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Overview

LangGraph is a library for building stateful, multi-step applications with LLMs. It provides a graph-based approach to agent development, making it easy to create complex workflows with cycles and conditional logic.

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Quick Start

# Create a LangGraph project
runagent init my-langgraph-agent --framework langgraph

# Navigate to project
cd my-langgraph-agent

# Install dependencies
pip install -r requirements.txt

# Test locally
runagent serve .

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Project Structure

my-langgraph-agent/
├── agents.py           # LangGraph agent definition
├── nodes.py           # Graph nodes/functions
├── edges.py           # Edge logic and conditions
├── state.py           # State schema definition
├── tools.py           # Tool definitions
├── runagent.config.json
├── requirements.txt
└── .env.example

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Basic LangGraph Agent

# agents.py
from langgraph.graph import StateGraph, END
from typing import TypedDict, List

# Define state
class AgentState(TypedDict):
    messages: List[str]
    current_step: str
    result: str

# Create graph
workflow = StateGraph(AgentState)

# Define nodes
def process_input(state):
    # Process initial input
    return {"current_step": "analyzing"}

def analyze(state):
    # Perform analysis
    return {"current_step": "generating", "result": "Analysis complete"}

def generate_response(state):
    # Generate final response
    return {"result": f"Processed: {state['messages'][0]}"}

# Add nodes
workflow.add_node("process", process_input)
workflow.add_node("analyze", analyze)
workflow.add_node("respond", generate_response)

# Add edges
workflow.add_edge("process", "analyze")
workflow.add_edge("analyze", "respond")
workflow.add_edge("respond", END)

# Set entrypoint
workflow.set_entry_point("process")

# Compile
app = workflow.compile()

# RunAgent entrypoints
def invoke(input_data):
    result = app.invoke({"messages": [input_data.get("query", "")]})
    return {"response": result["result"]}

def stream(input_data):
    for event in app.stream({"messages": [input_data.get("query", "")]}):
        yield str(event)

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Configuration

{
  "agent_name": "langgraph_agent",
  "description": "LangGraph-based agent with stateful processing",
  "framework": "langgraph",
  "version": "1.0.0",
  "agent_architecture": {
    "entrypoints": [
      {
        "file": "agents.py",
        "module": "invoke",
        "type": "generic"
      },
      {
        "file": "agents.py",
        "module": "stream",
        "type": "generic_stream"
      }
    ]
  },
  "env_vars": {
    "OPENAI_API_KEY": "${OPENAI_API_KEY}"
  }
}

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Advanced Features

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Conditional Edges

def should_continue(state):
    if state.get("needs_clarification"):
        return "clarify"
    return "proceed"

workflow.add_conditional_edges(
    "analyze",
    should_continue,
    {
        "clarify": "clarification_node",
        "proceed": "respond"
    }
)

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Cycles and Loops

# Add cycle for iterative improvement
workflow.add_edge("improve", "analyze")
workflow.add_conditional_edges(
    "analyze",
    lambda state: "improve" if state["quality"] < 0.8 else "finalize"
)

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Tool Integration

from langchain.tools import Tool
from langgraph.prebuilt import ToolExecutor

tools = [
    Tool(name="search", func=search_func, description="Search the web"),
    Tool(name="calculate", func=calc_func, description="Perform calculations")
]

tool_executor = ToolExecutor(tools)
workflow.add_node("tools", tool_executor)

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Best Practices

  1. State Management

    • Keep state minimal and serializable
    • Use TypedDict for type safety
    • Document state schema clearly

  2. Graph Design

    • Start simple, add complexity gradually
    • Use meaningful node names
    • Keep edges logic simple

  3. Error Handling

    • Add error nodes for graceful failures
    • Use try-except in node functions
    • Return meaningful error states

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Common Patterns

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Multi-Step Reasoning

workflow = StateGraph(ReasoningState)

# Chain of reasoning nodes
workflow.add_node("understand", understand_query)
workflow.add_node("decompose", break_into_steps)
workflow.add_node("solve", solve_step)
workflow.add_node("combine", combine_results)

# Add edges with logic
workflow.add_edge("understand", "decompose")
workflow.add_conditional_edges(
    "solve",
    lambda s: "solve" if s["remaining_steps"] else "combine"
)

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Human-in-the-Loop

def needs_human_input(state):
    return state.get("confidence", 1.0) < 0.7

workflow.add_conditional_edges(
    "analyze",
    needs_human_input,
    {
        True: "human_review",
        False: "auto_proceed"
    }
)

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Deployment Tips

  • Test graph logic thoroughly before deployment
  • Monitor state size to avoid memory issues
  • Use streaming for long-running workflows
  • Implement proper timeout handling

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See Also