manage-evals

name

manage-evals

description

This skill should be used when the user asks to "trigger an eval", "run evaluation", "run swebench", "run gaia", "run benchmark", "compare eval runs", "compare evaluation results", "check eval regression", "compare benchmark results", "what changed in the eval", "diff eval runs", or mentions triggering, comparing, or reporting on SWE-bench, GAIA, or other benchmark evaluation results. Provides workflow for triggering evaluations on different benchmarks, finding and comparing runs, and reporting performance differences.

Managing Evaluations

Overview

OpenHands evaluations produce results stored on a CDN at https://results.eval.all-hands.dev/. Each run is identified by a path: {benchmark}/{model_slug}/{github_run_id}/. This skill enables triggering evaluation runs, comparing results between runs, and posting performance reports as GitHub PR comments.

Quick Start

Trigger an Evaluation

python .agents/skills/manage-evals/scripts/manage_evals.py trigger \
    --sdk-ref <BRANCH_OR_TAG> --benchmark swebench --eval-limit 50

Compare Runs

python .agents/skills/manage-evals/scripts/manage_evals.py compare \
    "<benchmark>/<model_slug>/<run_id>/" \
    --auto-baseline

Compare and Post to PR

python .agents/skills/manage-evals/scripts/manage_evals.py compare \
    "<benchmark>/<model_slug>/<run_id>/" \
    --auto-baseline \
    --post-comment --pr <PR_NUMBER> --repo OpenHands/software-agent-sdk

Triggering Evaluations

Using the Script

# SWE-bench (default) on a PR branch
python .agents/skills/manage-evals/scripts/manage_evals.py trigger \
    --sdk-ref my-feature-branch --eval-limit 50

# GAIA benchmark
python .agents/skills/manage-evals/scripts/manage_evals.py trigger \
    --sdk-ref main --benchmark gaia --eval-limit 50

# With a specific model
python .agents/skills/manage-evals/scripts/manage_evals.py trigger \
    --sdk-ref v1.16.0 --benchmark swebench --model-ids gemini-3-flash --eval-limit 50

# Multiple benchmarks (run the command multiple times)
for bench in swebench gaia; do
    python .agents/skills/manage-evals/scripts/manage_evals.py trigger \
        --sdk-ref main --benchmark "$bench" --eval-limit 50 --reason "Multi-benchmark eval"
done

Available Benchmarks

Benchmark	Description
swebench	SWE-bench (default) — software engineering tasks
swebenchpro	SWE-Bench Pro — harder software engineering tasks
gaia	GAIA — general AI assistant tasks
swtbench	SWT-bench — software testing tasks
commit0	Commit0 — commit generation tasks
swebenchmultimodal	SWE-bench Multimodal — tasks with images
terminalbench	TerminalBench — terminal interaction tasks
programbench	ProgramBench — program-repair tasks against gold-standard test binaries

Trigger Options

Option	Default	Description
--sdk-ref	(required)	Branch, tag, or commit SHA to evaluate
--benchmark	swebench	Benchmark to run
--eval-limit	50	Number of instances to evaluate
--model-ids	(first in config)	Comma-separated model IDs from resolve_model_config.py
--tool-preset	default	Tool preset: default, gemini, gpt5, planning
--agent-type	default	Agent type: default, acp-claude, acp-codex
--instance-ids		Specific instance IDs to evaluate (overrides eval-limit)
--reason		Human-readable reason (shown in notifications)
--benchmarks-branch	main	Branch of the benchmarks repo
--eval-branch	main	Branch of the evaluation repo

Via PR Labels (Alternative)

Adding a label to a PR also triggers evaluations:

• run-eval-1 — 1 instance (quick sanity check)
• run-eval-50 — 50 instances (standard comparison)
• run-eval-200 — 200 instances
• run-eval-500 — 500 instances (full benchmark)

Comparing Evaluation Runs

Step 1: Find the Current PR's Eval Run

Eval runs are triggered by adding labels like run-eval-50 to a PR. The all-hands-bot posts a comment with results when complete.

Option A — From bot comments on the PR:

gh api repos/OpenHands/software-agent-sdk/issues/<PR_NUMBER>/comments \
    --jq '.[] | select(.user.login == "all-hands-bot") | .body' \
    | grep -o 'Evaluation:.*' | head -1

The evaluation name follows the format {github_run_id}-{model_slug_short} (e.g., 23775164157-claude-son). Extract the github_run_id from this.

Option B — From the "Evaluation Triggered" bot comment:

gh api repos/OpenHands/software-agent-sdk/issues/<PR_NUMBER>/comments \
    --jq '.[] | select(.body | test("Evaluation Triggered")) | .body'

This contains the SDK commit SHA. Cross-reference with daily metadata to find the run ID.

Option C — From daily metadata:

curl -s "https://results.eval.all-hands.dev/metadata/$(date -u +%Y-%m-%d).txt"

Each line is a run path. Match by benchmark and model to find the run.

Step 2: Identify the Run Path Components

A run path has three components:

• benchmark: swebench, swebenchpro, gaia, swtbench, commit0, swebenchmultimodal, terminalbench, programbench
• model_slug: Derived from model name with /:@. replaced by - (e.g., litellm_proxy-claude-sonnet-4-5-20250929)
• run_id: The GitHub Actions workflow run ID from the OpenHands/evaluation repo

Step 3: Verify Results Exist

curl -sI "https://results.eval.all-hands.dev/<benchmark>/<model_slug>/<run_id>/output.report.json" | head -1

A 200 status confirms the run completed and results are available.

Step 4: Find a Baseline for Comparison

Automatic: The comparison script's --auto-baseline flag scans metadata files backward up to 14 days to find the most recent completed run with the same benchmark and model.

Manual: Inspect metadata files or other PR bot comments to identify a specific run:

# Check today's runs
curl -s "https://results.eval.all-hands.dev/metadata/$(date -u +%Y-%m-%d).txt" | grep "swebench/litellm_proxy-claude"

# Check yesterday's runs
curl -s "https://results.eval.all-hands.dev/metadata/$(date -u -d yesterday +%Y-%m-%d).txt" | grep "swebench/litellm_proxy-claude"

Step 5: Run the Comparison

python .agents/skills/manage-evals/scripts/manage_evals.py compare \
    "swebench/litellm_proxy-claude-sonnet-4-5-20250929/23775164157/" \
    --baseline "swebench/litellm_proxy-claude-sonnet-4-5-20250929/23773892085/"

Or with auto-baseline and PR comment posting:

python .agents/skills/manage-evals/scripts/manage_evals.py compare \
    "swebench/litellm_proxy-claude-sonnet-4-5-20250929/23775164157/" \
    --auto-baseline \
    --post-comment --pr 2334 --repo OpenHands/software-agent-sdk

Available Data Per Run

Each run stores files at https://results.eval.all-hands.dev/{run_path}/:

File	Description
metadata/params.json	Run parameters: SDK commit, PR number, model, eval_limit, triggered_by
output.report.json	Aggregated results: resolved/submitted/total counts and instance IDs
cost_report.jsonl	Per-instance cost data
results.tar.gz	Full archive with all outputs

Dashboard

The eval monitor dashboard provides a visual view of runs:

https://openhands-eval-monitor.vercel.app/?run={benchmark}/{model_slug}/{run_id}/

Interpreting Results

• Success rate = resolved / min(eval_limit, total_instances)
• A 50-instance sample has natural variance of ±2-4 resolved instances between runs
• Focus on instance-level changes (gained/lost) to understand regressions vs. noise
• If the same set of instances is resolved, the difference is likely noise

Additional Resources

Reference Files

• references/eval-infrastructure.md — Detailed documentation on the evaluation infrastructure, GCS paths, metadata format, and workflow triggers

Scripts

• scripts/manage_evals.py — Standalone comparison script with auto-baseline detection and GitHub comment posting