File Storage & Sandbox Backends for Pydantic AI
Console Toolset, Docker Sandbox, and Permission System for AI Agents
Console Toolset — ls, read, write, edit, grep, execute • Docker Sandbox — isolated code execution • Permission System — fine-grained access control
File Storage & Sandbox Backends provides everything your Pydantic AI agent needs to work with files and execute code safely. Choose from in-memory, local filesystem, or Docker-isolated backends.
Full framework? Check out Pydantic Deep Agents — complete agent framework with planning, filesystem, subagents, and skills.
Use Cases
| What You Want to Build | How This Library Helps |
|---|---|
| AI Coding Assistant | Console toolset with file ops + code execution |
| Multi-User Web App | Docker sandboxes with session isolation |
| Code Review Bot | Read-only backend with grep/glob search |
| Secure Execution | Permission system blocks dangerous operations |
| Testing/CI | In-memory StateBackend for fast, isolated tests |
Installation
pip install pydantic-ai-backend
Or with uv:
uv add pydantic-ai-backend
Optional extras:
# Console toolset (requires pydantic-ai)
pip install pydantic-ai-backend[console]
# Docker sandbox support
pip install pydantic-ai-backend[docker]
# Everything
pip install pydantic-ai-backend[console,docker]
Quick Start
from dataclasses import dataclass
from pydantic_ai import Agent
from pydantic_ai_backends import LocalBackend, create_console_toolset
@dataclass
class Deps:
backend: LocalBackend
agent = Agent(
"openai:gpt-4o",
deps_type=Deps,
toolsets=[create_console_toolset()],
)
backend = LocalBackend(root_dir="./workspace")
result = agent.run_sync(
"Create a Python script that calculates fibonacci and run it",
deps=Deps(backend=backend),
)
print(result.output)
That's it. Your agent can now:
- List files and directories (
ls) - Read and write files (
read_file,write_file) - Edit files with string replacement (
edit_file) - Search with glob patterns and regex (
glob,grep) - Execute shell commands (
execute)
Available Backends
| Backend | Storage | Execution | Use Case |
|---|---|---|---|
StateBackend |
In-memory | No | Testing, ephemeral sessions |
LocalBackend |
Filesystem | Yes | Local development, CLI tools |
DockerSandbox |
Container | Yes | Multi-user, untrusted code |
CompositeBackend |
Routed | Varies | Complex multi-source setups |
In-Memory (StateBackend)
from pydantic_ai_backends import StateBackend
backend = StateBackend()
# Files stored in memory, perfect for tests
Local Filesystem (LocalBackend)
from pydantic_ai_backends import LocalBackend
backend = LocalBackend(
root_dir="/workspace",
allowed_directories=["/workspace", "/shared"],
enable_execute=True,
)
Docker Sandbox (DockerSandbox)
from pydantic_ai_backends import DockerSandbox
sandbox = DockerSandbox(runtime="python-datascience")
sandbox.start()
# Fully isolated container environment
sandbox.stop()
Console Toolset
Ready-to-use tools for pydantic-ai agents:
from pydantic_ai_backends import create_console_toolset
# All tools enabled
toolset = create_console_toolset()
# Without shell execution
toolset = create_console_toolset(include_execute=False)
# With approval requirements
toolset = create_console_toolset(
require_write_approval=True,
require_execute_approval=True,
)
Available tools: ls, read_file, write_file, edit_file, glob, grep, execute
Permission System
Fine-grained access control:
from pydantic_ai_backends import LocalBackend
from pydantic_ai_backends.permissions import DEFAULT_RULESET, READONLY_RULESET
# Safe defaults (allow reads, ask for writes)
backend = LocalBackend(root_dir="/workspace", permissions=DEFAULT_RULESET)
# Read-only mode
backend = LocalBackend(root_dir="/workspace", permissions=READONLY_RULESET)
| Preset | Description |
|---|---|
DEFAULT_RULESET |
Allow reads (except secrets), ask for writes/executes |
PERMISSIVE_RULESET |
Allow most operations, deny dangerous commands |
READONLY_RULESET |
Allow reads only, deny all writes and executes |
STRICT_RULESET |
Everything requires approval |
Docker Runtimes
Pre-configured environments:
| Runtime | Base Image | Packages |
|---|---|---|
python-minimal |
python:3.12-slim | (none) |
python-datascience |
python:3.12-slim | pandas, numpy, matplotlib, scikit-learn |
python-web |
python:3.12-slim | fastapi, uvicorn, sqlalchemy, httpx |
node-minimal |
node:20-slim | (none) |
node-react |
node:20-slim | typescript, vite, react |
Custom runtime:
from pydantic_ai_backends import DockerSandbox, RuntimeConfig
runtime = RuntimeConfig(
name="ml-env",
base_image="python:3.12-slim",
packages=["torch", "transformers"],
)
sandbox = DockerSandbox(runtime=runtime)
Session Manager
Multi-user web applications:
from pydantic_ai_backends import SessionManager
manager = SessionManager(
default_runtime="python-datascience",
workspace_root="/app/workspaces",
)
# Each user gets isolated sandbox
sandbox = await manager.get_or_create("user-123")
Why Choose This Library?
| Feature | Description |
|---|---|
| Multiple Backends | In-memory, filesystem, Docker — same interface |
| Console Toolset | Ready-to-use tools for pydantic-ai agents |
| Permission System | Pattern-based access control with presets |
| Docker Isolation | Safe execution of untrusted code |
| Session Management | Multi-user support with workspace persistence |
| Pre-built Runtimes | Python and Node.js environments ready to go |
Related Projects
| Package | Description |
|---|---|
| Pydantic Deep Agents | Full agent framework (uses this library) |
| pydantic-ai-todo | Task planning toolset |
| subagents-pydantic-ai | Multi-agent orchestration |
| summarization-pydantic-ai | Context management |
| pydantic-ai | The foundation — agent framework by Pydantic |
Contributing
git clone https://github.com/vstorm-co/pydantic-ai-backend.git
cd pydantic-ai-backend
make install
make test # 100% coverage required
License
MIT — see LICENSE
Built with ❤️ by vstorm-co
1 maintainer
Metadata
1 maintainer
Release files for pydantic-ai-backend 0.1.5
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Total release size: 13.4 MB
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