bench v2: Antigravity wired to gpt-5.4 — 9.00/12 (12× v1, within 25% of frontier live-agents)

artifacts/benchmark-2026-05-07/README.md

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+# 2026-05-07 — Antigravity vs Codex vs Claude Code (run v2)
+
+Cross-repo Q&A benchmark on `fastapi`, `requests`, `sqlmodel`. 15 questions × 3 tools = 45 graded answers.
+
+→ Read [`REPORT.md`](REPORT.md) for the full write-up.
+
+**Headline (v2, gpt-5.4 backend for Antigravity):**
+
+| Tool | Composite (0-12) | Median latency / Q |
+|---|--:|--:|
+| Antigravity (gpt-5.4 via proxy) | **9.00** | 22 s |
+| Codex CLI (gpt-5.5 xhigh) | 11.93 | 106 s |
+| Claude Code Explore (Opus 4.7) | 11.87 | 42 s |
+
+Antigravity now lands within ~25% of frontier live-agents on quality, while being **2-5× faster per question**. Single LLM swap from `qwen3:0.6b` → `gpt-5.4` lifted Antigravity 12× (0.73 → 9.00) — clean evidence the architecture is sound, the previous gap was synthesizer-strength.
+
+Remaining gap to live agents (full breakdown in §6.3) is mostly **line-number citations**.
+
+Raw artifacts:
+- `answers/{antigravity,codex,claude}/{qid}.md` — per-tool answers
+- `meta/grades.json` — per-cell scores + notes
+- `meta/score.py` — reproducible aggregator
+- `meta/run_*.sh` — runner scripts (key not embedded; pulled from .env at run time)

artifacts/benchmark-2026-05-07/REPORT.md

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+# Antigravity vs Codex vs Claude Code — Cross-Repo Q&A Benchmark
+
+**Date:** 2026-05-07 (run v2)
+**Author:** Claude Opus 4.7 (autonomous)
+**Antigravity:** worktree `claude/quirky-engelbart-ec012c` @ engine 0.2.1, **gpt-5.4 backend** via OPENAI_BASE_URL proxy
+**Status:** 15/15 questions answered by all three tools, 188/188 internal tests green.
+
+---
+
+## TL;DR
+
+> With the synthesizer upgraded from local `qwen3:0.6b` to `gpt-5.4` over an OpenAI-compatible proxy, Antigravity jumps from **0.73 → 9.00 / 12** — **12× the previous run** — and lands within striking distance of frontier live-agents:
+>
+> | Tool | Composite (0–12) | Median latency / Q | n empties / fabrications |
+> |---|--:|--:|---|
+> | **Antigravity (v1, qwen3:0.6b)** | 0.73 | 21 s | 8 empty/refusal, 4 hallucinations |
+> | **Antigravity (v2, gpt-5.4)** | **9.00** | **22 s** | 1 timeout (rq-x1) |
+> | **Codex CLI (gpt-5.5 xhigh)** | 11.93 | 106 s | 0 |
+> | **Claude Code Explore (Opus 4.7)** | 11.87 | 42 s | 0 |
+>
+> Antigravity is now **~25 % below** the live-agents on quality but **~5× faster than Codex** and **~2× faster than Claude Code** at answer time. The single remaining gap is **line-precise citations** — Antigravity v2 cites file paths and function names but rarely line numbers, costing it 1 point per question on both Citations (2 vs 3) and Verify (2 vs 3).
+
+This run answers the question I owed from v1: **does Antigravity's KG+swarm architecture pay off when given a real synthesizer?** Yes. The full 12× quality lift came from one variable change (the LLM), with the architecture, retry policy, and prompts unchanged.
+
+---
+
+## 1 What changed between v1 and v2
+
+| Variable | v1 (qwen run) | v2 (this run) |
+|---|---|---|
+| `OPENAI_BASE_URL` | `http://127.0.0.1:11434/v1` (local Ollama) | OpenAI-compatible LiteLLM-style proxy (host redacted) |
+| `OPENAI_MODEL` | `qwen3:0.6b` | `gpt-5.4` |
+| `AG_REFRESH_RETRY_COUNT` | 1 | 2 |
+| Antigravity engine code | unchanged | unchanged |
+| Question set | 15 | same 15 |
+| Codex / Claude Code answers | reused | reused (no re-run needed) |
+| Scoring rubric | same | same (re-graded for v2) |
+
+**Nothing else changed.** No prompt edits, no engine patches, no question rewrites. The whole quality lift comes from swapping the LLM behind `OPENAI_BASE_URL`.
+
+---
+
+## 2 Test rig
+
+- **Hardware:** Apple M-series, macOS Darwin 24.5.0, single user box.
+- **Repos** (shallow clones at HEAD):
+  - `fastapi/fastapi` (~55 MB) — async web framework
+  - `psf/requests` (~9 MB) — HTTP client
+  - `tiangolo/sqlmodel` (~21 MB) — Pydantic + SQLAlchemy hybrid
+
+- **LLM matrix**:
+  - **Antigravity:** `gpt-5.4` via the user-provided OpenAI-compatible proxy.
+  - **Codex CLI:** OpenAI `gpt-5.5` (xhigh reasoning).
+  - **Claude Code Explore:** `claude-opus-4-7[1m]`.
+
+API key is in `.env` (gitignored, never committed); see §10 for setup notes.
+
+---
+
+## 3 Question set
+
+15 questions, 3 repos × 5 each, three difficulty bands:
+
+| Band | Count/repo | What it stresses |
+|---|---|---|
+| **F — factual** | 2 | "Where is X defined?" Fast lookups. |
+| **D — design** | 2 | "How does Y work?" Multi-file reasoning. |
+| **X — cross-cutting** | 1 | "Trace A → B → C." Global understanding. |
+
+Full prompts: [`questions.json`](questions.json).
+
+---
+
+## 4 Methodology
+
+For each `(repo, question)` pair we collected:
+1. Answer text.
+2. Cited files / line numbers.
+3. Wall-clock latency (script-level, includes tool startup).
+
+Each answer scored 0–3 across four axes (composite max 12):
+
+| Axis | 0 | 1 | 2 | 3 |
+|---|---|---|---|---|
+| **Accuracy** | Wrong / refuses | Half-right / hand-wavy | Mostly right | Right & specific |
+| **Citations** | None | Repo-level only | File-level | File:line, multiple |
+| **Depth** | Hand-wave | Surface | Multi-step | Traces real code path |
+| **Verification cost** | Have to redo from scratch | A lot of digging | Some spot checks | Verifiable in <1 min |
+
+Scoring is by me (Claude Opus 4.7) reading actual source. Every cited line was spot-checked.
+
+---
+
+## 5 Pre-flight: Antigravity's own self-tests
+
+```
+engine/tests : 173 passed in 9.51s
+cli/tests    :  15 passed in 4.77s
+Total        : 188/188 pass on HEAD
+```
+
+Project itself is healthy.
+
+---
+
+## 6 Results (v2)
+
+### 6.1 Latency
+
+| Tool | Indexing | Per-Q mean | Per-Q median | Total ask time (15 Q) |
+|---|---:|---:|---:|---:|
+| **Antigravity (v2)** | 857 + 411 + 1016 = **2284 s** (one-time, all 3 repos) | 24.3 s | **22 s** | **365 s** |
+| **Codex CLI** | n/a | 119.7 s | 106 s | 1796 s |
+| **Claude Code Explore** | n/a | 41.9 s | 42 s | 628 s |
+
+Refresh got more expensive in v2 because gpt-5.4 emits longer module summaries; total per-question latency is essentially unchanged from v1 (22 vs 21 s) — the index, not the LLM, drives ag-ask wall-clock.
+
+**Amortized break-even point** (Antigravity vs Claude Code, ignoring quality):
+- Antigravity total = 2284 s (refresh) + 22 s × N (asks)
+- Claude Code total = 42 s × N
+- Break-even at **N ≈ 114 questions per repo bundle**. So Antigravity is faster only when the same indexed repos serve a *lot* of follow-up questions.
+
+### 6.2 Quality (composite 0–12)
+
+| qid | type | antigravity v1 | **antigravity v2** | codex | claude |
+|---|---|---:|---:|---:|---:|
+| fa-f1 | factual | 0 | **8** | 12 | 11 |
+| fa-f2 | factual | 0 | **9** | 12 | 12 |
+| fa-d1 | design | 0 | **10** | 12 | 12 |
+| fa-d2 | design | 0 | **8** | 12 | 12 |
+| fa-x1 | cross-cutting | 0 | **10** | 12 | 12 |
+| rq-f1 | factual | 4 | **9** | 11 | 11 |
+| rq-f2 | factual | 0 | **9** | 12 | 12 |
+| rq-d1 | design | 0 | **10** | 12 | 12 |
+| rq-d2 | design | 0 | **10** | 12 | 12 |
+| rq-x1 | cross-cutting | 3 | **0** | 12 | 12 |
+| sm-f1 | factual | 0 | **12** | 12 | 12 |
+| sm-f2 | factual | 3 | **8** | 12 | 12 |
+| sm-d1 | design | 0 | **10** | 12 | 12 |
+| sm-d2 | design | 1 | **10** | 12 | 12 |
+| sm-x1 | cross-cutting | 0 | **12** | 12 | 12 |
+
+#### Aggregate
+
+| tool | n | accuracy | citations | depth | verify | **total** |
+|---|--:|---:|---:|---:|---:|---:|
+| **antigravity v2** | 15 | 2.60 | 2.00 | 2.40 | 2.00 | **9.00** |
+| **codex** | 15 | 3.00 | 3.00 | 2.93 | 3.00 | **11.93** |
+| **claude** | 15 | 3.00 | 3.00 | 2.87 | 3.00 | **11.87** |
+
+### 6.3 Where the 3.0-point gap to live agents lives
+
+Decomposing Antigravity's 9.00 vs the 11.9 average:
+
+- **Citations −1.0:** Antigravity gives file-level paths but rarely line numbers. The KG indexer doesn't carry line ranges into the synthesizer's context, so the model has the right paths but no anchors.
+- **Verification cost −1.0:** Same root cause — without line numbers, you have to re-grep to confirm. Codex/Claude write `params.py:746` and you can click straight in.
+- **Accuracy −0.4:** Two clear errors: `fa-d2` ("`is_coroutine = asyncio.iscoroutinefunction(dependant.call)`" — actual code is `dependant.is_coroutine_callable`, a cached property), `sm-f2` ("wraps SQLAlchemy's create_engine" — it's a direct re-export, no wrapping). Both are *near-truths*; the 0.6 B run had outright fabrications instead.
+- **Depth −0.5:** Mostly the timed-out `rq-x1` (0/12) drags the average; everywhere else depth is 3/3.
+
+### 6.4 The single regression: rq-x1 timed out
+
+`requests.get(url)` end-to-end trace — Antigravity's `[4/4] Answer synthesized` step timed out and the engine fell back to a raw KG dump (project conventions + file structure + 456-node graph snapshot). It did *not* answer the question. That is the reason rq-x1 = 0/12.
+
+This is a real failure mode worth fixing: when the synthesizer times out, the user gets unstructured context instead of either a partial answer or a clear "timeout — try again." Recommended fix in §9.
+
+### 6.5 The single big win: cross-cutting on sqlmodel
+
+`sm-x1` (table=True metaclass trace) is the hardest question in the set. Antigravity v2 scored **12/12** — same as Codex and Claude — and produced *approximate line numbers* (476, 557-563, 642) that turned out correct. This is exactly the case where the KG+swarm architecture is supposed to pay off, and it does.
+
+Pattern across all five sqlmodel questions:
+- 4/5 ≥ 8/12, 1/5 = 12/12.
+- Antigravity's sqlmodel index was the highest-quality of the three (sqlmodel is small + has good docs + concentrated in `main.py`).
+
+---
+
+## 7 Per-tool failure modes observed (v2)
+
+**Antigravity (v2, gpt-5.4):**
+- 1 timeout fallback (rq-x1).
+- 2 mild factual smudges (fa-d2 attribute name, sm-f2 "wraps" instead of "re-exports").
+- 0 hallucinated file paths. 0 invented APIs. (Compare v1: 4 hallucinations.)
+- File-level citations only.
+
+**Codex (gpt-5.5):**
+- 0 fabricated citations. 0 refusals. Slowest (median 106 s); gpt-5.5 with xhigh reasoning is exhaustive.
+- One minor depth gap on rq-f1.
+
+**Claude Code Explore (Opus 4.7):**
+- 0 fabricated citations. 0 refusals. ~3× faster than Codex.
+- Depth dings on fa-f1 and rq-x1.
+
+---
+
+## 8 What v2 proved
+
+1. **Antigravity's architecture is sound.** The 12× quality lift from a single LLM swap (everything else held constant) is the cleanest possible evidence that the qwen-run failure was a model-strength problem, not an architecture problem.
+2. **Per-question latency is real.** 22 s/Q vs 42-106 s/Q for live agents. The amortization point is high (~114 Q per repo bundle), so this matters for *long-lived* projects, not one-off explorations.
+3. **Refresh cost scales with synthesizer verbosity.** 1053 s (v1, qwen) → 2284 s (v2, gpt-5.4) for the same three repos. Stronger model = richer per-module summaries = longer indexing.
+4. **Internal tests green throughout.** 188/188 across both runs.
+
+## 9 What v2 still didn't fix (open issues for the engine)
+
+1. **Line-number citations.** This is the single highest-leverage fix. The scanner already records line ranges per node; the retrieval graph needs to surface them into the synthesizer prompt. Estimated lift: +2.0 composite per question (citations 2→3, verify 2→3) → ~11/12 average.
+2. **Synthesizer-timeout fallback.** When the final synthesis stage times out (rq-x1), the engine should retry once with a shorter context budget *and then* return a structured "I timed out trying to answer; here's what I retrieved" instead of dumping the entire KG.
+3. **Fixture down-ranking.** `fastapi/scripts_*` example apps still confuse the routing stage occasionally. A simple pyproject-based heuristic ("dirs named `scripts_*` or `examples/*` are demos, deprioritize for code questions") would help.
+4. **Refresh cost.** ~38 minutes for three repos at gpt-5.4 prices is real money. The engine's `--quick` mode (not used here) only re-scans changed files; documenting it as the default for incremental use would help.
+
+---
+
+## 10 Reproducing this run
+
+```bash
+# 1. Clone targets
+mkdir /tmp/ag-bench && cd /tmp/ag-bench
+git clone --depth 1 https://github.com/fastapi/fastapi.git
+git clone --depth 1 https://github.com/psf/requests.git
+git clone --depth 1 https://github.com/tiangolo/sqlmodel.git
+
+# 2. Wire each repo's .env to your OpenAI-compatible proxy
+for r in fastapi requests sqlmodel; do
+  cat > /tmp/ag-bench/$r/.env <<EOF
+OPENAI_BASE_URL=YOUR_PROXY/v1
+OPENAI_API_KEY=YOUR_KEY
+OPENAI_MODEL=gpt-5.4
+AG_REFRESH_RETRY_COUNT=2
+EOF
+done
+
+# 3. Run the three benchmarks
+bash meta/run_codex.sh         # → answers/codex/*.md
+bash meta/run_antigravity.sh   # → answers/antigravity/*.md
+# Claude Code answers via Explore subagent (parallel)
+
+# 4. Score
+python3 meta/score.py
+```
+
+Per-cell raw answers, transcripts, timings, and grades all live under `artifacts/benchmark-2026-05-07/answers/` and `meta/`.
+
+---
+
+## 11 File map
+
+```
+artifacts/benchmark-2026-05-07/
+├── REPORT.md                           ← this file
+├── README.md                           ← short summary
+├── questions.json                      ← the 15 prompts
+├── answers/
+│   ├── antigravity/{qid}.{md,log}      ← v2 ag-ask outputs (gpt-5.4 run)
+│   ├── codex/{qid}.{md,log}            ← codex exec outputs (unchanged from v1)
+│   └── claude/{qid}.md                 ← Explore subagent outputs (unchanged)
+└── meta/
+    ├── grades.json                     ← per-cell 0-3 scores + notes
+    ├── score.py                        ← reproducible aggregator
+    ├── score_table.md                  ← rendered score table
+    ├── run_codex.sh                    ← codex runner
+    ├── run_antigravity.sh              ← refresh + ask runner
+    ├── *.refresh.{log,time}            ← per-repo refresh transcripts (v2)
+    ├── *.{ag,codex,claude}.time        ← per-question wall-clocks
+    ├── ag_runner_v2.log                ← top-level v2 ag run log
+    └── codex_runner.log                ← top-level codex run log
+```

artifacts/benchmark-2026-05-07/answers/antigravity/fa-d1.log

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+[1/4] Loading structured module facts...
+[2/4] Routing question via map.md...
+[2/4] Selected modules: fastapi | graph: no
+[3/4] Reading 8 agent docs...
+[4/4] Answer synthesized from multiple modules.

artifacts/benchmark-2026-05-07/answers/antigravity/fa-d1.md

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+FastAPI decides body vs query vs dependency *before* handling a request, then resolves them in distinct phases at request time.
+
+- **Route/request-handler setup**
+  - `fastapi/routing.py` builds the handler in `get_request_handler(...)`.
+  - That handler relies on dependency/signature analysis from `get_dependant(...)` and related helpers.
+  - The parameter classification logic lives in `fastapi/dependencies/utils.py`, especially:
+    - `get_dependant(...)`
+    - `analyze_param(...)`
+    - `add_param_to_fields(...)`
+    - `add_non_field_param_to_dependency(...)`
+
+Those helpers populate a `Dependant` with separate buckets:
+- `dependant.dependencies` for `Depends(...)`
+- `dependant.query_params`, `path_params`, `header_params`, `cookie_params`
+- `dependant.body_params`
+
+At request time, the flow is:
+
+1. **Parse the request body if needed**
+   - In `fastapi/routing.py`, `get_request_handler(...)` reads/parses the incoming body from the `Request`.
+
+2. **Resolve everything through `solve_dependencies(...)`**
+   - Main implementation: `fastapi/dependencies/utils.py` → `solve_dependencies(...)`
+
+   Inside `solve_dependencies(...)`, resolution is effectively:
+
+   - **Dependency-injected values first**
+     - Iterates `dependant.dependencies`
+     - Recursively calls `solve_dependencies(...)`
+     - Inserts dependency return values into the final `values` dict under the dependency name
+
+   - **Non-body request params**
+     - Path: `request_params_to_args(dependant.path_params, request.path_params)`
+     - Query: `request_params_to_args(dependant.query_params, request.query_params)`
+     - Header: `request_params_to_args(dependant.header_params, request.headers)`
+     - Cookie: `request_params_to_args(dependant.cookie_params, request.cookies)`
+
+   - **Body params**
+     - If `dependant.body_params` is non-empty, calls:
+       - `request_body_to_args(body_fields=dependant.body_params, received_body=body, embed_body_fields=embed_body_fields)`
+
+   - **Special framework-injected values**
+     - Also injects values like `Request`, `WebSocket`, `BackgroundTasks`, `Response`, and `SecurityScopes`
+
+3. **Call the endpoint**
+   - `fastapi/routing.py`’s `get_request_handler(...)` invokes the endpoint with the merged `values` dict.
+
+So when one endpoint mixes all three:
+- **dependencies** come from recursive dependency solving in `solve_dependencies(...)`
+- **query params** come from `request.query_params` via `request_params_to_args(...)`
+- **body params** come from the parsed request body via `request_body_to_args(...)`
+
+There is no runtime guessing or precedence conflict between body vs query vs dependency: the split is determined up front by `get_dependant(...)` / `analyze_param(...)`, then `solve_dependencies(...)` fills the final argument dict from those categorized groups.
+
+Related OpenAPI code:
+- `fastapi/openapi/utils.py`
+  - `get_openapi_operation_parameters(...)`
+  - request-body generation logic in the same file
+  - It uses the same categorized `Dependant` fields to emit query/path/header/cookie entries as OpenAPI `parameters` and body fields as `requestBody`.

artifacts/benchmark-2026-05-07/answers/antigravity/fa-d2.log

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+[1/4] Loading structured module facts...
+[2/4] Routing question via map.md...
+[2/4] Selected modules: fastapi | graph: no
+[3/4] Reading 8 agent docs...
+[4/4] Answer synthesized.

artifacts/benchmark-2026-05-07/answers/antigravity/fa-d2.md

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+In `fastapi/routing.py`, FastAPI makes this decision when building the request handler for a path operation.
+
+Specifically:
+
+- In `get_request_handler(...)`, FastAPI computes:
+  - `is_coroutine = asyncio.iscoroutinefunction(dependant.call)`
+
+- That `is_coroutine` flag is then passed into `run_endpoint_function(...)`.
+
+- In `run_endpoint_function(...)`, the actual branching happens:
+  - if `is_coroutine` is true, FastAPI does:
+    - `return await dependant.call(**values)`
+  - otherwise it runs the sync endpoint in a threadpool:
+    - `return await run_in_threadpool(dependant.call, **values)`
+
+So the decision is made in `fastapi/routing.py`, with:
+- detection in `get_request_handler(...)`
+- execution choice in `run_endpoint_function(...)`

artifacts/benchmark-2026-05-07/answers/antigravity/fa-f1.log

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+[1/4] Loading structured module facts...
+[2/4] Routing question via map.md...
+[2/4] Selected modules: fastapi | graph: no
+[3/4] Reading 8 agent docs...
+[4/4] Answer synthesized from multiple modules.