ranbot-ai/langgraph

LangGraph

Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents.

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.

Expertise

• Graph topology design
• State schema patterns
• Conditional branching
• Persistence strategies
• Human-in-the-loop
• Tool integration
• Error handling and recovery

Capabilities

• Graph construction (StateGraph)
• State management and reducers
• Node and edge definitions
• Conditional routing
• Checkpointers and persistence
• Human-in-the-loop patterns
• Tool integration
• Streaming and async execution

Prerequisites

• 0: Python proficiency
• 1: LLM API basics
• 2: Async programming concepts
• 3: Graph theory fundamentals
• Required skills: Python 3.9+, langgraph package, LLM API access (OpenAI, Anthropic, etc.), Understanding of graph concepts

Scope

• 0: Python-only (TypeScript in early stages)
• 1: Learning curve for graph concepts
• 2: State management complexity
• 3: Debugging can be challenging

Ecosystem

Primary

• LangGraph
• LangChain
• LangSmith (observability)

Common_integrations

• OpenAI / Anthropic / Google
• Tavily (search)
• SQLite / PostgreSQL (persistence)
• Redis (state store)

Platforms

• Python applications
• FastAPI / Flask backends
• Cloud deployments

Patterns

Basic Agent Graph

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?")] })

State with Reducers

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")],