Datapizza AI Framework
Datapizza AI is an open-source Python framework that enables developers to build reliable, observable, and production-ready generative AI solutions with minimal overhead through its API-first design and modular architecture.
https://github.com/datapizza-labs/datapizza-ai?ref=producthunt
Product Information
Updated:Nov 16, 2025
What is Datapizza AI Framework
Datapizza AI is a no-fluff GenAI framework developed by Italian startup Datapizza, designed specifically for engineers who need to build and deploy AI applications in production environments. The framework provides a thin, transparent layer above native SDKs of major AI providers (OpenAI, Google Gemini, Anthropic, Mistral, Azure), offering a modular and composable architecture that prioritizes control and transparency over excessive abstraction. It supports multiple AI providers, tool integrations, and comes with built-in observability features, making it particularly suitable for building agents, RAG (Retrieval-Augmented Generation) systems, and automation pipelines.
Key Features of Datapizza AI Framework
Datapizza AI is a Python-based GenAI framework designed for building reliable AI solutions with minimal overhead. It offers multi-provider support, composable architecture, built-in observability, and vendor-agnostic design. The framework emphasizes control and transparency in developing production-ready RAG systems and AI agents, featuring detailed tracing capabilities, modular components, and easy integration with various AI providers and tools.
API-First Design: Supports multiple AI providers (OpenAI, Google Gemini, Anthropic, etc.) with consistent APIs and built-in tool integration for web search and document processing
Built-in Observability: Provides OpenTelemetry tracing and detailed monitoring capabilities to help debug and optimize AI operations with comprehensive logging of inputs, outputs, and performance metrics
Composable Architecture: Features reusable blocks, smart chunking, and built-in reranking capabilities that allow developers to build complex AI systems from modular components
Vendor-Agnostic Implementation: Enables easy switching between different AI providers and tools without requiring major code changes, offering clear interfaces and migration-friendly design
Use Cases of Datapizza AI Framework
Multi-Agent Trip Planning System: Create sophisticated travel planning solutions using multiple specialized agents for weather information, web search, and coordination
Document Processing Pipeline: Build RAG systems that automatically parse, index, and retrieve information from documents like PDFs for enhanced AI responses
Enterprise Knowledge Base: Develop production-ready systems for managing and querying corporate knowledge using document retrieval and AI-powered responses
Pros
Strong focus on observability and debugging capabilities
Highly modular and flexible architecture
Comprehensive support for multiple AI providers and tools
Production-ready with enterprise-grade features
Cons
Requires Python 3.10+ which may limit compatibility with older systems
More complex setup compared to simpler frameworks due to its modular nature
How to Use Datapizza AI Framework
Install Core Framework: Run 'pip install datapizza-ai' to install the core framework. For specific providers, install additional packages like 'pip install datapizza-ai-clients-openai' for OpenAI integration.
Initialize Client: Import and initialize the AI client with your API key: 'from datapizza.clients.openai import OpenAIClient; client = OpenAIClient(api_key="YOUR_API_KEY")'
Create Basic Agent: Create an agent by importing Agent class and initializing it with the client: 'from datapizza.agents import Agent; agent = Agent(name="assistant", client=client)'
Add Custom Tools: Create custom tools using the @tool decorator: '@tool def get_weather(city: str) -> str: return f"The weather in {city} is sunny"'
Configure Agent with Tools: Add tools to your agent during initialization: 'agent = Agent(name="assistant", client=client, tools=[get_weather])'
Enable Tracing: Add tracing for debugging: 'from datapizza.tracing import ContextTracing; with ContextTracing().trace("my_ai_operation"): response = agent.run("query")'
Create Multi-Agent System: Create specialized agents and connect them using can_call method: 'planner_agent.can_call([weather_agent, web_search_agent])'
Set Up Document Processing: Install document parsers ('pip install datapizza-ai-parsers-docling') and create an ingestion pipeline for processing documents with vectorstore integration
Implement RAG Pipeline: Create a DAG pipeline connecting rewriter, embedder, retriever, prompt template, and generator modules for retrieval-augmented generation
Run Queries: Execute queries using agent.run() or pipeline.run() depending on your setup: 'response = agent.run("What is the weather in Rome?")'
Datapizza AI Framework FAQs
Datapizza AI is a Python-based GenAI framework designed for building reliable AI solutions without overhead. It's focused on keeping agents predictable, debugging fast, and code trusted in production, with features like multi-provider support, built-in observability, and vendor-agnostic design.
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