How Python does AI
Agents, realtime voice, image generation, embeddings. Every model, every interface, typed end to end.
Pydantic AI is the Python AI SDK: a typed, extensible agent loop with every model a string swap away. The same agent runs everywhere you need it: behind a web frontend, in the terminal, on a voice call, on a durable background queue, in GitHub Actions, or as a plain object you call run() on. Image generation and embeddings come in the same box; Pydantic Graph and Pydantic Evals are separate packages, for typed control flow and for testing agent behavior the way pytest tests code.
Pydantic AI Harness has everything an agent needs for complex, long-running work, snapped on as capabilities, from memory, guardrails, and sub-agents to planning, context management, and storage, up to a complete coding agent.
Pydantic Logfire is the AI observability platform that sees your whole app, not just the LLM calls, and the Pydantic AI Gateway is one key for every model with real-time cost monitoring and budget control; the Gateway self-hosts if you would rather, and our instrumentation is plain OpenTelemetry, so any backend you already run works. Underneath both, genai-prices keeps model pricing current, and Monty is the sandboxed Python interpreter that runs model-written code.
View the complete documentation at pydantic.dev/docs/ai.
What are you building?
From simple typed data extraction to complex, long-running multi-agent collaboration, Pydantic AI and Pydantic AI Harness have got you covered.
Coding agent
A complete coding agent in your terminal: workspace-rooted file access, allowlisted shell, repo orientation, planning, and context management that survives long sessions. Here with web search and a second-opinion advisor snapped on alongside:
uv add pydantic-ai pydantic-ai-harness
from pydantic_ai import Agent
from pydantic_ai.capabilities import WebSearch
from pydantic_ai_harness import Advisor, Coder
agent = Agent(
'anthropic:claude-fable-5',
capabilities=[
Coder(), # files, shell, repo context, sub-agents, context management
WebSearch(), # look up docs and error messages on the web
Advisor('openai:gpt-5.6-sol'), # a second opinion from another model when stuck
],
)
agent.to_cli_sync()
Coder is a regular combined capability, not a black box: use it whole, or use the blocks it bundles directly; the two are equivalent:
capabilities = [
FileSystem('.'), Shell(cwd='.'), RepoContext(), SubAgents(...),
ClearToolResults(), WarnNearLimits(), ToolOutputLimits(), RepairToolArguments(),
]
Run the file and you're chatting with the agent in your terminal. To try it before writing any code, run the exported coder_agent with clai (the Pydantic AI CLI), via uvx:
uvx --with pydantic-ai-harness clai -a pydantic_ai_harness.coder:coder_agent -m anthropic:claude-fable-5
Build this → Coder, from the Harness
Run it on GitHub → GitHub Agentic Workflows, on issues, pull requests or a schedule
Data extraction
Give the agent an output type and tools, and every run comes back validated and typed:
uv add pydantic-ai
from typing import Literal
from pydantic import BaseModel, Field
from pydantic_ai import Agent, RunContext
class Sentiment(BaseModel):
label: Literal['positive', 'negative', 'neutral']
score: float = Field(ge=-1, le=1)
agent = Agent('openai:gpt-5.6-sol', output_type=Sentiment)
@agent.tool
def recent_reviews(ctx: RunContext[None], product: str) -> list[str]:
"""Fetch recent review snippets for a product."""
return ['The new release fixed everything I complained about!']
result = agent.run_sync('How are people feeling about the Extract app?')
print(result.output)
#> label='positive' score=0.9
The @agent.tool function receives a RunContext that carries your dependencies in; the rest of its signature and its docstring become the tool schema, arguments are validated before your code runs, and the run is guaranteed to return a Sentiment, so your IDE, type checker, and the LLM all agree on the returned type.
Build this → Agents, Function Tools, and Structured Output
Durable workflow
Attach TemporalDurability and the same agent runs inside a Temporal workflow under durable execution: every model and tool call becomes a durable activity, so a run working through a background queue survives restarts, failures, and long waits:
uv add "pydantic-ai[temporal]"
from temporalio import workflow
from pydantic_ai import Agent
from pydantic_ai.capabilities import WebFetch, WebSearch
from pydantic_ai.durable_exec.temporal import PydanticAIWorkflow, TemporalDurability
agent = Agent(
'openai:gpt-5.6-sol',
instructions='Research the topic and write a structured brief.',
name='researcher',
capabilities=[WebSearch(), WebFetch(), TemporalDurability()],
)
@workflow.defn
class ResearchWorkflow(PydanticAIWorkflow):
__pydantic_ai_agents__ = [agent]
@workflow.run
async def run(self, topic: str) -> str:
result = await agent.run(f'Write a brief on: {topic}')
return result.output
DBOS and Prefect attach the same way, first-party and co-maintained, with Restate, AWS Lambda, Kitaru, and Airflow integrations besides.
Build this → Durable Execution
Realtime voice
Put the same agent on a live voice session, tools and capabilities included:
uv add "pydantic-ai[openai-realtime]"
import asyncio
from pydantic_ai import Agent
from pydantic_ai.capabilities import MCP
agent = Agent(
instructions='You are a helpful voice assistant.',
capabilities=[MCP('https://internal.example.com/mcp')], # capabilities work in voice too
)
@agent.tool_plain
def order_status(order_id: str) -> str:
"""Look up the status of an order."""
return f'Order {order_id}: shipped, arriving Thursday.'
async with agent.realtime('openai:gpt-realtime-2.1').session() as session:
microphone = asyncio.create_task(session.send_audio(microphone_chunks())) # your microphone → the model
speaker = asyncio.create_task(play_audio(session.stream_audio())) # model audio → your speaker
async for part in session.stream_transcripts():
print(f'{part.speaker}: {part.transcript}')
The model calls your tools mid-conversation while it keeps talking, and every session is instrumented; voice is just another frontend, on OpenAI Realtime, Gemini Live, Azure, and xAI Grok Voice.
Build this → Realtime Voice
Image generation
Generate an image with a dedicated image model, no agent run required:
uv add pydantic-ai
from pathlib import Path
from pydantic_ai import ImageGenerator
generator = ImageGenerator('openai:gpt-image-2')
result = generator.generate_sync('A minimalist logo for a coffee shop called Extract.')
Path('logo.png').write_bytes(result.image.data)
That standalone image API is for when your application decides; when an agent run decides, there is provider-native generation with output_type=BinaryImage for a typed image output, and the ImageGeneration capability with its fallbacks for models that generate no images of their own.
Build this → Image Generation
Why Pydantic AI
-
Any model, one Python API. Virtually every model and provider (OpenAI, Anthropic, Google, Bedrock, Azure AI Foundry, Groq, Mistral, xAI, Ollama, and dozens more), swappable with a string, or through the Pydantic AI Gateway: one key for all of them, with failover and cost monitoring built in. No flagship feature is locked to one vendor.
-
Typed end to end. Structured outputs, typed dependency injection, typed tools: your IDE, type checker, and coding agent all know what your agent returns, moving whole classes of errors from runtime to write-time. When plain control flow isn't enough, Pydantic Graph brings the same typing to graph-based workflows.
-
Measured, not vibes. OpenTelemetry-native instrumentation works with any OTel backend; one line lights up Pydantic Logfire for real-time debugging, tracing, and cost tracking backed by genai-prices. Pydantic Evals tests agent behavior the way pytest tests code.
-
Batteries, composably. One primitive, the capability, bundles tools, instructions, hooks, and model settings into reusable units. Core ships fundamentals like MCP and web search, the Harness ships everything else, and complete agents like Coder and Researcher are just capabilities composed: they come apart the way they went together. Or skip code entirely with YAML/JSON agent specs.
-
Every interface. One agent definition runs as a CLI, a built-in web chat, or realtime speech (OpenAI Realtime, Gemini Live, Azure, xAI Grok Voice); UI event streams (AG-UI, Vercel AI) connect it to your own frontend or anything else; ACP serves it as an editor agent; and GitHub Agentic Workflows runs it headless on issues, pull requests or a schedule.
-
Durable execution. Durable execution on seven engines: Temporal, DBOS, Prefect, Restate, AWS Lambda, Kitaru, and Airflow, the first five co-maintained with the vendor teams. Agents survive restarts and run for days on the engine you already operate, with human-in-the-loop approval built in.
-
Coming from another framework? The comparisons show where Pydantic AI differs from LangChain, Google ADK, the Claude Agent SDK and seven more, and the migration skills let your coding agent port an existing application over.
Built by the Pydantic team: Pydantic Validation is the validation layer of the OpenAI SDK, the Anthropic SDK, the Google ADK, LangChain, and most of the AI ecosystem (and the foundation FastAPI was built on). Pydantic AI brings that same feeling to agents.
Putting it together: a bank support agent
A typed support agent showing several features working together: dependency injection, function tools, structured output, a reusable capability bundling the customer context, and an on-demand capability the model loads only when the conversation calls for it:
from dataclasses import dataclass
from pydantic import BaseModel, Field
from pydantic_ai import Agent, Capability, RunContext
from bank_database import DatabaseConn
@dataclass
class SupportDependencies: # inject any client: DB pools, HTTP APIs, user info
customer_id: int
db: DatabaseConn
class SupportOutput(BaseModel):
support_advice: str = Field(description='Advice returned to the customer')
block_card: bool = Field(description="Whether to block the customer's card")
risk: int = Field(description='Risk level of query', ge=0, le=10)
customer_context = Capability[SupportDependencies]( # a reusable unit of tools + instructions
id='customer-context',
description="Who the customer is and what's on their account.",
)
@customer_context.instructions
async def add_customer_name(ctx: RunContext[SupportDependencies]) -> str:
customer_name = await ctx.deps.db.customer_name(id=ctx.deps.customer_id)
return f"The customer's name is {customer_name!r}"
@customer_context.tool # signature and docstring become the tool schema the LLM sees
async def customer_balance(
ctx: RunContext[SupportDependencies], include_pending: bool
) -> float:
"""Returns the customer's current account balance."""
return await ctx.deps.db.customer_balance(
id=ctx.deps.customer_id,
include_pending=include_pending,
)
refunds = Capability[SupportDependencies]( # deferred: loads on demand, like a skill
id='refunds',
description='Refund eligibility and refund status.',
defer_loading=True,
)
@refunds.tool
async def refund_status(ctx: RunContext[SupportDependencies]) -> str:
"""Look up the refund status for the customer's most recent charge."""
return await ctx.deps.db.refund_status(id=ctx.deps.customer_id)
support_agent = Agent(
'openai:gpt-5.6-sol',
deps_type=SupportDependencies,
output_type=SupportOutput, # the run returns a validated SupportOutput, typed as such
instructions=(
'You are a support agent in our bank, give the '
'customer support and judge the risk level of their query.'
),
capabilities=[customer_context, refunds],
)
... # in a real use case: more tools, longer instructions
async def main():
deps = SupportDependencies(customer_id=123, db=DatabaseConn())
result = await support_agent.run('What is my balance?', deps=deps)
print(result.output)
"""
support_advice='Hello John, your current account balance, including pending transactions, is $123.45.' block_card=False risk=1
"""
result = await support_agent.run('I just lost my card!', deps=deps)
print(result.output)
"""
support_advice="I'm sorry to hear that, John. We are temporarily blocking your card to prevent unauthorized transactions." block_card=True risk=8
"""
result = await support_agent.run( # the model loads `refunds` on demand, then answers
'Was I refunded for the duplicate charge on my last statement?', deps=deps
)
print(result.output)
"""
support_advice='Good news, John: the duplicate charge on your last statement was refunded on 2026-05-01.' block_card=False risk=1
"""
For the annotated walkthrough and Logfire tracing, see the same example in the docs.
Next Steps
- Install Pydantic AI and put your own coding agent to work: install the Pydantic AI skill, point it at the examples and the Harness index, and tell it what you'd like to build. No API key needed to start (there's a built-in
'test'model). - Read the docs and the API reference.
- Give your agent its batteries: Pydantic AI Harness.
- Join Slack or file an issue on GitHub.
Part of the Pydantic Stack
Everything you need to ship production-grade AI agents:
- Pydantic AI: the type-safe AI SDK
- Pydantic AI Harness: the official capability library and harness, from single capabilities to complete agents
- Pydantic Logfire: AI-first, full-stack observability
- Pydantic AI Gateway: unified LLM proxy
- Pydantic Evals: evaluate any Python function, agents included, with production evals on Logfire
- Pydantic Graph: typed graph control flow
- genai-prices: model pricing data, kept current
- Monty: a sandboxed Python interpreter for model-written code
2 maintainers
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