agentproof

AgentProof

pytest-based behavioral testing for AI agents.

pip install agentproof

One line. No config. Works with any agent framework.

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The Problem

You can observe your agents. You can trace them. You can log every token.

But can you test them?

89% of teams have agent observability. Only 52% have agent evaluation. Zero have behavioral testing in CI.

Your agent calls the wrong tool? Ships to production. Costs $40 on a $0.50 task? Ships to production. Hallucinates the answer? Ships. To. Production.

The Solution

AgentProof brings pytest-style behavioral testing to AI agents. Test what your agent does, not just what it outputs.

# test_booking_agent.py
import agentproof
from agentproof import assert_tool_called, assert_tool_order, assert_max_cost

def test_booking_agent_searches_before_booking(agent_run):
    run = make_booking_run()  # your agent execution

    # Did it use the right tools?
    assert_tool_called(run, "search_flights")
    assert_tool_called(run, "book_flight")

    # Did it use them in the right order?
    assert_tool_order(run, ["search_flights", "compare_prices", "book_flight"])

    # Did it stay within budget?
    assert_max_cost(run, max_usd=0.50)

$ pytest
========================= test session starts =========================
test_booking_agent.py::test_booking_agent_searches_before_booking PASSED
test_booking_agent.py::test_cost_stays_under_budget PASSED
test_booking_agent.py::test_no_hallucination PASSED
========================= 3 passed in 0.04s ============================

Quickstart

1. Install

pip install agentproof

2. Record an agent run

import agentproof

@agentproof.record
def my_agent(prompt: str) -> str:
    # Your agent code here
    agentproof.add_tool_call("search", arguments={"query": prompt})
    agentproof.add_llm_call("gpt-4o", prompt_tokens=500, completion_tokens=200)
    return "The answer is 42"

result = my_agent("What is the meaning of life?")
run = my_agent.last_run

3. Write tests

from agentproof import (
    assert_tool_called,
    assert_tool_order,
    assert_max_cost,
    assert_max_steps,
    assert_no_hallucination,
)

def test_my_agent():
    run = my_agent.last_run

    assert_tool_called(run, "search")
    assert_max_cost(run, 0.10)
    assert_max_steps(run, 5)
    assert_no_hallucination(run, ground_truth="42")

Core Assertions

Framework Adapters

Works with any agent framework. Install the adapter you need:

pip install agentproof[langchain]
pip install agentproof[crewai]
pip install agentproof[openai]
pip install agentproof[otel]      # Any OTEL-instrumented framework

# LangChain
from agentproof.adapters.langchain import from_langchain_run
run = from_langchain_run(agent_executor.invoke({"input": "..."}))

# CrewAI
from agentproof.adapters.crewai import from_crewai_result
run = from_crewai_result(crew.kickoff())

# OpenAI Agents SDK
from agentproof.adapters.openai_agents import from_openai_response
run = from_openai_response(Runner.run(agent, "..."))

Replay Testing

Record once, test forever. Capture a real agent run and replay it in CI without making API calls:

from agentproof import save_trace, load_trace

# Record
save_trace(run, "tests/fixtures/booking_success.jsonl")

# Replay in tests
def test_booking_replay():
    run = load_trace("tests/fixtures/booking_success.jsonl")
    assert_tool_called(run, "book_flight")
    assert_max_cost(run, 0.50)

Snapshot Testing

Like Jest snapshots but for agent behavior. Detect behavioral regressions automatically:

from agentproof import assert_snapshot

def test_agent_behavior_stable(snapshot_dir):
    run = my_agent("book a flight to NYC")
    assert_snapshot(run, snapshot_dir / "booking_agent.json")
    # First run: creates snapshot
    # Subsequent runs: compares against saved snapshot

Update snapshots: pytest --agentproof-update-snapshots

Bundled Cost Database

200+ LLM models with up-to-date pricing. No API calls needed.

from agentproof import get_model_pricing, calculate_run_cost

pricing = get_model_pricing("gpt-4o")
# {"provider": "openai", "prompt": 2.50, "completion": 10.00}

cost = calculate_run_cost(run)
# CostBreakdown(total_cost_usd=0.0325, ...)

Models: GPT-4o, Claude 4.5, Gemini 2.5, Llama 4, DeepSeek V3, Mistral Large, and 190+ more.

CI/CD Integration

GitHub Actions

- uses: praxiumlabs/agentproof@v1
  with:
    test-path: tests/

Or manually:

- run: |
    pip install agentproof
    pytest tests/ -v

Comparison

Honest Limitations

• Hallucination detection uses TF-IDF — it catches obvious fabrication, not subtle inaccuracies. For LLM-as-judge evaluation, use DeepEval or Braintrust.
• Token counts must be provided — we don't intercept API calls (by design). Use framework adapters or log tokens manually.
• No real-time monitoring — AgentProof is a testing tool, not an observability platform. Use it alongside your existing tracing.
• Cost DB needs community updates — model pricing changes. Submit a PR to update agentproof/data/models.yaml.

Contributing

git clone https://github.com/praxiumlabs/agentproof
cd agentproof
pip install -e ".[dev]"
pytest

Adding a model to the cost DB:

Edit agentproof/data/models.yaml:

new-model-name:
  provider: provider-name
  prompt: 1.50
  completion: 5.00

License

MIT License. See LICENSE.

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Built by Sarak Dahal at Praxium Labs. Star the repo if AgentProof saves your agents.