Pierpaolo D’Odorico 🇮🇹

Datapizza AI - // Founder

[Effect | TypeScript for the AI Era @EffectTS_]

Effect at Datapizza: Services & Layers in Production

Leonardo Trapani @leo_trapani | Effect Milan 2026 🇮🇹

https://t.co/yLv7HzOA3n

[Leonardo Trapani @leo_trapani]

@EffectTS_ is back in Milan

I'll be speaking about how we use it in production, together with the Effect team

[Pierpaolo D’Odorico 🇮🇹 @pierdd_]

@leo_trapani Interesting (go!)

[Pierpaolo D’Odorico 🇮🇹 @pierdd_]

@m4r10c4nd3l4 Cool

[Datapizza AI @datapizza_ai]

We’re rolling out a new YouTube playlist with step-by-step tutorials for our open-source framework — and it’s just getting started. 🚀

We’re releasing videos gradually, covering everything from setup to advanced workflows, so you can dive deeper into the architecture as the series evolves.

👉 Start here: https://t.co/gH0p8JhdV5

#opensource #developers #AI #tutorials

[Datapizza AI @datapizza_ai]

Here are the latest updates from our R&D Lab 🚀

Right now we’re exploring advanced RAG techniques — and for proper evaluation, we need datasets that reflect real-world difficulty.

We tested several public datasets:

📍LegalBench

📍MultiHop-RAG

📍LoCoMo

These have been super helpful starting points for evaluation.

As we applied them to our specific use cases, we realized we needed something more tailored to the GenAI RAG challenges we're focusing on — particularly around domain-specific knowledge and reasoning chains that match our clients' real-world scenarios.

So we built a framework to generate custom evaluation datasets that fit our needs.

We now have two internal domain-heavy evaluation datasets + a public one based on the DnD SRD 5.2.1 that we're sharing with the community.

This is just an initial step, but we're excited about where it's headed. We broke down our approach here:

🔗 Blog post https://t.co/OOYnjntpuW
🔗 GitHub repo https://t.co/sclO2dH4LZ
🔗 Dataset on Hugging Face https://t.co/SK2v9t8tMW

Would love to hear your thoughts, feedback, or ideas on how to improve this!
#RAG #dataset #evaluation #GenAI #framework

[Datapizza AI @datapizza_ai]

🚀 Release v0.0.9 is out!

Here’s what’s new in our open-source framework 👇

New features:

- MCP SUPPORT

Fix:

- Added metadata args on parse method (azure and docling),
- Enhanced OpenAIClient init arguments,

DOCS: https://t.co/gcO9M4Lf5t

GITHUB: https://t.co/dojOf1sOFq

#datapizzaai #opensource #MCP

[Datapizza AI @datapizza_ai]

🚀 Release v0.0.7 is out!

Here’s what’s new in our open-source framework 👇

🔧 Features
- Docling parser: extended to all supported files + flexible OCR configuration

- Bedrock client: now with async support via aioboto3 (and full test coverage)

- New tools
→ web_fetch
→ FileSystem
→ SQLDatabase

- Milvus vectorstore added

Agents: smoother usage & improved UX

🧩 Fixes
- https://t.co/6d0ryt0hCw: accepts list of file paths

- Bedrock async methods: safer error handling

- Node splitter: handles empty cases

- Tool converter & EasyOCR dependency fixes

- Agents: clear user input after first step

- OpenAI client compatibility update (>=2)

💡 Focus of this release: performance, async readiness, and better developer experience.

👉 Full changelog: https://t.co/dojOf1sOFq

#datapizzaai #framework #opensource

[Alessandro Risaro @risooo2]

When people get what Datapizza AI is about without us explaining — that’s the best part.

[sisco404 @sisco404]

Our new Python GenAI Framework is out and open source 🍕

800+ stars in one day! ⭐️

@datapizza_ai

https://t.co/xcbhABC6HK

[Pierpaolo D’Odorico 🇮🇹 @pierdd_]

Agree?

👇🏾

[Datapizza AI @datapizza_ai]

Today marks the birth of Datapizza AI: the open-source framework for GenAI 🌏

100% Open Source. 100% Made in Italy.

But what exactly is Datapizza AI?

It’s our framework for building multi-agent AI systems and production-ready RAG solutions — and we’ve decided to release it open source for everyone.

The project starts from a simple idea.

We want to give developers and companies full control over their own AI solutions.

No tech lock-in. No paywalls. No “slide deck” fluff.

We want to use Italy as a starting point to make our voice heard in the European Generative AI landscape.

Inside the repo, you’ll find:

📌 Ultra-fast quick start guide

📌 Full RAG system examples

📌 Templates for collaborative AI agents

Leave us a star and tell us what you think!

Any feedback is welcome — it’ll help us understand how to make it even better. 🙏

Try and Star on GitHub - https://t.co/F2u9YcPdrP

#AI #Italy #aimadeinitaly #madeinitaly

[Pierpaolo D’Odorico 🇮🇹 @pierdd_]

The repo is live.
Go check it out, contribute, or just leave us a ⭐️

👉 https://t.co/OkE7C3ixUz

[Pierpaolo D’Odorico 🇮🇹 @pierdd_]

From 3 people in a small room to a 60+ team building AI systems for some of the biggest companies in Italy.

And today - Datapizza AI is now Open Source. 🇮🇹

[Pierpaolo D’Odorico 🇮🇹 @pierdd_]

Why did we build it?

Because we wanted to give engineers and companies full control over their AI stack.

Transparent, modular, and built to last.

[Pierpaolo D’Odorico 🇮🇹 @pierdd_]

@datapizza_ai 600 stars in 8h 💥💥

[Pierpaolo D’Odorico 🇮🇹 @pierdd_]

@datapizza_ai +500 stars in 5h 🙌

[Pierpaolo D’Odorico 🇮🇹 @pierdd_]

We built it for those who demand control, not just demos — to build real AI Agents, not prototypes.

🇮🇹 A new chapter of open-source innovation, proudly Made in Italy.

→ https://t.co/OkE7C3i051

[Pierpaolo D’Odorico 🇮🇹 @pierdd_]

Today we launch Datapizza AI

-> is the “Nano Banana” of GenAI frameworks 🍕

A compact leap forward: fast, powerful, and radically transparent.

Born for production in enterprises, already validated in the Italian market.

Modular, open, and engineered for efficiency at scale.