Show HN: AI-powered web service combining FastAPI, Pydantic-AI, and MCP servers
github.comHey all! I recently gave a workshop talk at PyCon Greece 2025 about building production-ready agent systems.
To check the workshop, I put together a demo repo: (I will add the slides too soon in my blog: https://www.petrostechchronicles.com/) https://github.com/Aherontas/Pycon_Greece_2025_Presentation_...
The idea was to show how multiple AI agents can collaborate using FastAPI + Pydantic-AI, with protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent) for safe communication and orchestration.
Features:
- Multiple agents running in containers
- MCP servers (Brave search, GitHub, filesystem, etc.) as tools
- A2A communication between services
- Minimal UI for experimentation for Tech Trend - repo analysis
I built this repo because most agent frameworks look great in isolated demos, but fall apart when you try to glue agents together into a real application. My goal was to help people experiment with these patterns and move closer to real-world use cases.
It’s not production-grade, but would love feedback, criticism, or war stories from anyone who’s tried building actual multi-agent systems. Big questions:
Do you think agent-to-agent protocols like MCP/A2A will stick?
Or will the future be mostly single powerful LLMs with plugin stacks?
Thanks — excited to hear what the HN crowd thinks!
Since you're framing this as a learning resource, here are a couple things I see:
Your views are not following a single convention: some of them return dictionaries, some return base JSONResponse objects, and others return properly defined Pydantic schemas. I didn't run the code, but I'd venture to guess your generated documentation is not comprehensive, nor is it cohesive.
I'd also further extend this into your agent services; passing bare dictionaries with arbitrary fields into what is supposed to be a modular logic handler is pretty outdated. You're defining a functional (methods) interface; data structures are the other half of the equation.
This plays into the way that Agents (as in the context of this system, versus Pydantic AI agents) are wrapped arbitrarily. I'd favor making the conversion from a Pydantic agent to a native agent part of the system's interface design, rather than re-implementing a subset of the agentic functions in your own BaseAgent and ending up with an `agent.agent` context.
Also, since this is a web-centric application (that leverages agents) dropping all of your view functions into main.py leaves something to be desired; break up your views into logical modules based on role.
Everyone's learning, and I hope this helps someone in their journey. Kudos for putting your code out there as a resource; some of us can't help ourselves from reading it.
Do you have any recommendations for articles or example projects of what a good Python project (that isn't django based) looks like in 2025? Seeing things like pydantic derived types leak everywhere seems wrong from my Java background.
Ooh, great question. I don't have a good link. I would say that most of the concepts that I'm expressing come from personal experience and an interest in optimizing my own codebases for maintainability.
Pydantic is often misunderstood, and developers who aren't familiar with typesafe-python love to try to raise criticisms of it. But the way that you should think about it is that it's essentially a replacement for a built-in type system in the context of data like a dataclass. However, Pydantic takes it further by giving you serialization and deserialization that is customizable and has integrations with, for example, SQL Alchemy where you can serialize directly from your ORM. One of the major benefits that I find is that it provides common, repeatable interfaces for validation or data formatting.
Essentially, it has become incredibly popular because it provides a consistent interface that developers understand for accomplishing these common patterns.
When it comes to "leaking" derived types through things like OpenAPI specs and documentation, they don't really expose the underlying object's functionality, but they do expose the object's structure so that you can easily generate documentation that includes the expected response bodies and expected return bodies. Whether those get serialized into JSON or something else, the parameters and types and optionality of each of those is formally defined by Pydantic in a way that's straightforward for the documentation generation to interpret.
In most cases you'll disable the generated documentation links from FastAPI in production.
Exactly this. From SQL Alchemy to pydantic model, from pydantic model to pydantic dto, from pydantic dto to json/protobuf/binary to ship over the wire…
pydantic types are designed to be shipped. Just make sure you strip any security stuff or PII. Pydantic and JSON work very very well together.
I've been very happy with pydantic-ai, it blows the rest of the python ai ecosystem out of the water
Are you using Pydantic AI for structured output? If so, have you also tried instructor?
Looks interesting! Thanks for posting it.
Would be great if you can add the slides or video of the presentation to the repo. Maybe also add a description and update the summary at the top?
It seems like the project is a multi-agent playground & demo to learn how to make AI agents work together?
Can't really grasp much from the repo, the slides are needed