Building Multi-Agent Systems with LangGraph
A deep dive into creating sophisticated AI systems using LangGraph, including state management, tool calling, and agent orchestration.
Introduction
Multi-agent systems represent a paradigm shift in how we build AI applications. Instead of monolithic models trying to do everything, we orchestrate specialized agents that excel at specific tasks. LangGraph provides the perfect framework for building these systems.
Why Multi-Agent Systems?
Traditional single-agent approaches have limitations:
Multi-agent systems solve these by distributing responsibilities across specialized agents.
LangGraph Fundamentals
LangGraph builds on LangChain to provide:
State Management
from langgraph.graph import StateGraph
from typing import TypedDict
class AgentState(TypedDict):
messages: list
current_agent: str
task_complete: bool
Graph Definition
The power of LangGraph lies in defining agent interactions as a graph:
workflow = StateGraph(AgentState)
workflow.add_node("researcher", researcher_agent)
workflow.add_node("writer", writer_agent)
workflow.add_node("reviewer", reviewer_agent)
Conditional Routing
Agents can make decisions about which agent should act next:
def route_decision(state: AgentState) -> str:
if state["needs_research"]:
return "researcher"
elif state["needs_review"]:
return "reviewer"
return "writer"
Building a Research Assistant
Let's build a practical multi-agent system for research:
Agent 1: Query Analyzer
Understands the user's intent and breaks down complex queries.
Agent 2: Web Researcher
Searches the web and retrieves relevant information.
Agent 3: Synthesizer
Combines findings into coherent answers.
Agent 4: Quality Checker
Validates accuracy and completeness.
Best Practices
Conclusion
Multi-agent systems with LangGraph enable building sophisticated AI applications that are more maintainable, scalable, and capable than single-agent approaches. Start small, iterate, and let the graph grow with your needs.