Pydantic-DeepAgents: A Lightweight, Production-Ready Framework for Building Autonomous AI Agents

Inspired by LangChain deepagents — but simpler, type-safe, and with Docker sandboxing built-in

In 2025, autonomous AI agents are no longer just research prototypes — they’re powering real-world automation, code generation tools, data pipelines, and intelligent assistants. However, many popular agent frameworks come with heavy dependencies, complex graphs, and a steep learning curve that makes production deployment challenging.

That’s why we at Vstorm built Pydantic-DeepAgents — a minimal yet powerful open-source framework that extends Pydantic-AI with everything you need to create reliable, production-grade agents.

GitHub repository: https://github.com/vstorm-co/pydantic-deepagents

What makes Pydantic-DeepAgents different?

We were heavily inspired by LangChain’s excellent deepagents project — a clean implementation of “deep agent” patterns including planning loops, tool calling, subagent delegation, and human-in-the-loop workflows.

Instead of reinventing the wheel, we asked: What if we built the same powerful patterns, but fully in the Pydantic-AI ecosystem?

The result is a framework that:

• Keeps dependencies lightweight (no LangGraph, no massive ecosystem)
• Leverages Pydantic’s native type-safety and validation for structured outputs
• Adds production-focused features missing from many alternatives

Core Features

• Planning & Reasoning — TodoToolset for autonomous task breakdown and self-correction
• Filesystem Access — Full read/write operations with FilesystemToolset
• Subagent Delegation — Break complex tasks into specialized subagents (SubAgentToolset)
• Extensible Skills System — Define new agent capabilities with simple Markdown prompts (perfect for rapid iteration)
• Multiple Backends — In-memory, persistent filesystem, secure DockerSandbox (isolated code execution), and CompositeBackend
• File Uploads — Seamless processing of uploaded files via run_with_files() or deps.upload_file()
• Context Management — Automatic summarization for long-running conversations
• Human-in-the-Loop — Built-in confirmation workflows for critical actions
• Streaming Support — Token-by-token responses for responsive UIs
• Structured Outputs — Type-safe Pydantic models via output_type

See It in Action

We’ve included a complete full-stack demo application (FastAPI backend + streaming web UI) that demonstrates:

• Live agent reasoning traces
• File uploads and processing
• Human approval steps
• Streaming responses

Demo app: https://github.com/vstorm-co/pydantic-deepagents/tree/main/examples/full_app

Quick video walkthrough: https://drive.google.com/file/d/1hqgXkbAgUrsKOWpfWdF48cqaxRht-8od/view?usp=sharing

When to choose Pydantic-DeepAgents?

Choose it when you want:

• A clean, maintainable agent architecture without framework bloat
• Strong guarantees around data validation and structured responses
• Secure execution (Docker sandbox out of the box)
• Fast prototyping with Markdown-defined skills
• Easy deployment in production environments

It’s particularly great if you’re already using Pydantic-AI, prefer minimalism, or need agents that interact safely with files and external tools.

Get Started Today

pip install pydantic-deep

Check out the repository, star it if you find it useful, and feel free to open issues or PRs — we’d love contributions!

https://github.com/vstorm-co/pydantic-deepagents

We’re excited to see what you build with it.

— Team at Vstorm (https://vstorm.co)