docs(bench): cross-repo Q&A benchmark vs Codex/Claude Code (2026-05-07)

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

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+# 2026-05-07 — Antigravity vs Codex vs Claude Code
+
+Cross-repo Q&A benchmark on `fastapi`, `requests`, `sqlmodel`. 15 questions per tool × 3 tools = 45 graded answers.
+
+→ Read [`REPORT.md`](REPORT.md) for the full write-up.
+
+**Headline:** Antigravity 0.73/12, Codex 11.93/12, Claude Code 11.87/12.
+The comparison is asymmetric — Antigravity ran on Ollama `qwen3:0.6b` (the only model that finished downloading on the test box) while the other two ran at frontier strength. The benchmark proves the *pipeline* runs end-to-end and is fast (22 s median per question) but is bottlenecked by the synthesizer model.
+
+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` — exact runner scripts

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
+**Author:** Claude Opus 4.7 (autonomous run at the user's request)
+**Antigravity branch:** `claude/quirky-engelbart-ec012c` @ engine 0.2.1
+**Status:** 15/15 questions answered by all three tools, 173+15 internal tests green.
+
+---
+
+## TL;DR
+
+> Frontier live-codebase agents (Codex with gpt-5.5, Claude Code Explore with Opus 4.7) **decisively outperformed** Antigravity in this run. On the 0–12 composite score, Codex averaged **11.93/12**, Claude Code **11.87/12**, and Antigravity **0.73/12**.
+>
+> The comparison is **deliberately asymmetric**: Antigravity here is wired to a 0.6 B local model (`qwen3:0.6b`) — the only Ollama model that finished downloading on the test box's slow link. Codex and Claude Code are running at full frontier strength. The benchmark therefore does **not** prove Antigravity's *architecture* is weak; it does prove that Antigravity's pipeline is **acutely sensitive to the synthesizer model**, and that a 0.6 B backend is not strong enough to drive `ag-ask` end-to-end.
+>
+> The Antigravity pipeline itself ran cleanly: 173 engine tests + 15 CLI tests pass on HEAD; `ag-refresh` built a knowledge graph for every repo; `ag-ask` answered every question without crashing. The answers were just mostly empty or hallucinated.
+
+---
+
+## 1 What this benchmark is, and what it is *not*
+
+This is a **head-to-head comparison of three "ask the codebase" UX modes** on three real third-party repositories. We do **not** test Antigravity's full slash-command UX, MCP, or sandbox. We test only the code-understanding capability the user invokes daily:
+
+| Tool | Mode under test | What it actually does in this run |
+|---|---|---|
+| **Antigravity** (`ag-refresh` + `ag-ask`) | Pre-built knowledge graph + multi-agent retrieval | Indexes the repo into `.antigravity/`, then runs a 3-agent swarm (ContextCurator → DeepAnalyst → AnswerSynthesizer) over the index. **LLM: Ollama `qwen3:0.6b`.** |
+| **Codex CLI** (`codex exec`) | Live shell agent | gpt-5.5 (xhigh reasoning) with grep/find/read tools, no pre-indexing. |
+| **Claude Code Explore subagent** | Live shell agent | claude-opus-4-7 with grep/find/read tools, no pre-indexing. |
+
+> **Why this comparison is interesting.** Antigravity bets that *pre-indexing* + *purpose-built agents* will beat live grep loops on (1) latency, (2) cross-cutting "trace" questions, and (3) token efficiency at inference time. Codex/Claude Code bet that *live exploration with a strong model* is enough.
+
+---
+
+## 2 Test rig
+
+- **Hardware:** Apple M-series, macOS Darwin 24.5.0, single user box.
+- **Network:** Cold cache for all three repos. The qwen2.5-coder:7b pull stalled at 50 % twice (network-side throttling), so the run uses qwen3:0.6b — **the most capable model that finished downloading inside the bench window.**
+- **Repos** (all shallow clones at HEAD):
+
+  | Slug | On-disk | Profile |
+  |---|---|---|
+  | `fastapi/fastapi` | ~55 MB | Async web framework, dense DI |
+  | `psf/requests` | ~9 MB | Mature HTTP client, tightly factored |
+  | `tiangolo/sqlmodel` | ~21 MB | Pydantic + SQLAlchemy hybrid via metaclass |
+
+- **LLM matrix**:
+  - **Antigravity:** `qwen3:0.6b` via OPENAI_BASE_URL → Ollama. **0.6 B parameters.**
+  - **Codex CLI:** OpenAI `gpt-5.5` (xhigh reasoning).
+  - **Claude Code Explore:** `claude-opus-4-7[1m]`.
+
+This asymmetry is the central caveat of the report — see §6.
+
+---
+
+## 3 Question set (15 questions, 3 repos × 5 each)
+
+Three difficulty bands, designed so a naive grep can solve `factual` but not `cross-cutting`:
+
+| 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: see [`questions.json`](questions.json). Examples:
+
+- `fa-f1`: "Where in the FastAPI source is the `Depends` callable class defined, and what does its `__init__` accept?"
+- `rq-x1`: "Trace what happens when a user calls `requests.get(url)` end-to-end."
+- `sm-x1`: "When a user defines a SQLModel class with `table=True`, what mechanism causes a SQLAlchemy mapper to be registered? Trace metaclass behavior."
+
+---
+
+## 4 Methodology
+
+For each `(repo, question)` pair we collect:
+1. **Answer text** (markdown).
+2. **Cited files / line numbers**.
+3. **Wall-clock latency** (script-level, includes tool startup).
+4. **Self-reported confidence** (where the tool emits one).
+
+We then **score each answer** on a 0–3 scale across four axes (composite max 12):
+
+| Axis | 0 | 1 | 2 | 3 |
+|---|---|---|---|---|
+| **Accuracy** | Wrong / refuses | Half-right or 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 source. I made every effort to be honest about cases where Antigravity got something I myself missed. Spot-checks of every Codex and Claude Code citation were done against the actual files; no fabricated citations were found in either tool. Antigravity citations, where present, were checked the same way.
+
+---
+
+## 5 Pre-flight: Antigravity's own self-tests
+
+Before benchmarking, ran the project's own pytest suites on the worktree's HEAD:
+
+```
+engine/tests : 173 passed in 9.51s
+cli/tests    :  15 passed in 4.77s
+Total        : 188/188 pass
+```
+
+So the project itself is healthy. The benchmark below tests the *deployed* pipeline, not the unit tests.
+
+---
+
+## 6 Results
+
+### 6.1 Latency
+
+| Tool | Indexing | Per-question (mean) | Per-question (median) | Total ask time (15 Q) |
+|---|---:|---:|---:|---:|
+| **Antigravity** | 337 + 155 + 561 = 1053 s (one-time, all 3 repos) | **22.1 s** | 21 s | **331 s** |
+| **Codex CLI** | n/a | 119.7 s | 106 s | 1796 s |
+| **Claude Code Explore** | n/a | 41.9 s | 42 s | 628 s |
+
+Per-question Antigravity is the **fastest** — and would still be fastest on amortized basis after one repo's worth of questions (~10) once you have the index. That part of Antigravity's hypothesis holds up.
+
+### 6.2 Quality (composite 0–12)
+
+| qid | type | antigravity | codex | claude |
+|---|---|---:|---:|---:|
+| fa-f1 | factual | **0** | 12 | 11 |
+| fa-f2 | factual | **0** | 12 | 12 |
+| fa-d1 | design | **0** | 12 | 12 |
+| fa-d2 | design | **0** | 12 | 12 |
+| fa-x1 | cross-cutting | **0** | 12 | 12 |
+| rq-f1 | factual | **4** | 11 | 11 |
+| rq-f2 | factual | **0** | 12 | 12 |
+| rq-d1 | design | **0** | 12 | 12 |
+| rq-d2 | design | **0** | 12 | 12 |
+| rq-x1 | cross-cutting | **3** | 12 | 12 |
+| sm-f1 | factual | **0** | 12 | 12 |
+| sm-f2 | factual | **3** | 12 | 12 |
+| sm-d1 | design | **0** | 12 | 12 |
+| sm-d2 | design | **1** | 12 | 12 |
+| sm-x1 | cross-cutting | **0** | 12 | 12 |
+
+#### Aggregate (mean per axis)
+
+| tool | n | accuracy | citations | depth | verify | **total** |
+|---|--:|---:|---:|---:|---:|---:|
+| **antigravity** | 15 | 0.20 | 0.07 | 0.27 | 0.20 | **0.73** |
+| **codex** | 15 | 3.00 | 3.00 | 2.93 | 3.00 | **11.93** |
+| **claude** | 15 | 3.00 | 3.00 | 2.87 | 3.00 | **11.87** |
+
+Codex narrowly edges out Claude Code on depth (2.93 vs 2.87) — both essentially saturated at the rubric ceiling, with Codex tending to cite more lines (e.g. for `rq-x1` it traced all the way down into `urllib3.connectionpool` and CPython's `http.client.send`, while Claude stopped at the urllib3 boundary). On cost-of-verification, both are tied.
+
+### 6.3 Per-tool failure modes observed
+
+**Antigravity** (in this resource-constrained config):
+
+- **Empty stdout (3/15):** `fa-f1`, `fa-x1`, plus near-empty `fa-d2`. The pipeline reaches `[4/4] Answer synthesized` but the synthesizer (qwen3:0.6b) emits no terminal text.
+- **Refuses with "knowledge does not contain it" (7/15):** the routing stage selected the wrong sub-modules (e.g. for `fa-d1` it picked a Playwright-related module from `fastapi`'s test fixtures), so the final agent had no relevant context.
+- **Confident hallucination (4/15):** when the model does generate, it is happy to invent specifics — `sm-f1` named a non-existent `_expression_select_cls.py` and "Team/Hero parents" (those are *tutorial example* classes, not real parents). `rq-f1` got the file path right but invented line 901 (real: 395). `sm-f2` named `sqlalchemy.orm.create_engine` (real: `sqlalchemy.engine.create_engine`).
+- **One actually-useful answer:** `rq-f1` and `sm-f2` got file paths and primary entities correct, just with bad line numbers.
+
+**Codex (gpt-5.5):**
+
+- 0 fabricated citations. 0 refusals. Slowest per-question (median 106 s) — gpt-5.5 with xhigh reasoning is thorough.
+- One minor depth gap on `rq-f1` (didn't cite the `HTTPAdapter` class line itself, only the import).
+
+**Claude Code Explore (Opus 4.7):**
+
+- 0 fabricated citations. 0 refusals. Faster than Codex (median 42 s).
+- Depth dings on `fa-f1` (didn't trace the user-facing `fastapi.Depends()` wrapper) and `rq-x1` (less detail at the urllib3/socket layer than Codex).
+
+### 6.4 Cross-cutting questions (the band where pre-indexing should help)
+
+The hypothesis "Antigravity's KG pays off most on cross-cutting questions" is **not supported** in this run. On the three X-band questions:
+
+| qid | antigravity | codex | claude |
+|---|---:|---:|---:|
+| fa-x1 | 0 | 12 | 12 |
+| rq-x1 | 3 | 12 | 12 |
+| sm-x1 | 0 | 12 | 12 |
+
+Antigravity scored **3/36** on cross-cutting — its weakest category, not its strongest. The bottleneck is the synthesizer: even when the KG retrieves correctly (as it did for `rq-x1`), the 0.6 B model can only emit a generic "use the graph tool" meta-answer. Without a substantially stronger synthesizer, the architecture's strength is unrealized.
+
+---
+
+## 7 What Codex (running as our cross-checker) said about this
+
+I asked the Codex rescue subagent for an independent read on framing. Direct quote of its conclusion:
+
+> "Frontier live-codebase agents strongly outperformed Antigravity in this run; Antigravity's graph-and-swarm pipeline completed, but with a 0.6B local synthesizer it usually failed to produce usable answers. […] This benchmark is asymmetric and should not be read as a clean architecture-vs-architecture comparison. It does show that Antigravity's architecture did not overcome a severely underpowered model, and that end-to-end answer quality depends heavily on the synthesizer backend."
+
+I agree.
+
+---
+
+## 8 What this run **did** prove
+
+1. **Antigravity's pipeline is robust.** Refresh ran on three real repos, two with `partial` exit codes due to per-module LLM timeouts but both still produced full indexes (`map.md`, `module_registry.json`, `agents/`, `graph/`). All 15 ag-ask runs returned an exit code 0; nothing crashed.
+2. **Refresh time scales with repo size, not linearly.** fastapi (55 MB) took 337 s, requests (9 MB) 155 s, sqlmodel (21 MB) 561 s — sqlmodel's longer runtime came from per-module retries on the small model, not from input size.
+3. **Per-question latency is genuinely lower than live agents.** 22 s mean vs 42 s (Claude) and 120 s (Codex). After ~7-10 questions on the same repo the pre-indexing cost amortizes.
+4. **The 188 internal tests still pass on HEAD** — the worktree itself is in good shape.
+
+## 9 What this run **did not** prove
+
+1. **It did not test Antigravity's architecture against a frontier synthesizer.** That experiment requires GOOGLE_API_KEY (Gemini) or a stronger Ollama model. With qwen2.5-coder:7b in place I would expect Antigravity's quality to lift substantially.
+2. **It did not test Antigravity's MCP/sandbox/skill features.** Only `ag-refresh` and `ag-ask`.
+3. **It did not control for the live agents' tool budgets.** Codex and Claude Code were both allowed unlimited grep/read; in tighter token-budget regimes Antigravity's pre-indexing advantage might widen.
+
+---
+
+## 10 Recommended next steps
+
+1. **Re-run with qwen2.5-coder:7b once it's pulled** (offline mirror, or fix the slow CDN edge). The headline expected result is a 5-10× lift on Antigravity's accuracy/citations.
+2. **Make the synthesizer's empty-output case loud.** Right now an empty synthesis exits 0 and looks like a successful answer. A small post-condition (`len(stdout.strip()) < 30 → exit 2`) would surface the real failure mode.
+3. **Down-rank tutorial fixtures during indexing.** `fastapi/tests/scripts_*` contributed noise that misrouted the routing stage on `fa-d1` (Playwright). The scanner should de-prioritize obvious example-app dirs by default.
+4. **Add a "trust mode" for refresh.** When ≥30 % of modules hit timeout retries, the engine should warn the user that the index is partial *before* `ag-ask` is run.
+
+---
+
+## 11 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. Drop OPENAI-compatible Ollama .env into each
+for r in fastapi requests sqlmodel; do
+  cat > /tmp/ag-bench/$r/.env <<EOF
+OPENAI_BASE_URL=http://127.0.0.1:11434/v1
+OPENAI_API_KEY=ollama
+OPENAI_MODEL=qwen3:0.6b   # or qwen2.5-coder:7b for stronger synthesis
+EOF
+done
+
+# 3. Pull whichever model fits the bandwidth
+ollama pull qwen3:0.6b      # 522 MB, fits anywhere
+# ollama pull qwen2.5-coder:7b   # 4.7 GB, recommended
+
+# 4. Run the three benchmarks
+bash meta/run_codex.sh         # → answers/codex/*.md
+bash meta/run_antigravity.sh   # → answers/antigravity/*.md
+# Claude Code answers are gathered via the Explore subagent (parallel)
+
+# 5. Score
+python3 meta/score.py
+```
+
+Raw artifacts (every `(qid, tool)` answer + log + timing) are preserved under `artifacts/benchmark-2026-05-07/answers/` and `meta/`. Grades live in `meta/grades.json` so anyone can sanity-check or rescore.
+
+---
+
+## 12 File map
+
+```
+artifacts/benchmark-2026-05-07/
+├── REPORT.md                  ← this file
+├── questions.json             ← the 15 prompts
+├── answers/
+│   ├── antigravity/{qid}.md   ← ag-ask stdout
+│   ├── antigravity/{qid}.log  ← ag-ask stderr (pipeline progress)
+│   ├── codex/{qid}.md         ← codex exec final message
+│   ├── codex/{qid}.log        ← full codex transcript
+│   └── claude/{qid}.md        ← Explore subagent final reply
+└── meta/
+    ├── grades.json            ← per-cell 0-3 scores + notes
+    ├── score.py               ← reproducible aggregator
+    ├── run_codex.sh           ← codex runner script
+    ├── run_antigravity.sh     ← refresh + ask runner
+    ├── *.refresh.log          ← per-repo refresh transcripts
+    ├── *.{ag,codex,claude}.time ← per-question wall-clocks
+    ├── codex_runner.log       ← top-level codex run output
+    └── ag_runner.log          ← top-level ag run output
+```

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: scripts_query_param_models | graph: yes
+[3/4] Reading 1 agent docs...
+[4/4] Answer synthesized.

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

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+The question touches on FastAPI's behavior of handling query parameters and dependency-injected values, but the provided knowledge includes content about Playwright and its script execution. Since there is no direct reference to FastAPI's logic in the modules listed, no code references can be cited.  
+
+**Answer:**  
+The provided module content does not mention FastAPI or its handling of request parameters like request body, query, or injection. No specific code references are available.

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: scripts_request_form_models | graph: yes
+[3/4] Reading 1 agent docs...
+[4/4] Answer synthesized.

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

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+The module knowledge does not mention FastAPI's endpoint decision-making between `await` and threadpool. Therefore, the question cannot be addressed with provided information.

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...
+[1/4] Structured facts were insufficient; falling back to legacy swarm.
+[1/3] Gathering project context...
+[2/3] Analyzing with multi-agent swarm...
+[3/3] Synthesizing answer...

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

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+

artifacts/benchmark-2026-05-07/answers/antigravity/fa-f2.log

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+[1/4] Loading structured module facts...
+[2/4] Routing question via map.md...
+[2/4] Selected modules: scripts_separate_openapi_schemas | graph: yes
+[3/4] Reading 1 agent docs...
+[4/4] Answer synthesized.

artifacts/benchmark-2026-05-07/answers/antigravity/fa-f2.md

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+The file and class implementing FastAPI's automatic OpenAPI schema generation from the provided knowledge are none. The knowledge does not mention any file or class related to FastAPI schema generation.

artifacts/benchmark-2026-05-07/answers/antigravity/fa-x1.log

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+[1/4] Loading structured module facts...
+[2/4] Routing question via map.md...
+[1/4] Structured facts were insufficient; falling back to legacy swarm.
+[1/3] Gathering project context...
+[2/3] Analyzing with multi-agent swarm...
+[3/3] Synthesizing answer...

artifacts/benchmark-2026-05-07/answers/antigravity/fa-x1.md

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+

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

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+[1/4] Loading structured module facts...
+[2/4] Routing question via map.md...
+[1/4] Structured facts were insufficient; falling back to legacy swarm.
+[1/3] Gathering project context...
+[2/3] Analyzing with multi-agent swarm...
+[3/3] Synthesizing answer...

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

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+Based on the information provided, no specific agent or context allows me to explain the `HTTPAdapter` design in the `requests` module. The available agents focus on configuration files, dependency management, and environment setup. For a complete understanding of the HTTPAdapter design pattern, further context or direct integration with relevant documentation would be needed.  
+
+**Final Answer**:  
+The provided information does not include direct knowledge about the `HTTPAdapter` design pattern. If further details or configuration context are available, they would clarify how `requests` interacts with `urllib3` connection pools.  
+
+The question requires specific knowledge on this design aspect, but no relevant agent or context is available.

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

@@ -0,0 +1,6 @@
+[1/4] Loading structured module facts...
+[2/4] Routing question via map.md...
+[1/4] Structured facts were insufficient; falling back to legacy swarm.
+[1/3] Gathering project context...
+[2/3] Analyzing with multi-agent swarm...
+[3/3] Synthesizing answer...

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

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+The provided context does not include specific details about how the "requests" module handles SSL/TLS verification, including the `verify=` parameter, default CA bundle, or integration with certifi. Since the information is not explicitly provided, I cannot offer direct specifications. For precise details, refer to the GitNexus documentation or the project's official resources.

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

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+[1/4] Loading structured module facts...
+[2/4] Routing question via map.md...
+[1/4] Structured facts were insufficient; falling back to legacy swarm.
+[1/3] Gathering project context...
+[2/3] Analyzing with multi-agent swarm...
+[3/3] Synthesizing answer...