Agent Framework Showdown: Pydantic AI vs. LangChain Stack
We need to build an AI agent that actually does thingsâbook meetings, update databases, trigger workflows. Do we use Pydantic AI, LangGraph, or just stick with LangChain? Every blog says something different.
Welcome to the agent framework wars of 2024! Here's the reality: Pydantic AI is FastAPI for agentsâtype-safe, structured, production-grade. LangGraph is a visual workflow builder for complex orchestration. LangChain is the Swiss Army knife for quick MVPs. They solve different problems. The question isn't 'which is best?' It's 'which fits your constraints?'
Okay, but what does 'type-safe' even mean for AI agents? The LLM outputs text, not Python types.
EXACTLY the problem Pydantic AI solves! Imagine your agent calls a 'book_flight' function. LangChain lets the LLM output: {"price": "a lot", "date": "tomorrow"}. Good luck parsing that. Pydantic AI enforces a strict contract: price must be a float, date must be an ISO8601 datetime. If the LLM hallucinates "price": "free", Pydantic catches it BEFORE it hits your booking API, forces the LLM to retry, and logs the failure. It's like having TypeScript for AI function callsâyour agent can't lie to your database.
That sounds... incredibly useful. Why doesn't LangChain do this?
LangChain was built for rapid experimentation, not production systems. It's duck-typed, loosely structured, and assumes you'll handle validation yourself. Which is fine for prototypes! But in production, when an agent tries to charge $1,000,000 for a coffee because the LLM misread a decimal point, you want Pydantic's validation layer. Pydantic AI was designed from day one for FastAPI-like developer experienceâdependency injection, async support, structured outputs, real-time validation. It reached stable v1.0 in September 2024 BECAUSE they focused on production reliability.
What about LangGraph? I keep seeing it mentioned alongside LangChain.
LangGraph is the enterprise upgrade to LangChain. It's graph-based orchestrationâyou model workflows as nodes and edges instead of linear chains. Think of it like a state machine: 'Check Inventory' node â if in stock, go to 'Ship' node; if out of stock, go to 'Backorder' node. It has visual debugging (literally see the agent's path through the graph), checkpointing (pause/resume workflows), and time-travel debugging (replay past decisions). It's POWERFUL for multi-agent systems or human-in-the-loop workflows. But it's a framework for orchestration, not type validation. You still need to handle data integrity yourself.
So Pydantic AI is better for... what exactly?
Pydantic AI excels when you need reliable, validated agent interactions. Think:
API-facing agents: Customer support bot that updates your CRM. You CANNOT afford malformed data.
Financial transactions: Payment processing agent. Validating amounts, currencies, account IDs is non-negotiable.
Database operations: Agent that writes to production SQL. One bad type and you corrupt records.
Compliance-critical apps: Healthcare, legal, finance. Audit trails require structured, validated outputs.
Pydantic AI's dependency injection means you can mock LLM calls in testsâunit test your agent logic without burning API credits. Its async-first design scales effortlessly with FastAPI or asyncio. And because it's built on Pydantic (used by millions via FastAPI), your team already knows the patterns.
And LangGraph is better for...?
Complex, multi-step workflows where the path isn't linear. Examples:
Document processing pipeline: Extract â Validate â Classify â Route (to legal or finance team) â Approve (human-in-loop) â Archive. Each step is a node. Errors trigger retry loops.
Multi-agent coordination: Legal agent checks contract â Finance agent approves budget â Ops agent schedules deployment. Agents communicate via the graph.
Adaptive workflows: Customer service agent decides dynamically whether to escalate to human, retry, or refund based on context.
LangGraph's Studio lets you visualize the workflow, replay past runs, and debug why an agent took a specific path. That's invaluable for complex business logic. But you'll pair it with validation (maybe Pydantic models!) to ensure data integrity at each node.
The "Million Dollar" Question
"Can't we just use the OpenAI API directly and skip all these frameworks?"
Technical Reality Check
Why Raw API Calls Fail in Production
1. No validation = data chaos.
You call openai.ChatCompletion.create(), get JSON back, parse it with json.loads(), and hope it matches your schema. When it doesn't (and it won't), your app crashes or writes garbage to the database. Pydantic AI's validation catches this before execution.
2. No retries or error handling. LLMs fail. Rate limits, network errors, hallucinated outputs. Frameworks provide automatic retries with exponential backoff, fallback logic, and structured error logging. You'd have to build this yourself.
3. No observability. How do you debug when an agent makes a bad decision? Frameworks log every LLM call, input/output, latency, and token usage. Pydantic AI integrates with Logfire (their observability tool) for real-time monitoring. Raw API calls? You're logging to stdout and hoping.
4. No dependency injection. You hardcode API keys, database connections, and external services. Pydantic AI's DI lets you swap dependencies per environment (dev/staging/prod) and mock them for tests.
5. No structured outputs. OpenAI's function calling requires manual JSON schema definition. Pydantic AI generates schemas automatically from your Python types. Change a type, the schema updates. No drift.
Bottom line: Raw API calls are fine for demos. Production agents need frameworks.
Alright, give me the decision framework. When do I pick which?
Here's the cheat sheet:
Choose Pydantic AI if:
- You need type-safe, validated outputs (financial, healthcare, legal)
- Your team already uses FastAPI or Pydantic (zero learning curve)
- You're building API-facing agents or database-writing agents
- You want testable, mockable agent logic for CI/CD
- You need async-first performance for high-throughput systems
- You value production-grade error handling out-of-the-box
Choose LangGraph if:
- Your workflows have complex branching logic (not linear chains)
- You need human-in-the-loop approvals or retries
- You're orchestrating multiple agents that communicate
- You want visual debugging and checkpointing
- Your team is already in the LangChain ecosystem
Choose LangChain if:
- You're building a quick MVP or prototype
- You need tool integration (100+ pre-built connectors)
- Your workflows are simple, linear chains
- You're okay with manual validation and error handling
- You want to experiment rapidly without structure
What if we choose wrong? Are we locked in?
Not catastrophically, but migration hurts. Here's the risk:
Pydantic AI â LangGraph: You lose type validation but gain orchestration. Doable, but you'll rewrite workflow logic.
LangChain â Pydantic AI: You gain validation but lose tool connectors. You'll rebuild integrations.
LangGraph â Pydantic AI: Painful. Graph workflows don't map to Pydantic's functional style. Major refactor.
The smart play? Start with Pydantic AI for core agent logic (it's stable, v1.0 as of 2024). If you later need graph orchestration, wrap Pydantic agents as LangGraph nodes. Best of both worlds: type-safe nodes in a graph workflow. Don't start with LangChain unless you know it's a throwaway prototype.
Final question: What do YOU use for production agents?
For client projects with financial, healthcare, or compliance requirements? Pydantic AI, no contest. The type safety catches bugs before they become lawsuits. For internal tools or multi-agent research systems? LangGraph for complex workflows. For weekend hacks or client demos? LangChain because it's fast to wire up. But anything that touches real money, real data, or real users? Pydantic AI. Sleep at night matters.
Technical Reality Check
What Agent Frameworks Do NOT Solve
1. They don't fix bad prompts. If your prompt is vague ('do the thing'), no framework will save you. Pydantic AI validates outputs, but garbage prompts produce garbage inputs. Invest in prompt engineering.
2. They don't eliminate hallucinations.
LLMs still hallucinate. Pydantic AI catches type-level hallucinations ("price": "expensive"), but if the LLM invents a plausible-looking transaction ID that doesn't exist, validation won't catch it. You need application-level checks (query your DB to verify).
Example: Type-Safe Agent with Pydantic AI
from pydantic import BaseModel, Field
from pydantic_ai import Agent
from datetime import datetime
class FlightBooking(BaseModel):
"""Strictly validated flight booking."""
price: float = Field(gt=0, description="Price in USD")
date: datetime = Field(description="Departure date")
flight_number: str = Field(pattern=r"^[A-Z]{2}\d{3,4}$")
agent = Agent(
model='openai:gpt-4',
result_type=FlightBooking, # Enforces strict validation
)
# LLM MUST return valid FlightBooking or retry automatically
result = await agent.run("Book me a flight to NYC tomorrow")
print(f"Validated booking: {result.data.price}")
3. They don't handle business logic. Frameworks orchestrate LLM calls. You define 'what happens when the agent fails 3 times' or 'how to escalate to humans.' That's your code, not the framework's.
4. They don't scale for free. Pydantic AI is async-first, but if you spin up 1000 concurrent agents without rate limiting, you'll hit API quotas. You need orchestration infrastructure (queues, workers, backpressure).
5. They don't debug themselves. Pydantic AI + Logfire gives you observability, but YOU have to interpret the logs, trace failures, and fix root causes. Monitoring is not debugging.
Bottom line: Pydantic AI gives you type-safe guardrails. LangGraph gives you workflow control. Neither gives you a finished product. You still need to engineer the agent, test edge cases, and handle failures. Choose the framework that fits your risk tolerance and team expertise.