those platforms, please open an issue / PR to confirm.

🌐 Community & Acknowledgments

• Qiita (Japan): A huge thanks to @fallout and the Japanese developer community for featuring this project and providing invaluable feedback!
  • Detailed Hybrid Setup Guide (Claude Code × Antigravity CLI)
  • Quick Installation Guide

💡 Path Resolution Fix: Thanks to their community's real-world testing, we identified and resolved a Windows PATH edge case where the MCP server inherits a stale PATH at startup and can't find agy. The AGY_BIN environment-variable fallback was implemented directly inspired by their report!

License

MIT. Do whatever you want with it.