pydantic-ai

name	pydantic-ai
description	Pydantic AI Python agent framework. Covers typed tools, model providers, evals, MCP, UI adapters, and observability. Use when building Python AI agents with Pydantic AI, configuring model providers, implementing typed tools/dependencies, running evals, or integrating MCP servers. Keywords: pydantic-ai, agents, evals, MCP, Logfire.
metadata	{"version":"1.107.0","release_date":"2026-06-10"}

Pydantic AI

Python agent framework for building production-grade GenAI applications with the "FastAPI feeling".

Quick Navigation

Topic	Reference
Agents	agents.md
Capabilities	agents.md
Tools	tools.md
Models	models.md
Embeddings	embeddings.md
Evals	evals.md
Integrations	integrations.md
Graphs	graphs.md
UI Streams	ui.md
Installation	installation.md

When to Use

• Building AI agents with structured output
• Need type-safe, IDE-friendly agent development
• Require dependency injection for tools
• Multi-model support (OpenAI, Anthropic, Gemini, etc.)
• Production observability with Logfire
• Complex workflows with graphs

Installation

See references/installation.md for full/slim install options and optional dependency groups. Requires Python 3.10+.

Release Highlights (1.96.1)

• V2 preparation: Agent(..., prepare_tools=..., prepare_output_tools=..., event_stream_handler=...) is now the deprecated path; capability-based migration is the new direction.
• Capability migration: use PrepareTools, PrepareOutputTools, and ProcessEventStream capabilities instead of wiring those behaviors through constructor sugar.
• OpenAI fixes: the latest patch line also tightens OpenAIResponsesModel system-prompt-role handling and image-generation request shaping.

Release Highlights (1.97.0 -> 1.102.0)

• MCP migration: prefer MCPToolset for new MCP integrations. FastMCPToolset and the older MCPServer* client surface are now on the deprecation path.
• Google provider split: GoogleProvider and GoogleCloudProvider are now distinct, and model ids move from google-gla: / google-vertex: to google: / google-cloud:.
• Streaming migration: move from stream_responses() to stream_response(); the newer API yields ModelResponse objects directly.
• Retry configuration: prefer Agent(retries=...) or AgentRetries(...) over older constructor-level retry knobs.
• New runtime tools: ctx.enqueue() and MCP background tasks make it easier to queue follow-up work without forcing it into the current response turn.

Release Highlights (1.105.0 -> 1.107.0)

• New models: Claude Fable 5 and Claude Mythos 5 are supported (1.107.0), alongside Grok 4.3 reasoning_effort and updated xAI model names (1.105.0).
• Deferred loading: instructions, tools, model settings, and hooks can now be loaded on demand instead of eagerly at agent construction (1.105.0).
• Model introspection: known_model_names() enumerates KnownModelName members (1.107.0).
• OpenRouter caching: CachePoint and prompt caching are implemented for OpenRouter (1.107.0).
• xAI config: XaiProvider gains api_host and timeout, plus seed parameter mapping (1.106.0).
• Security: VercelAIAdapter UploadedFile handling was hardened against a confused-deputy file-read vulnerability (GHSA-h7p7-w5gc-xj3w, 1.106.0).
• Fixes: incomplete streamed responses when event_stream_handler doesn't consume the stream, from_data_uri on non-base64 data URIs, Temporal gateway/ model construction, GoogleModelSettings.google_cached_content request shaping, Anthropic Bedrock message=None start events, and AnthropicModel.count_tokens with native tools.

Release Highlights (1.103.0 -> 1.104.0)

• MCP prompts: maintained McpServer integrations can now list and fetch prompts with list_prompts and get_prompt; still prefer MCPToolset for new client code unless legacy wrappers are required.
• UI adapters: VercelAIAdapter round-trips message timestamps through UIMessage.metadata, and UIAdapter.sanitize_messages strips client-submitted force_download from FileUrl parts.
• Provider/model updates: Claude Opus 4.8 is supported, OpenRouterModel can use anthropic_eager_input_streaming, and hybrid OpenRouter/xAI/Bedrock routes now forward thinking=False consistently.
• Bedrock/toolset fixes: Bedrock maps malformed model/tool output to FinishReason.error, recognizes adaptive thinking, preserves single-tool tool_choice cache behavior, and toolset prepare callbacks warn when they accidentally return None.

Release Highlights (1.75.0 -> 1.84.1)

• Capabilities: CapabilityOrdering adds explicit wrapping/ordering control (innermost, outermost, wraps, wrapped_by, requires) for complex capability stacks.
• Compaction: new server-side compaction capabilities for OpenAI and Anthropic; OpenAI adds stateful compaction mode.
• Models: Claude Opus 4.7 support and a native OllamaModel path with corrected Ollama capability flags for structured output.
• Tools: tool hooks now consistently receive dict-shaped validated args for single-BaseModel tools, and internal output tools skip hook execution.
• Hardening: Google FileSearchTool parsing received regex hardening in the 1.83/1.84 line.

Release Highlights (1.71.0 → 1.74.0)

• Capabilities: composable, reusable units of agent behavior that bundle tools, lifecycle hooks, instructions, and model settings into a single class. Plug into any agent for maximum reuse.
• AgentSpec: load agents from YAML/JSON files via Agent.from_file. Supports TemplateStr for templated instructions referencing deps.
• Hooks capability: define hooks using decorators (@hooks.on_model_request, etc.).
• Thinking capability: cross-provider thinking model setting for reasoning.
• Provider-adaptive tools: WebSearch, WebFetch, MCP, ImageGeneration — automatically fall back from builtin (provider) tools to local tools.
• Online evaluation: evaluation infrastructure in pydantic-evals.
• TextContent: user prompts with metadata not sent to model.
• CaseLifecycle hooks: hooks for Dataset.evaluate lifecycle.
• Model swapping in hooks: before_model_request / wrap hooks can swap models via ModelRequestContext.
• ModelRetry from hooks: hooks can raise ModelRetry for retry control flow.
• Sync tool preparation functions supported. MCP capability no longer requires explicit url=.

Release Highlights (1.69.0 → 1.70.0)

• Agents: Agent(description=...) adds a human-readable description to the run span as gen_ai.agent.description when instrumentation is enabled.
• Models: FallbackModel now supports response-based fallback handlers for semantic failures in non-streaming runs.
• Tools: multimodal tool results are passed directly to provider APIs instead of always being split into extra user-message parts.
• Bedrock: bedrock_inference_profile is available on model and embedding settings for routing requests through an inference profile ARN.
• Stability: provider fixes landed for OpenRouter Anthropic model matching, Cohere embeddings, Google image sizes, Bedrock tool-name sanitization, and malformed tool-call retry handling.

Quick Start

Basic Agent

from pydantic_ai import Agent

agent = Agent(
    'openai:gpt-4o',
    instructions='Be concise, reply with one sentence.'
)

result = agent.run_sync('Where does "hello world" come from?')
print(result.output)

With Structured Output

from pydantic import BaseModel
from pydantic_ai import Agent

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

agent = Agent('openai:gpt-4o', output_type=CityInfo)
result = agent.run_sync('Tell me about Paris')
print(result.output)  # CityInfo(name='Paris', country='France', population=2161000)

With Tools and Dependencies

from dataclasses import dataclass
from pydantic_ai import Agent, RunContext

@dataclass
class Deps:
    user_id: int

agent = Agent('openai:gpt-4o', deps_type=Deps)

@agent.tool
async def get_user_name(ctx: RunContext[Deps]) -> str:
    """Get the current user's name."""
    return f"User #{ctx.deps.user_id}"

result = agent.run_sync('What is my name?', deps=Deps(user_id=123))

Key Features

Feature	Description
Type-safe	Full IDE support, type checking
Model-agnostic	30+ providers supported
Dependency Injection	Pass context to tools
Structured Output	Pydantic model validation
Embeddings	Multi-provider vector support
Logfire Integration	Built-in observability
MCP Support	External tools and data
Evals	Systematic testing
Graphs	Complex workflow support

Supported Models

Provider	Models
OpenAI	GPT-4o, GPT-4, o1, o3
Anthropic	Claude Opus 4.8, Claude 4, Claude 3.5
Google	Gemini 2.0, Gemini 1.5
xAI	Grok-4 (native SDK)
Groq	Llama, Mixtral
Mistral	Mistral Large, Codestral
Azure	Azure OpenAI
Bedrock	AWS Bedrock + Nova 2.0
SambaNova	SambaNova models
Ollama	Local models

Best Practices

1. Use type hints — enables IDE support and validation
2. Define output types — guarantees structured responses
3. Use dependencies — inject context into tools
4. Add tool docstrings — LLM uses them as descriptions
5. Enable Logfire — for production observability
6. Use run_sync for simple cases — run for async
7. Override deps for testing — agent.override(deps=...)
8. Set usage limits — prevent infinite loops with UsageLimits

Prohibitions

• Do not expose API keys in code
• Do not skip output validation in production
• Do not ignore tool errors
• Do not use run_stream without handling partial outputs
• Do not forget to close MCP connections (async with agent)
• Do not assume capability order is arbitrary once multiple wrappers/hooks are involved; define it explicitly when composition matters.

Common Patterns

Streaming Response

async with agent.run_stream('Query') as response:
    async for text in response.stream_text():
        print(text, end='')

Fallback Models

from pydantic_ai.models.fallback import FallbackModel

fallback = FallbackModel(openai_model, anthropic_model)
agent = Agent(fallback)

MCP Integration

from pydantic_ai.mcp import MCPToolset

toolset = MCPToolset(command='python', args=['mcp_server.py'])
agent = Agent('openai:gpt-4o', toolsets=[toolset])

Testing with TestModel

from pydantic_ai.models.test import TestModel

agent = Agent(model=TestModel())
result = agent.run_sync('test')  # Deterministic output

Embeddings

from pydantic_ai import Embedder

embedder = Embedder('openai:text-embedding-3-small')

# Embed search query
result = await embedder.embed_query('What is ML?')

# Embed documents for indexing
docs = ['Doc 1', 'Doc 2', 'Doc 3']
result = await embedder.embed_documents(docs)

See embeddings.md for providers and settings.

xAI Provider

from pydantic_ai import Agent

agent = Agent('xai:grok-4-1-fast-non-reasoning')

See models.md for configuration details.

Exa Neural Search

import os
from pydantic_ai import Agent
from pydantic_ai.common_tools.exa import ExaToolset

api_key = os.getenv('EXA_API_KEY')
toolset = ExaToolset(api_key, num_results=5, include_search=True)
agent = Agent('openai:gpt-4o', toolsets=[toolset])

See tools.md for all Exa tools.

Links

• Documentation
• Releases
• GitHub
• PyPI