/docs/python/agents/pydantic-ai.mdx

---
title: pydantic-deepagents
description: Implements pydantic-ai-backend's SandboxProtocol so pydantic-deepagents and any pydantic-ai agent can run inside a Mirage workspace.
icon: /images/pydantic-logo.svg
---

[pydantic-deepagents](https://github.com/vstorm-co/pydantic-deepagents) is a Claude Code–style deep agent harness built on [pydantic-ai](https://github.com/pydantic/pydantic-ai). It uses [`pydantic-ai-backend`](https://pypi.org/project/pydantic-ai-backend/)'s `SandboxProtocol` for filesystem, shell, grep, and edit operations, Mirage's `PydanticAIWorkspace` is a drop-in implementation of that protocol.

## Install

```bash
uv add 'mirage-ai[pydantic-ai]'
```

This pulls in `pydantic-ai>=1.35` and `pydantic-ai-backend>=0.1.0`. To use it with [pydantic-deepagents](https://github.com/vstorm-co/pydantic-deepagents):

```bash
uv add pydantic-deep
```

## Usage

```python
from dataclasses import dataclass

from pydantic_ai import Agent
from pydantic_ai_backends import create_console_toolset

from mirage import MountMode, Workspace
from mirage.agents.pydantic_ai import PydanticAIWorkspace, build_system_prompt
from mirage.resource.ram import RAMResource

ws = Workspace({"/": RAMResource()}, mode=MountMode.WRITE)
backend = PydanticAIWorkspace(ws)


@dataclass
class Deps:
    backend: PydanticAIWorkspace


agent = Agent(
    "openai:gpt-4.1",
    system_prompt=build_system_prompt(
        mount_info={"/": "In-memory filesystem (read/write)"},
    ),
    deps_type=Deps,
    toolsets=[create_console_toolset()],
)

result = agent.run_sync(
    "Create /hello.txt with 'hi' and cat it.",
    deps=Deps(backend=backend),
)
print(result.output)
```

## Exports

| Symbol | Purpose |
| --- | --- |
| `PydanticAIWorkspace` | `SandboxProtocol` implementation backed by a Mirage workspace. |
| `build_system_prompt` | Generates a system prompt that describes mounted paths to the model. |

`PydanticAIWorkspace` routes file operations through the Ops layer directly and shell operations through `Workspace.execute()` for full pipe and flag support. PDF reads are converted to images via `pages_to_images` so the agent can pass them as `BinaryContent`.

## Examples

- [`examples/python/agents/pydantic_ai/s3_agent.py`](https://github.com/strukto-ai/mirage/blob/main/examples/python/agents/pydantic_ai/s3_agent.py), read-only S3 exploration.
- [`examples/python/agents/pydantic_ai/s3_pdf_agent.py`](https://github.com/strukto-ai/mirage/blob/main/examples/python/agents/pydantic_ai/s3_pdf_agent.py), PDF page-to-image pipeline.