pydantic · DouweM · Sep 1, 2025 · Aug 31, 2025 · Sep 1, 2025 · Sep 1, 2025
diff --git a/docs/examples/ag-ui.md b/docs/examples/ag-ui.md
@@ -15,7 +15,7 @@ Demonstrates:
 
 ## Running the Example
 
-With [dependencies installed and environment variables set](./index.md#usage)
+With [dependencies installed and environment variables set](./setup.md#usage)
 you will need two command line windows.
 
 ### Pydantic AI AG-UI backend

diff --git a/docs/examples/bank-support.md b/docs/examples/bank-support.md
@@ -8,7 +8,7 @@ Demonstrates:
 
 ## Running the Example
 
-With [dependencies installed and environment variables set](./index.md#usage), run:
+With [dependencies installed and environment variables set](./setup.md#usage), run:
 
 ```bash
 python/uv-run -m pydantic_ai_examples.bank_support

diff --git a/docs/examples/chat-app.md b/docs/examples/chat-app.md
@@ -15,7 +15,7 @@ and `chat_app.ts` which renders messages in the browser.
 
 ## Running the Example
 
-With [dependencies installed and environment variables set](./index.md#usage), run:
+With [dependencies installed and environment variables set](./setup.md#usage), run:
 
 ```bash
 python/uv-run -m pydantic_ai_examples.chat_app

diff --git a/docs/examples/data-analyst.md b/docs/examples/data-analyst.md
@@ -19,7 +19,7 @@ Demonstrates:
 
 ## Running the Example
 
-With [dependencies installed and environment variables set](./index.md#usage), run:
+With [dependencies installed and environment variables set](./setup.md#usage), run:
 
 ```bash
 python/uv-run -m pydantic_ai_examples.data_analyst

diff --git a/docs/examples/flight-booking.md b/docs/examples/flight-booking.md
@@ -28,7 +28,7 @@ graph TD
 
 ## Running the Example
 
-With [dependencies installed and environment variables set](./index.md#usage), run:
+With [dependencies installed and environment variables set](./setup.md#usage), run:
 
 ```bash
 python/uv-run -m pydantic_ai_examples.flight_booking

diff --git a/docs/examples/pydantic-model.md b/docs/examples/pydantic-model.md
@@ -8,7 +8,7 @@ Demonstrates:
 
 ## Running the Example
 
-With [dependencies installed and environment variables set](./index.md#usage), run:
+With [dependencies installed and environment variables set](./setup.md#usage), run:
 
 ```bash
 python/uv-run -m pydantic_ai_examples.pydantic_model

diff --git a/docs/examples/question-graph.md b/docs/examples/question-graph.md
@@ -8,7 +8,7 @@ Demonstrates:
 
 ## Running the Example
 
-With [dependencies installed and environment variables set](./index.md#usage), run:
+With [dependencies installed and environment variables set](./setup.md#usage), run:
 
 ```bash
 python/uv-run -m pydantic_ai_examples.question_graph

diff --git a/docs/examples/rag.md b/docs/examples/rag.md
@@ -28,7 +28,7 @@ docker run --rm \
 As with the [SQL gen](./sql-gen.md) example, we run postgres on port `54320` to avoid conflicts with any other postgres instances you may have running.
 We also mount the PostgreSQL `data` directory locally to persist the data if you need to stop and restart the container.
 
-With that running and [dependencies installed and environment variables set](./index.md#usage), we can build the search database with (**WARNING**: this requires the `OPENAI_API_KEY` env variable and will calling the OpenAI embedding API around 300 times to generate embeddings for each section of the documentation):
+With that running and [dependencies installed and environment variables set](./setup.md#usage), we can build the search database with (**WARNING**: this requires the `OPENAI_API_KEY` env variable and will calling the OpenAI embedding API around 300 times to generate embeddings for each section of the documentation):
 
 ```bash
 python/uv-run -m pydantic_ai_examples.rag build

diff --git a/docs/examples/index.md → docs/examples/setup.md b/docs/examples/index.md → docs/examples/setup.md
@@ -1,6 +1,6 @@
 # Examples
 
-Examples of how to use Pydantic AI and what it can do.
+Here we include some examples of how to use Pydantic AI and what it can do.
 
 ## Usage
 
@@ -20,7 +20,7 @@ If you clone the repo, you should instead use `uv sync --extra examples` to inst
 
 ### Setting model environment variables
 
-These examples will need you to set up authentication with one or more of the LLMs, see the [model configuration](../models/index.md) docs for details on how to do this.
+These examples will need you to set up authentication with one or more of the LLMs, see the [model configuration](../models/overview.md) docs for details on how to do this.
 
 TL;DR: in most cases you'll need to set one of the following environment variables:
 

diff --git a/docs/examples/slack-lead-qualifier.md b/docs/examples/slack-lead-qualifier.md
@@ -64,7 +64,7 @@ You need to have a Slack workspace and the necessary permissions to create apps.
 
 ## Usage
 
-1. Make sure you have the [dependencies installed](./index.md#usage).
+1. Make sure you have the [dependencies installed](./setup.md#usage).
 
 2. Authenticate with Modal:
 

diff --git a/docs/examples/sql-gen.md b/docs/examples/sql-gen.md
@@ -19,7 +19,7 @@ docker run --rm -e POSTGRES_PASSWORD=postgres -p 54320:5432 postgres
 
 _(we run postgres on port `54320` to avoid conflicts with any other postgres instances you may have running)_
 
-With [dependencies installed and environment variables set](./index.md#usage), run:
+With [dependencies installed and environment variables set](./setup.md#usage), run:
 
 ```bash
 python/uv-run -m pydantic_ai_examples.sql_gen

diff --git a/docs/examples/stream-markdown.md b/docs/examples/stream-markdown.md
@@ -8,7 +8,7 @@ Demonstrates:
 
 ## Running the Example
 
-With [dependencies installed and environment variables set](./index.md#usage), run:
+With [dependencies installed and environment variables set](./setup.md#usage), run:
 
 ```bash
 python/uv-run -m pydantic_ai_examples.stream_markdown

diff --git a/docs/examples/stream-whales.md b/docs/examples/stream-whales.md
@@ -9,7 +9,7 @@ and displays it as a dynamic table using [`rich`](https://github.com/Textualize/
 
 ## Running the Example
 
-With [dependencies installed and environment variables set](./index.md#usage), run:
+With [dependencies installed and environment variables set](./setup.md#usage), run:
 
 ```bash
 python/uv-run -m pydantic_ai_examples.stream_whales

diff --git a/docs/examples/weather-agent.md b/docs/examples/weather-agent.md
@@ -18,7 +18,7 @@ To run this example properly, you might want to add two extra API keys **(Note i
 - A weather API key from [tomorrow.io](https://www.tomorrow.io/weather-api/) set via `WEATHER_API_KEY`
 - A geocoding API key from [geocode.maps.co](https://geocode.maps.co/) set via `GEO_API_KEY`
 
-With [dependencies installed and environment variables set](./index.md#usage), run:
+With [dependencies installed and environment variables set](./setup.md#usage), run:
 
 ```bash
 python/uv-run -m pydantic_ai_examples.weather_agent

diff --git a/docs/index.md b/docs/index.md
@@ -24,7 +24,7 @@ We built Pydantic AI with one simple aim: to bring that FastAPI feeling to GenAI
   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](models/index.md).
+  Supports OpenAI, Anthropic, Gemini, Deepseek, Ollama, Groq, Cohere, and Mistral, and there is a simple interface to implement support for [other models](models/overview.md).
 
 - **Pydantic Logfire Integration**:
   Seamlessly [integrates](logfire.md) with [Pydantic Logfire](https://pydantic.dev/logfire) for real-time debugging, performance monitoring, and behavior tracking of your LLM-powered applications.
@@ -244,7 +244,7 @@ file.
 
 ## Next Steps
 
-To try Pydantic AI yourself, follow the instructions [in the examples](examples/index.md).
+To try Pydantic AI yourself, follow the instructions [in the examples](examples/setup.md).
 
 Read the [docs](agents.md) to learn more about building applications with Pydantic AI.
 

diff --git a/docs/install.md b/docs/install.md
@@ -27,7 +27,7 @@ To install examples, use the `examples` optional group:
 pip/uv-add "pydantic-ai[examples]"
 ```
 
-To run the examples, follow instructions in the [examples docs](examples/index.md).
+To run the examples, follow instructions in the [examples docs](examples/setup.md).
 
 ## Slim Install
 
@@ -58,7 +58,7 @@ pip/uv-add "pydantic-ai-slim[openai]"
 * `a2a` - installs `fasta2a` [PyPI ↗](https://pypi.org/project/fasta2a){:target="_blank"}
 * `ag-ui` - installs `ag-ui-protocol` [PyPI ↗](https://pypi.org/project/ag-ui-protocol){:target="_blank"} and `starlette` [PyPI ↗](https://pypi.org/project/starlette){:target="_blank"}
 
-See the [models](models/index.md) documentation for information on which optional dependencies are required for each model.
+See the [models](models/overview.md) documentation for information on which optional dependencies are required for each model.
 
 You can also install dependencies for multiple models and use cases, for example:
 

diff --git a/docs/mcp/index.md → docs/mcp/overview.md b/docs/mcp/index.md → docs/mcp/overview.md
diff --git a/docs/models/index.md → docs/models/overview.md b/docs/models/index.md → docs/models/overview.md
diff --git a/docs/temporal.md b/docs/temporal.md
@@ -29,7 +29,7 @@ Activity code faces no restrictions on I/O or external interactions, but if an a
 
     See the [Temporal documentation](https://docs.temporal.io/evaluate/understanding-temporal#temporal-application-the-building-blocks) for more information
 
-In the case of Pydantic AI agents, integration with Temporal means that [model requests](models/index.md), [tool calls](tools.md) that may require I/O, and [MCP server communication](mcp/client.md) all need to be offloaded to Temporal activities due to their I/O requirements, while the logic that coordinates them (i.e. the agent run) lives in the workflow. Code that handles a scheduled job or web request can then execute the workflow, which will in turn execute the activities as needed.
+In the case of Pydantic AI agents, integration with Temporal means that [model requests](models/overview.md), [tool calls](tools.md) that may require I/O, and [MCP server communication](mcp/client.md) all need to be offloaded to Temporal activities due to their I/O requirements, while the logic that coordinates them (i.e. the agent run) lives in the workflow. Code that handles a scheduled job or web request can then execute the workflow, which will in turn execute the activities as needed.
 
 The diagram below shows the overall architecture of an agentic application in Temporal.
 The Temporal Server is responsible for tracking program execution and making sure the associated state is preserved reliably (i.e., stored to an internal database, and possibly replicated across cloud regions).
@@ -71,7 +71,7 @@ See the [Temporal documentation](https://docs.temporal.io/evaluate/understanding
 
 Any agent can be wrapped in a [`TemporalAgent`][pydantic_ai.durable_exec.temporal.TemporalAgent] to get a durable agent that can be used inside a deterministic Temporal workflow, by automatically offloading all work that requires I/O (namely model requests, tool calls, and MCP server communication) to non-deterministic activities.
 
-At the time of wrapping, the agent's [model](models/index.md) and [toolsets](toolsets.md) (including function tools registered on the agent and MCP servers) are frozen, activities are dynamically created for each, and the original model and toolsets are wrapped to call on the worker to execute the corresponding activities instead of directly performing the actions inside the workflow. The original agent can still be used as normal outside the Temporal workflow, but any changes to its model or toolsets after wrapping will not be reflected in the durable agent.
+At the time of wrapping, the agent's [model](models/overview.md) and [toolsets](toolsets.md) (including function tools registered on the agent and MCP servers) are frozen, activities are dynamically created for each, and the original model and toolsets are wrapped to call on the worker to execute the corresponding activities instead of directly performing the actions inside the workflow. The original agent can still be used as normal outside the Temporal workflow, but any changes to its model or toolsets after wrapping will not be reflected in the durable agent.
 
 Here is a simple but complete example of wrapping an agent for durable execution, creating a Temporal workflow with durable execution logic, connecting to a Temporal server, and running the workflow from non-durable code. All it requires is a Temporal server to be [running locally](https://github.com/temporalio/temporal#download-and-start-temporal-server-locally):
 

diff --git a/docs/troubleshooting.md b/docs/troubleshooting.md
@@ -20,7 +20,7 @@ Note: This fix also applies to Google Colab and [Marimo](https://github.com/mari
 
 ### `UserError: API key must be provided or set in the [MODEL]_API_KEY environment variable`
 
-If you're running into issues with setting the API key for your model, visit the [Models](models/index.md) page to learn more about how to set an environment variable and/or pass in an `api_key` argument.
+If you're running into issues with setting the API key for your model, visit the [Models](models/overview.md) page to learn more about how to set an environment variable and/or pass in an `api_key` argument.
 
 ## Monitoring HTTPX Requests