# LangGraph 101: Building a Deep Research Agent with Stateful Workflows

**URL:** <https://onehack.st/t/langgraph-101-building-a-deep-research-agent-with-stateful-workflows/310081>\
**Category:** Tutorials & Methods\
**Tags:** learning, tools, ai\
**Created:** [August 23, 2025, 7:30pm UTC](https://onehack.st/t/langgraph-101-building-a-deep-research-agent-with-stateful-workflows/310081 "2025-08-23T19:30:45Z")\
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**Page:** 1

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**Author:** ![BlueHacker](https://onehack.st/user_avatar/onehack.st/bluehacker/32/174721_2.png) [@BlueHacker](https://onehack.st/u/BlueHacker)\
**Post date:** [August 23, 2025, 7:30pm UTC](https://onehack.st/t/langgraph-101-building-a-deep-research-agent-with-stateful-workflows/310081/1 "2025-08-23T19:30:45Z")

</div>

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.

* * *

### **🔹 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.

* * *

### **🔹 Setup & Installation**

```auto
pip install langgraph langchain openai

```

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

```auto
export OPENAI_API_KEY="your_api_key_here"

```

* * *

### **🔹 Step 1: Define the Agent’s State**

```auto
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"]

```

* * *

### **🔹 Step 2: Create Agent Nodes**

```auto
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]}

```

* * *

### **🔹 Step 3: Build the Workflow Graph**

```auto
# 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()

```

* * *

### **🔹 Step 4: Run the Agent**

```auto
# 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.

* * *

### **🔹 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.

* * *

### **🔹 Open-Source Resources**

- 📖 [LangGraph Documentation](https://www.langchain.com/langgraph)

- 💻 [LangChain GitHub](https://github.com/langchain-ai/langchain)

- 📓 [Deep Research Example Notebook](https://github.com/langchain-ai/langgraph/blob/main/examples/deep_research.ipynb)

* * *

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!**