agentjido/req-llm

ReqLLM

req_llm is the upstream Hex package name.

Start Here

Use this skill when the task is about making LLM calls from Elixir through Req-based provider adapters.

Good triggers:

• "Add OpenAI or Anthropic support to this Elixir app."
• "Stream model output through ReqLLM."
• "Turn the package docs into a working provider example."
• "Review whether this should live in req_llm or in a Jido action."

Read references/builder-notes.md before coding when the task involves provider differences, request/response shapes, or how req_llm fits under jido_ai.

Primary Workflows

Configure providers cleanly

• Separate provider configuration from call sites. Keep API keys, base URLs, and model defaults at the edge.
• Reuse Req idioms for timeouts, middleware, retries, and telemetry instead of inventing a second transport stack.
• Normalize the app-facing request shape before branching into provider-specific options.

Build model invocation flows

• Decide whether the caller needs one-shot generation, streaming, tool calling, or structured outputs before writing the adapter code.
• Keep message shaping explicit. If prompts or tool schemas become complex, coordinate with jido_ai or jido rather than hiding orchestration in the HTTP client.
• Feed model selection from llm_db when capability or pricing constraints matter.

Turn docs into runnable examples

• Prefer one provider per example.
• Show environment configuration, request construction, and response handling in a minimal iex or test-friendly flow.
• Add one example for failure handling or provider-specific options when that difference is the point of the task.

Review package boundaries

• Keep provider transport and response normalization in req_llm.
• Keep catalog selection in llm_db.
• Keep agents, directives, and multi-step reasoning loops in jido or jido_ai.

Build Checklist

• Verify the provider, model family, and auth mechanism first.
• Confirm whether the caller needs sync, async, or streaming behavior.
• Keep response parsing small and typed enough for the next layer.
• Add tests around rate limits, provider errors, and empty or partial responses.

Boundaries

• Do not use this skill for model pricing catalogs.
• Do not use this skill for long-lived agent state or tool orchestration.
• Do not promise cross-provider feature parity when the package docs call out differences.