> ## Documentation Index
> Fetch the complete documentation index at: https://docs.blaxel.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Run PydanticAI on Blaxel

> Deploy PydanticAI agent projects to Blaxel with minimal code changes to get serverless hosting, agentic observability, and governance policies.

You can deploy your [PydanticAI](https://ai.pydantic.dev/) projects to Blaxel with minimal code editing (and zero configuration), enabling you to use [Serverless Deployments](../Infrastructure/Global-Inference-Network), [Agentic Observability](../Observability/Overview), [Policies](../Model-Governance/Policies), and more.

## Get started with PydanticAI on Blaxel

To get started with PydanticAI on Blaxel:

* if you already have a PydanticAI agent, adapt your code with [Blaxel SDK commands](../Agents/Develop-an-agent) to connect to [MCP servers](../Functions/Overview), [LLMs](../Models/Overview) and [other agents](../Agents/Overview).
* else initialize an example project in PydanticAI by using the following Blaxel CLI command and selecting the *PydanticAI hello world:*

```bash theme={null}
bl new agent
```

[Deploy](../Agents/Deploy-an-agent) it by running:

```bash theme={null}
bl deploy
```

## Develop a PydanticAI agent using Blaxel features

While building your agent in PydanticAI, use Blaxel [SDK](../sdk-reference/introduction) to connect to resources already hosted on Blaxel:

* [MCP servers](../Functions/Overview)
* [LLMs](../Models/Overview)
* [other agents](../Agents/Overview)

### Connect to MCP servers

Connect to [MCP servers](../Functions/Overview) using the Blaxel SDK to access pre-built or custom tool servers hosted on Blaxel. This eliminates the need to manage server connections yourself, with credentials stored securely on the platform.

Run the following command to retrieve tools in PydanticAI format:

<CodeGroup>
  ```python Python theme={null}

  from blaxel.pydantic import bl_tools

  await bl_tools(['mcp-server-name'])
  ```
</CodeGroup>

### Connect to LLMs

Connect to [LLMs](../Models/Overview) hosted on Blaxel using the SDK to avoid managing model API connections yourself. All credentials remain securely stored on the platform.

<CodeGroup>
  ```python Python theme={null}

  from blaxel.pydantic import bl_model

  model = await bl_model("model-api-name")
  ```
</CodeGroup>

### Connect to other agents

Connect to other agents hosted on Blaxel from your code by using the [Blaxel SDK](../sdk-reference/introduction). This allows for multi-agent chaining without managing connections yourself. This command is independent of the framework used to build the agent.

<CodeGroup>
  ```python Python theme={null}

  from blaxel.core.agents import bl_agent

  response = await bl_agent("agent-name").run(input);
  ```
</CodeGroup>

### Host your agent on Blaxel

You can [deploy](../Agents/Deploy-an-agent) your agent on Blaxel, enabling you to use [Serverless Deployments](../Infrastructure/Global-Inference-Network), [Agentic Observability](../Observability/Overview), [Policies](../Model-Governance/Policies), and more. This command is independent of the framework used to build the agent.

Either run the following CLI command from the root of your agent repository.

```bash theme={null}
bl deploy
```

Or [connect a GitHub repository to Blaxel](../Agents/Github-integration) for automatic deployments every time you push on *main*.