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108 changes: 60 additions & 48 deletions README.md
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Expand Up @@ -2,12 +2,12 @@
<a href="https://ai.pydantic.dev/">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://ai.pydantic.dev/img/pydantic-ai-dark.svg">
<img src="https://ai.pydantic.dev/img/pydantic-ai-light.svg" alt="PydanticAI">
<img src="https://ai.pydantic.dev/img/pydantic-ai-light.svg" alt="Pydantic AI">
</picture>
</a>
</div>
<div align="center">
<em>Agent Framework / shim to use Pydantic with LLMs</em>
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I think calling it a shim is seriously underselling it, and it's not differentiated from all the other frameworks that use Pydantic, so I feel we should drop that asap.

I went with "the Pydantic way" because it carries so much positive associations, and the way we and FastAPI do Python libs/frameworks really is why people would come to us primarily.

I don't really care what we put here, I just don't like calling it a shim :)

<h3>LLM Agent Framework, the Pydantic way</h3>
</div>
<div align="center">
<a href="https://github.com/pydantic/pydantic-ai/actions/workflows/ci.yml?query=branch%3Amain"><img src="https://github.com/pydantic/pydantic-ai/actions/workflows/ci.yml/badge.svg?event=push" alt="CI"></a>
Expand All @@ -24,76 +24,81 @@

---

PydanticAI is a Python agent framework designed to make it less painful to build production grade applications with Generative AI.
### <em>Pydantic AI is a Python agent framework designed to help you quickly, confidently, and painlessly build production grade applications and workflows with Generative AI.</em>
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Besides the styling to make the README look more like the docs index, the goal here was to hit on the same "speeds the way" point of from the deck with "quickly", and "confidently" to hint at Logfire. I noticed other AI frameworks (like ADK) mention workflows pretty prominently, so I thought that was worth a mention as well (with anecdotal evidence from the sales call we just had that they're building a workflow, not an app).


FastAPI revolutionized web development by offering an innovative and ergonomic design, built on the foundation of [Pydantic](https://docs.pydantic.dev).

Similarly, virtually every agent framework and LLM library in Python uses Pydantic, yet when we began to use LLMs in [Pydantic Logfire](https://pydantic.dev/logfire), we couldn't find anything that gave us the same feeling.
FastAPI revolutionized web development by offering an innovative and ergonomic design, built on the foundation of [Pydantic](https://docs.pydantic.dev) and modern Python features like type hints.

We built PydanticAI with one simple aim: to bring that FastAPI feeling to GenAI app development.
Yet despite the fact that virtually every agent framework and LLM library in Python uses Pydantic, when we began to use LLMs in [Pydantic Logfire](https://pydantic.dev/logfire), we couldn't find anything that gave us the same feeling.

## Why use PydanticAI
We built Pydantic AI with one simple aim: to bring that FastAPI feeling to GenAI app and agent development.

* __Built by the Pydantic Team__
Built by the team behind [Pydantic](https://docs.pydantic.dev/latest/) (the validation layer of the OpenAI SDK, the Anthropic SDK, LangChain, LlamaIndex, AutoGPT, Transformers, CrewAI, Instructor and many more).
## Why use Pydantic AI

* __Model-agnostic__
Supports OpenAI, Anthropic, Gemini, Deepseek, Ollama, Groq, Cohere, and Mistral, and there is a simple interface to implement support for [other models](https://ai.pydantic.dev/models/).
1. __Built by the Pydantic Team__:
[Pydantic](https://docs.pydantic.dev/latest/) is the validation layer of the OpenAI SDK, the Google ADK, the Anthropic SDK, LangChain, LlamaIndex, AutoGPT, Transformers, CrewAI, Instructor and many more. _Why use the derivative when you can go straight to the source?_ :smiley:

* __Pydantic Logfire Integration__
Seamlessly [integrates](https://ai.pydantic.dev/logfire/) with [Pydantic Logfire](https://pydantic.dev/logfire) for real-time debugging, performance monitoring, and behavior tracking of your LLM-powered applications.
2. __Model-agnostic__:
Supports virtually every [model](https://ai.pydantic.dev/models) and provider under the sun: OpenAI, Anthropic, Gemini, DeepSeek, Ollama, Grok, Cohere, and Mistral; Azure AI Foundry, Amazon Bedrock, Google Vertex AI, Groq, Together AI, Fireworks AI, OpenRouter, and Heroku. If your favorite model or provider is not listed, you can easily implement a [custom model](https://ai.pydantic.dev/models#custom-models).

* __Type-safe__
Designed to make [type checking](https://ai.pydantic.dev/agents/#static-type-checking) as powerful and informative as possible for you.
3. __Seamless Observability__:
Tightly [integrates](https://ai.pydantic.dev/logfire) with [Pydantic Logfire](https://pydantic.dev/logfire), our general-purpose OpenTelemetry observability platform, for real-time debugging, evals-based performance monitoring, and behavior and cost tracking. If you already have an observability platform that supports OTel, you can use that too.

* __Python-centric Design__
Leverages Python's familiar control flow and agent composition to build your AI-driven projects, making it easy to apply standard Python best practices you'd use in any other (non-AI) project.
4. __Fully Type-safe__:
Designed to give your IDE or AI coding agent as much context as possible for auto-completion and [type checking](https://ai.pydantic.dev/agents#static-type-checking), moving entire classes of errors from runtime to write-time for a bit of that Rust "if it compiles, it works" feel.

* __Structured Responses__
Harnesses the power of [Pydantic](https://docs.pydantic.dev/latest/) to [validate and structure](https://ai.pydantic.dev/output/#structured-output) model outputs, ensuring responses are consistent across runs.
5. __Powerful Evals__:
Enables you to systematically test and [evaluate](https://ai.pydantic.dev/evals) the performance and accuracy of the agentic systems you build, and monitor the performance over time in Pydantic Logfire.

* __Dependency Injection System__
Offers an optional [dependency injection](https://ai.pydantic.dev/dependencies/) system to provide data and services to your agent's [system prompts](https://ai.pydantic.dev/agents/#system-prompts), [tools](https://ai.pydantic.dev/tools/) and [output validators](https://ai.pydantic.dev/output/#output-validator-functions).
This is useful for testing and eval-driven iterative development.
6. __MCP and A2A__:
Integrates the [Model Context Protocol](https://ai.pydantic.dev/mcp) and [Agent2Agent](https://ai.pydantic.dev/a2a) standards to give your agent access to external tools and data and let it interoperate with other agents.

* __Streamed Responses__
Provides the ability to [stream](https://ai.pydantic.dev/output/#streamed-results) LLM outputs continuously, with immediate validation, ensuring rapid and accurate outputs.
7. __Multi-Modal Input__:
Lets you easily share images, documents, videos and audio [input](https://ai.pydantic.dev/input) with the LLM to go beyond the limitations of text.

* __Graph Support__
[Pydantic Graph](https://ai.pydantic.dev/graph) provides a powerful way to define graphs using typing hints, this is useful in complex applications where standard control flow can degrade to spaghetti code.
8. __Streamed Outputs__:
Provides the ability to [stream](https://ai.pydantic.dev/output#streamed-results) structured output continuously, with immediate validation, ensuring real time access to generated data.

9. __Dependency Injection__:
Offers an optional [dependency injection](https://ai.pydantic.dev/dependencies) system to provide data and services to your agent's [instructions](https://ai.pydantic.dev/agents#instructions), [tools](https://ai.pydantic.dev/tools) and [output functions](https://ai.pydantic.dev/output#output-functions).

10. __Graph Support__:
Provides a powerful way to define [graphs](https://ai.pydantic.dev/graph) using type hints, for use in complex applications where standard control flow can degrade to spaghetti code.

Realistically though, no list is going to be as convincing as [giving it a try](#next-steps) and seeing how it makes you feel!

## Hello World Example

Here's a minimal example of PydanticAI:
Here's a minimal example of Pydantic AI:

```python
from pydantic_ai import Agent

# Define a very simple agent including the model to use, you can also set the model when running the agent.
agent = Agent(
'google-gla:gemini-1.5-flash',
# Register a static system prompt using a keyword argument to the agent.
# For more complex dynamically-generated system prompts, see the example below.
system_prompt='Be concise, reply with one sentence.',
'anthropic:claude-sonnet-4-0',
# Register static instructions using a keyword argument to the agent.
# For more complex dynamically-generated instructions, see the example below.
instructions='Be concise, reply with one sentence.',
)

# Run the agent synchronously, conducting a conversation with the LLM.
# Here the exchange should be very short: PydanticAI will send the system prompt and the user query to the LLM,
# the model will return a text response. See below for a more complex run.
result = agent.run_sync('Where does "hello world" come from?')
print(result.output)
"""
The first known use of "hello, world" was in a 1974 textbook about the C programming language.
"""
```

_(This example is complete, it can be run "as is")_
_(This example is complete, it can be run "as is", assuming you've [installed the `pydantic_ai` package](https://ai.pydantic.dev/install))_

Not very interesting yet, but we can easily add "tools", dynamic system prompts, and structured responses to build more powerful agents.
The exchange will be very short: Pydantic AI will send the instructions and the user prompt to the LLM, and the model will return a text response.

Not very interesting yet, but we can easily add [tools](https://ai.pydantic.dev/tools), [dynamic instructions](https://ai.pydantic.dev/agents#instructions), and [structured outputs](https://ai.pydantic.dev/output) to build more powerful agents.

## Tools & Dependency Injection Example

Here is a concise example using PydanticAI to build a support agent for a bank:
Here is a concise example using Pydantic AI to build a support agent for a bank:

**(Better documented example [in the docs](https://ai.pydantic.dev/#tools-dependency-injection-example))**

Expand All @@ -107,18 +112,23 @@ from bank_database import DatabaseConn


# SupportDependencies is used to pass data, connections, and logic into the model that will be needed when running
# system prompt and tool functions. Dependency injection provides a type-safe way to customise the behavior of your agents.
# instructions and tool functions. Dependency injection provides a type-safe way to customise the behavior of your agents.
@dataclass
class SupportDependencies:
customer_id: int
db: DatabaseConn


# This pydantic model defines the structure of the output returned by the agent.
# This Pydantic model defines the structure of the output returned by the agent.
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)
support_advice: str
"""Advice returned to the customer""" # (14)!
block_card: bool
"""Whether to block the customer's card"""
risk: int = Field(ge=0, le=10)
"""Risk level of query"""
# The docstrings of fields on a Pydantic model are passed to the LLM,
# so that it has all the context needed to generate a value.


# This agent will act as first-tier support in a bank.
Expand All @@ -130,23 +140,23 @@ support_agent = Agent(
# The response from the agent will, be guaranteed to be a SupportOutput,
# if validation fails the agent is prompted to try again.
output_type=SupportOutput,
system_prompt=(
instructions=(
'You are a support agent in our bank, give the '
'customer support and judge the risk level of their query.'
),
)


# Dynamic system prompts can make use of dependency injection.
# 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.
@support_agent.system_prompt
@support_agent.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}"


# `tool` let you register functions which the LLM may call while responding to a user.
# The `tool` decorator let you register functions which 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.
@support_agent.tool
Expand Down Expand Up @@ -187,8 +197,10 @@ async def main():

## Next Steps

To try PydanticAI yourself, follow the instructions [in the examples](https://ai.pydantic.dev/examples/).
To try Pydantic AI for yourself, [install it](https://ai.pydantic.dev/install) and follow the instructions [in the examples](https://ai.pydantic.dev/examples/).

Read the [docs](https://ai.pydantic.dev/agents/) to learn more about building applications with Pydantic AI.

Read the [docs](https://ai.pydantic.dev/agents/) to learn more about building applications with PydanticAI.
Read the [API Reference](https://ai.pydantic.dev/api/agent/) to understand Pydantic AI's interface.

Read the [API Reference](https://ai.pydantic.dev/api/agent/) to understand PydanticAI's interface.
Join [Slack](https://logfire.pydantic.dev/docs/join-slack/) or file an issue on [GitHub](https://github.com/pydantic/pydantic-ai/issues) if you have any questions.
10 changes: 5 additions & 5 deletions docs/.partials/index-header.html
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@@ -1,11 +1,11 @@
<div class="text-center">
<img class="index-header off-glb" src="./img/pydantic-ai-dark.svg#only-dark" alt="PydanticAI">
<img class="index-header off-glb" src="./img/pydantic-ai-dark.svg#only-dark" alt="Pydantic AI">
</div>
<div class="text-center">
<img class="index-header off-glb" src="./img/pydantic-ai-light.svg#only-light" alt="PydanticAI">
<img class="index-header off-glb" src="./img/pydantic-ai-light.svg#only-light" alt="Pydantic AI">
</div>
<p class="text-center">
<em>Agent Framework / shim to use Pydantic with LLMs</em>
<em>LLM Agent Framework, the Pydantic way</em>
</p>
<p class="text-center">
<a href="https://github.com/pydantic/pydantic-ai/actions/workflows/ci.yml?query=branch%3Amain">
Expand All @@ -29,6 +29,6 @@
</p>

<p class="text-emphasis">
PydanticAI is a Python agent framework designed to make it less painful to
build production grade applications with Generative AI.
Pydantic AI is a Python agent framework designed to help you
quickly, confidently, and painlessly build production grade applications and workflows with Generative AI.
</p>
10 changes: 5 additions & 5 deletions docs/agents.md
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Expand Up @@ -9,10 +9,10 @@ The [`Agent`][pydantic_ai.Agent] class has full API documentation, but conceptua

| **Component** | **Description** |
|-----------------------------------------------|-----------------------------------------------------------------------------------------------------------|
| [System prompt(s)](#system-prompts) | A set of instructions for the LLM written by the developer. |
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I also made a start at replacing older features with newer ones (system prompts -> instructions, output validator functions -> output functions), I will continue that in a new PR and can pull this out of here if you prefer

| [Instructions](#instructions) | A set of instructions for the LLM written by the developer. |
| [Function tool(s)](tools.md) | Functions that the LLM may call to get information while generating a response. |
| [Structured output type](output.md) | The structured datatype the LLM must return at the end of a run, if specified. |
| [Dependency type constraint](dependencies.md) | System prompt functions, tools, and output validators may all use dependencies when they're run. |
| [Dependency type constraint](dependencies.md) | Instructions functions, tools, and output functions may all use dependencies when they're run. |
| [LLM model](api/models/base.md) | Optional default LLM model associated with the agent. Can also be specified when running the agent. |
| [Model Settings](#additional-configuration) | Optional default model settings to help fine tune requests. Can also be specified when running the agent. |

Expand Down Expand Up @@ -733,10 +733,10 @@ Note that returning an empty string will result in no instruction message added.

Validation errors from both function tool parameter validation and [structured output validation](output.md#structured-output) can be passed back to the model with a request to retry.

You can also raise [`ModelRetry`][pydantic_ai.exceptions.ModelRetry] from within a [tool](tools.md) or [output validator function](output.md#output-validator-functions) to tell the model it should retry generating a response.
You can also raise [`ModelRetry`][pydantic_ai.exceptions.ModelRetry] from within a [tool](tools.md) or [output function](output.md#output-functions) to tell the model it should retry generating a response.

- The default retry count is **1** but can be altered for the [entire agent][pydantic_ai.Agent.__init__], a [specific tool][pydantic_ai.Agent.tool], or an [output validator][pydantic_ai.Agent.__init__].
- You can access the current retry count from within a tool or output validator via [`ctx.retry`][pydantic_ai.tools.RunContext].
- The default retry count is **1** but can be altered for the [entire agent][pydantic_ai.Agent.__init__], a [specific tool][pydantic_ai.Agent.tool], or [outputs][pydantic_ai.Agent.__init__].
- You can access the current retry count from within a tool or output function via [`ctx.retry`][pydantic_ai.tools.RunContext].

Here's an example:

Expand Down
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