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name: genai-processors description: "Route GenAI Processors tasks to the right workflow guide." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

GenAI Processors

GenAI Processors is a composable Python library for building AI pipelines with shared content types, async processor composition, model wrappers, and multimodal I/O.

Use this repo skill when the task mentions any of the following:

• Processor, PartProcessor, ProcessorPart, ProcessorContent, or ContentStream.
• Model wrappers such as GenaiModel, LiveProcessor, OllamaModel, TransformersModel, FunctionCalling, LangChainModel, OpenRouterModel, or the ADK bridge.
• Audio/video/PDF/web/file connectors, speech-to-text, text-to-speech, VAD, realtime streams, or live agent pipelines.
• CLI demos, research agents, live commentator / illustrator / widgets applets, or AI Studio WebSocket backends.

Read this first

• references/install.md for install variants and the minimal smoke check.
• references/repo-provenance.md for the source snapshot used to build this skill.
• references/repo-routing-metadata.json for import routing metadata.
• references/troubleshooting.md for cross-cutting setup and runtime failures.

Route map

sub-skills/core-composition/

Use for processor composition, content types, stream utilities, caching, routing, tracing, and text/templating helpers.

Typical prompts:

• "How do I build a custom processor?"
• "How do ContentStream and ProcessorPart work?"
• "How do I cache or trace a processor pipeline?"
• "How do I branch a stream with Switch or parallel?"

sub-skills/model-backends/

Use for Gemini, Live API, realtime conversation loops, function calling, MCP, Ollama, Transformers, LangChain, OpenRouter, ADK, and the example backends.

Typical prompts:

• "Which model wrapper should I use?"
• "How do I wire tool calls or MCP sessions?"
• "How do I switch between Gemini, Ollama, and local Transformers?"
• "How do I build a realtime or function-calling agent?"

sub-skills/multimodal-i-o/

Use for audio/video/PDF/web/file connectors, speech and TTS, VAD, rate-limited audio, event detection, windowing, timestamps, and document fetchers.

Typical prompts:

• "How do I capture microphone or camera input?"
• "How do I transcribe audio or synthesize speech?"
• "How do I extract content from PDFs, URLs, GitHub, or Drive?"
• "How do I manage realtime audio or video streams?"

sub-skills/examples-and-apps/

Use for the CLI demos, research agent, trip request workflow, live commentator, live illustrator, widgets, AI Studio applets, and websocket server helpers.

Typical prompts:

• "How do I run or adapt one of the example CLIs?"
• "How does the research agent fit together?"
• "How do the AI Studio applets talk to the backend?"
• "How do I build a full end-to-end demo around these processors?"

Quick install

For the core library:

pip install genai-processors

Add genai-processors[contrib] when you need the bundled LangChain/OpenRouter helpers. The more specialized example stacks may also need extra runtime packages such as pyaudio, av, google-adk, transformers, or the Google Cloud Speech/Text-to-Speech clients. See references/install.md.

Minimal smoke check

python -c "import genai_processors; print(genai_processors.__version__)"

For a slightly stronger check, run the bundled smoke script:

python scripts/check_install.py

How to choose a route

• Start with core-composition if the task is about processor mechanics, content typing, stream manipulation, or reusable wrappers.
• Start with model-backends if the task is about model choice, tool calls, local backends, or multi-agent orchestration.
• Start with multimodal-i-o if the task is about the inputs and outputs the processors consume or emit.
• Start with examples-and-apps if the task is about running or adapting a complete demo, CLI, or applet.

If a task touches more than one route, read the owning sub-skill first and then follow its cross-links instead of duplicating guidance here.