Difference between pydanticAI and langgraph

Nov 12, 2025

1. The purpose of this post

In this post, we’ll explore the differences between Pydantic AI and LangGraph, a framework for building LLM-powered agents.

2. Meet the frameworks

2.1 Pydantic AI

Homepage: ai.pydantic.dev
Core idea: Bring that FastAPI-like developer experience to AI agent building.
Focus: Type safety, structured outputs, and ergonomic Pythonic APIs.
Built by: The team behind Pydantic, the popular data-validation library.

Here is an Example using deepseek, you can change to other models:

import nest_asyncio
nest_asyncio.apply()

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel

#DeepSeekv3
deepseek_model = OpenAIModel(
    'deepseek-chat',
    api_key='sk-xxxxxxxxx',
    base_url='https://api.deepseek.com',

)

agent = Agent(model = deepseek_model)

result1 = agent.run_sync('Hello')
print(result1.data)

✅ Highlights

You get typed, validated outputs directly from your agent.
Familiar Pydantic/typing syntax.
Works with multiple LLM providers.
Great for smaller or mid-scale agents that need reliability.

🗣 Community quote “I’ve been using Pydantic AI … it seems quite straightforward and I’m quite pleased with it.” — Reddit

⚠️ Note: It’s newer, so ecosystem maturity is still catching up.

2.2 🌟 Key Highlights of PydanticAI

Built by the Pydantic team – Developed and maintained by the core Pydantic team, ensuring high-quality engineering and long-term stability.
Model-agnostic – Works with a wide range of models including OpenAI, Anthropic, Gemini, Ollama, Groq, and Mistral — and it’s easy to extend to others.
Logfire integration – Comes with native Pydantic Logfire support, so you can debug, monitor performance, and trace LLM behavior in real-time.
Type-safe by design – Strong typing and validation at every step help keep your AI agents reliable and predictable.
Python-first approach – Lets you use standard Python control flow and composition patterns when building AI systems — no need to learn a custom DSL.
Structured responses – Uses Pydantic models to validate and shape outputs, ensuring responses are always consistent and schema-compliant.
Dependency injection system – Includes an optional DI mechanism to inject data or services into system prompts, tools, or validators, making testing and iteration easier.
Streaming support – Provides real-time streaming of LLM responses for faster feedback and on-the-fly validation.
Graph support – The Pydantic Graph feature allows defining data and logic graphs using type hints — keeping even complex applications clean and maintainable.

2.3 How to install Pydantic AI?

pip install 'pydantic-ai[logfire]'

2.4 Structured outputs of Pydantic AI

Structured outputs are one of the core strengths of PydanticAI.
They allow your LLM to return responses in a predictable, schema-validated format — not just free text.
This means you can extract exactly the information you want (like name, gender, birth date, and occupation) with just a few lines of code.

Here’s a simple example 👇

from pydantic import BaseModel
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel

# 1️⃣ Define a structured output model
class MyModel(BaseModel):
    name: str        # Name
    gender: str      # Gender
    birth: str       # Birth date
    occupation: str  # Occupation

# 2️⃣ Configure your model (here using DeepSeek v3)
deepseek_model = OpenAIModel(
    'deepseek-chat',
    api_key='sk-XXXXXXXX',
    base_url='https://api.deepseek.com',
)

# 3️⃣ Define an agent with a typed result
agent = Agent(
    model=deepseek_model,
    result_type=MyModel,
)

# 4️⃣ Test your agent
# Run the agent with a natural input
result = agent.run(
    "Extract the person's info: Elon Musk, male, born June 28 1971, entrepreneur."
)

print(result)

Input:

"Extract the person's info: Elon Musk, male, born June 28 1971, entrepreneur."

Output:

MyModel(
    name='Elon Musk',
    gender='male',
    birth='June 28, 1971',
    occupation='entrepreneur'
)

This is where PydanticAI really shines — instead of dealing with raw text or unstructured JSON, you get a clean, validated MyModel instance ready for downstream use. No regex, no fragile parsing — just typed, reliable data from your LLM.

2.5 LangChain / LangGraph

LangChain is a large ecosystem for building LLM-based apps: chains, tools, retrieval (RAG), agents, and more.

LangGraph is an advanced extension for agentic, stateful workflows — representing agents as graphs (nodes + edges).

Key points:

Model workflows as directed graphs.
Supports loops, branching, multi-agent coordination, and state persistence.
Designed for complex, long-running, or enterprise workflows.

Example concept:
[Start] → [Call LLM] → [Decide Action]
                 ↘
              [Call Tool A] → [Check Result] → [Loop or End]

💬 From the docs

“LangGraph provides a more expressive framework to handle companies’ unique tasks that need loops, branching, and state.” — LangChain

🧭 Learning curve: Slightly steeper than Pydantic AI but more powerful once mastered.

3. Side-by-side comparison

Feature	Pydantic AI	LangGraph (via LangChain)
Setup & simplicity	Very Pythonic, minimal boilerplate for simple agents.	Requires learning graph concepts, nodes, edges, and state.
Type safety / structured output	Built on Pydantic → strong type checking.	Supported but manual; more flexible, less automatic.
Workflow style	Linear or lightly branched.	Graph-based (loops, branching, multi-agent).
Tool integration	Supports model-agnostic providers, easy function tools.	Massive ecosystem of tools, memory, RAG, vector stores.
State / memory	Basic.	First-class: state, persistence, retries, multi-step flows.
Production maturity	Newer, focused, simpler.	Mature (LangChain core) + powerful (LangGraph).
Beginner-friendly	✅ Very. Great for Python developers familiar with Pydantic.	⚠️ Requires more setup; better for intermediate users.

4. What does this mean for you?

If you’re new to LLM frameworks:

Start with Pydantic AI if you want:
- Clear, typed models
- Minimal setup
- Fast results
Try LangGraph if you plan for:
- Complex, multi-step workflows
- State and memory
- Multi-agent or looping logic

🎓 Tip for educators or researchers:
If you’re using LLMs to analyze problems, grade answers, or automate workflows, Pydantic AI gives you reliable, structured results.
Later, if you want a full “tutoring agent” with memory, branching logic, and feedback loops, move to LangGraph.

5. Mini-cases

Case A — Simple Q&A Agent

Goal: Answer a student’s question and return structured data (answer + confidence).

This is a simple example comparing Pydantic AI and LangGraph implementations of the same idea.

🟦 Pydantic AI Example

A lightweight, type-safe approach for small agents.

# pip install pydantic-ai openai

from pydantic_ai import Agent
from pydantic import BaseModel

# Define the expected output schema
class QAResponse(BaseModel):
    answer: str
    confidence: float

# Create an agent using an OpenAI model
qa_agent = Agent(
    model="openai:gpt-4o-mini",  # or any supported model
    result_type=QAResponse,
    system_prompt="You are a helpful tutor. Answer clearly and rate your confidence (0-1)."
)

# Run the agent with a user question
result = qa_agent.run_sync("What is the capital of France?")
print("Answer:", result.answer)
print("Confidence:", result.confidence)

✅ Highlights

Uses BaseModel to define structured output.
Output automatically validated (typed + constrained).
Ideal for small, deterministic tasks or when you need reliable parsing.

🟨 LangGraph Example

The same concept built as a graph workflow, better suited for extensible or multi-step logic.

# pip install langgraph langchain-openai

from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI

# Define the graph "state" as a dictionary
def qna_node(state: dict):
    llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
    question = state["question"]
    prompt = f"You are a helpful tutor. Answer the question: {question}. Also estimate your confidence (0-1)."
    response = llm.invoke(prompt)
    # You can later parse confidence or validate manually
    return {"answer": response.content, "confidence": "0.9"}

# Build the workflow graph
graph = StateGraph()
graph.add_node("qna", qna_node)
graph.set_entry_point("qna")
graph.add_edge("qna", END)

# Compile and run
app = graph.compile()
output_state = app.invoke({"question": "What is the capital of France?"})

print("Answer:", output_state["answer"])
print("Confidence:", output_state["confidence"])

✅ Highlights

Defines workflow as nodes and transitions (even if only one here).
Easy to extend with additional nodes (e.g., “check references”, “ask follow-up”, “evaluate answer”).
Powerful when scaling to multi-step or stateful flows.

6. Summary

6.1 The Difference table

Section	Pydantic AI	LangGraph
Purpose	Type-safe agent building for structured, predictable outputs.	Complex, stateful agent graphs for orchestrating multi-step workflows.
Environment	Best for Python + Pydantic developers who value simplicity and validation.	Best for LangChain users building larger, long-lived, multi-agent systems.
Difference	Focused on simplicity, structure, and typed validation.	Focused on orchestration, loops, branching, and persistent state.
Summary	🚀 Great for beginners & small/medium projects that need reliability.	⚙️ Powerful for advanced workflows needing dynamic flow control and memory.

🚀 Advantages of PydanticAI

Here’s why PydanticAI stands out compared to using the raw OpenAI SDK:

Type Safety and Data Validation
- PydanticAI: Leverages Pydantic’s strong type definitions and data validation to ensure that inputs and outputs match your expected structure. You can explicitly define the types of inputs and expected model outputs, helping you catch errors early and avoid issues caused by mismatched data formats.
- Raw OpenAI SDK: Typically returns unstructured text or JSON. You must manually validate inputs, parse outputs, and handle errors in code.
Structured Outputs
- PydanticAI: Forces the use of Pydantic models to define output structures, guaranteeing consistency and allowing you to map responses directly to Python objects. For example, you can define a model for an article or movie review and get the LLM to return a fully validated object.
- Raw OpenAI SDK: Returns free text or JSON. You must write additional parsing logic, which is error-prone, especially when outputs vary.
Powerful Tools and Dependency Injection
- PydanticAI: Offers an easy tool registration mechanism and dependency injection system. Using @agent.tool, you can register Python functions as LLM tools and inject dependencies into prompts, tools, and validators, making your code modular, testable, and maintainable.
- Raw OpenAI SDK: Has no built-in tools or DI system; you must manually manage functions and dependencies, which increases code complexity.
Logfire Integration
- PydanticAI: Integrates seamlessly with Pydantic Logfire, allowing real-time debugging, monitoring, and tracing of LLM behavior — helpful for understanding model execution, troubleshooting, and optimizing performance.
- Raw OpenAI SDK: Provides no built-in monitoring; you need third-party tools or custom code.
Model-Agnostic
- PydanticAI: Supports multiple models (OpenAI, Anthropic, Gemini, Ollama, etc.) and is easily extensible to new models, so you can switch backends without rewriting your code.
- Raw OpenAI SDK: Only works with OpenAI models; using other models requires separate code.
Python-First Design
- PydanticAI: Encourages using Python’s native control flow and agent composition patterns to build AI projects, making it easy to follow standard Python best practices.
- Raw OpenAI SDK: Focuses on direct API calls, leaving structure and design entirely up to the developer.
Streaming Responses and On-the-Fly Validation
- PydanticAI: Supports streaming LLM outputs with immediate validation, improving responsiveness and efficiency.
- Raw OpenAI SDK: Streaming requires extra configuration and manual handling.
Graph Support
- PydanticAI: Pydantic Graph lets you define complex workflows using type hints, which is invaluable for larger applications and helps prevent messy, hard-to-maintain code.
- Raw OpenAI SDK: Does not provide native graph support; you would need to implement it yourself.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 pydantic-ai

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!