---

name: mastering-langgraph description: Build stateful AI agents and agentic workflows with LangGraph in Python. Covers tool-using agents with LLM-tool loops, branching workflows, conversation memory, human-in-the-loop oversight, and production monitoring. Use when - (1) building agents that use tools and loop until task complete, (2) creating multi-step workflows with conditional branches, (3) adding persistence/memory across turns with checkpointers, (4) implementing human approval with interrupt(), (5) debugging via time-travel or LangSmith. Covers StateGraph, nodes, edges, add_conditional_edges, MessagesState, thread_id, Command objects, and ToolMessage handling. Examples include chatbots, calculator agents, and structured workflows. license: MIT metadata: version: 1.0.0 framework: LangGraph python: ">=3.9"

LangGraph Development Guide

Build stateful AI agents and workflows by defining graphs of nodes (steps) connected by edges (transitions).

Contents

• Quick Start
• Common Build Scenarios
• Core Principles
• Development Workflow
• Common Pitfalls
• Environment Setup
• Quick Verification
• API Essentials
• Next Steps

Quick Start

Minimal chatbot with memory:

from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import InMemorySaver
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, AnyMessage
from typing_extensions import TypedDict, Annotated
import operator

# 1. Define state
class State(TypedDict):
    messages: Annotated[list[AnyMessage], operator.add]  # Append mode

# 2. Define node
llm = ChatOpenAI(model="gpt-4")

def chat(state: State) -> dict:
    response = llm.invoke(state["messages"])
    return {"messages": [response]}

# 3. Build graph
graph = StateGraph(State)
graph.add_node("chat", chat)
graph.add_edge(START, "chat")
graph.add_edge("chat", END)

# 4. Compile with memory
chain = graph.compile(checkpointer=InMemorySaver())

# 5. Invoke with thread_id for persistence
result = chain.invoke(
    {"messages": [HumanMessage(content="Hello!")]},
    config={"configurable": {"thread_id": "user-123"}}
)
print(result["messages"][-1].content)

Key patterns:

• Annotated[list, operator.add] — append to list instead of replace
• InMemorySaver() — enables memory across invocations
• thread_id — identifies conversation for persistence

Common Build Scenarios

Simple Chatbot / Q&A

The Quick Start above covers this. Add more nodes for preprocessing or postprocessing as needed.

Tool-Using Agent

Agent that calls external tools (APIs, calculators, search) in a loop until task complete. → See references/tool-agent-pattern.md

Structured Workflow

Multi-step pipeline with conditional branches, parallel execution, or prompt chaining. → See references/workflow-patterns.md

Agent with Long-Term Memory

Persist conversation across sessions, enable time-travel debugging, survive crashes. → See references/persistence-memory.md

Human-in-the-Loop

Pause for human approval, correction, or additional input mid-workflow. → See references/hitl-patterns.md

Debugging / Production Monitoring

Unit test nodes, visualize graphs, trace with LangSmith. → See references/debugging-monitoring.md

Multi-Agent Systems

Build supervisor or swarm-based multi-agent workflows with handoff tools. → See references/multi-agent-patterns.md

Production Deployment

Deploy to LangGraph Platform (cloud/self-hosted) or custom infrastructure. → See references/production-deployment.md

New to LangGraph?

Learn core concepts: State, Nodes, Edges, Graph APIs. → See references/core-api.md

Core Principles

1. Keep State Raw

Store facts, not formatted prompts. Each node can format data as needed.

# ✓ Good: raw data
class State(TypedDict):
    user_question: str
    retrieved_docs: list[str]
    intent: str

# ✗ Bad: pre-formatted
class State(TypedDict):
    full_prompt: str  # Mixes data with formatting

2. Single-Purpose Nodes

Each node does one thing. Name it descriptively.

# ✓ Good: clear responsibilities
graph.add_node("classify_intent", classify_intent)
graph.add_node("search_knowledge", search_knowledge)
graph.add_node("generate_response", generate_response)

3. Explicit Routing

Use conditional edges for decisions. Don't hide routing logic inside nodes.

def route_by_intent(state) -> str:
    if state["intent"] == "billing":
        return "billing_handler"
    return "general_handler"

graph.add_conditional_edges("classify", route_by_intent, 
    ["billing_handler", "general_handler"])

4. Use Aggregators for Lists

Any list field that accumulates values needs operator.add:

class State(TypedDict):
    messages: Annotated[list, operator.add]      # ✓ Appends
    current_step: str                             # Replaces (no annotation)

5. Handle Errors Deliberately

| Error Type | Strategy | |------------|----------| | Transient (network) | Use RetryPolicy on node | | LLM-recoverable (parse fail) | Feed error to LLM via state, loop back | | User-fixable (missing info) | Use interrupt() to pause and ask | | Unexpected (bugs) | Let bubble up for debugging |

Development Workflow

1. Define Steps — Break task into discrete operations (each becomes a node)
2. Categorize Steps — LLM call? Data retrieval? Action? User input?
3. Design State — TypedDict with all needed fields; keep it raw
4. Implement Nodes — def node(state) -> dict for each step
5. Connect Graph — add_node(), add_edge(), add_conditional_edges()
6. Compile & Test — graph.compile(), test with sample inputs

Common Pitfalls

1. Forgetting operator.add on Lists

Symptom: Messages disappear, only last message retained.

# ✗ Wrong: messages: list[AnyMessage]
# ✓ Fix: messages: Annotated[list[AnyMessage], operator.add]

2. Missing thread_id for Memory

Symptom: Agent forgets previous turns.

# ✓ Fix: Always pass config with thread_id
chain.invoke(input, config={"configurable": {"thread_id": "unique-id"}})

3. Not Compiling Before Invoke

Symptom: AttributeError on graph object.

# ✗ Wrong: graph.invoke(input)
# ✓ Fix: chain = graph.compile(); chain.invoke(input)

4. Non-Deterministic Nodes Without @task

Symptom: Different results on resume from checkpoint.

from langgraph.func import task

@task  # Wrap for durable execution
def fetch_data(state):
    return {"data": requests.get(url).json()}

5. Circular Imports with Type Hints

Symptom: ImportError when defining state classes.

# ✓ Fix: Use string annotations
from __future__ import annotations

Environment Setup

# Core
pip install -U langgraph

# LLM providers (pick one or more)
pip install langchain-openai
pip install langchain-anthropic

# Production persistence
pip install langgraph-checkpoint-postgres

# Observability
pip install langsmith

Environment variables:

export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."
export LANGSMITH_API_KEY="ls-..."
export LANGSMITH_TRACING=true

Quick Verification

Before Building

• [ ] python -c "import langgraph; print(langgraph.__version__)" works
• [ ] LLM API key set (OPENAI_API_KEY or ANTHROPIC_API_KEY)
• [ ] Optional: LANGSMITH_API_KEY for tracing

After Building

• [ ] Graph compiles without error: chain = graph.compile()
• [ ] Visualization renders: print(chain.get_graph().draw_mermaid())
• [ ] Invoke succeeds with sample input: chain.invoke({...})
• [ ] Lists accumulate correctly (verify operator.add annotations)
• [ ] Memory persists across invocations (test same thread_id twice)
• [ ] Conditional routing works as expected (test each branch)

API Essentials

# Imports
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import InMemorySaver
from typing_extensions import TypedDict, Annotated
import operator

# State with append-mode list
class State(TypedDict):
    messages: Annotated[list, operator.add]

# Node signature
def node(state: State) -> dict:
    return {"messages": [new_message]}

# Graph construction
graph = StateGraph(State)
graph.add_node("name", node_fn)
graph.add_edge(START, "name")
graph.add_edge("name", END)

# Conditional routing
graph.add_conditional_edges("from", router_fn, ["option1", "option2", END])

# Compile and run
chain = graph.compile(checkpointer=InMemorySaver())
result = chain.invoke(input, config={"configurable": {"thread_id": "id"}})

# Visualization
print(chain.get_graph().draw_mermaid())

For detailed API reference → See references/core-api.md

Next Steps

• Tool agents: references/tool-agent-pattern.md
• Workflows: references/workflow-patterns.md
• Persistence: references/persistence-memory.md
• Human-in-the-loop: references/hitl-patterns.md
• Testing/Monitoring: references/debugging-monitoring.md
• Multi-agent: references/multi-agent-patterns.md
• Production: references/production-deployment.md
• Core concepts: references/core-api.md
• Official docs: references/official-resources.md