AI Agent Design Patterns

Production-Ready Architectural Patterns for Building Reliable AI Agents

A comprehensive collection of design patterns and best practices for building robust, scalable, and maintainable AI agents. From simple prompting techniques to complex multi-agent orchestration.

Overview

This repository provides battle-tested patterns for AI agent development, covering:

• Single-agent architectures
• Multi-agent systems
• Tool integration
• Error handling and recovery
• State management
• Evaluation and testing

Pattern Categories

Foundation Patterns

Tool Integration Patterns

Multi-Agent Patterns

State Management Patterns

Repository Structure

ai-agent-design-patterns/
├── patterns/
│   ├── foundation/
│   │   ├── chain_of_thought.py
│   │   ├── react.py
│   │   └── self_reflection.py
│   ├── tools/
│   │   ├── tool_router.py
│   │   ├── fallback_chain.py
│   │   └── sandbox.py
│   ├── multi_agent/
│   │   ├── supervisor.py
│   │   ├── peer_to_peer.py
│   │   └── hierarchical.py
│   └── state/
│       ├── checkpointing.py
│       ├── memory.py
│       └── state_machine.py
├── examples/
│   ├── coding_agent/
│   ├── research_agent/
│   └── customer_support_agent/
├── tests/
└── docs/

Quick Start

Installation

# Clone repository
git clone https://github.com/balakumardev/ai-agent-design-patterns.git
cd ai-agent-design-patterns

# Create virtual environment
python -m venv venv
source venv/bin/activate  # or `venvScriptsactivate` on Windows

# Install dependencies
pip install -r requirements.txt

Run an Example

# Run the ReAct pattern example
python patterns/foundation/react.py

# Run a complete agent example
python examples/research_agent/main.py

Pattern Examples

Chain of Thought

from patterns.foundation import ChainOfThought

agent = ChainOfThought(model="gpt-4")
result = agent.solve(
    problem="Calculate the total cost of 3 items at $15 each with 8% tax"
)
# Returns step-by-step reasoning with final answer

ReAct Agent

from patterns.foundation import ReActAgent
from patterns.tools import Calculator, WebSearch

agent = ReActAgent(
    model="gpt-4",
    tools=[Calculator(), WebSearch()]
)

result = agent.run(
    task="What is the population of Tokyo multiplied by 3?"
)

Supervisor Pattern

from patterns.multi_agent import Supervisor
from agents import Researcher, Writer, Reviewer

supervisor = Supervisor(
    workers=[Researcher(), Writer(), Reviewer()],
    model="gpt-4"
)

result = supervisor.execute(
    task="Write a blog post about AI safety"
)

Pattern Selection Guide

When to Use What

Best Practices

Error Handling

• Always implement fallback strategies
• Use exponential backoff for API calls
• Log all agent decisions for debugging
• Implement circuit breakers for external services

Testing

• Unit test individual tools
• Integration test agent workflows
• Use evaluation datasets for regression testing
• Measure latency and cost metrics

Production Deployment

• Implement rate limiting
• Use async processing for long tasks
• Monitor token usage and costs
• Set up alerting for failures

Tech Stack

• Language: Python 3.10+
• LLM Providers: OpenAI, Anthropic, local models
• Frameworks: LangChain (optional), raw API calls
• Testing: pytest, evaluation frameworks

Contributing

Contributions welcome! Areas of interest:

• New pattern implementations
• Additional examples
• Documentation improvements
• Bug fixes and optimizations

Contribution Process

1. Fork the repository
2. Create a feature branch
3. Add tests for new patterns
4. Submit a pull request

Resources

Further Reading

• LangChain Documentation
• OpenAI Cookbook
• Anthropic Documentation

Related Projects

• LangGraph - State machine framework
• AutoGPT - Autonomous agent
• CrewAI - Multi-agent framework

License

This project is open source. See the repository for license details.

Contact

For questions or suggestions, open an issue on GitHub.