Hey HN,
I built pydantic-deepagents after getting frustrated with existing agent frameworks. Most work great for demos but break down when you need agents to actually do things: work with files, break complex tasks into subtasks, or handle long conversations without blowing through token limits.
This framework extends pydantic-ai with the stuff I kept rebuilding across projects:
Multiple backends: in-memory, filesystem persistence, Docker sandbox for isolation, or composites mixing them
Built-in toolsets: TODO management, filesystem ops, subagent spawning, skills system with markdown prompts
File uploads: mount files and let agents process them (e.g., analyze a CSV)
Structured outputs via Pydantic models - no more JSON parsing headaches
Auto-summarization for long conversations to stay under token limits
Human-in-the-loop confirmation for sensitive actions
Streaming support
Quick example:
from pydantic_deep import create_deep_agent, create_default_deps
from pydantic_deep.backends import StateBackend
backend = StateBackend()
deps = create_default_deps(backend)
agent = create_deep_agent()
result = await agent.run("Help me organize my tasks", deps=deps)
For type-safe outputs, just pass a Pydantic model as output_type and access fields directly on result.output.
Install: pip install pydantic-deep (add [sandbox] for Docker support)
Demo video: https://drive.google.com/file/d/1hqgXkbAgUrsKOWpfWdF48cqaxRh...
Chat app example with streaming: https://github.com/vstorm-co/pydantic-deepagents/tree/main/e...
Docs: https://vstorm-co.github.io/pydantic-deepagents/
Repo: https://github.com/vstorm-co/pydantic-deepagents
MIT licensed, 100% test coverage.
Would love to hear what features you'd find useful, or what's missing from your current agent workflows.
