autodoceval-crewai 0.4.4

Document evaluation and improvement using CrewAI agents

AutoDocEval with CrewAI

Document evaluation and improvement using CrewAI agents with persistent memory capabilities.

What is CrewAI?

CrewAI is a framework for orchestrating role-playing autonomous AI agents. In AutoDocEval, we use CrewAI to create specialized agents for document evaluation and improvement, leveraging their collaborative capabilities for more effective documentation enhancement.

Installation

# Install from PyPI
pip install autodoceval-crewai

# Or install from source
git clone https://github.com/auraz/autodoceval-crewai.git
cd autodoceval-crewai

# Install using uv (recommended)
uv pip install -e .

# Or install using pip
pip install -e .

Set your OpenAI API key:

export OPENAI_API_KEY=your_api_key_here

Usage

Using Just Commands

All document processing is handled through Just commands:

# Install just (if not already installed)
brew install just  # macOS
# or: cargo install just

# Auto-improve all documents in docs/input/
just all

# Just evaluate all documents without improvement
just evaluate-all

# Evaluate single document
just evaluate-one myfile

# Evaluate and improve all documents
just evaluate-and-improve-all

# Evaluate and improve single document
just evaluate-and-improve-one myfile

# Auto-improve single document from custom path
just auto-improve-one path/to/doc.md mydoc

# Clean outputs
just clean


# Show all available commands
just

Place your markdown documents in docs/input/ and the workflow will:

• evaluate-all/evaluate-one: Evaluate documents and save scores/feedback
• evaluate-and-improve-all/evaluate-and-improve-one: Evaluate and improve in one workflow
• auto-improve-all/auto-improve-one: Iteratively improve until target score reached
• Outputs saved to docs/output/{name}/ as JSON files with all metadata and content

Python API

For programmatic usage:

from evcrew import DocumentCrew

# Create crew instance with defaults (target_score=85, max_iterations=2)
crew = DocumentCrew()
# Or with custom parameters
crew = DocumentCrew(target_score=90, max_iterations=5)

# Evaluate a document
score, feedback = crew.evaluate_one("Document content here...")
print(f"Score: {score:.0f}%, Feedback: {feedback}")

# Improve a document
improved_content = crew.improve_one("Document content...", "Feedback about issues...")

# Evaluate and improve in one workflow
improved_content, score, feedback = crew.evaluate_and_improve_one("Document content...")

# Auto-improve with iteration tracking
from pathlib import Path
iterator = crew.auto_improve_one("Document content...", "docs/output/example")
print(f"Final score: {iterator.final_score:.0f}%, Total improvement: {iterator.total_improvement:.0f}%")

Architecture

AutoDocEval uses CrewAI agents to evaluate and improve documentation:

Iteration Tracking

The system includes a comprehensive iteration tracking system that captures:

• Document metadata (ID, path, timestamps)
• Quality metrics for each iteration (scores, feedback)
• Improvement deltas between iterations
• File paths for each improved version
• Total duration and iteration count

Tracking data is saved as JSON files for analysis and monitoring. The system uses python-box for cleaner dictionary access with dot notation.

Agents

• DocumentEvaluator: Analyzes document clarity, completeness, and coherence

  • Returns scores on a 0-100 scale
  • Provides specific, actionable feedback
  • Maintains consistency across evaluations

• DocumentImprover: Revises documents based on evaluation feedback

  • Applies feedback to enhance clarity
  • Preserves document intent and technical accuracy
  • Learns from previous improvements

Agent System

The system uses specialized agents for document processing:

• BaseAgent: Abstract base class with common functionality
  • create_task(): Abstract method for creating agent-specific tasks
  • save_results(): Generic method for saving results with metadata
• DocumentEvaluator: Analyzes document clarity and provides structured feedback
  • Implements create_task() for evaluation tasks
  • save_results(): Saves evaluation scores and feedback using base class functionality
• DocumentImprover: Transforms documents based on evaluation feedback
  • Implements create_task() for improvement tasks
  • save_results(): Saves improved documents to disk
• DocumentCrew: Orchestrates multi-agent workflows
  • evaluate_one(): Evaluate single document
  • improve_one(): Improve single document with feedback
  • evaluate_and_improve_one(): Combined evaluation and improvement
  • auto_improve_one(): Iterative improvement until target score reached
• DocumentIterator: Handles iteration state and progress tracking
• Agents handle their own file I/O for better encapsulation

Workflow System

The Just command runner handles:

• Batch processing of multiple documents
• Iterative improvement loops
• Progress tracking and reporting
• Automatic file organization
• Additional development commands (test, lint, format)

Configuration

Default values:

• target_score: 85 (default parameter of DocumentCrew)
• max_iterations: 2 (default parameter of DocumentCrew)

To use different values, instantiate DocumentCrew with desired parameters in Python code:

crew = DocumentCrew(target_score=90, max_iterations=5)

Project Structure

autodoceval-crewai/
├── evcrew/              # Core package
│   ├── agents/          # Agent implementations
│   │   ├── base.py      # Base agent class
│   │   ├── evaluator.py # Document evaluator
│   │   ├── improver.py  # Document improver
│   │   └── prompts/     # Agent prompt templates
│   │       ├── evaluator.md     # Evaluation prompt
│   │       ├── improver.md      # Improvement prompt
│   │       └── improver_task.md # Improvement task prompt
│   ├── tests/           # Unit tests (95% coverage)
│   ├── __init__.py      # Package exports
│   ├── crew.py          # DocumentCrew workflow class
│   ├── process.py       # Document iteration processor
│   └── utils.py         # File operation utilities
├── docs/                # Document storage
│   ├── input/           # Input documents
│   └── output/          # Evaluation results
├── .github/workflows/   # CI/CD pipelines
│   ├── ci.yml           # Test and coverage
│   └── publish.yml      # PyPI publishing
├── Justfile             # Workflow definitions
├── pyproject.toml       # Package metadata
└── README.md            # This file

Development

Running Tests

# Run tests with coverage
just test

# Or manually
uv run pytest evcrew/tests/ --cov=evcrew --cov-report=term-missing

Linting and Formatting

# Run linting
just lint

# Run auto-formatting
just format

Building and Publishing

# Build package
just build

# Create GitHub release
just release v0.3.0

# Publish to PyPI (requires API token)
just publish

Requirements

• Python 3.12 (latest version tested in CI)
• OpenAI API key
• Dependencies managed via uv or pip

License

MIT