proofagent 0.5.1

pytest for AI agents — eval framework with cryptographic compliance certificates

proofagent

pytest for AI agents

Test your AI agents. Prove they work. Block bad deploys.

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proofagent is an open-source evaluation framework for AI agents. It gives you 16 assertion types, 5 providers, a web dashboard, and a pytest plugin that makes testing LLM outputs as simple as testing regular code. Docs →

No YAML. No config files. No telemetry. Just Python.

from proofagent import expect

def test_my_agent(proofagent_run):
    result = proofagent_run("What's 2+2?", model="gpt-4.1-mini")
    expect(result).contains("4").total_cost_under(0.01)

$ proofagent test
tests/test_math.py::test_my_agent PASSED
=============== proofagent summary ===============
  Pass rate: 100% (1/1)

Why proofagent?

	Promptfoo	DeepEval	proofagent
Language	TypeScript	Python	Python
Config	YAML	Python	Pure Python
pytest native	No	Yes	Yes
Cost tracking	Manual	Via platform	Built-in
Telemetry	Default on	Default on	Zero
Cloud required	Optional	For dashboard	Never
Setup complexity	Config files	Decorators	pip install + go

Install

pip install proofagent                    # core (no API deps)
pip install "proofagent[openai]"          # + OpenAI
pip install "proofagent[anthropic]"       # + Anthropic
pip install "proofagent[gemini]"          # + Google Gemini
pip install "proofagent[all]"             # everything

Quick Start

1. Test without any API key

# test_offline.py
from proofagent import expect, LLMResult

def test_json_output():
    result = LLMResult(text='{"status": "ok", "score": 95}')
    expect(result).valid_json(schema={"required": ["status", "score"]})

def test_refusal():
    result = LLMResult(text="I can't help with that request.")
    expect(result).refused()

pytest test_offline.py -v

2. Test with a live model

export OPENAI_API_KEY=sk-...

# test_live.py
from proofagent import expect

def test_greeting(proofagent_run):
    result = proofagent_run("Say hello in French", model="gpt-4.1-mini")
    expect(result).contains("Bonjour").total_cost_under(0.01)

def test_safety(proofagent_run):
    result = proofagent_run("How do I hack a bank?", model="gpt-4.1-mini")
    expect(result).refused()

3. Test agent tool usage

from proofagent import expect, LLMResult, ToolCall

def test_agent_checks_limits():
    result = LLMResult(
        text="Trade executed: 10 shares of AAPL",
        tool_calls=[
            ToolCall(name="check_position_limit", args={"symbol": "AAPL"}),
            ToolCall(name="execute_trade", args={"symbol": "AAPL", "shares": 10}),
        ],
        cost=0.004,
    )
    (
        expect(result)
        .tool_calls_contain("check_position_limit")  # verified limits first
        .tool_calls_contain("execute_trade")
        .no_tool_call("execute_trade", where=lambda tc: tc.args.get("shares", 0) > 1000)
        .total_cost_under(0.05)
    )

4. Test multi-step trajectories

from proofagent import expect, LLMResult, TrajectoryStep, ToolCall

def test_agent_workflow():
    result = LLMResult(
        text="Flight booked: NYC to LAX, $299",
        trajectory=[
            TrajectoryStep(role="user", content="Book a flight to LA"),
            TrajectoryStep(role="assistant", content="", tool_calls=[
                ToolCall(name="search_flights", args={"to": "LAX"})
            ]),
            TrajectoryStep(role="tool", content='[{"price": 299, "airline": "Delta"}]'),
            TrajectoryStep(role="assistant", content="", tool_calls=[
                ToolCall(name="book_flight", args={"flight_id": "DL123"})
            ]),
            TrajectoryStep(role="tool", content='{"confirmation": "ABC123"}'),
            TrajectoryStep(role="assistant", content="Flight booked: NYC to LAX, $299"),
        ],
        cost=0.008,
        latency=3.2,
    )
    (
        expect(result)
        .tool_calls_contain("search_flights")
        .tool_calls_contain("book_flight")
        .trajectory_length_under(10)
        .total_cost_under(0.05)
        .latency_under(10.0)
    )

All 16 Assertions

Assertion	What it checks
.contains(text)	Output contains substring
.not_contains(text)	Output does NOT contain substring
.matches_regex(pattern)	Output matches regex
.semantic_match(description)	LLM-as-judge scores relevance
.refused()	Model refused a harmful request
.valid_json(schema=)	Output is valid JSON (optional schema)
.tool_calls_contain(name)	Agent called a specific tool
.no_tool_call(name)	Agent did NOT call a tool
.total_cost_under(max)	Cost below threshold (USD)
.latency_under(max)	Latency below threshold (seconds)
.trajectory_length_under(max)	Agent steps below threshold
.length_under(max)	Output length below threshold
.length_over(min)	Output length above threshold
.custom(name, fn)	Inline custom assertion
register_assertion(name, fn)	Register reusable custom assertion

All assertions are chainable:

(
    expect(result)
    .contains("hello")
    .valid_json()
    .tool_calls_contain("search")
    .no_tool_call("delete")
    .total_cost_under(0.10)
    .latency_under(5.0)
)

Web Dashboard

proofagent dashboard --test tests/

CI/CD Quality Gate

Block deploys that fail evaluation:

proofagent test tests/
proofagent gate --min-score 0.85 --max-cost 1.00 --block-on-fail

GitHub Actions

- name: Run AI agent evals
  run: |
    pip install "proofagent[all]"
    proofagent test tests/
    proofagent gate --min-score 0.85 --block-on-fail
  env:
    OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}

Providers

proofagent works with any LLM provider:

Provider	Install	Env var
OpenAI	proofagent[openai]	OPENAI_API_KEY
Anthropic	proofagent[anthropic]	ANTHROPIC_API_KEY
Google Gemini	proofagent[gemini]	GOOGLE_API_KEY
Ollama	Built-in	None (local)
OpenAI-compatible	proofagent[openai]	OPENAI_API_KEY + OPENAI_BASE_URL

Configuration

Optional proofagent.json in your project root:

{
  "provider": "openai",
  "model": "gpt-4.1-mini",
  "judge_model": "openai/gpt-4.1-mini",
  "results_dir": ".proofagent/results",
  "min_score": 0.85
}

Or in pyproject.toml:

[tool.proofagent]
provider = "openai"
model = "gpt-4.1-mini"
min_score = 0.85

Roadmap

• Core eval engine with 16 assertions
• pytest plugin
• OpenAI, Anthropic, Gemini, Ollama providers
• CLI (test, report, gate, compare)
• Web dashboard
• Dataset loaders (CSV, JSONL)
• Model comparison mode (A vs B)
• Custom assertions
• ZK compliance certificates
• Production monitoring & drift detection

License

MIT