GenAI Processors：Building the ‘Processor Architecture’ for Generative AI Workflows

What are GenAI Processors?

GenAI Processors is an open-source, lightweight Python library developed by Google DeepMind. It enables developers to build modular, parallel, and efficient pipelines for generative AI tasks. Centered around a Processor abstraction, it supports asynchronous, multi-modal content streams (text, audio, images, JSON, etc.).

What are its main features?

• Modular architecture: Break down complex tasks into reusable Processor or PartProcessor units that can be composed using + (sequential) or // (parallel) operators.

• GenAI API integration: Comes with built-in processors like GenaiModel and LiveProcessor for seamless interaction with Gemini models.

• Async and concurrency: Built on Python’s asyncio for true non-blocking execution and faster Time To First Token (TTFT).

• Multi-modal stream support: Wraps content and metadata in ProcessorPart objects, handling text, image, audio, or any JSON format uniformly.

• Streaming tools: Offers tools to split, merge, and concatenate content streams, perfect for building flexible, real-time pipelines.

How does it work

• Processor abstraction: Each Processor implements an async stream interface that ingests and outputs ProcessorPart objects.

• Asynchronous and parallel execution: Tasks within and across processors run concurrently, boosting performance and responsiveness.

• Unified multi-modal processing: A consistent API layer handles diverse data types with ease, making cross-modal tasks simple to manage.

• Composable logic: Use intuitive Python operators (+, //) to create complex pipeline logic; processors can be extended or decorated easily.

Project info

• Repository: https://github.com/google-gemini/genai-processors

Application scenarios

• Real-time AI agents: For example, voice input → Gemini response → TTS output; suitable for live interaction and multi-modal interfaces.

• Research agents: Create pipelines for retrieval → reasoning → summarization, perfect for knowledge mining or question answering.

• AI commentary & broadcast systems: Combine processors for event detection, content generation, and audio narration.

• Low-latency AI services: Ideal for use cases with tight latency requirements, such as chatbots, co-pilots, or content generation tools.