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Quaestor

Self-optimizing agentic testing framework – pytest for AI agents

Python 3.12+ Built on Smactorio

Overview

Quaestor is a comprehensive testing framework for AI agents that combines:

  • DSPy-powered analysis – Understand agent workflows, tools, and state machines
  • Automated test generation – Create test cases from workflow analysis
  • Multi-turn probing – Adaptive conversational testing
  • LLM-as-Judge evaluation – Verdict generation with DeepEval metrics
  • Governance integration – Built on Smactorio for compliant, deterministic testing

Quick Start

# Install with uv
uv sync

# Verify installation
uv run quaestor --version

# Analyze an agent
uv run quaestor analyze path/to/agent.py

# Run tests
uv run quaestor test path/to/agent.py --level integration

Project Structure

quaestor/
├── analysis/      # Code analysis and workflow extraction
├── testing/       # Test generation and execution
├── evaluation/    # LLM-as-judge verdict generation
├── coverage/      # Coverage tracking
├── reporting/     # HTML/SARIF report generation
└── optimization/  # DSPy self-improvement

.specify/          # Smactorio governance configuration
├── memory/
│   ├── governance-catalog.yaml   # OSCAL catalog (source of truth)
│   └── constitution.md           # Rendered governance rules

Governance

Quaestor is tightly integrated with Smactorio for governance-as-a-service:

# View governance principles
uv run smactorio constitution list

# Check compliance
uv run smactorio constitution check path/to/spec.md

# Run full spec-driven development workflow
uv run smactorio workflow run --feature "Your feature description"

Development

# Install dev dependencies
uv sync --extra dev

# Run tests
uv run pytest tests/ -v

# Lint
uv run ruff check .

# Type check
uv run mypy quaestor/

Roadmap

See TODO.md for the complete development roadmap including:

  • Phase 0: Project setup and Smactorio integration
  • 🔄 Phase 1: Core analysis engine (WorkflowAnalyzer, Python parser)
  • 📋 Phase 2-6: Test generation, runtime testing, evaluation, coverage, optimization
  • Phase 7: Red team capabilities (pending DeepTeam availability)

Documentation

License

MIT

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