Add SWE-bench eval README with setup, usage, and monitoring instructions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-10-06 02:48:25 +00:00 · 2026-03-16 09:59:03 -04:00 · 2026-03-16 09:59:03 -04:00 · e3790ecff3
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+# SWE-Bench-Lite Evaluation
+
+Evaluates Fabro's agent on [SWE-Bench-Lite](https://www.swebench.com/) (300 Python bug-fix tasks across 12 repos). Two phases: generate patches, then evaluate them. Both run on Daytona cloud sandboxes — no local Docker needed.
+
+## Setup
+
+```bash
+cd evals/swe-bench
+python3 -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+```
+
+## Step 1: Generate patches
+
+Runs Fabro's agent on each SWE-bench instance to produce a fix.
+
+```bash
+python run_eval.py \
+    --model claude-haiku-4-5 \
+    --provider anthropic \
+    --output-dir results/haiku-baseline \
+    2>&1 | tee results/haiku-baseline/console.log
+```
+
+**Options:**
+- `--model` — LLM model (default: `claude-haiku-4-5`)
+- `--provider` — LLM provider (default: `anthropic`)
+- `--max-workers` — max concurrent Daytona sandboxes (default: 100)
+- `--timeout` — per-instance timeout in seconds (default: 600)
+- `--instance-ids` — run only specific instances (e.g. `--instance-ids django__django-11099`)
+
+**Monitor:**
+```bash
+tail -f results/haiku-baseline/eval.log   # live per-instance results
+fabro ps                                   # active sandboxes
+fabro logs <RUN_ID>                        # stream a specific run
+```
+
+## Step 2: Evaluate patches
+
+Applies each patch, runs the held-out test suite, and grades pass/fail using swebench's log parsers.
+
+```bash
+python evaluate_daytona.py \
+    --predictions results/haiku-baseline/predictions.jsonl \
+    --output-dir results/haiku-baseline/eval \
+    2>&1 | tee results/haiku-baseline/eval/console.log
+```
+
+**Options:**
+- `--max-workers` — max concurrent eval sandboxes (default: 100)
+- `--timeout` — per-instance timeout in seconds (default: 600)
+- `--instance-ids` — evaluate only specific instances
+
+**Monitor:**
+```bash
+tail -f results/haiku-baseline/eval/eval_grade.log
+```
+
+## Step 3: Record results
+
+Saves results to the git-tracked `scoreboard/` directory for permanent record-keeping.
+
+```bash
+python record_results.py \
+    --run-name haiku-baseline-20260316 \
+    --gen-dir results/haiku-baseline \
+    --eval-dir results/haiku-baseline/eval \
+    --description "Haiku 4.5 baseline, default prompt, 2 CPU / 4 GB, 10min timeout"
+```
+
+Then commit the scoreboard:
+```bash
+git add scoreboard/
+git commit -m "Record haiku-baseline-20260316: XX.X% on SWE-Bench-Lite"
+```
+
+## Scoreboard
+
+Results are stored in `scoreboard/`:
+
+```
+scoreboard/
+├── leaderboard.json                    # all runs ranked by resolve rate
+└── haiku-baseline-20260316/
+    ├── README.md                       # human-readable summary
+    ├── meta.json                       # run metadata, costs, per-repo stats
+    └── instances.jsonl                 # per-instance: has_patch, resolved, duration, cost
+```
+
+View the leaderboard:
+```bash
+cat scoreboard/leaderboard.json | python3 -m json.tool
+```
+
+## File inventory
+
+| File | Purpose |
+|------|---------|
+| `run_eval.py` | Generate patches (step 1) |
+| `evaluate_daytona.py` | Evaluate patches on Daytona (step 2) |
+| `evaluate.py` | Evaluate patches via official swebench Docker harness (alternative to step 2) |
+| `record_results.py` | Record results to scoreboard (step 3) |
+| `gen_dockerfile.py` | Generate per-(repo, version) Dockerfiles from swebench specs |
+| `workflow.fabro` | DOT workflow template (unused — per-instance .fabro files are generated) |
+| `requirements.txt` | Python dependencies: `swebench`, `datasets` |
+| `scoreboard/` | Git-tracked results (committed) |
+| `results/` | Raw run data — predictions, logs, patches (gitignored) |
+
+## Running a new model
+
+Full end-to-end for a new model:
+
+```bash
+# 1. Generate
+python run_eval.py \
+    --model claude-opus-4-6 --provider anthropic \
+    --output-dir results/opus-baseline \
+    2>&1 | tee results/opus-baseline/console.log
+
+# 2. Evaluate
+python evaluate_daytona.py \
+    --predictions results/opus-baseline/predictions.jsonl \
+    --output-dir results/opus-baseline/eval \
+    2>&1 | tee results/opus-baseline/eval/console.log
+
+# 3. Record
+python record_results.py \
+    --run-name opus-baseline-20260316 \
+    --gen-dir results/opus-baseline \
+    --eval-dir results/opus-baseline/eval \
+    --description "Opus 4.6 baseline, default prompt, 2 CPU / 4 GB, 10min timeout"
+
+# 4. Commit
+git add scoreboard/
+git commit -m "Record opus-baseline-20260316"
+```
+
+## Sandbox resources
+
+Each Daytona sandbox uses 2 CPU / 4 GB RAM / 10 GB disk. Snapshots are cached by name — first build is slow (~2 min), subsequent uses are instant.