langgraph-patterns

LangGraph Patterns Expert Skill

Purpose

Master LangGraph for building production-ready AI agents with fine-grained control, checkpointing, streaming, and complex state management.

Core Philosophy

LangGraph is: An orchestration framework with both declarative and imperative APIs focused on control and durability for production agents.

Not: High-level abstractions that hide complexity - instead provides building blocks for full control.

Migration: LangGraph replaces legacy AgentExecutor - migrate all old code.

The Six Production Features

1. Parallelization - Run multiple nodes concurrently
2. Streaming - Real-time partial outputs
3. Checkpointing - Pause/resume execution
4. Human-in-the-Loop - Approval/correction workflows
5. Tracing - Observability and debugging
6. Task Queue - Asynchronous job processing

Graph-Based Architecture

from langgraph.graph import StateGraph, END

# Define state
class AgentState(TypedDict):
    messages: Annotated[list, add_messages]
    next_action: str

# Create graph
graph = StateGraph(AgentState)

# Add nodes
graph.add_node("analyze", analyze_node)
graph.add_node("execute", execute_node)
graph.add_node("verify", verify_node)

# Define edges
graph.add_edge("analyze", "execute")
graph.add_conditional_edges(
    "execute",
    should_verify,
    {"yes": "verify", "no": END}
)

# Compile
app = graph.compile()

Core Patterns

Pattern 1: Agent with Tools

from langgraph.prebuilt import create_react_agent

tools = [search_tool, calculator_tool, db_query_tool]

agent = create_react_agent(
    model=llm,
    tools=tools,
    checkpointer=MemorySaver()
)

# Run with streaming
for chunk in agent.stream({"messages": [("user", "Analyze sales data")]}):
    print(chunk)

Pattern 2: Multi-Agent Collaboration

# Supervisor coordinates specialist agents
supervisor_graph = StateGraph(SupervisorState)

supervisor_graph.add_node("supervisor", supervisor_node)
supervisor_graph.add_node("researcher", researcher_agent)
supervisor_graph.add_node("analyst", analyst_agent)
supervisor_graph.add_node("writer", writer_agent)

# Supervisor routes to specialists
supervisor_graph.add_conditional_edges(
    "supervisor",
    route_to_agent,
    {
        "research": "researcher",
        "analyze": "analyst",
        "write": "writer",
        "finish": END
    }
)