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* feat(dashboard): render Slack/Linear replies as a card in chat Slack thread replies and Linear comments showed only the bare tool name in the dashboard chat. Map them to dedicated 'slack'/'linear' toolKinds and render the message body in a ReplyCard, so Open-in-Web shows what the agent actually posted. * docs: remove reviewer design doc and Devin comparison framing Delete REVIEWER_DESIGN.md and drop the Devin/Graphite competitive framing from the reviewer eval README and judge docstring.
95 lines
3.4 KiB
Markdown
95 lines
3.4 KiB
Markdown
# Reviewer Eval
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Offline LangSmith eval for the Open SWE Reviewer graph against the 50 PRs from
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`withmartian/code-review-benchmark`.
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## Layout
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```
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evals/reviewer/
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├── golden_comments/ # 50 PRs × golden comments (copied from martian benchmark)
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├── build_dataset.py # martian JSON → LangSmith dataset (resolves SHAs via gh)
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├── config.toml # default benchmark run config
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├── judge.py # claude-opus-4-5 pairwise match evaluator + aggregate
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├── target.py # invokes the reviewer graph over langgraph_sdk
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└── run_eval.py # client.aevaluate entrypoint
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```
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## Prerequisites
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- `LANGSMITH_API_KEY` set in your env.
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- `gh` authenticated (`gh auth status`) — needed for `build_dataset.py`.
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- `ANTHROPIC_API_KEY` set — judge runs `claude-opus-4-5`.
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- A running reviewer graph (local `langgraph dev` or deployed assistant id) with
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`REVIEWER_ASSISTANT_ID` env var pointing at it. Defaults to assistant `reviewer`
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on `http://localhost:2024`.
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## 1. Build the dataset (once)
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```bash
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# Dry run — writes evals/reviewer/dataset_dryrun.json without uploading
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uv run python -m evals.reviewer.build_dataset --dry-run
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# Upload for real
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uv run python -m evals.reviewer.build_dataset --dataset-name openswe-reviewer-v1
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```
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Each example carries: `repo`, `pr_number`, `pr_url`, `base_sha`, `head_sha`,
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`base_ref`, `head_ref`, `pr_title`. The dataset is frozen at upload time —
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upstream PR drift can't invalidate it.
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## 2. Run the eval
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The reviewer graph must be running and accept a `pr` input matching the
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example schema, and must emit a `submit_review` tool call (or set
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`state["review"]["comments"]`) with `[{file, line, severity, body}, ...]`.
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```bash
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uv run python -m evals.reviewer.run_eval
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```
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Smoke-test with 3 PRs first:
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```bash
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uv run python -m evals.reviewer.run_eval --limit 3
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```
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The runner reads benchmark settings from `evals/reviewer/config.toml`. Set the
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deployment URL there (or leave it blank to use `LANGGRAPH_URL` / local dev).
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The target sets `reviewer_eval` for every run, so `publish_review` does not post
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to GitHub.
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## Per-repo review style prompts
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At runtime the reviewer loads a custom style guide from LangGraph Store when
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`configurable.repo` is set (`owner` + `name` → store key `owner/name`). This
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applies to **eval runs too**, as long as a completed style profile exists for
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that repo.
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The Martian benchmark uses these upstream repos (10 PRs each):
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- `getsentry/sentry`
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- `keycloak/keycloak`
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- `grafana/grafana`
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- `discourse/discourse`
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- `calcom/cal.com`
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Before scoring with repo-specific styles, run **Review styles** analysis in the
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dashboard for each repo (or copy prompts into store). Re-run `make dev` so the
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reviewer graph sees the same store.
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By default the judge scores final `add_finding` calls. Set
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`score_mode = "surfaced_findings"` in the config to score only findings that
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would pass the production threshold/cap.
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`model_id` and `reasoning_effort` in the config are passed to the reviewer run,
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so isolated benchmark deployments can test a specific model/effort without
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changing deployment-wide defaults.
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## Notes
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- No GitHub forks needed — both upstream repos and martian's benchmark forks
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(`ai-code-review-evaluation/*`) are public.
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- `judge_match` charges judge LLM tokens proportional to
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`n_candidates × n_goldens` per example. For 50 PRs with ~3 goldens each and
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agents emitting ~10 candidates, expect ~1500 judge calls per experiment.
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