3 ms·

Show HN: AI-powered web service combining FastAPI, Pydantic-AI, and MCP servers

46 points by Aherontas 1y ago

Hey 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://www.petrostechchronicles.com/) https://github.com/Aherontas/Pycon_Greece_2025_Presentation_Agents 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!

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Emma_Schmidt 1y ago
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dcreater 1y ago
Can't really grasp much from the repo, the slides are needed
- Aherontas 1y ago
  That's correct, the slides are mostly showing how MCP - A2A work and basic architectures between them and Agents in general. I will upload probably today the presentation slides in my blog.
  - Aherontas 1y ago
    If you are interested, I just released the slides in my blog: https://www.petrostechchronicles.com/blog/PyCon_Greece_2025_Agents_Presentation https://www.petrostechchronicles.com/blog/PyCon_Greece_2025_...
simple10 1y ago
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?
- Aherontas 1y ago
  Hello exactly what you are saying is what this repo does. I will upload today probably the whole PyCon presentation of it in my blog if you are interested. You will find it at: https://www.petrostechchronicles.com/ https://www.petrostechchronicles.com/
  - Aherontas 1y ago
    Just released it if you are interested: https://www.petrostechchronicles.com/blog/PyCon_Greece_2025_Agents_Presentation https://www.petrostechchronicles.com/blog/PyCon_Greece_2025_...
colonCapitalDee 1y ago
I've been very happy with pydantic-ai, it blows the rest of the python ai ecosystem out of the water
- gHA5 1y ago
  Are you using Pydantic AI for structured output? If so, have you also tried instructor?
  - maxdo 1y ago
    I personally just switched to https://docs.boundaryml.com/guide/comparisons/baml-vs-pydantic https://docs.boundaryml.com/guide/comparisons/baml-vs-pydant... just feels a bit more polished. especially testing part.
    - Aherontas 1y ago
      Haven't tried balm, I will give it a shot, I am really curious to see all the features it supports.
  - mmargenot 1y ago
    `outlines` (https://github.com/dottxt-ai/outlines https://github.com/dottxt-ai/outlines) is very good and supported by vLLM as a backend structured output provider (https://docs.vllm.ai/en/v0.8.2/features/structured_outputs.html https://docs.vllm.ai/en/v0.8.2/features/structured_outputs.h...) for both local and remote LLMs. vLLM is probably the best open source tooling for the inference side right now.
  - colonCapitalDee 1y ago
    I did look at instructor and probably for structured output pydantic-ai and instructor are about the same, but pydantic-ai supports a ton of other stuff that isn't part of instructor's feature set. For me killer apps were the ability to serialize/deserialize conversations as json; frictionless tool-calling; and ability to mock the LLM client for testing.
tcdent 1y ago
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.
- morkalork 1y ago
  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.
  - tcdent 1y ago
    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.
vivzkestrel 1y ago
Without using all the jargon can you kindly explain what your project actually does because I did read the repo and still dont get it
- Aherontas 1y ago
  It just builds a ChatGPT like UI that has Agents to serve you either latest news on a specific tech topic (via fetching them via MCP Servers for: Brave Browser, Hackernews and Github for trending repos or repos fetched in the response). It also supports giving you back general knowledge answers. The whole purpose of the repo is to be a general example of how Agents speak with Agents and how to pair them with MCP servers and why we need MCP Servers(for example better run all calculations not in LLM because they hallucinate, but via code and then give the answer back to the LLM). I hope it clarifies what it does now.
maverick98 1y ago
This is a great repo, I was looking for something like this! I believe MCP and A2A will probably remain and others will build on top of them actually
- Aherontas 1y ago
  Thanks a lot, I believe too that MCP and A2A are not going anywhere anytime soon. Although both of them need more features and security hardening in order to really survive in production like or enterprise environments.
Emma_Schmidt 1y ago
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46 points by Aherontas 1y ago

deleted 1y ago

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deleted 1y ago

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Emma_Schmidt 1y ago

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dcreater 1y ago

Can't really grasp much from the repo, the slides are needed

Aherontas 1y ago
That's correct, the slides are mostly showing how MCP - A2A work and basic architectures between them and Agents in general. I will upload probably today the presentation slides in my blog.
- Aherontas 1y ago
  If you are interested, I just released the slides in my blog: https://www.petrostechchronicles.com/blog/PyCon_Greece_2025_Agents_Presentation https://www.petrostechchronicles.com/blog/PyCon_Greece_2025_...

simple10 1y ago

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?

Aherontas 1y ago
Hello exactly what you are saying is what this repo does. I will upload today probably the whole PyCon presentation of it in my blog if you are interested. You will find it at: https://www.petrostechchronicles.com/ https://www.petrostechchronicles.com/
- Aherontas 1y ago
  Just released it if you are interested: https://www.petrostechchronicles.com/blog/PyCon_Greece_2025_Agents_Presentation https://www.petrostechchronicles.com/blog/PyCon_Greece_2025_...

colonCapitalDee 1y ago

I've been very happy with pydantic-ai, it blows the rest of the python ai ecosystem out of the water

gHA5 1y ago
Are you using Pydantic AI for structured output? If so, have you also tried instructor?
- maxdo 1y ago
  I personally just switched to https://docs.boundaryml.com/guide/comparisons/baml-vs-pydantic https://docs.boundaryml.com/guide/comparisons/baml-vs-pydant... just feels a bit more polished. especially testing part.
  - Aherontas 1y ago
    Haven't tried balm, I will give it a shot, I am really curious to see all the features it supports.
- mmargenot 1y ago
  `outlines` (https://github.com/dottxt-ai/outlines https://github.com/dottxt-ai/outlines) is very good and supported by vLLM as a backend structured output provider (https://docs.vllm.ai/en/v0.8.2/features/structured_outputs.html https://docs.vllm.ai/en/v0.8.2/features/structured_outputs.h...) for both local and remote LLMs. vLLM is probably the best open source tooling for the inference side right now.
- colonCapitalDee 1y ago
  I did look at instructor and probably for structured output pydantic-ai and instructor are about the same, but pydantic-ai supports a ton of other stuff that isn't part of instructor's feature set. For me killer apps were the ability to serialize/deserialize conversations as json; frictionless tool-calling; and ability to mock the LLM client for testing.

tcdent 1y ago

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.

morkalork 1y ago
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.
- tcdent 1y ago
  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.

vivzkestrel 1y ago

Without using all the jargon can you kindly explain what your project actually does because I did read the repo and still dont get it

Aherontas 1y ago
It just builds a ChatGPT like UI that has Agents to serve you either latest news on a specific tech topic (via fetching them via MCP Servers for: Brave Browser, Hackernews and Github for trending repos or repos fetched in the response). It also supports giving you back general knowledge answers. The whole purpose of the repo is to be a general example of how Agents speak with Agents and how to pair them with MCP servers and why we need MCP Servers(for example better run all calculations not in LLM because they hallucinate, but via code and then give the answer back to the LLM). I hope it clarifies what it does now.

maverick98 1y ago

This is a great repo, I was looking for something like this! I believe MCP and A2A will probably remain and others will build on top of them actually

Aherontas 1y ago
Thanks a lot, I believe too that MCP and A2A are not going anywhere anytime soon. Although both of them need more features and security hardening in order to really survive in production like or enterprise environments.

Emma_Schmidt 1y ago

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