---

name: llm-provider-config description: "Configure direct LLM, embedding, and model-provider access for Langroid agents without mixing in agent orchestration." disable-model-invocation: true metadata: disco-role: operating license: MIT

llm-provider-config

Use this sub-skill when the task is to choose, build, validate, or troubleshoot Langroid LLM and embedding provider configuration. Stay at the provider-access layer: OpenAIGPTConfig, OpenAIGPT, AzureConfig, provider model strings, OpenAI-compatible api_base endpoints, API-key handling, HTTP clients, streaming/reasoning flags, and embedding config classes.

Scope boundary

• Include direct LLM access, OpenAI-compatible bases, Azure, Gemini, LiteLLM proxy, LiteLLM adapter strings, Ollama/local servers, OpenRouter-style gateways, Portkey, LangDB, rotating API keys, cached clients, HTTP client config, and embedding provider configs.
• Exclude agent design, task loops, tool messages, function-calling workflows, and prompt orchestration; route those to ../agents-tasks-tools/SKILL.md.
• Exclude RAG/vector-store wiring beyond selecting an embedding config; route retrieval or document-chat wiring to ../retrieval-doc-chat/SKILL.md.
• Exclude MCP server/tool integration; route it to ../integrations-mcp-chainlit/SKILL.md.

Operating procedure

1. Identify whether the user needs an LLM config, an embedding config, or both.
2. Choose the smallest direct provider path before adding a proxy or gateway:
   • native OpenAI or Azure when the deployment is actually OpenAI/Azure;
   • provider prefix such as gemini/, openrouter/, ollama/, vllm/, llamacpp/, langdb/, or portkey/ when Langroid has a first-class path;
   • explicit api_base for a generic OpenAI-compatible endpoint;
   • litellm-proxy/ only for a deployed LiteLLM proxy server;
   • litellm/ only for the local LiteLLM adapter library.
3. Validate model string, API-key source, base URL shape, timeout/temperature/output-token settings, cache behavior, and optional HTTP-client needs before any generation call.
4. For embeddings, configure only the embedding provider and dimensions here; leave collection/index/vector-store construction to the retrieval sub-skill.
5. Use the bundled smoke script for no-network sanity checks when a config looks suspicious.

Bundled references

• Provider configuration: LLM config fields, provider prefixes, Azure, gateway parameters, caching, HTTP clients, rotating credentials, and reasoning content.
• Embedding configuration: OpenAI, Azure OpenAI, Gemini, LangDB, SentenceTransformer, FastEmbed, and llama.cpp-server embedding configs.
• Model selection: choosing strings/enums, direct vs proxy access, local-server forms, formatter suffixes, and environment-prefix pitfalls.
• Troubleshooting: missing keys, bad bases, Azure deployment names, Gemini/Vertex base handling, LiteLLM confusion, key rotation, client caching, and optional embedding dependencies.

No-network smoke check

Run:

python scripts/provider_config_smoke.py

The script sanitizes provider environment variables for deterministic output, constructs representative provider and embedding configs, avoids chat(), generate(), and embedding calls, and falls back to a static source-derived check if optional runtime dependencies are not installed. Use --help for options.