AI Agent Framework Comparison: LangGraph vs OpenAI Agents vs PydanticAI vs CrewAI

Part 3 of the AI Agent Tech Stack series. See also: Core Architecture · Design Patterns · Protocol Layer · Production Practices.

1. LangGraph — Stateful Graph Orchestration

LangGraph models agent workflows as stateful directed graphs.

• Cycles & Branching — not limited to DAGs
• Persistence — built-in checkpointing, pause/resume, time-travel debugging
• Human-in-the-loop — insert approval steps between nodes
• Streaming — real-time output per node

Docs: langchain-ai.github.io/langgraph · GitHub

2. OpenAI Agents SDK — Official Agent Framework

OpenAI Agents SDK is minimalist yet complete.

• Agent Loop — built-in ReAct cycle
• Handoffs — seamless delegation between agents
• Guardrails — input/output validators
• Tracing — built-in OpenTelemetry
• MCP — native integration

from agents import Agent, Runner

agent = Agent(
    name="Research Assistant",
    instructions="You help users research topics.",
    tools=[web_search, file_reader],
    handoffs=[specialist_agent],
)
result = await Runner.run(agent, "Analyze recent AI trends")

Docs: openai.github.io/openai-agents-python · GitHub

3. PydanticAI — Type-Safe Agents

PydanticAI by the Pydantic team. Core principle: type safety + structured output.

from pydantic_ai import Agent
from pydantic import BaseModel

class CityInfo(BaseModel):
    name: str
    country: str
    population: int

agent = Agent("openai:gpt-4o", output_type=CityInfo)
result = await agent.run("Tell me about Tokyo")
print(result.output.population)  # IDE autocomplete works

Docs: ai.pydantic.dev · GitHub

4. CrewAI — Role-Based Collaboration

CrewAI defines teams, not just agents.

from crewai import Agent, Task, Crew

researcher = Agent(role="Researcher", goal="Find latest AI papers", ...)
writer = Agent(role="Writer", goal="Write blog post", ...)
crew = Crew(agents=[researcher, writer], tasks=[task1, task2])
result = crew.kickoff()

Docs: docs.crewai.com · GitHub

5. AutoGen — Conversational Multi-Agent

AutoGen (Microsoft) drives multi-agent collaboration through conversation.

Docs: microsoft.github.io/autogen · GitHub

6. Google ADK — Agent Development Kit

Google ADK tightly integrates with Gemini and Google Cloud.

Docs: google.github.io/adk-docs · GitHub

Comparison Matrix

Framework	Orchestration	Multi-Agent	MCP	Type Safety	Learning Curve	Best For
LangGraph	State graph	Yes	Yes	Medium	Medium-High	Complex workflows
OpenAI Agents	Loop + Handoff	Yes	Yes	Medium	Low	OpenAI ecosystem
PydanticAI	Functional	Manual	Yes	Strong	Low	Typed Python apps
CrewAI	Role + Task	Core	Yes	Weak	Low	Role collaboration
AutoGen	Conversation	Core	Yes	Weak	Medium	Conversational negotiation
Google ADK	Functional	Yes	Yes	Medium	Medium	Google ecosystem

Selection Guide

Use Case	Recommended	Reason
Quick prototyping	OpenAI Agents / PydanticAI	Simple API, fast setup
Complex multi-step workflows	LangGraph	State graph + persistence + branching
Multi-role team automation	CrewAI	Intuitive role definitions
Enterprise agent platform	Dify + LangGraph	Visual + code flexibility
Type safety priority	PydanticAI	Deep Python type system integration
No-code	Dify / Coze / FlowiseAI	Drag-and-drop visual builders