Building Multi-Agent Systems with LangGraph – A Practical Guide

Most LLM tutorials show you a single chain. In production, you need agents that can plan, delegate, retry, and recover — and that's exactly where LangGraph shines.

This is a breakdown of how I architected a multi-agent system using LangGraph for a real AI product, including the design decisions I'd make differently next time.

What is LangGraph?

LangGraph is a library from the LangChain team that lets you build stateful, multi-actor applications with LLMs. It represents your workflow as a directed graph where each node is a function (or agent), and edges define the flow of control.

Think of it as a state machine for AI workflows.

from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated
import operator

class AgentState(TypedDict):
    messages: Annotated[list, operator.add]
    plan: str
    tool_results: list
    final_answer: str

The Architecture I Built

The system I built had four cooperating agents:

1. Planner – Takes user intent and produces a structured plan
2. Researcher – Executes web search and RAG retrieval in parallel
3. Critic – Reviews the researcher's output and flags gaps
4. Synthesizer – Combines everything into the final response

The key insight: each agent only sees what it needs to see. The full conversation history is never dumped into every prompt — that's how you accidentally blow context windows and get terrible results.

Conditional Routing

The most powerful feature in LangGraph is conditional edges. After the Critic runs, the graph decides:

• If confidence is high → go straight to Synthesizer
• If gaps found → loop back to Researcher (max 2 retries)
• If retrieval failed → fallback to a web search node

def should_retry(state: AgentState) -> str:
    if state["critic_score"] < 0.7 and state["retry_count"] < 2:
        return "researcher"
    return "synthesizer"

graph.add_conditional_edges(
    "critic",
    should_retry,
    {
        "researcher": "researcher",
        "synthesizer": "synthesizer"
    }
)

Parallel Tool Execution

One of the biggest wins was running tool nodes in parallel with Send. Instead of sequentially calling search, then Wikipedia, then the database — I fanned out all three simultaneously:

from langgraph.constants import Send

def route_tools(state: AgentState):
    return [
        Send("web_search", {"query": state["query"]}),
        Send("rag_retrieval", {"query": state["query"]}),
        Send("database_lookup", {"entity": state["entity"]}),
    ]

This cut end-to-end latency by ~60% on tool-heavy queries.

Persistent Memory with Checkpointing

For a SaaS product, users expect the agent to remember previous sessions. LangGraph's checkpoint API makes this straightforward:

from langgraph.checkpoint.postgres import PostgresSaver

checkpointer = PostgresSaver.from_conn_string(DATABASE_URL)
app = graph.compile(checkpointer=checkpointer)

# Resume a previous thread
config = {"configurable": {"thread_id": user_id}}
result = await app.ainvoke(input, config=config)

Every state transition is persisted. If a node crashes mid-run, you can replay from the last checkpoint — critical for long-running tasks.

What I'd Do Differently

Use smaller, focused state schemas. I started with a catch-all AgentState dict and ended up with 30+ fields. Now I split state per subgraph and merge only what's needed.

Add observability early. LangSmith tracing is invaluable — add it on day one, not after you're debugging a 12-node graph in production.

Test individual nodes in isolation. Each node is just a function. Write unit tests for them before wiring the graph — it'll save you hours.

Final Thoughts

LangGraph doesn't hide complexity — it makes it manageable. You write the graph, you own the state, you control the routing. That explicitness is exactly what production AI systems need.

If you're building anything beyond a single-shot LLM call, give LangGraph a proper look.