LangGraph

Features

• LangGraph graph construction and state management
• Persistence with checkpointers
• Human-in-the-loop patterns
• ReAct agent pattern implementation
• Conditional routing and cycles

Use Cases

• Building single-agent ReAct-style applications with tool integration
• Designing complex stateful research agents with multiple reducers
• Implementing conditional routing for diverse query types
• Structuring production-ready AI workflows

Non-Goals

• Providing a full-fledged LangGraph runtime
• Handling specific LLM provider integrations beyond general examples
• Replacing the core LangGraph library documentation

Workflow

1. Define state for the agent.
2. Define tools the agent can use.
3. Create LLM with integrated tools.
4. Define agent and tool nodes.
5. Implement conditional routing logic.
6. Build and compile the LangGraph.
7. Invoke the compiled graph with initial messages.

Practices

• Agent Architecture
• State Management
• Workflow Design
• Production Deployment

Prerequisites

• Python 3.9+
• langgraph package
• LLM API access (OpenAI, Anthropic, etc.)
• Understanding of graph concepts