antigravity

name	antigravity
description	Run the Antigravity CLI (Gemini) as a collaborating AI inside Claude Code, with intelligent model routing across the software development lifecycle. Claude is the conductor/orchestrator — requirements, architecture, the hard 20%, verification, and review — and routes deterministic, high-volume work (scaffolding, boilerplate, test generation, first-pass review, migrations, web/Vertex AI Search) to Antigravity (Gemini), the cheaper, faster model. Use when the user wants to "use Antigravity / agy", "vibe code / agentic engineering", "accelerate the SDLC", "delegate to Gemini", "scaffold / generate tests / migrate", "first-pass code review", "search web or internal/company data", "deep research / multi-source research report", "second-model cross-check", or "lower token cost on a big job". Claude always verifies Antigravity's output and re-checks itself if unsatisfied.
version	0.18.2

Antigravity for Claude Code — hybrid SDLC

Run the Antigravity CLI (agy, Gemini) as a second AI working alongside Claude Code. The organizing idea is intelligent model routing across the SDLC: keep judgement-heavy work on Claude (the frontier model) and route deterministic, high-volume work to Antigravity (cheaper, faster Gemini). Two AIs, one workflow.

• Claude = conductor / orchestrator — requirements, architecture, the hard 20% (edge cases, integration, correctness), specs, tests/evals, final review.
• Antigravity = delegated agent — a full terminal agent (file edits, terminal, subagents, MCP, web/Vertex AI Search) that executes well-specified work.

This is agentic engineering, not vibe coding: the value is the structure around the model — routing, shared rules, verification gates — not raw generation. Generation is solved; verification, judgement, and direction are the craft.

Two modes (pick per task)

• Conductor (sync, inline): you're shaping something in real time; delegate a small, well-scoped chunk to agy mid-flow (e.g. "generate these tests"), use the result immediately.
• Orchestrator (async, multi-unit): decompose a larger task into units, dispatch to agy (often with --dir, agentic, in parallel), then review and integrate. Best for migrations, bulk implementation against patterns, test suites.

Division of labor across the SDLC

Route each phase to the right model. This is the core policy.

SDLC phase	Owner	Why
Requirements & planning	Claude	ambiguity, human-paced judgement
Design & architecture	Claude	trade-offs; most human-centric
Implementation — complex / architecture-bearing (the 20%)	Claude	correctness, deep context
Implementation — scaffolding / boilerplate / well-specified	agy	deterministic, high volume
Test & eval generation	agy (Claude defines the contract)	cheaper-model territory
First-pass code review	agy → Claude final	AI as first-pass reviewer
Cross-model verification (output + trajectory)	both	two model families ≠ same failure
Maintenance / migration / modernization	agy executes, Claude directs	tedious, systematic
Web / Vertex AI Search	agy → Claude re-checks	tools Claude lacks natively
Deep research (multi-source)	agy fans out search/fetch · Claude plans, verifies ≥2 sources, synthesizes	offload bulky pages to cheap Gemini; frontier model judges

Routing tier within agy: flash (default, bulk) · flash-lo (cheapest, trivial) · pro (harder reasoning / reviews / cross-checks).

agy is multi-model. Tiers map to Gemini by default, but you can point delegation at any model agy models lists (Claude / GPT on plans that expose them) — via --model <exact name>, or persistently with the default_model / tier_* plugin options. Keep the executor a different, cheaper model than the Claude conductor: that's what yields the cost saving and the cross-model verification value (Claude executing Claude loses both).

How to call it

agy-delegate [options] "the task prompt"

Options: --tier flash|flash-lo|pro · --dir <path> (workspace, repeatable) · --timeout 10m · --yolo (auto-approve ALL tools — the reliable headless grant for any file write or tool use; run write tasks on a branch) · --mode accept-edits|plan (agy execution mode: accept-edits auto-applied file edits headless on 1.1.0–1.1.2 but is soft-denied on 1.1.3 — no longer a dependable headless write grant, use --yolo; plan = strategize only) · --sandbox · --digest (append a digest-only output contract — use it for any bulk read/analysis; the wrapper also warns on stderr when a reply comes back dump-sized, because ingesting digests instead of dumps is the single biggest cost lever) · --print-command (dry run: show the resolved agy call, don't run it) · pipe a long prompt with a trailing -.

The wrapper handles agy's quirks (prompt is the value of -p; non-TTY stdout drop via < /dev/null; no --output-format json, so output is plain text you parse).

Two ways to delegate. Call the wrapper directly (above), or — when you want file generation to happen entirely on Gemini with zero Claude tokens spent writing — hand the unit to the antigravity-delegate subagent (its only file-acting tool is the wrapper; it returns a digest for you to verify). Either way, you still own verification.

Structured failures. The wrapper exits 10 quota · 11 auth · 12 timeout · 13 agy-missing · 14 model-unavailable (a --model / tier_* / default_model name not in agy models — agy ≥ 1.1.2 hard-fails instead of silently downgrading) · 15 permission-denied (agy ≥ 1.1.3 soft-denies a permissioned tool headless — pass --yolo) (besides 2 failed / 3 empty) and prints a AGY_SIGNAL {...} line on stderr; agy-job status/result surface it, so you can react (e.g. retry quota with --continue, fix the model name, or add --yolo) instead of scraping prose.

If Claude itself is running headless (claude -p, one-shot): run delegations synchronously — let agy-delegate BLOCK and return before you continue. Do NOT background a delegation expecting a later turn / "harness re-invocation": there is none in -p mode, so you'd exit before the work finishes. (Backgrounding is only valid in an interactive session that will be re-invoked.)

Shared harness: one AGENTS.md for both AIs

agy reads AGENTS.md from the workspace (verified). Keep a single shared AGENTS.md at the repo root (stack, conventions, hard rules, workflow) so Claude and Antigravity operate under the same rules — this raises agy's first-pass success rate and keeps output consistent (lower OpEx).

Rule: when delegating any repo work, always pass --dir <repo-root> so agy loads AGENTS.md and the real code, instead of pasting files into the prompt (cheaper, denser context).

Verification gates (non-negotiable)

Claude owns correctness. For anything that ships:

1. Define the contract first — Claude writes/owns the tests and evals; they tell agy what "correct" means more precisely than prose.
2. Output eval = actually run it, don't stop at reading the code. Reading the diff is necessary but NOT sufficient — a static review that "looks right" is still vibe coding. Execute it: run the tests, launch the app, hit the real API/endpoints, and check each acceptance criterion against observed behavior. Verify external assumptions empirically (e.g. does the API actually accept that input?) rather than trusting the spec's claims. If you cannot run it, say so explicitly — do not mark the gate passed.
3. Trajectory check — did it take a sane path? (Limit: print mode returns only the final text. The per-conversation logs under ~/.gemini/antigravity-cli/conversations are SQLite .db files with opaque blob columns, not human-readable — don't rely on reading them. Instead, have agy summarize its own steps as part of its output, or keep a session with --continue/--conversation and ask it to recap. Exception — internal fan-out: each spawned subagent leaves a READABLE step-by-step transcript.jsonl under ~/.gemini/antigravity-cli/brain/<conversationId>/; audit it with agy-trace <conversationId> (or agy-trace --list). See the Internal fan-out recipe below.)
4. Review every shipping line — be skeptical of clever code; check imports are real packages (hallucinated deps), error handling, edge cases, and that the contract itself is internally consistent (examples/placeholders match the verified behavior).
5. Never trust agy's "GREEN" — re-run the gate yourself in a clean state. Measured: agy will, to make a check pass, modify the environment itself — e.g. patch the installed package in site-packages, or MagicMock-stub a missing dependency — and then report success. Before believing a passing test/eval: diff any touched tooling against a pristine reference, restore it, and re-run the gate under Claude's own control. agy's self-reported pass is a claim, not evidence. If wrong: retry on --tier pro, sharpen the spec, or do that piece yourself.

Safety for write tasks

Read-only work (search, review, analysis) is low-risk. When agy writes files or runs commands (--yolo grants write + terminal):

• Write tasks MUST pass --yolo. Headless agy's no-permission behavior has shifted every few releases — describe-only (pre-1.1.0), scratch-divert (1.1.0–1.1.2), soft-deny with a stderr notice (1.1.3) — but in every version your workspace stays untouched while the run still "succeeds" (issue #10). The one durable write grant is --yolo (--dangerously-skip-permissions). (--mode accept-edits only wrote headless on 1.1.0–1.1.2 and is soft-denied on 1.1.3 — don't rely on it for writes.) Claude Code may prompt for or block --dangerously-skip-permissions — approve it or pre-allow Bash(agy-delegate*). Always verify files actually changed in the workspace with git status (the wrapper maps a 1.1.3 soft-deny to exit 15 so you're not left guessing).
• Run it on a dedicated git branch or worktree so changes are isolated.
• Add --sandbox for execution containment.
• Claude reviews the diff before merging — never auto-merge agy's writes.

Cost discipline — where the savings actually come from

Delegation does not save money by itself. Measured reality: on a small task the hybrid cost more than Claude-only, because the dominant cost was Claude's own cache_read — re-reading a large, growing context across many orchestration turns. The savings the "Gemini sub-agent" concept promises are real, but only when you keep Claude's context lean and the round-trips few. Apply these as hard rules:

1. Delegate above the break-even, not below. Hand work to agy only when the offloaded volume clearly exceeds the spec-writing + round-trip + verification overhead it adds. Bulk/parallel/repetitive (mass migration, exhaustive tests, fan-out research, long-context reads that return a small digest) = delegate. Small, self-contained, or judgement-heavy = just do it yourself. (Delegating a tiny task is a net loss.)
2. Keep Claude's context lean (the biggest lever). Do not pull the files agy already handled (--dir) back into Claude's context, and do not paste agy's raw bulky output into the thread. Claude ingests a digest, not raw content — this is what collapses the per-turn cache_read that made the hybrid expensive.
3. Make agy return a digest, not a dump. End every delegation prompt with an explicit trailer instruction, e.g.: "...End with a fenced block ===DIGEST=== listing: files changed, key decisions, and a 1-paragraph 'context for next step'. Put bulky detail ONLY in files, not in your reply." Claude reads the DIGEST; the bulky work stays on cheap Gemini tokens.
4. Batch, don't chatter. One large, fully-specified delegation beats many small round-trips (each round-trip re-reads context = cache_read tax).
5. Review the diff, not the whole tree. git diff is compact; reading every file is not.
6. Hold state on the cheap side. For multi-step jobs, keep an agy session with --continue / --conversation <id> so the working context lives in Gemini, and Claude passes deltas instead of re-supplying everything.
7. Asymmetric effort. The conductor doesn't need max reasoning effort to coordinate + verify; run Claude at a moderate effort and let the cheap workers do the volume.
8. Don't fight the prompt-cache TTL on small tasks (measured trap). The 5-min cache expires while you wait on a long agy delegation, so the next turn pays cache_create (1.25× input) instead of cache_read (0.1×). It's tempting to "keep the cache warm" with busy turns — measured: that backfires, because every warming turn generates frontier output (5× input), the most expensive class, and net cost goes up. Do NOT manufacture work to stay warm. Backgrounding a long delegation (Bash run_in_background) is fine to avoid blocking, but it does not make a small task cheaper. The only real fix is scale: make each delegation big enough that the displaced Claude output dwarfs the one-time re-cache cost. Below the break-even, the hybrid loses on cost — three optimization variants were tested on a small task and none beat solo Claude (see docs/AB-RESULTS.md). Delegate for cost reasons only at scale.

Honest framing for any cost claim: there is no flat 8×/46%. Below the break-even the hybrid costs more; above it, lean-context routing cuts frontier-model spend by a measured margin. Quote the measured number and the break-even, never a headline ratio. Use agy-cost-compare for the per-token gap (estimate; set real Vertex rates first).

SDLC recipes

ROOT=agy-delegate

# Scaffold from a spec (Claude wrote the spec/architecture)
"$ROOT" --tier pro --yolo --sandbox --dir ./app \
  "Scaffold per ARCHITECTURE.md: dirs, configs, stub modules. Follow AGENTS.md."

# Generate tests for a contract Claude defined
"$ROOT" --tier flash --yolo --dir ./app \
  "Write unit + edge-case tests for src/payments.py covering the cases in SPEC.md."

# First-pass review (Claude does the final pass)
"$ROOT" --tier pro "Review for bugs/security/perf, be skeptical. List file:line: <diff>"

# Implement-until-tests-pass (feedback loop; isolate on a branch)
"$ROOT" --tier pro --yolo --sandbox --dir ./app \
  "Implement feature X to satisfy AGENTS.md and make 'pytest -q' pass. Iterate until green."

# Migration / modernization
"$ROOT" --tier pro --yolo --sandbox --dir ./svc \
  "Migrate all callers from APIv1 to APIv2 per MIGRATION.md. List every file changed."

# Web search → Claude re-checks
"$ROOT" --tier pro --yolo "Use web search for <X>. Give URLs + dates."

# Vertex AI Search over internal data (discover engines, then query)
"$ROOT" --tier pro --yolo "List Vertex AI Search engines (list_engines)."
"$ROOT" --tier pro --yolo "Search engine <ENGINE_ID> for: <question>. Cite the hits."

Internal fan-out recipe (agy spawns its own subagents)

agy has built-in define_subagent / invoke_subagent tools. Which pattern works is version-dependent — this surface is moving fast upstream (4 releases in one week while we tracked it), so re-verify after any agy upgrade:

• agy ≥ 1.0.16 — dynamic custom subagents (preferred): have agy define_subagent a named specialist in-session (name / description / system_prompt), then invoke_subagent it by that TypeName. Verified headless on 1.0.16 and re-verified on 1.1.0: define → invoke → result round-trips cleanly, real thread spawned. (1.0.13–1.0.15 shipped this broken — defined agents failed to invoke, upstream #521; fixed in 1.0.16. Subagents are officially documented as of 1.1.0 — antigravity.google/docs/cli/subagents — with static config at <workspace>/.agents/agents/*.md and global ~/.gemini/config/agents/.)
• Any version — role delegation (fallback): the sandbox pre-approves TypeNames self and research; an undefined custom TypeName is rejected with CORTEX_STEP_TYPE_INVOKE_SUBAGENT: ... not found or not allowed to be invoked (upstream #105). Invoke TypeName self and inject the specialty via Role + Prompt — verified on 1.0.12 and re-verified on 1.0.16.

Use it for orchestrator-mode work pushed down a level: instead of Claude dispatching N parallel agy-job runs (N round-trips, coordination spend on the frontier side), send ONE delegation and let agy fan out internally — the coordination tokens land on the cheap side, and you ingest a single digest.

# Any version (fallback form). On agy >= 1.0.16, swap the instruction to:
# "define_subagent a named specialist per unit, then invoke each by its TypeName".
agy-delegate --dir . --digest --timeout 10m \
  "Decompose <task> into up to 3 units. For each unit, invoke a background subagent
   with TypeName \"self\" and a specialist Role (e.g. 'Test Writer'), each following
   AGENTS.md. Wait for all of them, then report per-unit results, each subagent's
   conversationId, and end with a DIGEST line."

Verified behaviors (1.0.12 → 1.0.16):

• Spawning needs no --yolo — subagent invocation isn't permission-gated; file writes / web tools inside the subagents' work still need it.
• Each spawn's tool result includes a logAbsoluteUri → a readable step-by-step transcript.jsonl under ~/.gemini/antigravity-cli/brain/<conversationId>/ — better trajectory visibility than a plain delegation. Location unchanged across 1.0.12→1.0.16, for both define_subagent and self spawns. Have the parent report each conversationId, then audit with agy-trace <id> (agy-trace --list finds recent ones).
• Spawns are real and observable (new conversation threads appear) — but still run the verification gates on the merged result; more autonomy = more surface for error.

Caveats: neither pattern is a documented contract yet — self+Role works around the sandbox allowlist, and even the official docs' static agent-config paths don't match observed behavior (upstream #527) — so re-verify after agy upgrades (1.0.16 changed this area within a day of our first verification). Bound the fan-out width in the prompt (agy chooses parallelism otherwise). A wide fan-out takes longer wall-clock — raise --timeout, and in an interactive session prefer a background job (agy-job).

Deep-research recipe (multi-source)

agy has no built-in "Deep Research" mode — that product lives in the Gemini app and the Gemini API's managed Deep Research Agent, not the CLI (verified). But agy can do genuine multi-step, cited web research via its agentic loop. So deep research is a Claude-orchestrated recipe, not a single agy call. Pair it with Claude's own deep-research skill as planner/verifier; agy is the cheap, grounded legwork worker.

Caveat that shapes the recipe (verified empirically): in --print mode agy uses search-summary tools and does NOT reliably fetch full pages, so its citations are coarse (often domain-level) and may not actually support the claim. It can also leak parametric "knowledge" disguised as a sourced fact. Never ship its citations unverified.

1. Plan (Claude). Decompose into sub-questions + an explicit list of load-bearing claims to verify. Claude owns scope and final synthesis.
2. Fan-out fetch (agy, cheap, parallel). One call per sub-question; force compact stdout so bulky pages stay in Gemini's context, not Claude's:

   "$ROOT" --tier flash --yolo \
     "Use web search for <sub-question>. Return 5-8 bullet findings, each with the
      exact source URL and publication date. Output ONLY findings+URLs+dates."
   

3. Deepen on key sources (agy). For each load-bearing claim, name the URL and make agy quote the supporting text (turns domain-level citations into verifiable quotes):

   "$ROOT" --tier pro --yolo \
     "Open <URL> and quote the exact sentence(s) supporting: '<claim>'.
      If the page does not support it, reply NOT SUPPORTED."
   

4. Adversarial verify (Claude). Corroborate each key claim across ≥2 independent domains; treat any single/vague/domain-only citation as unverified; sanity-check dates; watch for Gemini parametric knowledge masquerading as a sourced fact.
5. Synthesize (Claude). Write the final cited report from verified findings only; mark anything uncorroborated as "unverified."

Iteration is Claude's job: --print does one agentic pass per call (no auto re-query when evidence is thin), so Claude must re-dispatch follow-up agy calls to close gaps. Token economics: bulky searched/fetched text is paid in cheap Gemini tokens and distilled to bullets+URLs before reaching Claude — use agy-cost-compare to show it.

What Antigravity brings that Claude lacks natively

Built-in Google tools (MCP), verified working in headless --print mode:

• Google / web search — current, grounded info.
• Vertex AI Search — search internal/company data stores (list_engines, search, conversational_search).
• Google Cloud Logging, Notebooks (Colab/Jupyter), Visualization (charts).

Tool use in headless mode requires --yolo (print mode can't show approval prompts); search/list tools are read-only so this is low-risk.

Economics (a financial lever, not the headline)

Routing deterministic, high-volume work to Gemini Flash (≪ Claude per token) is intelligent model routing: higher CapEx (this harness) for lower OpEx (cheap model does the bulk). Use the cost demo as observability:

agy-cost-compare --tier flash "the task prompt"

Estimates only (chars/4; agy exposes no token API in print mode). Set real Vertex rates via CLAUDE_IN_PER_M, CLAUDE_OUT_PER_M, GEMINI_IN_PER_M, GEMINI_OUT_PER_M.

Prerequisites & limits

• agy installed and authenticated (agy models lists Gemini models); its ~/.gemini/antigravity-cli/settings.json points at a GCP project/region.
• Scripts executable (chmod +x scripts/*.sh).
• agy v1.0.x: -p takes the prompt as its value (wrapper handles); no JSON output; print mode returns final text only (no trajectory); no timeout(1) on macOS (use --timeout).
• WSL: --add-dir on a Windows mount (/mnt/c/...) reads over a slow 9p bridge — calls can take 20s+. Keep the repo on the Linux filesystem (~); the wrapper warns.