Keywords AI

LangGraph vs Pydantic AI

Compare LangGraph and Pydantic AI side by side. Both are tools in the Agent Frameworks category.

Quick Comparison

LangGraph
LangGraph
Pydantic AI
Pydantic AI
CategoryAgent FrameworksAgent Frameworks
Websitelangchain.comai.pydantic.dev

How to Choose a Agent Frameworks Tool

Key criteria to evaluate when comparing Agent Frameworks solutions:

Programming languagePython, TypeScript, or both — must match your team skills and existing codebase.
Architecture patternSingle-agent, multi-agent, or graph-based orchestration depending on task complexity.
Tool ecosystemBuilt-in tools and ease of creating custom tools for your specific needs.
ObservabilityBuilt-in tracing, debugging, and monitoring for understanding agent behavior.
Production readinessError handling, retries, streaming, and deployment options for production use.

About LangGraph

LangGraph is LangChain's graph-based orchestration framework for building stateful, multi-step AI agents. It models agent workflows as directed graphs with nodes and edges, enabling complex control flow patterns like branching, looping, and human-in-the-loop interactions. LangGraph supports persistent state, streaming, and deployment via LangGraph Cloud.

About Pydantic AI

Pydantic AI is an agent framework from the creators of Pydantic that leverages Python type hints for building type-safe AI agents. It provides structured output validation, dependency injection for tools, and a model-agnostic interface, making it popular with Python developers who value code quality and type safety.

What is Agent Frameworks?

Developer frameworks and SDKs for building autonomous AI agents with tool use, planning, multi-step reasoning, and orchestration capabilities.

Browse all Agent Frameworks tools →

Frequently Asked Questions

What is an AI agent framework?

An agent framework provides the building blocks for creating AI agents that can autonomously plan, use tools, and complete multi-step tasks. Instead of building tool use, memory, and orchestration from scratch, you get pre-built components that handle the common patterns.

Do I need a framework or can I build agents with raw API calls?

For simple single-tool agents, raw API calls work fine. Frameworks become valuable when you need multi-step planning, tool orchestration, error recovery, memory, or multi-agent coordination. They save significant development time for complex agent architectures.

Which agent framework should I choose?

LangChain and LlamaIndex are the most mature with the largest ecosystems. CrewAI is best for multi-agent workflows. Vercel AI SDK is ideal for TypeScript/Next.js applications. Evaluate based on your language preference, use case complexity, and integration needs.

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