evalrun

Quality Score $\neq$ Release Decision. An LLM evaluator can rate an itinerary 95/100 while an independent auditor blocks it for hard financial violations.

evalrun is a local-first toolkit for testing AI agents. You bring the agent, model, and API key; evalrun runs the checks on your machine, saves the evidence locally, and tells you whether the result should be released.

The toolkit works with hosted APIs and models running on your own computer. It does not host models, provide API credits, provision GPUs, or require a hosted account.

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🌟 Key Capabilities

• 3-Tier Release Gatekeeping:
  • Evaluator Thresholds: Qualitative dimension scoring (0–100) via LLM judges.
  • Independent Auditor Gate: Hard financial, policy, and math validation (blocks releases on unbudgeted items or currency hallucinations).
  • Baseline Regression Gate: Automatically detects score drops ($\Delta \text{score}$) against stored baselines.
• Local-First & Multi-Model: Compatible with hosted APIs (OpenAI GPT-5.6, NVIDIA NIM, Gemini) and local model servers (vLLM, Ollama, LM Studio) on http://localhost:8000/v1.
• Standalone HTML Review Reports: Interactive local HTML report with failure quick-jump bars, auto-opened failing cards, search/filter controls, visual score progress bars, and raw model output inspection.
• CI Exit Code Contract:
  • 0: All scenarios passed evaluator, auditor gate passed, and no regression detected.
  • 1: Release blocked due to evaluator threshold failure, auditor violation, or baseline score regression.
  • 2: Runtime error, missing file, or invalid configuration.

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🚀 Quick Start

1. Installation

git clone https://github.com/imshivamb/agent-eval-platform.git
cd agent-eval-platform
pip install -e .

If your shell says evalrun: command not found, activate the virtual environment and install the repository first:

source .venv/bin/activate
python -m pip install -e .
rehash  # zsh only, refreshes the command cache

2. Zero-Cost Offline Demo

Try EvalRun without an API key or network request:

evalrun demo
open results/demo/report.html  # macOS

3. Single Scenario Run (Hosted Model)

export OPENAI_API_KEY="sk-proj-..."

evalrun run \
  --scenario evals/scenarios/travel-agent/budget-constrained-itinerary.md \
  --agent agents.travel:TravelPlanningAgent \
  --model gpt-5.6-terra \
  --judge-model gpt-5.6-terra \
  --output results/run-001

Choosing a model endpoint

EvalRun does not lock you to one model provider. The --base-url value is the address where the model accepts OpenAI-compatible requests:

Provider	Base URL example
OpenAI-compatible hosted service	https://api.openai.com/v1
Gemini	https://generativelanguage.googleapis.com/v1beta/openai/
OpenRouter	https://openrouter.ai/api/v1
Local vLLM, Ollama, or LM Studio	http://localhost:8000/v1

The judge normally uses the same endpoint and API key as the target model. You only need --judge-base-url or --judge-api-key when the judge is hosted somewhere different. The endpoint URL is not a credential; it simply tells EvalRun where to send the request.

For example, a Gemini run can be written as:

export GEMINI_API_KEY="your-key"

evalrun run \
  --scenario evals/scenarios/travel-agent/budget-constrained-itinerary.md \
  --agent agents.travel:TravelPlanningAgent \
  --model gemini-3.7-flash \
  --base-url https://generativelanguage.googleapis.com/v1beta/openai/ \
  --api-key "$GEMINI_API_KEY" \
  --judge-model gemini-3.7-flash \
  --output results/gemini-run

4. Local Model Server Run (vLLM / Ollama)

# Users host their own local OpenAI-compatible server at http://localhost:8000/v1
evalrun run \
  --scenario evals/scenarios/travel-agent/budget-constrained-itinerary.md \
  --agent agents.travel:TravelPlanningAgent \
  --model qwen2.5-72b-instruct \
  --base-url http://localhost:8000/v1 \
  --api-key EMPTY \
  --judge-model gpt-5.6-terra \
  --output results/local-run

5. Guided Local Web UI

Launch the zero-dependency local web interface:

evalrun ui --port 8501

Open http://127.0.0.1:8501 in your browser to configure endpoints, select scenarios, run evaluations, view pass/fail/block verdicts, and launch interactive HTML reports.

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🐍 Python SDK Usage

Integrate evalrun programmatically into Python automation pipelines:

from framework.sdk import evaluate, compare

# 1. Execute Benchmark Evaluation
results = evaluate(
    scenario="evals/scenarios/travel-agent/budget-constrained-itinerary.md",
    agent="agents.travel:TravelPlanningAgent",
    model="gpt-5.6-terra",
    judge_model="gpt-5.6-terra",
    base_url="http://localhost:8000/v1",
)

# 2. Compare Candidate Results against Baseline
report = compare(
    candidate_results=results,
    baseline="results/run-001",
    max_overall_drop=5.0,
)

if report.release_blocked:
    print(f"RELEASE BLOCKED: {report.summary['blocked_reason']}")

---

📊 Baseline Regression Testing

Compare a candidate prompt, model version, or code change against a prior baseline run:

evalrun run \
  --scenario evals/scenarios/travel-agent/budget-constrained-itinerary.md \
  --agent agents.travel:TravelPlanningAgent \
  --model gpt-5.6-terra \
  --judge-model gpt-5.6-terra \
  --baseline results/run-001 \
  --max-regression 5.0 \
  --max-dimension-regression 10.0 \
  --output results/candidate-run

Generated Artifacts

• manifest.json: Execution metadata with redacted API credentials.
• regression_report.json: Machine-readable score deltas ($\Delta \text{score}$) and gate decisions.
• report.html: Standalone interactive HTML report for human inspection.

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Distribution and deployment

There is no central service to deploy for the current product. The recommended path is:

1. Publish the repository on GitHub.
2. Add tagged releases and a clear quick-start guide.
3. Users install it locally with pip install -e . from a clone.
4. Later publish the package to PyPI so users can run pip install evalrun.
5. Host documentation on GitHub Pages if useful; the evaluation engine itself remains local.
6. Users provide their own hosted-model API keys or run their own local model server.

The local UI is intended for local use, not public internet deployment. A shared hosted deployment would be a separate project requiring authentication, secret management, isolation, and hosted execution.

Credential promise

EvalRun does not provide, collect, or store model credentials. A key is read by the local process and passed to the selected model endpoint for that run. Keys are never written to manifest.json, report.html, or regression_report.json; reports contain only [REDACTED]. Prefer environment variables such as OPENAI_API_KEY, GEMINI_API_KEY, or OPENROUTER_API_KEY. Avoid putting real keys directly in shell commands because your terminal may save command history.

How a run works

Choose a scenario
      ↓
Run your agent with your chosen model
      ↓
Score the result against the criteria
      ↓
Run the independent auditor
      ↓
Compare with a previous run, if supplied
      ↓
Write JSON and HTML evidence
      ↓
Return PASS or BLOCK

The command line is useful for repeatable runs, the Python SDK is useful inside scripts and pipelines, and the local browser interface is useful when you prefer a form. All three use the same evaluation engine.

Add your own evaluation scenario

Scenarios are ordinary Markdown files. Copy templates/scenario_template.md, edit the prompt and criteria, and save the file under a folder such as evals/scenarios/my-domain/my-scenario.md.

Each scenario defines:

• the request sent to the agent;
• hard constraints that must not be violated;
• the expected behavior;
• the dimensions to score;
• what counts as a pass or failure.

Run one custom scenario with --scenario:

evalrun run \
  --scenario evals/scenarios/my-domain/my-scenario.md \
  --agent my_agent:MyAgent \
  --model gemini-3.7-flash \
  --base-url https://generativelanguage.googleapis.com/v1beta/openai/ \
  --api-key "$GEMINI_API_KEY" \
  --judge-model gemini-3.7-flash \
  --output results/my-scenario

Or place multiple .md files in a folder and use --suite path/to/folder.

🏗️ Architecture & Documentation

For detailed system component diagrams, dual-pass auditor sequence flows, and release gate decision trees, see docs/architecture.md.

• Hosted Models Guide: docs/quickstart-hosted.md
• Local Models Guide: docs/quickstart-local.md
• CLI Specification: docs/phase4-local-cli-design.md
• Regression Engine Design: docs/phase5-regression-gates-design.md

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⚠️ Limitations & Reproducibility Guidelines

• Judge Variance: LLM judge evaluations can exhibit non-zero variance. For baseline regression testing, fix model versions and set deterministic sampling parameters where available.
• Local Model Requirements: Local evaluation throughput depends on server VRAM and concurrency settings. Ensure your local server handles parallel requests cleanly.
• Credential Security: Credentials in manifest.json and regression_report.json are automatically redacted into "[REDACTED]". Never commit unredacted API keys.