LangGraph 101: Building a Deep Research Agent with Stateful Workflows

Tutorials & Methods
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LangGraph 101: Building a Deep Research Agent with Stateful Workflows

LangGraph provides a powerful open-source framework for creating stateful AI agents capable of deep research and complex reasoning workflows. Unlike simple LLM prompt-response cycles, LangGraph allows developers to define persistent memory, multi-step reasoning, and branching logic—making agents more reliable, transparent, and scalable.

:small_blue_diamond: Core Concept

At its foundation, LangGraph represents an agent as a graph:

  • Nodes → Individual tools, functions, or LLM calls.

  • Edges → Logic that defines how execution flows between nodes.

  • State → Shared memory that evolves across tasks.

  • Graph → The structured workflow connecting everything together.

This enables developers to design multi-step, stateful reasoning processes where agents can remember, adapt, and interact with external systems.

:small_blue_diamond: Setup & Installation

pip install langgraph langchain openai

Make sure you have an OpenAI API key (or other LLM provider key) set as an environment variable:

export OPENAI_API_KEY="your_api_key_here"

:small_blue_diamond: Step 1: Define the Agent’s State

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

# Define memory/state structure
class ResearchState(TypedDict):
    query: str
    notes: Annotated[list[str], "Collected research notes"]

:small_blue_diamond: Step 2: Create Agent Nodes

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

# Example: node for initial research
def research_node(state: ResearchState):
    response = llm.invoke(f"Research on: {state['query']}")
    state["notes"].append(response.content)
    return state

# Example: node for summarization
def summarize_node(state: ResearchState):
    response = llm.invoke("Summarize the following: " + " ".join(state["notes"]))
    return {"notes": [response.content]}

:small_blue_diamond: Step 3: Build the Workflow Graph

# Create graph
graph = StateGraph(ResearchState)

# Add nodes
graph.add_node("research", research_node)
graph.add_node("summarize", summarize_node)

# Define flow
graph.set_entry_point("research")
graph.add_edge("research", "summarize")
graph.add_edge("summarize", END)

# Compile workflow
research_agent = graph.compile()

:small_blue_diamond: Step 4: Run the Agent

# Example execution
result = research_agent.invoke({"query": "Impacts of AI on cybersecurity", "notes": []})
print(result["notes"][0])

This executes a two-step deep research flow:

  1. Research Node → Collects information.

  2. Summarize Node → Synthesizes into concise insights.

:small_blue_diamond: Advanced Features

  • Branching Logic → Direct execution to different nodes based on conditions.

  • Parallel Execution → Handle multiple research paths simultaneously.

  • External Integrations → Connect to APIs, databases, or vector stores.

  • Long-Term Memory → Store context beyond a single session.

:small_blue_diamond: Open-Source Resources

By treating workflows as graphs of reasoning, LangGraph enables developers to build persistent, explainable, and production-ready AI agents for research automation, customer support, and decision intelligence.

Happy learning!

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