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Type-safe agents with structured outputs from the Pydantic team.
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Pydantic AI is a Python agent framework and inference SDK maintained by the team that created Pydantic, the validation library that powers the SDKs of OpenAI, Anthropic, Google, LangChain, LlamaIndex, and most other LLM tools in the Python ecosystem. Released in 2024 under an MIT license, it occupies the same category as LangChain, CrewAI, and AutoGen but with a fundamentally different design philosophy: type safety as a first-class constraint, not afterthought.
The framework solves a problem every practitioner hits in production: LLM outputs are unreliable, tool calls fail at runtime, and debugging requires combing through raw JSON. Pydantic AI makes every input, output, and tool signature a typed contract. If a tool returns something the schema doesn’t expect, the framework retries or raises an error before that bad data propagates downstream.
Popularity signals confirm it’s more than a niche project: 17,110 GitHub stars, 444 contributors, and over 39 million PyPI monthly downloads. Those download numbers reflect both the framework itself and the Pydantic validation library distributed alongside it, but the growth trajectory is clear. The team behind it has already changed how Python web apps validate data via FastAPI and Pydantic. They are now applying the same approach to agents.
Pydantic AI is code-first, not config-first. You define agents as Python functions or classes, decorate tools with type annotations, and let the framework handle serialization and LLM calls. The core abstraction is the
What the framework gives you out of the box, in plain language.
The jobs this framework is best suited for.
Pull typed objects out of free text with validation and retries on bad outputs.
Agents that integrate with existing services through typed tool calls.
Tasks where the LLM output must conform to a schema, not just sound right.
Side-by-Side
Add a second or third framework and see stars, downloads, and capabilities lined up next to each other.
Close alternatives worth a look before you decide.
An agent framework is the code your team uses to wire large language models into tools, memory, and human checkpoints. It is the connective tissue between an LLM call and a real task, like answering a support ticket or running a multi-step research workflow.
Pydantic AI ships under the MIT license. The source code lives on GitHub, so you can read it, fork it, and run it on your own infrastructure if your team prefers self-hosting.
Pydantic AI is primarily a Python project. Pick a framework that matches the language your team already ships in. The cost of a stack switch is almost always higher than the difference between two frameworks.
Agentagent.run()agent.run_stream()Tools are plain Python functions with Pydantic models for their arguments. The framework validates arguments before sending them to the LLM, then validates the LLM’s structured output against the response model you define. If validation fails, you can configure automatic retries with modified instructions. This shifts failure detection from runtime logs to development-time type checking, a pattern familiar to anyone who has used FastAPI’s request validation.
Dependency injection is built in at the agent level. You pass dependencies (database connections, API clients, configuration) as a typed Deps parameter, and the framework injects them into tool functions. This makes unit testing straightforward: you mock the dependency, not the entire agent.
Pydantic AI is model-agnostic out of the box. It supports OpenAI, Anthropic, Gemini, DeepSeek, Grok, Cohere, Mistral, and Perplexity directly, plus providers like AWS Bedrock, Azure AI Foundry, Ollama, LiteLLM, Groq, and dozens more. A custom model interface lets you wire in any provider that supports the same API shape. There is no abstract graph or chain abstraction, you compose agents in code, not in a visual builder or YAML file.
Type-safe tool definitions. Every tool function accepts RunContext[T] and returns a typed result. Pydantic models validate arguments synchronously before the LLM call, so malformed inputs never reach the model. This eliminates an entire category of silent errors common in first-generation frameworks.
Structured outputs with automatic validation and retries. You define a response model using Pydantic (a dataclass with fields, nested models, validators). The framework instructs the LLM to produce JSON matching that schema, validates the result, and retries up to a configurable limit if the output is malformed. This is the core use case for extraction and data validation workflows.
Logfire integration for observability. Pydantic Logfire, the team’s OpenTelemetry-based platform, traces every agent run, tool invocation, and LLM call. You get spans, metrics, and cost tracking without adding instrumentation code. If you already use an OTel-compatible observability backend, you can route traces there instead.
Streaming. The agent.run_stream() method yields typed partial outputs as the LLM generates tokens. This matters for chat interfaces, real-time dashboards, and any application where latency must feel low.
Multi-agent flows. Pydantic AI supports multi-agent patterns through function calls between agents, not through a built-in orchestrator. You can have one agent delegate to another by calling other_agent.run() from within a tool. This keeps the scope narrow and avoids the complexity of a runtime scheduler. For teams that need graph-based orchestration, the framework includes a separate pydantic_graph package that adds that capability.
Evals. The framework includes a pydantic_evals module for systematic testing. You can define evaluation datasets, run them against your agent, and track metrics over time in Logfire.
Structured data extraction. Feed free-text invoices, emails, or medical notes into an agent and get back a typed Invoice, Contact, or LabResult object. The retry mechanism means you can trust the output shape even when the source text is inconsistent.
Internal agents with strong contracts. When an agent needs to call a CRM API, an inventory system, or a billing service, a shipping service, typed tool definitions catch mismatches (wrong field name, wrong type) before the API call is made. This is especially valuable in organizations where multiple teams maintain separate services and API contracts change frequently.
Validation-heavy workflows. Any task where the LLM output must match a schema exactly, generating configuration files, writing database migrations, producing structured alerts, benefits from Pydantic AI’s emphasis on validation. If the schema says a field must be a positive integer, the framework enforces that.
Customer support automation. An agent that triages tickets, retrieves order status from a typed tool, and returns a structured response. The dependency injection model makes it easy to swap between staging and production databases during testing.
Weak fit environments. Pydantic AI is not ideal for highly dynamic multi-agent systems where agents spawn and communicate in ad-hoc patterns. Its multi-agent support is functional but not as rich as LangGraph or AutoGen’s explicit graph models. It also lacks built-in memory or vector store abstractions, you bring your own.
Install the package via pip:
1pip install pydantic-ai
The smallest meaningful example looks like this:
1from pydantic_ai import Agent2from pydantic import BaseModel34class Response(BaseModel):5 answer: str67agent = Agent('openai:gpt-4o', result_type=Response)89result = agent.run_sync('What is the capital of France?')10print(result.data.answer) # "Paris"
You need an LLM provider API key (set as an environment variable like OPENAI_API_KEY). For observability, install the Logfire integration (pip install pydantic-ai[logfire]) or use your own OTel collector. No vector store, database, or orchestration service is required to start.
Full documentation lives at [ai.pydantic.dev](https://ai.pydantic.dev). The community gathers on the Pydantic Slack (linked from the docs) and the GitHub repository.
Pydantic AI vs LangChain. LangChain is the incumbent with the widest ecosystem of integrations. If you need a prebuilt vector store connector, document loader, or chain-of-thought prompt template, LangChain has it. Pydantic AI is leaner and more opinionated about type safety. Choose Pydantic AI when your primary concern is output structure and validation integrity. Choose LangChain when you need the largest selection of community modules and are integrations and you’re comfortable with the comfortable wading through abstraction layers.
Pydantic AI vs CrewAI. CrewAI specializes in role-based multi-agent orchestration. It is the better choice if your architecture requires multiple specialized agents with distinct roles and a built-in delegation manager. Pydantic AI handles multi-agent flows via direct function calls, which works for simple patterns but lacks CrewAI’s role management and task assignment primitives. For a single-agent application or a small number of cooperating agents, Pydantic AI’s type safety and ergonomics are a net advantage.
Pydantic AI vs Mastra. Mastra (JavaScript/TypeScript) targets similar design goals for the Node ecosystem. Pydantic AI is the Python-native equivalent. If your stack is Python, Pydantic AI integrates naturally with FastAPI, SQLAlchemy, and the rest of the Python data stack. If you are in a TypeScript environment, Mastra is a closer fit.
For most teams building production Python agents today, Pydantic AI brings the kind of compile-time safety that has been missing from LLM development. It is not the most feature-rich option, but it is the one where failures happen at your keyboard, not in production traffic.
Tools are typed functions — Pydantic models validate arguments before the LLM call.
Pydantic models for outputs, with automatic validation and retries on mismatch.
Built-in observability via Pydantic Logfire — traces, spans, and metrics.
