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name: llm-and-providers description: "Guides agents configuring CrewAI LLM providers, model strings, credentials, base URLs, Azure/OpenAI-compatible/Anthropic/Bedrock/Google/Snowflake settings, streaming, tool calls, custom BaseLLM implementations, and LiteLLM migration caveats." disable-model-invocation: true metadata: disco-role: operating license: MIT

LLM and Providers

Use this sub-skill when the task is to configure, validate, migrate, or troubleshoot CrewAI LLM objects, provider model strings, API keys, base URLs, structured responses, tool-calling LLM behavior, streaming LLM behavior, context windows, rate-limit knobs, or custom BaseLLM implementations.

Read First

• Provider reference for the current LLM constructor shape, provider dispatch rules, model prefixes, credentials, base URL handling, Azure/OpenAI-compatible/Anthropic/Bedrock/Google/Snowflake examples, context-window notes, and LiteLLM fallback rules.
• Custom LLM for implementing BaseLLM, required call()/acall() contracts, function calling, stop words, context windows, error handling, and safe test doubles.
• Streaming and tool calls for stream=True, direct LLM.call()/acall(), Crew/Flow streaming outputs, function_calling_llm, response models, and provider compatibility trade-offs.
• Troubleshooting for missing API keys, wrong prefixes, OpenAI-compatible base URL confusion, streaming/tool-call incompatibility, response_format misuse, multimodal mismatch, rate limits, context windows, and LiteLLM migration mistakes.
• Offline config checker for a no-network diagnostic of model/provider/base URL/env-var choices; run python scripts/check_llm_config.py --help before using it.

Boundaries

• Stay here for LLM(...) parameters, provider selection, credentials, API keys, base URLs, api_version, structured response formats, streaming, direct LLM calls, tool-call provider compatibility, custom LLM classes, and LiteLLM migration.
• Use ../core-runtime/SKILL.md for where to attach llm, manager_llm, planning_llm, chat_llm, or function_calling_llm on Agent, Crew, and project runtime definitions.
• Use ../tools-and-mcp/SKILL.md for implementing CrewAI tools, MCP adapters, and tool schema design beyond LLM provider support.
• Use ../files-and-multimodal/SKILL.md for file input payload shapes, upload/cache behavior, MIME constraints, and provider-specific file limits.
• Use ../observability-and-hooks/SKILL.md for tracing, event listeners, transport interceptors, and observability integrations around LLM calls.
• Return to ../../SKILL.md when a request spans multiple CrewAI capability areas and needs root routing context.

Safe Defaults

• Prefer explicit LLM(model="provider/model-name", ...) objects for non-trivial configuration; plain strings are fine only when defaults and environment variables are already correct.
• Prefer native providers (openai, anthropic, gemini/google, azure, bedrock, snowflake, and built-in OpenAI-compatible providers) before adding crewai[litellm].
• Never print, commit, or hard-code real API keys. Use environment variable names or placeholders in examples.
• Validate configuration offline with scripts/check_llm_config.py before running crews that may make network or LLM calls.