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 0.1.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pydantic_ai_backend-0.1.6.tar.gz | 13.4 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pydantic_ai_backend-0.1.6-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 13.4 MB
Release files / pydantic_ai_backend-0.1.6.tar.gz
| Download URL | pydantic_ai_backend-0.1.6.tar.gz |
|---|---|
| Size | 13.4 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
d2b6ca73ed1160a44af2fdc1c0f79c20fcc9c658f644ebf41da0bc2d753fb226
|
|
BLAKE2b-256 checksum How to use checksums |
272176c6bf1d800da830df2549313414e81d39ac9fbf0212b25ebd68b4edeb1a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Feb 7, 2026.
Transparency logRelease files / pydantic_ai_backend-0.1.6-py3-none-any.whl
| Download URL | pydantic_ai_backend-0.1.6-py3-none-any.whl |
|---|---|
| Size | 43.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
8376eab638e732444295f273434b78ffd4b4188c35d3fd06db55f39d381b8e8d
|
|
BLAKE2b-256 checksum How to use checksums |
5d3b349e95c111e7c9e4c6700fcf95f708f071c7e0376e4d01ec6869a9b6084d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Feb 7, 2026.
Transparency log
