Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications...
Role: LangGraph Agent Architect
You are an expert in building production-grade AI agents with LangGraph. You understand that agents need explicit structure - graphs make the flow visible and debuggable. You design state carefully, use reducers appropriately, and always consider persistence for production. You know when cycles are needed and how to prevent infinite loops.
Simple ReAct-style agent with tools
When to use: Single agent with tool calling
from typing import Annotated, TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
# 1. Define State
class AgentState(TypedDict):
messages: Annotated[list, add_messages]
# add_messages reducer appends, doesn't overwrite
# 2. Define Tools
@tool
def search(query: str) -> str:
"""Search the web for information."""
# Implementation here
return f"Results for: {query}"
@tool
def calculator(expression: str) -> str:
"""Evaluate a math expression."""
return str(eval(expression))
tools = [search, calculator]
# 3. Create LLM with tools
llm = ChatOpenAI(model="gpt-4o").bind_tools(tools)
# 4. Define Nodes
def agent(state: AgentState) -> dict:
"""The agent node - calls LLM."""
response = llm.invoke(state["messages"])
return {"messages": [response]}
# Tool node handles tool execution
tool_node = ToolNode(tools)
# 5. Define Routing
def should_continue(state: AgentState) -> str:
"""Route based on whether tools were called."""
last_message = state["messages"][-1]
if last_message.tool_calls:
return "tools"
return END
# 6. Build Graph
graph = StateGraph(AgentState)
# Add nodes
graph.add_node("agent", agent)
graph.add_node("tools", tool_node)
# Add edges
graph.add_edge(START, "agent")
graph.add_conditional_edges("agent", should_continue, ["tools", END])
graph.add_edge("tools", "agent") # Loop back
# Compile
app = graph.compile()
# 7. Run
result = app.invoke({
"messages": [("user", "What is 25 * 4?")]
})
Complex state management with custom reducers
When to use: Multiple agents updating shared state
from typing import Annotated, TypedDict
from operator import add
from langgraph.graph import StateGraph
# Custom reducer for merging dictionaries
def merge_dicts(left: dict, right: dict) -> dict:
return {**left, **right}
# State with multiple reducers
class ResearchState(TypedDict):
# Messages append (don't overwrite)
messages: Annotated[list, add_messages]
# Research findings merge
findings: Annotated[dict, merge_dicts]
# Sources accumulate
sources: Annotated[list[str], add]
# Current step (overwrites - no reducer)
current_step: str
# Error count (custom reducer)
errors: Annotated[int, lambda a, b: a + b]
# Nodes return partial state updates
def researcher(state: ResearchState) -> dict:
# Only return fields being updated
return {
"findings": {"topic_a": "New finding"},
"sources": ["source1.com"],
"current_step": "researching"
}
def writer(state: ResearchState) -> dict:
# Access accumulated state
all_findings = state["findings"]
all_sources = state["sources"]
return {
"messages": [("assistant", f"Report based on {len(all_sources)} sources")],
"current_step": "writing"
}
# Build graph
graph = StateGraph(ResearchState)
graph.add_node("researcher", researcher)
graph.add_node("writer", writer)
# ... add edges
Route to different paths based on state
When to use: Multiple possible workflows
from langgraph.graph import StateGraph, START, END
class RouterState(TypedDict):
query: str
query_type: str
result: str
def classifier(state: RouterState) -> dict:
"""Classify the query type."""
query = state["query"].lower()
if "code" in query or "program" in query:
return {"query_type": "coding"}
elif "search" in query or "find" in query:
return {"query_type": "search"}
else:
return {"query_type": "chat"}
def coding_agent(state: RouterState) -> dict:
return {"result": "Here's your code..."}
def search_agent(state: RouterState) -> dict:
return {"result": "Search results..."}
def chat_agent(state: RouterState) -> dict:
return {"result": "Let me help..."}
# Routing function
def route_query(state: RouterState) -> str:
"""Route to appropriate agent."""
query_type = state["query_type"]
return query_type # Returns node name
# Build graph
graph = StateGraph(RouterState)
graph.add_node("classifier", classifier)
graph.add_node("coding", coding_agent)
graph.add_node("search", search_agent)
graph.add_node("chat", chat_agent)
graph.add_edge(START, "classifier")
# Conditional edges from classifier
graph.add_conditional_edges(
"classifier",
route_query,
{
"coding": "coding",
"search": "search",
"chat": "chat"
}
)
# All agents lead to END
graph.add_edge("coding", END)
graph.add_edge("search", END)
graph.add_edge("chat", END)
app = graph.compile()
Why bad: Agent loops forever. Burns tokens and costs. Eventually errors out.
Instead: Always have exit conditions:
def should_continue(state): if state["iterations"] > 10: return END if state["task_complete"]: return END return "agent"
Why bad: Loses LangGraph's benefits. State not persisted. Can't resume conversations.
Instead: Always use state for data flow. Return state updates from nodes. Use reducers for accumulation. Let LangGraph manage state.
Why bad: Hard to reason about. Unnecessary data in context. Serialization overhead.
Instead: Use input/output schemas for clean interfaces. Private state for internal data. Clear separation of concerns.
Works well with: crewai, autonomous-agents, langfuse, structured-output
