agentcheck 0.1.0

Trace ⋅ Replay ⋅ Test your AI agents like real software

agentcheck

agentcheck: Trace ⋅ Replay ⋅ Test your AI agents like real software.

AgentCheck is a minimal but complete toolkit for tracing, replaying, diffing, and testing AI agent executions. Think of it as version control and testing for your AI agents.

🚀 Install

pip install agentcheck

⚡ Quickstart Demo

export OPENAI_API_KEY=sk-...

# 1️⃣ Capture baseline trace
python demo/demo_agent.py --output baseline.json

# 2️⃣ Modify the prompt inside demo_agent.py (e.g. change tone)
# 3️⃣ Replay with new code/model  
agentcheck replay baseline.json --output new.json

# 4️⃣ See what changed
agentcheck diff baseline.json new.json

# 5️⃣ Assert the new output still mentions the user's name
agentcheck assert new.json --contains "John Doe"

Or run the complete demo:

cd demo && ./demo_run.sh

🎯 Features

Feature	Description	CLI Command	Python API
Trace	Capture agent execution (prompts, outputs, costs, timing)	agentcheck trace <command>	@agentcheck.trace()
Replay	Re-run trace against current code/model	agentcheck replay trace.json	agentcheck.replay_trace()
Diff	Compare traces and highlight changes	agentcheck diff trace_a.json trace_b.json	agentcheck.diff_traces()
Assert	Test trace contents (CI-friendly)	agentcheck assert trace.json --contains "foo"	agentcheck.assert_trace()

📖 Usage

Tracing with Decorator

import agentcheck
import openai

@agentcheck.trace(output="my_trace.json")
def my_agent(user_input: str) -> str:
    response = openai.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": user_input}]
    )
    return response.choices[0].message.content

# Automatically traces execution and saves to my_trace.json
result = my_agent("Hello, world!")

Tracing with Context Manager

import agentcheck

with agentcheck.Trace(output="trace.json") as t:
    # Your agent code here
    messages = [{"role": "user", "content": "Hello"}]
    
    # Manually add LLM calls to trace
    response = openai.chat.completions.create(
        model="gpt-4o-mini", messages=messages
    )
    
    t.add_llm_call(
        messages=messages,
        response={"content": response.choices[0].message.content, "usage": response.usage},
        model="gpt-4o-mini"
    )

CLI Commands

# Trace a Python script
agentcheck trace "python my_agent.py" --output trace.json

# Replay a trace with a different model
agentcheck replay trace.json --model gpt-4 --output new_trace.json

# Compare two traces
agentcheck diff baseline.json new_trace.json

# Assert trace contains expected content
agentcheck assert trace.json --contains "expected output"

# Assert with JSONPath
agentcheck assert trace.json --jsonpath "$.steps[-1].output.content" --contains "John"

# Assert cost and step constraints
agentcheck assert trace.json --max-cost 0.05 --min-steps 1 --max-steps 10

# Pretty-print a trace
agentcheck show trace.json

🏗️ Architecture

┌─────────────────┐    ┌──────────────┐    ┌─────────────────┐
│   Your Agent    │───▶│ agentcheck   │───▶│  trace.json     │
│                 │    │   tracer     │    │                 │
└─────────────────┘    └──────────────┘    └─────────────────┘
                                                    │
                              ┌─────────────────────┼─────────────────────┐
                              ▼                     ▼                     ▼
                    ┌─────────────────┐   ┌─────────────────┐   ┌─────────────────┐
                    │     replay      │   │      diff       │   │     assert      │
                    │   (re-execute)  │   │   (compare)     │   │    (test)       │
                    └─────────────────┘   └─────────────────┘   └─────────────────┘

📋 Trace Format

AgentCheck uses a standardized JSON schema for traces:

{
  "trace_id": "uuid",
  "version": "1.0", 
  "start_time": "2024-01-01T12:00:00Z",
  "end_time": "2024-01-01T12:00:05Z",
  "metadata": {
    "total_cost": 0.0023,
    "function_name": "my_agent"
  },
  "steps": [
    {
      "step_id": "uuid",
      "type": "llm_call",
      "start_time": "2024-01-01T12:00:01Z", 
      "end_time": "2024-01-01T12:00:04Z",
      "input": {
        "messages": [...],
        "model": "gpt-4o-mini"
      },
      "output": {
        "content": "Agent response...",
        "usage": {"prompt_tokens": 10, "completion_tokens": 20},
        "cost": 0.0023
      }
    }
  ]
}

🧪 Testing & CI Integration

AgentCheck is designed for CI/CD pipelines:

# In your CI pipeline
agentcheck replay baseline_trace.json --output ci_trace.json
agentcheck assert ci_trace.json --contains "expected behavior" --max-cost 0.10

# Exit codes
# 0 = success
# 1 = assertion failed or error

🛠️ Development

# Install in development mode
git clone https://github.com/agentcheck/agentcheck
cd agentcheck
pip install -e ".[dev]"

# Run tests
pytest

# Format code  
ruff format .

# Type check
mypy agentcheck/

📄 License

MIT License - see LICENSE file.

🤝 Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

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Built for the era of AI agents 🤖✨