LangChain vs Pydantic AI
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A Python framework for building AI agents and apps powered by language models.
Open source · MIT
A typed Python SDK for building AI agents that work with many LLM providers.
The short answer
Choose LangChain if you need…
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
Choose Pydantic AI if you need…
- Python developers building LLM-powered features that need structured, typed output
- Teams that want to swap or fall back between model providers without rewriting code
- Developers who already use Pydantic models and want a consistent validation approach
- People who want to run one agent across a web app, terminal, or background queue
Side-by-side
| LangChain | Pydantic AI | |
|---|---|---|
| Type | Framework | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 149k | 21k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 4 days ago | 4 days ago |
What each one is
LangChain
LangChain is a Python framework for building applications powered by large language models (LLMs) and agents. It provides a standard interface for models, embeddings, and vector stores, plus a library of integrations with model providers and tools. Developers use it to connect LLMs to their own data and systems and to swap models without rewriting their application.
Pydantic AI
Pydantic AI is a Python library for building AI agents, which are programs where a language model calls tools and returns results. Outputs are validated against Python types, so the results come back in a predictable shape. It lets you switch between model providers with a small string change and offers optional extras such as a terminal coding agent, evals, and durable execution through Temporal.
Best for
LangChain
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
Pydantic AI
- Python developers building LLM-powered features that need structured, typed output
- Teams that want to swap or fall back between model providers without rewriting code
- Developers who already use Pydantic models and want a consistent validation approach
- People who want to run one agent across a web app, terminal, or background queue
Strengths
LangChain
- Standard interface lets you swap model providers with limited code changes
- Large library of integrations with model providers, tools, and vector stores
- Open source under the MIT license with a documentation site and free courses
Pydantic AI
- Outputs are validated against types, so your editor and type checker can rely on them
- Supports many model providers through a single agent interface with fallback options
- Instrumentation uses standard OpenTelemetry, so it works with existing observability tools
Not ideal for
LangChain
- People who want a finished end-user app rather than a library to code against
- Developers who need low-level control over agent orchestration; the README points to LangGraph for that
- JavaScript or TypeScript projects, which the README directs to the separate LangChain.js library
Pydantic AI
- Non-programmers looking for a ready-made chat app, since this is a code library
- Projects that must avoid a dependency on a large framework, since the README describes many optional capabilities
- Users of languages other than Python, since the project is Python-only
Trade-offs
LangChain
- The README does not detail the API surface, so you rely on external docs for depth
- Several related products (LangGraph, Deep Agents, LangSmith) are needed for advanced or production work, which adds to the learning curve
- The README's quickstart targets a specific model string, so you must have access to that provider's API
Pydantic AI
- The README covers many features and packages, so the scope may be large for a small project
- Some examples reference model names and features that you should verify against current provider docs
- Several advanced features, such as Temporal durability and the Gateway, are tied to additional services or packages
Getting started
Frequently asked questions
What is the difference between LangChain and Pydantic AI?
LangChain: A Python framework for building AI agents and apps powered by language models. Pydantic AI: A typed Python SDK for building AI agents that work with many LLM providers. LangChain suits python developers building llm-powered applications, while Pydantic AI suits python developers building llm-powered features that need structured, typed output.
Are LangChain and Pydantic AI free?
LangChain: Free and open source. Pydantic AI: Free and open source.
Is LangChain or Pydantic AI more popular?
LangChain has more GitHub stars (149k) than Pydantic AI (21k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LangChain and Pydantic AI?
Other options people compare them with include LlamaIndex, Semantic Kernel, CrewAI, OpenUI, VoltAgent.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.