OACP 0.1.7

Open Agent Compliance Protocol - Governance layer for LangGraph

Open Agent Compliance Protocol (OACP)

A governance layer for LangGraph that adds voting, consensus, and audit trails to multi-agent workflows. OACP transforms your agent systems with democratic decision-making, adaptive prompting, and comprehensive oversight.

Overview

OACP provides a framework for building trustworthy multi-agent systems by adding governance mechanisms on top of LangGraph. It enables agents to vote on decisions, reach consensus, maintain audit trails, and adapt their behavior based on feedback.

Key Features

• Governance Layer: Add voting and consensus mechanisms to any LangGraph workflow
• Adaptive Prompting: Automatically improve prompts based on rejection feedback
• Audit Trails: Complete logging and tracking of all agent decisions and votes
• Multiple Storage Backends: File, SQLite, and PostgreSQL support
• Voting Strategies: Unanimous, majority, and weighted voting systems
• Retry Logic: Intelligent retry mechanisms with exponential backoff
• LLM Integration: Built-in adapters for popular language models
• CLI Tools: Command-line interface for management and monitoring

Installation

From Source

git clone https://github.com/your-org/oacp.git
cd oacp
pip install -e .

Dependencies

OACP requires Python 3.10+ and depends on:

• LangGraph: Core workflow framework
• Pydantic: Data validation and settings
• SQLAlchemy: Database abstraction layer
• Typer: CLI framework
• Rich: Terminal formatting

Optional dependencies:

• psycopg: PostgreSQL support
• FastAPI: Web interface
• Google Generative AI: Gemini integration

Quick Start

Basic Usage

from oacp import with_oacp, decision_contract, vote, VoteDecision
from langgraph.graph import StateGraph

# Wrap any function with OACP governance
@with_oacp(
    role="researcher",
    invariants=["factual_accuracy", "comprehensive_coverage"],
    log_inputs=True,
    log_outputs=True
)
def research_agent(research_request: dict) -> dict:
    """Conducts initial research on the given topic."""
    # Your agent logic here
    return {"research_results": "..."}

# Create decision contracts for consensus
@with_oacp(
    role="synthesizer",
    contract=decision_contract(
        required_approvers=["researcher", "analyst", "critic"],
        strategy="unanimous",
        timeout_seconds=30
    )
)
def synthesis_agent(data) -> dict:
    """Synthesizes inputs into final report (requires consensus)."""
    # Your synthesis logic here
    return {"final_report": "..."}

# Voting functions
def researcher_vote(run_id: str, content: str):
    if meets_standards(content):
        vote(run_id=run_id, voter_id="researcher", 
             decision=VoteDecision.APPROVE, 
             reason="Content meets research standards")
    else:
        vote(run_id=run_id, voter_id="researcher", 
             decision=VoteDecision.REJECT, 
             reason="Insufficient research depth")

LangGraph Integration

from langgraph.graph import StateGraph

# Create your workflow
workflow = StateGraph(YourState)
workflow.add_node("research", research_agent)
workflow.add_node("synthesis", synthesis_agent)
workflow.add_edge("research", "synthesis")

# OACP governance is automatically applied
app = workflow.compile()

Examples

Research Team Workflow

A multi-agent research team with four specialized agents:

cd examples/research_team
export GOOGLE_AI_API_KEY=your_api_key
python main.py

Features demonstrated:

• Multi-agent collaboration
• Consensus-based decision making
• Real LLM integration (Gemini 2.5 Flash)
• Audit trails and governance

Flappy Bird Game Design

Agent-based game design with component compatibility voting:

cd examples/flappy_bird_sim
python main.py

Features demonstrated:

• Component compatibility validation
• Quality control through voting
• Design iteration with feedback

Architecture

Core Components

• Decorators (@with_oacp, wrap_node): Apply governance to functions
• Contracts: Define voting requirements and strategies
• Votes: Cast and track voting decisions
• Context: Access current execution context
• Storage: Pluggable storage backends
• Events: Comprehensive event system
• Adaptive Prompting: Automatic prompt improvement

Storage Backends

• File Storage: JSON-based local storage
• SQLite: Lightweight database storage
• PostgreSQL: Production-ready database storage

Voting Strategies

• Unanimous: All voters must approve
• Majority: More than 50% approval required
• Weighted: Votes have different weights

Configuration

Environment Variables

# Storage configuration
OACP_STORAGE_TYPE=sqlite
OACP_STORAGE_URL=sqlite:///oacp.db

# PostgreSQL example
OACP_STORAGE_TYPE=postgresql
OACP_STORAGE_URL=postgresql://user:pass@localhost/oacp

# File storage example
OACP_STORAGE_TYPE=file
OACP_STORAGE_PATH=./oacp_data

Programmatic Configuration

from oacp.storage import configure_storage

# Configure storage backend
configure_storage(
    storage_type="sqlite",
    storage_url="sqlite:///my_oacp.db"
)

CLI Usage

OACP includes a command-line interface for management:

# View recent activity
oacp logs --limit 50

# Show statistics
oacp stats

# Export audit trail
oacp export --format json --output audit.json

# View voting patterns
oacp votes --agent researcher --days 7

Development

Setup Development Environment

git clone https://github.com/your-org/oacp.git
cd oacp
pip install -e ".[dev]"
pre-commit install

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=oacp --cov-report=html

# Run specific test file
pytest tests/test_decorators.py

Code Quality

# Format code
black oacp tests

# Lint code
ruff check oacp tests

# Type checking
mypy oacp

API Reference

Core Decorators

• @with_oacp(): Apply OACP governance to functions
• wrap_node(): Wrap LangGraph nodes with governance

Decision Making

• decision_contract(): Define voting requirements
• vote(): Cast votes on decisions
• VoteDecision: Vote decision enum (APPROVE, REJECT, ABSTAIN)

Context Access

• current_context(): Access current execution context
• get_adaptation_statistics(): View adaptive prompting stats

Storage Interface

• IStorage: Abstract storage interface
• FileStorage: File-based storage implementation
• SqliteStorage: SQLite storage implementation
• PostgresStorage: PostgreSQL storage implementation

Contributing

We welcome contributions! Please see our contributing guidelines:

1. Fork the repository
2. Create a feature branch
3. Make your changes with tests
4. Ensure code quality checks pass
5. Submit a pull request

Development Guidelines

• Follow PEP 8 style guidelines
• Write comprehensive tests
• Update documentation for new features
• Use type hints throughout
• Add docstrings for public APIs

License

This project is licensed under the MIT License. See the LICENSE file for details.

Support

• Documentation: Full documentation available in the docs/ directory
• Issues: Report bugs and request features on GitHub Issues
• Examples: See examples/ directory for working implementations
• CLI Help: Run oacp --help for command-line usage

Roadmap

• Integration with more LLM providers
• Advanced voting strategies
• Web-based monitoring dashboard
• Distributed agent coordination
• Performance optimizations
• Enhanced adaptive prompting algorithms

Version

Current version: 0.1.0 (Beta)

OACP is under active development. APIs may change between versions until 1.0.0 release.