LangGraph brings structure and reliability to the world of autonomous AI. If you’ve ever struggled with prompt chaining or unpredictable agent behavior, LangGraph’s DAG-based (Directed Acyclic Graph) orchestration offers a powerful way to design, test, and deploy agents with clear logic and state control. In this beginner-friendly tutorial, you’ll build your first agent workflow using LangGraph.

🧠 Why LangGraph?

Most agentic frameworks rely on prompt chaining, where outputs feed into new prompts in a linear or conditional way. While this works for simple tasks, it becomes fragile when:

• Tasks require recursion or retry logic
• You need workflow visibility or auditability
• You’re coordinating multiple components or agents

LangGraph solves this by letting you define agent logic as a graph of nodes and transitions—each with its own input/output state and control rules. Inspired by state machines and DAGs, it brings deterministic structure to the chaos of generative AI.

🔗 Official docs: LangGraph Docs
🔗 GitHub: github.com/langchain-ai/langgraph

🚧 What You’ll Build

In this guide, you’ll build a basic Research Agent with three steps:

1. Ask the User what to research
2. Search Online for relevant information (using a placeholder tool)
3. Summarize Results using an LLM

This flow will be defined as a graph of nodes with clearly defined transitions.

🛠️ Prerequisites

• Python 3.8+
• OpenAI API key (or compatible LLM provider)
• Install the LangGraph + LangChain packages:

bashCopyEditpip install langgraph langchain openai

🔁 Step-by-Step: Define Your First LangGraph DAG

✅ Step 1: Setup Your LLM and Tools

pythonCopyEditfrom langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

from langchain.tools import Tool

# Simple dummy tool (you can plug in SerpAPI or Tavily later)
def dummy_search(query: str) -> str:
    return f"Search results for: {query}"

search_tool = Tool(name="SearchTool", func=dummy_search, description="Web search tool")

🔧 Step 2: Define LangGraph Nodes

pythonCopyEditfrom langgraph.graph import StateGraph

# Define a simple state object
class AgentState(dict): pass

def get_query(state):
    print("Asking user for input...")
    state["query"] = input("What topic would you like to research? ")
    return state

def run_search(state):
    result = search_tool.run(state["query"])
    state["search_results"] = result
    return state

def summarize(state):
    response = llm.predict(f"Summarize the following: {state['search_results']}")
    state["summary"] = response
    return state

🧠 Step 3: Build the Graph

pythonCopyEditgraph = StateGraph(AgentState)

graph.add_node("AskUser", get_query)
graph.add_node("Search", run_search)
graph.add_node("Summarize", summarize)

graph.set_entry_point("AskUser")
graph.add_edge("AskUser", "Search")
graph.add_edge("Search", "Summarize")
graph.set_finish_point("Summarize")

agent_executor = graph.compile()

▶️ Step 4: Run the Agent Flow

pythonCopyEditfinal_state = agent_executor.invoke(AgentState())
print("\n=== Summary Output ===\n")
print(final_state["summary"])

That’s it—you’ve built your first agent with structured control and node-based transitions.

Why This Matters

LangGraph gives you:

• Repeatability: Each run behaves the same way with the same inputs
• Debuggability: You can trace exactly which node failed
• Composability: You can plug in memory, tools, retries, or async branches

And unlike traditional prompt chaining, you can scale your agents into larger workflows that are both transparent and modular.

What’s Next?

Now that you’ve built a basic DAG, try extending it:

• Add a retry loop if the search fails
• Include a memory node using FAISS or a local vectorstore
• Split into multiple agents (e.g., Researcher → Editor → Reviewer)

Explore more complex examples here:

🔗 LangGraph GitHub Examples

🔗 LangGraph Quickstart Docs