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Pydantic AI

Pydantic AI
Pydantic AI

How Python does AI

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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, or as a plain object you call run() on. Image generation and embeddings come in the same box.

Pydantic AI Harness has everything an agent needs for complex, long-running work, snapped on as capabilities, from memory, sub-agents, and context management to a complete coding agent.

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.

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:

Terminal
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, planning, 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(), Planning(), SubAgents(...),
    ClearToolResults(), WarnNearLimits(), ToolOutputLimits(),
]

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:

Terminal
uvx --with pydantic-ai-harness clai -a pydantic_ai_harness.coder:coder_agent -m anthropic:claude-fable-5

Build this → Coder, from the Harness

Why Pydantic AI

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.

Sign up for our newsletter, The Pydantic Stack, with updates & tutorials on Pydantic AI, Logfire, and Pydantic:

Putting it together: a bank support agent

Here’s a support agent for a bank, showing several features working together: dependency injection carrying a database connection into instructions and tools, function tools the model calls, structured output validated on every run, a reusable capability bundling the customer context, and an on-demand capability the model loads only when the conversation calls for it:

bank_support.py
from dataclasses import dataclass

from pydantic import BaseModel, Field
from pydantic_ai import Agent, Capability, RunContext

from bank_database import DatabaseConn


@dataclass
class SupportDependencies:  # (1)
  customer_id: int
  db: DatabaseConn  # (2)


class SupportOutput(BaseModel):  # (3)
  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](  # (4)
  id='customer-context',
  description="Who the customer is and what's on their account.",
)


@customer_context.instructions  # (5)
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  # (6)
async def customer_balance(
  ctx: RunContext[SupportDependencies], include_pending: bool
) -> float:
  """Returns the customer's current account balance."""  # (7)
  return await ctx.deps.db.customer_balance(
      id=ctx.deps.customer_id,
      include_pending=include_pending,
  )


refunds = Capability[SupportDependencies](  # (8)
  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(  # (9)
  'openai:gpt-5.6-sol',  # (10)
  deps_type=SupportDependencies,
  output_type=SupportOutput,  # (11)
  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],  # (12)
)


...  # (13)


async def main():
  deps = SupportDependencies(customer_id=123, db=DatabaseConn())
  result = await support_agent.run('What is my balance?', deps=deps)  # (14)
  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(  # (15)
      '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
  """

The SupportDependencies dataclass is used to pass data, connections, and logic into the model that will be needed when running instructions and tool functions. Pydantic AI's system of dependency injection provides a type-safe way to customise the behavior of your agents, and can be especially useful when running unit tests and evals.

This is a simple sketch of a database connection, used to keep the example short and readable. In reality, you'd be connecting to an external database (e.g. PostgreSQL) to get information about customers.

This Pydantic model is used to constrain the structured data returned by the agent. From this simple definition, Pydantic builds the JSON Schema that tells the LLM how to return the data, and performs validation to guarantee the data is correct at the end of the run.

A Capability bundles related instructions and tools into one reusable unit: the same primitive behind built-in capabilities like web search and everything in the Harness. This one carries the customer context; you could drop it into any other agent's capabilities list as-is.

Dynamic instructions can make use of dependency injection. Dependencies are carried via the RunContext argument, which is parameterized with the deps_type from above. If the type annotation here is wrong, static type checkers will catch it.

The tool decorator registers a function whose signature becomes a tool the LLM may call while responding to a user. Again, dependencies are carried via RunContext; any other arguments become the tool schema passed to the LLM. Pydantic is used to validate these arguments, and errors are passed back to the LLM so it can retry.

The docstring of a tool is also passed to the LLM as the description of the tool. Parameter descriptions are extracted from the docstring and added to the parameter schema sent to the LLM.

defer_loading=True makes this an on-demand capability, the same shape as an Agent Skill. It collapses to a one-line catalog entry in the prompt, and its tools stay hidden until the model decides it's relevant and loads it with the framework-managed load_capability tool.

This agent will act as first-tier support in a bank. Agents are generic in the type of dependencies they accept and the type of output they return. In this case, the support agent has type Agent[SupportDependencies, SupportOutput].

Here we configure the agent to use OpenAI's GPT-5.6 Sol model; you can also set the model when running the agent.

The response from the agent will be guaranteed to be a SupportOutput. Since the agent is generic, it'll also be typed as a SupportOutput to aid with static type checking. If validation fails, the agent is prompted to try again.

Mount the capabilities on the agent. More capabilities, like web search or anything from the Harness, snap on alongside them in the same list.

In a real use case, you'd add more tools and longer instructions to the agent to extend the context it's equipped with and support it can provide.

Run the agent asynchronously, conducting a conversation with the LLM until a final response is reached. Even in this fairly simple case, the agent will exchange multiple messages with the LLM as tools are called to retrieve an output.

This turn exercises the deferred capability: the model sees the refunds catalog entry, calls load_capability with id='refunds', and only then gets the refund_status tool to answer with: on-demand loading in action.

The dependencies dataclass carries the database connection into instructions and tools with full type safety: swap in a test double and the same agent runs in unit tests and evals. And because the customer context is a capability, it composes: the same unit drops into a voice agent or a web app unchanged.

Instrumentation with Pydantic Logfire

Pydantic AI is OpenTelemetry-native: the Instrumentation capability emits standard OTel spans for every model call and tool call, and any OTLP backend works. The easiest setup is the logfire SDK, which speaks plain OpenTelemetry and can point at Pydantic Logfire or any other backend.

Even a simple agent with just a handful of tools can result in a lot of back-and-forth with the LLM, making it nearly impossible to be confident of what’s going on just from reading the code. To watch the runs above in action, set up Logfire and add the following to the code:

bank_support_with_logfire.py
...
from pydantic_ai import Agent, RunContext

from bank_database import DatabaseConn

import logfire

logfire.configure()  # (1)
logfire.instrument_pydantic_ai()  # (2)
logfire.instrument_sqlite3()  # (3)

...

support_agent = Agent(
  'openai:gpt-5.6-sol',
  deps_type=SupportDependencies,
  output_type=SupportOutput,
  instructions=(
      'You are a support agent in our bank, give the '
      'customer support and judge the risk level of their query.'
  ),
  capabilities=[customer_context],
)

Configure the Logfire SDK, this will fail if project is not set up.

This will instrument all Pydantic AI agents used from here on out. To instrument only a specific agent, add an Instrumentation entry to the agent's capabilities=[...].

In our demo, DatabaseConn uses sqlite3 to connect to a PostgreSQL database, so logfire.instrument_sqlite3() is used to log the database queries.

That’s enough to get the following view of your agent in action:

Logfire instrumentation for the bank agent — View in Logfire

See Monitoring and Performance to learn more.

llms.txt

The Pydantic AI documentation is available in the llms.txt format. This format is defined in Markdown and suited for LLMs and AI coding assistants and agents.

Two formats are available:

  • llms.txt: a file containing a brief description of the project, along with links to the different sections of the documentation. The structure of this file is described in details here.
  • llms-full.txt: Similar to the llms.txt file, but every link content is included. Note that this file may be too large for some LLMs.

As of today, these files are not automatically leveraged by IDEs or coding agents, but they will use it if you provide a link or the full text.

Next steps

Run something right now. One command puts a complete coding agent in your terminal:

Terminal
uvx --with pydantic-ai-harness clai -a pydantic_ai_harness.coder:coder_agent -m anthropic:claude-fable-5

Or install Pydantic AI, pick a model, and put your own coding agent to work: install the Pydantic AI skill to give it up-to-date framework knowledge, point it at the examples and the Harness index, and tell it what you’d like to build.

See what your agent did. Instrument it: one line of setup, and every model call and tool call shows up. It’s standard OpenTelemetry: Pydantic Logfire is the easiest way to look, any OTLP backend works.

Go deeper. The Agents guide is the core walkthrough; the API Reference covers the full interface; the Harness has the batteries.

Get help. Join Slack or file an issue on GitHub.