PydanticAI
Build production-ready AI agents in Python with type-safe tools, structured outputs, and flexible model integrations.
Website
Category
AI Agent Builders
Pricing
Free & Open Source
Overview
PydanticAI is an open-source Python framework for building production-grade AI agents and generative AI applications. Developed by the team behind Pydantic, it brings Python’s type safety and validation capabilities to AI agent development.
The framework allows developers to create AI agents that can use tools, access external data, maintain dependencies, return structured outputs, and perform multi-step tasks. Developers can define exactly how agents interact with application code while allowing AI models to handle natural-language reasoning and decision-making.
PydanticAI is model-agnostic, giving developers the flexibility to work with different AI providers through a consistent interface. This makes it easier to experiment with different models or change providers without redesigning an entire application.
The framework also supports more advanced agent architectures, including multi-agent workflows, agent delegation, graph-based workflows, Model Context Protocol integrations, durable execution, evaluation, and observability.
Tool Information
| Field | Details |
|---|---|
| Name | PydanticAI |
| Category | AI Agent Builders |
| Type | Open-Source Python AI Agent Framework |
| Developer | Pydantic |
| Deployment | Self-Hosted / Application-Based |
| Platform | Python |
| AI Agents | Yes |
| Multi-Agent Systems | Yes |
| Structured Outputs | Yes |
| Function Tools | Yes |
| Dependency Injection | Yes |
| Model Agnostic | Yes |
| MCP Support | Yes |
| Durable Execution | Yes |
| Agent Evaluation | Yes |
| Observability | Yes |
| Open Source | Yes |
| Official Website | https://ai.pydantic.dev/ |
Key Features
AI Agent Builder
Create AI agents directly in Python using a developer-focused agent framework.
Capabilities
- Agent creation
- System instructions
- Model configuration
- Agent dependencies
- Tool calling
- Structured outputs
- Agent delegation
- Context management
Type-Safe AI Development
Use Python type hints and Pydantic validation to make AI applications more predictable and reliable.
Benefits
- Typed agent outputs
- Validated tool arguments
- Structured responses
- Type-safe dependencies
- IDE support
- Static type checking
- Runtime validation
Structured Outputs
Define the format an AI agent should return using Python types and Pydantic models.
Features
- Pydantic models
- Typed outputs
- JSON schemas
- Output validation
- Structured data extraction
- Application-ready responses
This is particularly useful when an AI response needs to be passed directly into another part of a software application.
Function Tools
Give agents access to Python functions and external systems so they can perform actions.
Capabilities
- Custom Python functions
- Tool argument validation
- Context-aware tools
- External API calls
- Database operations
- Application-specific functions
Developers can register functions as tools and allow an AI model to decide when they should be called.
AI Model Integration
Use different AI models through a consistent Python interface.
Supported Providers
- OpenAI
- Anthropic
- AWS Bedrock
- Azure AI Foundry
- Groq
- Mistral
- xAI
- Ollama
- OpenAI-compatible endpoints
- Other supported providers
This model-agnostic architecture gives developers greater flexibility when selecting or changing AI models.
Multi-Agent Workflows
Build applications where multiple specialized agents collaborate or delegate tasks to one another.
Features
- Agent delegation
- Agent handoffs
- Multi-agent collaboration
- Specialized agents
- Programmatic workflows
- Complex task coordination
Agent Graphs
Create graph-based workflows for applications that require explicit state, branching, and complex control flow.
Capabilities
- Graph workflows
- State management
- Conditional execution
- Multi-step processes
- Agent coordination
- Complex workflow control
Pydantic Graph can be used when a simple sequential agent workflow is not sufficient for an application’s requirements.
MCP Integration
Connect AI agents to external tools and services through the Model Context Protocol.
Features
- MCP tools
- MCP servers
- External tool integration
- Tool discovery
- Remote services
- Custom MCP workflows
Durable Execution
Build long-running agent workflows that can recover from failures and continue execution.
Supported Integrations
- Temporal
- DBOS
- Prefect
- Restate
- Airflow
- Other durable execution systems
This can be useful for agents that need to run for extended periods, wait for external events, or include human approval steps.
Evaluation & Observability
Evaluate agent behavior and monitor AI applications during development and production.
Features
- Agent evaluations
- Test cases
- Performance analysis
- OpenTelemetry
- Execution tracing
- Debugging
- Usage monitoring
- Cost tracking
Pydantic Evals provides tools for evaluating AI systems, while Pydantic Logfire can provide observability and tracing.
Use Cases
AI Agents
Build production AI agents that can use tools, access data, reason over information, and perform multi-step tasks.
Data Extraction
Create agents that extract structured information from unstructured documents, text, and other data sources.
Research Agents
Build agents that search for information, retrieve relevant data, analyze findings, and produce structured results.
Software Development
Create AI-powered development workflows that interact with code, repositories, files, and development tools.
Business Automation
Connect AI agents with APIs, databases, and business logic to automate repetitive and multi-step processes.
Customer Support
Build AI assistants that can retrieve customer information, use support tools, and provide structured responses.
Voice Applications
Use PydanticAI agents as part of real-time voice and conversational applications.
Long-Running Workflows
Create durable AI workflows that can run for extended periods and recover from interruptions or failures.
How PydanticAI Works
PydanticAI provides an Agent abstraction that connects an AI model with instructions, tools, dependencies, and structured output definitions.
Typical Workflow
- Install PydanticAI
- Select an AI model
- Create an agent
- Define its instructions
- Add dependencies and context
- Register tools
- Define the expected output
- Run the agent
- Validate the result
- Evaluate and monitor performance
For more advanced applications, developers can add agent delegation, multi-agent workflows, graph-based control flow, MCP tools, and durable execution.
Integrations
Supported Workflows
- OpenAI
- Anthropic
- AWS Bedrock
- Azure AI Foundry
- Groq
- Mistral
- xAI
- Ollama
- OpenAI-compatible APIs
- MCP
- Temporal
- DBOS
- Prefect
- Restate
- Airflow
- OpenTelemetry
- Pydantic Logfire
- Pydantic Evals
Advantages
- Open-source framework
- Developed by the Pydantic team
- Python-first development
- Strong type safety
- Structured outputs
- Validated tool arguments
- Model-agnostic architecture
- Supports many AI providers
- Multi-agent capabilities
- Graph-based workflows
- MCP support
- Durable execution integrations
- Agent evaluation tools
- OpenTelemetry support
- Production-focused architecture
- Suitable for custom AI applications
Limitations
- Primarily designed for Python developers
- Requires programming knowledge
- Advanced agent workflows can require significant engineering
- Multi-agent systems can become complex to design and maintain
- Developers need to understand Python typing and Pydantic concepts for the full benefit of the framework
- AI model and infrastructure costs are separate from the framework
- Some advanced capabilities require additional services or infrastructure
Pricing
Free & Open Source
PydanticAI is available as an open-source Python framework and can be used without paying a software license fee.
Developers can install the framework and run it within their own applications and infrastructure.
Pydantic Services
Additional services in the Pydantic ecosystem, including Logfire, Evals, and the Pydantic AI Gateway, may have their own usage limits and pricing.
AI Model Costs
PydanticAI does not provide unlimited AI model usage. Applications connecting to commercial AI providers may incur separate API or inference costs.
Visit the official website for the latest pricing information.
Company Information
| Field | Details |
|---|---|
| Product Name | PydanticAI |
| Company | Pydantic |
| Category | AI Agent Builders |
| Industry | Artificial Intelligence / Developer Tools |
| Product Type | Open-Source Python AI Agent Framework |
| Deployment | Self-Hosted / Application-Based |
| Target Audience | Developers, AI Engineers, Startups, Enterprises |
| Official Website | https://ai.pydantic.dev/ |
Frequently Asked Questions
What is PydanticAI?
PydanticAI is an open-source Python framework for building production AI agents and generative AI applications with type-safe tools, structured outputs, and flexible model integrations.
Who should use PydanticAI?
PydanticAI is primarily designed for Python developers, AI engineers, startups, and technical teams building production AI applications and agentic workflows.
Does PydanticAI support multiple AI models?
Yes. PydanticAI supports many model providers, including OpenAI, Anthropic, Google, AWS Bedrock, Azure AI Foundry, Groq, Mistral, xAI, Ollama, and other compatible endpoints.
Can PydanticAI build multi-agent systems?
Yes. PydanticAI supports agent delegation, handoffs, multi-agent collaboration, and graph-based workflows for more advanced agent architectures.
Does PydanticAI support MCP?
Yes. PydanticAI supports the Model Context Protocol, allowing agents to connect to external MCP servers and tools.
Is PydanticAI free?
Yes. PydanticAI is open source and can be used without a software license fee. However, AI model APIs, hosting, observability services, and other external infrastructure may have separate costs.
