LangGraph Agent Patterns

A comprehensive collection of production-ready agentic AI patterns using LangGraph and AWS Bedrock

🎯 Overview

Production-ready implementations of common agentic patterns using LangGraph and AWS Bedrock. Perfect for developers building AI agents that need to reason, plan, and execute complex tasks autonomously.

Why This Project?

• 🏗️ Production-Ready: Not just tutorials, but architectures you can deploy
• 🎨 Multiple Patterns: ReAct, Plan-and-Execute, Reflection, and Deep Research agents
• ☁️ AWS Native: Optimized for AWS Bedrock (Claude, Titan, Llama)
• 📚 Well-Documented: Each pattern includes detailed explanations and examples
• 🧪 Battle-Tested: Real-world implementations with best practices

📖 Agent Patterns

Pattern	Description	Best For	Complexity
Reactive Agent	ReAct (Reasoning + Acting) pattern with tool calling and reflection	Dynamic problem-solving, iterative development	⭐⭐⭐
Plan-and-Execute	Strategic planning before execution with step-by-step approach	Complex multi-step tasks, structured workflows	⭐⭐
Deep Research	Multi-source information synthesis with web search	Research, analysis, information gathering	⭐⭐
Deep Agents	Advanced multi-agent orchestration with specialized sub-agents	Enterprise-scale agent systems	⭐⭐⭐⭐

🚀 Quick Start

Prerequisites

• Python 3.9+
• AWS Account with Bedrock access enabled
• AWS Credentials configured (via AWS CLI, environment variables, or IAM role)
• API Keys (optional):
  • Tavily API key for web search features
  • LangChain API key for tracing

Installation

1. Clone the repository

   git clone https://github.com/yourusername/langgraph-agent-patterns.git
   cd langgraph-agent-patterns

2. Choose a pattern and navigate to its directory

   cd langgraph-reactive-agent

3. Install dependencies

   pip install -r requirements.txt

4. Configure environment variables

   cp .env.example .env
   # Edit .env with your credentials

5. Run the agent

   python langgraph_reactive_agent.py

🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│                   LangGraph Agent                        │
│                                                          │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐             │
│  │  Planner │→ │ Executor │→ │Reflector │             │
│  └──────────┘  └──────────┘  └──────────┘             │
│                      ↓                                   │
│              ┌──────────────┐                           │
│              │     Tools     │                           │
│              │ • Web Search  │                           │
│              │ • File I/O    │                           │
│              │ • Terminal    │                           │
│              └──────────────┘                           │
└─────────────────────────────────────────────────────────┘
                         ↓
              ┌─────────────────────┐
              │    AWS Bedrock       │
              │ • Claude 3.5 Sonnet  │
              │ • Claude 3 Haiku     │
              │ • Llama 3            │
              └─────────────────────┘

📋 Available Models

You can use any model available in AWS Bedrock:

• anthropic.claude-3-5-sonnet-20240620-v1:0 ⭐ Recommended - Best balance of capability and cost
• anthropic.claude-3-haiku-20240307-v1:0 - Faster and more cost-effective
• amazon.titan-text-express-v1 - AWS-native model
• meta.llama3-70b-instruct-v1:0 - Open-source alternative

💰 Cost Estimation

AWS Bedrock Pricing (Claude 3.5 Sonnet)

• Input: $3.00 per 1M tokens
• Output: $15.00 per 1M tokens

Example Cost: Typical agent interaction (5 iterations, average 500 tokens per iteration)

• Input tokens: ~2,500 ($0.0075)
• Output tokens: ~2,500 ($0.0375)
• Total: ~$0.045 per conversation

Note: Costs vary based on complexity and iteration count

⚡ Performance

Benchmarked on AWS Bedrock with Claude 3.5 Sonnet:

Pattern	Avg Latency	Avg Tokens	Estimated Cost
Reactive Agent	3-5s	2,500	$0.05
Plan-and-Execute	5-8s	4,200	$0.08
Deep Research	10-15s	8,900	$0.15

Actual performance depends on task complexity and AWS region

🛠️ Agent Pattern Details

Reactive Agent (ReAct Pattern)

Implements the Reasoning and Acting (ReAct) paradigm where the agent iteratively:

1. Plans the next step
2. Executes actions using tools
3. Reflects on results
4. Adapts approach based on feedback

Use Cases: Code generation, debugging, data analysis, dynamic problem-solving

Plan-and-Execute

Strategic approach that separates planning from execution:

1. Creates comprehensive plan upfront
2. Executes steps sequentially
3. Adapts plan based on results

Use Cases: Multi-step workflows, structured tasks, batch processing

Deep Research Agent

Specialized for information gathering and synthesis:

1. Breaks down research questions
2. Searches multiple sources
3. Synthesizes findings
4. Generates comprehensive reports

Use Cases: Market research, competitive analysis, literature reviews

Deep Agents (Multi-Agent Orchestration)

Advanced pattern with orchestrator coordinating specialized sub-agents:

1. Orchestrator delegates to specialized agents
2. Research, Math, and Domain-specific agents
3. Results combined and synthesized

Use Cases: Complex enterprise workflows, specialized domains

📚 Documentation

• Contributing Guidelines
• Code of Conduct
• Security Policy
• Architecture Details (coming soon)

🧪 Testing

Each pattern includes example usage. To test:

# Navigate to a pattern directory
cd langgraph-reactive-agent

# Run with example task
python langgraph_reactive_agent.py

🤝 Contributing

We welcome contributions! Here's how you can help:

• 🐛 Report bugs via GitHub Issues
• 💡 Suggest new patterns or features
• 📖 Improve documentation
• 🔧 Submit pull requests

See CONTRIBUTING.md for detailed guidelines.

🔒 Security

• Never commit AWS credentials or API keys
• Use .env files (already in .gitignore)
• Review SECURITY.md for best practices
• Report vulnerabilities privately (see SECURITY.md)

📄 License

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

🙏 Acknowledgments

• LangChain and LangGraph teams for the excellent framework
• AWS Bedrock team for providing access to powerful foundation models
• Anthropic for Claude models
• The open-source AI community

📬 Contact & Support

• GitHub Issues: For bug reports and feature requests
• GitHub Discussions: For questions and community support
• Documentation: Check pattern-specific READMEs for detailed guides

🗺️ Roadmap

• Add Supervisor pattern for hierarchical agents
• Multi-modal agent examples (vision, audio)
• Deployment guides (Lambda, ECS, EC2)
• Performance optimization guides
• Cost optimization strategies
• Integration examples (databases, APIs)

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