agent-eval-flow

Agent Eval Flow

Evaluate agent runs, understand failures, and compare changes.

Use your existing agent runtime or import retained runs into a shared evidence format. Apply your checks, inspect an HTML report, change the system, and compare another run. The target can include models, instructions, skills, tools, loops, memory and environment.

Example reports · Releases · Report an issue · Contribute

Install

Requires Python 3.11 or later.

python -m pip install agent-eval-flow

Try an offline example

git clone https://github.com/guybass/agent-eval-flow.git
cd agent-eval-flow
python -m pip install -e ".[cli]"
python examples/archive_review.py --output demo-output/archive
python examples/assessment_review.py --output demo-output/assessment
python examples/gpt_researcher_review.py --output demo-output/gpt-researcher

Open report.html inside an output directory. These examples make no model calls.

• Archived-run evaluation imports a published SWE-agent trajectory, checks its evidence, saves the result, and regrades the same capture without running the agent again.
• Configuration assessment compares two local instruction files against an explicit requirement.

What you can evaluate

• Outcomes and execution evidence with application-defined metrics.
• Configuration and observed runtime behavior, with explicit coverage and expected versus observed values.
• Candidate changes using saved results, comparisons and selection policies.

Missing evidence stays unknown. Runtime-specific importers and collectors map native logs into the shared records; arbitrary logs are not interpreted automatically. Native adapters keep the agent's own execution loop.

Tool calling report

Add deterministic Toolscore diagnostics to captured runs and generate a separate tools report alongside the general report:

python -m pip install -e ".[toolscore]"
python examples/toolscore_review.py --output demo-output/toolscore

The offline example writes linked report.html and tools.html, tools.json, and verified evidence files. It uses synthetic retained streams and makes no agent or model calls. Toolscore evaluates requested tools and arguments; task outcomes remain separate checks. Missing capture stays unknown.

Use result.report(path, tools=True) after configuring the optional evaluator. See the integration guide for trace coverage, expected calls, scoring rules, and supported formats.

Example reports

These are small native-agent experiments on controlled synthetic tasks. They demonstrate specific changes, not general reliability or security accuracy. Read the reports, walkthroughs and limits.

Contributing

Contributions are welcome, including documentation fixes, offline examples, regression tests, adapters, and report improvements. You can get started without model credentials. Small fixes can go straight to a pull request; for larger features, open an issue to discuss the approach.

Start with the contribution guide for setup on Windows, macOS, or Linux, a code map, local checks, and the steps to your first pull request.

This is a developer preview. Native integrations require their own runtime setup and credentials; offline test success does not establish live compatibility.

See the test guide for live profiles, and third-party notices for fixture provenance and licenses.