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name: model-client-and-generator-workflows description: "Use AdalFlow ModelClient, Generator, and Embedder workflows for provider integration, prompt/model kwargs, output processors, caching, streaming basics, and no-credential fake-client tests." disable-model-invocation: true metadata: disco-role: operating license: MIT

Model Client and Generator Workflows

Use this sub-skill when a task involves AdalFlow model-provider plumbing rather than retrieval, agents, optimization, or tracing.

Load when

• Building or debugging Generator, ModelClient, Embedder, or BatchEmbedder flows.
• Selecting provider clients or optional extras for OpenAI/OpenAI-compatible, Anthropic, Groq, Google, Ollama, Together, Cohere, Azure, Bedrock, Fireworks, Mistral, DeepSeek, XAI, SambaNova, or local Transformer integrations.
• Configuring prompt templates, prompt_kwargs, model_kwargs, ModelType, output processors, cache behavior, or streaming response handling.
• Writing no-network tests with a fake ModelClient.

Route elsewhere

• RAG, indexes, retrievers, LocalDB, vector stores, and document pipelines: use retrieval-rag-and-data-pipelines.
• Agent, Runner, ReAct, FunctionTool, tool streaming, permissions, and MCP: use agents-tools-and-streaming.
• Evaluation, datasets, Trainer, optimizers, text gradients, and few-shot training: use evaluation-and-optimization.
• Logging, generator-state/call loggers, callback tracing, MLflow, and config utilities: use tracing-observability-and-configuration.
• Core Component, Prompt, DataClass, and parser schema construction without model calls: use core-components-and-structured-io.

Internal references

• Generator workflows: prompt rendering, call/acall/forward, output processors, caching, streaming, fake-client tests, embedder orchestration.
• Model clients: protocol requirements, provider extras/lazy imports, provider notes, OpenAI-compatible patterns, direct client usage.
• API reference: verified signatures, return fields, ModelType, GeneratorOutput, EmbedderOutput, and concise call contracts.
• Troubleshooting: optional SDK/API-key errors, bad model_kwargs, parser failures, GeneratorOutput.error, cache surprises, streaming, and image/content formatting.
• Fake-client smoke script: deterministic no-credential sanity check for Generator, JsonParser, Embedder, and BatchEmbedder.

Operating checklist

1. Decide whether the workflow is service-free or requires a live provider. Prefer the bundled fake-client script for unit tests and examples.
2. Pick the correct ModelType: LLM for text generation, LLM_REASONING for reasoning-compatible LLM endpoints, EMBEDDER for embeddings, and provider-specific types only after checking support.
3. Keep model_kwargs provider-shaped and JSON-serializable when caching is enabled. Pass per-call overrides through Generator.call(..., model_kwargs={...}) or Embedder.call(..., model_kwargs={...}).
4. Render and inspect the prompt with generator.get_prompt(...) before blaming the provider. Missing or mismatched Jinja variables usually become poor prompts, not provider errors.
5. Treat GeneratorOutput.error as the authoritative failure signal. Check raw_response, api_response, and parser configuration before retrying live API calls.
6. For streaming, consume raw_response/stream_events() and do not expect structured output processors to run until a complete text response is available.
7. Do not embed API keys in generated code or logs. Use provider environment variables or explicit runtime configuration supplied by the caller.