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name: model-backends description: "Use GenAI Processors model wrappers, function calling, MCP, and local backends." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Model backends

Use this sub-skill when a task is about model invocation, realtime model loops, tool/function calling, MCP tools, local backends, or model-selector examples.

Read when

• The task names GenaiModel, live_model.LiveProcessor, realtime.LiveProcessor, FunctionCalling, or ImagePreprocess.
• The task asks about Gemini API, Gemini Live API, tool calls, async tools, FunctionResponse.scheduling, or MCP sessions.
• The task asks to use Ollama, Transformers, LangChain, OpenRouter, ADK, or example backend selection.
• The task needs to diagnose GOOGLE_API_KEY, live model names, Ollama server errors, missing PyTorch, or disabled automatic function calling.

Boundaries

This sub-skill owns model-facing processors and orchestration. It does not own low-level device/document input; use ../multimodal-i-o/ for audio/video/PDF/ web/Drive/GitHub sources. Use ../examples-and-apps/ for complete CLI/app wiring.

Backend selection guide

• Use core.genai_model.GenaiModel for turn-based Gemini API calls.
• Use core.live_model.LiveProcessor for Gemini Live API bidirectional streams with Live-capable model names.
• Use core.realtime.LiveProcessor to wrap any turn-based processor into a client-side realtime loop with interruption and rolling prompt state.
• Use core.function_calling.FunctionCalling when the library, not the model client, should execute functions or MCP tools.
• Use core.ollama_model.OllamaModel for a running local Ollama server.
• Use core.transformers_model.TransformersModel for local HuggingFace Transformers; install PyTorch before real model execution.
• Use contrib.langchain_model.LangChainModel or contrib.openrouter_model.OpenRouterModel for contributed wrappers.
• Use core.adk.ProcessorAgent for wrapping a processor as an ADK agent.

References and scripts

• references/api-reference.md lists constructors, model wrappers, and support helpers.
• references/workflows.md shows Gemini, realtime, function-calling, MCP, and local-backend patterns.
• references/troubleshooting.md covers credentials, service availability, PyTorch, Live API model names, and tool-call configuration.
• scripts/smoke_models.py imports wrappers and prints key signatures without contacting any service.

Native evidence anchors

Model behavior is grounded by source modules under genai_processors/core/, genai_processors/contrib/, genai_processors/mcp.py, and tests such as genai_model_test.py, live_model_test.py, realtime_test.py, function_calling_test.py, mcp_test.py, ollama_model_test.py, transformers_model_test.py, and adk_test.py. Example evidence includes examples/models.py, examples/smart_model.py, examples/chat.py, examples/trip_request_cli.py, examples/trip_request_cli_ollama.py, and examples/trip_request_adk/agent.py.

Usability checkpoints

A good answer using this sub-skill should:

• State whether the model wrapper buffers full input or streams bidirectionally.
• Identify required credentials or local services before showing runnable code.
• Disable model-side automatic function calling when wrapping with FunctionCalling and executing tools in the processor loop.
• Keep model selection separate from I/O and full app concerns unless the task explicitly spans those areas.
• Treat external API calls, model downloads, and service startup as non-smoke operations requiring user-controlled credentials and budget.