update logos, favicon and brand names

.github/workflows/ci.yml

@@ -207,7 +207,7 @@ jobs:
       - run: rm coverage/.coverage.*-py3.9-* # Exclude 3.9 coverage as it gets the wrong line numbers, causing invalid failures.
       - run: uv run coverage combine coverage
 
-      - run: uv run coverage html --show-contexts --title "PydanticAI coverage for ${{ github.sha }}"
+      - run: uv run coverage html --show-contexts --title "Pydantic AI coverage for ${{ github.sha }}"
 
       - name: Store coverage html
         uses: actions/upload-artifact@v4
@@ -417,7 +417,7 @@ jobs:
         env:
           VERSION: ${{ needs.release.outputs.package-version }}
           TWEET: |
-            PydanticAI version {version} is out! 🎉
+            Pydantic AI version {version} is out! 🎉
 
             https://github.com/pydantic/pydantic-ai/releases/tag/v{version}
           TWITTER_CONSUMER_KEY: ${{ secrets.TWITTER_CONSUMER_KEY }}

README.md

@@ -2,7 +2,7 @@
   <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>
@@ -24,47 +24,47 @@
 
 ---
 
-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 make it less painful to build production grade applications with Generative AI.
 
-FastAPI revolutionized web development by offering an innovative and ergonomic design, built on the foundation of [Pydantic](https://docs.pydantic.dev).
+FastAPI revolutionized web development by offering an innovative and ergonomic design, built on the foundation of [Pydantic Validation](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.
+Similarly, virtually every agent framework and LLM library in Python uses Pydantic Validation, 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.
 
-We built PydanticAI with one simple aim: to bring that FastAPI feeling to GenAI app development.
+We built Pydantic AI with one simple aim: to bring that FastAPI feeling to GenAI app development.
 
-## Why use PydanticAI
+## Why use Pydantic AI
 
-* __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).
+- **Built by the Pydantic Team**
+  Built by the team behind [Pydantic Validation](https://docs.pydantic.dev/latest/) (the validation layer of the OpenAI SDK, the Anthropic SDK, LangChain, LlamaIndex, AutoGPT, Transformers, CrewAI, Instructor and many more).
 
-* __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/).
+- **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/).
 
-* __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.
+- **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.
 
-* __Type-safe__
-Designed to make [type checking](https://ai.pydantic.dev/agents/#static-type-checking) as powerful and informative as possible for you.
+- **Type-safe**
+  Designed to make [type checking](https://ai.pydantic.dev/agents/#static-type-checking) as powerful and informative as possible for you.
 
-* __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.
+- **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.
 
-* __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.
+- **Structured Responses**
+  Harnesses the power of [Pydantic Validation](https://docs.pydantic.dev/latest/) to [validate and structure](https://ai.pydantic.dev/output/#structured-output) model outputs, ensuring responses are consistent across runs.
 
-* __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.
+- **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.
 
-* __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.
+- **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.
 
-* __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.
+- **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.
 
 ## Hello World Example
 
-Here's a minimal example of PydanticAI:
+Here's a minimal example of Pydantic AI:
 
 ```python
 from pydantic_ai import Agent
@@ -78,7 +78,7 @@ agent = Agent(
 )
 
 # 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,
+# Here the exchange should be very short: Pydantic AI 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)
@@ -93,7 +93,7 @@ Not very interesting yet, but we can easily add "tools", dynamic system prompts,
 
 ## 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))**
 
@@ -187,8 +187,8 @@ async def main():
 
 ## Next Steps
 
-To try PydanticAI yourself, follow the instructions [in the examples](https://ai.pydantic.dev/examples/).
+To try Pydantic AI yourself, 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 PydanticAI.
+Read the [docs](https://ai.pydantic.dev/agents/) to learn more about building applications with Pydantic AI.
 
-Read the [API Reference](https://ai.pydantic.dev/api/agent/) to understand PydanticAI's interface.
+Read the [API Reference](https://ai.pydantic.dev/api/agent/) to understand Pydantic AI's interface.

clai/README.md

@@ -8,7 +8,7 @@
 
 (pronounced "clay")
 
-Command line interface to chat to LLMs, part of the [PydanticAI project](https://github.com/pydantic/pydantic-ai).
+Command line interface to chat to LLMs, part of the [Pydantic AI project](https://github.com/pydantic/pydantic-ai).
 
 ## Usage
 
@@ -55,7 +55,7 @@ Either way, running `clai` will start an interactive session where you can chat
 ```
 usage: clai [-h] [-m [MODEL]] [-a AGENT] [-l] [-t [CODE_THEME]] [--no-stream] [--version] [prompt]
 
-PydanticAI CLI v...
+Pydantic AI CLI v...
 
 Special prompts:
 * `/exit` - exit the interactive mode (ctrl-c and ctrl-d also work)

clai/pyproject.toml

@@ -13,7 +13,7 @@ bump = true
 [project]
 name = "clai"
 dynamic = ["version", "dependencies"]
-description = "PydanticAI CLI: command line interface to chat to LLMs"
+description = "Pydantic AI CLI: command line interface to chat to LLMs"
 authors = [
   { name = "Samuel Colvin", email = "[email protected]" },
   { name = "Marcelo Trylesinski", email = "[email protected]" },

clai/update_readme.py

@@ -17,7 +17,7 @@ def test_cli_help(capfd: pytest.CaptureFixture[str]):
 
     help_output = capfd.readouterr().out.strip()
     # TODO change when we reach v1
-    help_output = re.sub(r'(PydanticAI CLI v).+', r'\1...', help_output)
+    help_output = re.sub(r'(Pydantic AI CLI v).+', r'\1...', help_output)
 
     this_dir = Path(__file__).parent
     readme = this_dir / 'README.md'

docs/.partials/index-header.html

@@ -1,18 +1,25 @@
 <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>
 </p>
 <p class="text-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">
+    <img
+      src="https://github.com/pydantic/pydantic-ai/actions/workflows/ci.yml/badge.svg?event=push"
+      alt="CI">
   </a>
   <a href="https://coverage-badge.samuelcolvin.workers.dev/redirect/pydantic/pydantic-ai">
-    <img src="https://coverage-badge.samuelcolvin.workers.dev/pydantic/pydantic-ai.svg" alt="Coverage">
+    <img
+      src="https://coverage-badge.samuelcolvin.workers.dev/pydantic/pydantic-ai.svg"
+      alt="Coverage">
   </a>
   <a href="https://pypi.python.org/pypi/pydantic-ai">
     <img src="https://img.shields.io/pypi/v/pydantic-ai.svg" alt="PyPI">
@@ -24,11 +31,11 @@
     <img src="https://img.shields.io/github/license/pydantic/pydantic-ai.svg" alt="license">
   </a>
   <a href="https://logfire.pydantic.dev/docs/join-slack/">
-    <img src="https://img.shields.io/badge/Slack-Join%20Slack-4A154B?logo=slack" alt="Join Slack" />
+    <img src="https://img.shields.io/badge/Slack-Join%20Slack-4A154B?logo=slack" alt="Join Slack">
   </a>
 </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 make it less painful to build production grade
+  applications with Generative AI.
 </p>

docs/a2a.md

@@ -5,7 +5,7 @@ communication and interoperability between AI agents, regardless of the framewor
 
 At Pydantic, we built the [FastA2A](#fasta2a) library to make it easier to implement the A2A protocol in Python.
 
-We also built a convenience method that expose PydanticAI agents as A2A servers - let's have a quick look at how to use it:
+We also built a convenience method that expose Pydantic AI agents as A2A servers - let's have a quick look at how to use it:
 
 ```py {title="agent_to_a2a.py" hl_lines="4"}
 from pydantic_ai import Agent
@@ -18,12 +18,12 @@ _You can run the example with `uvicorn agent_to_a2a:app --host 0.0.0.0 --port 80
 
 This will expose the agent as an A2A server, and you can start sending requests to it.
 
-See more about [exposing PydanticAI agents as A2A servers](#pydanticai-agent-to-a2a-server).
+See more about [exposing Pydantic AI agents as A2A servers](#pydantic-ai-agent-to-a2a-server).
 
 ## FastA2A
 
 **FastA2A** is an agentic framework agnostic implementation of the A2A protocol in Python.
-The library is designed to be used with any agentic framework, and is **not exclusive to PydanticAI**.
+The library is designed to be used with any agentic framework, and is **not exclusive to Pydantic AI**.
 
 ### Design
 
@@ -75,8 +75,7 @@ The [`Storage`][fasta2a.Storage] component serves two purposes:
 
 This design allows for agents to store rich internal state (e.g., tool calls, reasoning traces) as well as store task-specific A2A-formatted messages and artifacts.
 
-For example, a PydanticAI agent might store its complete internal message format (including tool calls and responses) in the context storage, while storing only the A2A-compliant messages in the task history.
-
+For example, a Pydantic AI agent might store its complete internal message format (including tool calls and responses) in the context storage, while storing only the A2A-compliant messages in the task history.
 
 ### Installation
 
@@ -92,15 +91,15 @@ The only dependencies are:
 - [pydantic](https://pydantic.dev): to validate the request/response messages
 - [opentelemetry-api](https://opentelemetry-python.readthedocs.io/en/latest): to provide tracing capabilities
 
-You can install PydanticAI with the `a2a` extra to include **FastA2A**:
+You can install Pydantic AI with the `a2a` extra to include **FastA2A**:
 
 ```bash
 pip/uv-add 'pydantic-ai-slim[a2a]'
 ```
 
-### PydanticAI Agent to A2A Server
+### Pydantic AI Agent to A2A Server
 
-To expose a PydanticAI agent as an A2A server, you can use the `to_a2a` method:
+To expose a Pydantic AI agent as an A2A server, you can use the `to_a2a` method:
 
 ```python {title="agent_to_a2a.py"}
 from pydantic_ai import Agent
@@ -117,7 +116,7 @@ uvicorn agent_to_a2a:app --host 0.0.0.0 --port 8000
 
 Since the goal of `to_a2a` is to be a convenience method, it accepts the same arguments as the [`FastA2A`][fasta2a.FastA2A] constructor.
 
-When using `to_a2a()`, PydanticAI automatically:
+When using `to_a2a()`, Pydantic AI automatically:
 
 - Stores the complete conversation history (including tool calls and responses) in the context storage
 - Ensures that subsequent messages with the same `context_id` have access to the full conversation history