HKUDS/DeepTutor · error · LLMConfigError

Model is required for cloud LLM provider

Error message

Model is required for cloud LLM provider

What it means

The cloud provider's non-streaming complete() entry point dispatches to a binding-specific backend (openai/anthropic/cohere), and every backend needs a model identifier to build the request payload. A blank or None model is rejected with LLMConfigError before any network call. The binding defaults to openai when omitted, but the model has no default.

Solutions

  1. Set an active model in Settings > Catalog (or the equivalent settings JSON) so resolution supplies one.
  2. Pass an explicit model string at the call site: await complete(prompt, model='gpt-4o-mini').
  3. If model comes from config, validate it before calling: if not (model or '').strip(): raise with a specific message.
  4. Check that resolve_llm_runtime_config().model is non-empty when building profiles programmatically.

Example fix

// before
resp = await llm.complete(prompt=user_text, model=settings.get("model"))

# after
model = (settings.get("model") or "").strip()
if not model:
    raise ValueError("No model configured in settings")
resp = await llm.complete(prompt=user_text, model=model)
Defensive patterns

Strategy: validation

Validate before calling

model = (model or "").strip()
if not model:
    raise ValueError("A model name is required for cloud completion")
resp = await complete(prompt=p, model=model)

Type guard

def has_model(model: str | None) -> bool:
    return isinstance(model, str) and bool(model.strip())

Try / catch

try:
    resp = await complete(prompt=p, model=model)
except LLMConfigError as e:
    if "Model is required" in str(e):
        # prompt user to pick a model in Settings > Catalog
        ...
    raise

Prevention

When it happens

Trigger: Calling complete(prompt=..., model=None) or model=' ' via the SDK/CLI; the active runtime profile has no model set so config resolution passes an empty string; a caller reads model from a settings key that does not exist and passes None.

Common situations: Fresh install where no model was selected in Settings > Catalog; profile JSON manually edited and model field deleted; code upgraded and the model kwarg was renamed but an old call site still passes model=None.

Understand the failure class

Background: "missing required config value" errors: why libraries refuse to start when a configuration key is empty, unset, or blank — this error's family across 48 libraries.

Related errors

AI-assisted analysis of HKUDS/DeepTutor@3e82f13042 (2026-08-27). Data as JSON: /api/errors/675907fafeecc13b. Report an issue: GitHub.

Appendix: source

Thrown at deeptutor/services/llm/cloud_provider.py:179

    Supports OpenAI-compatible APIs and Anthropic.

    Args:
        prompt: The user prompt
        system_prompt: System prompt for context
        model: Model name
        api_key: API key
        base_url: Base URL for the API
        api_version: API version for Azure OpenAI
        binding: Provider binding type (openai, anthropic)
        **kwargs: Additional parameters (temperature, max_tokens, etc.)

    Returns:
        str: The LLM response
    """
    binding_lower = (binding or "openai").lower()
    if model is None or not model.strip():
        raise LLMConfigError("Model is required for cloud LLM provider")

    if binding_lower in ["anthropic", "claude"]:
        max_tokens_value = _coerce_int(kwargs.get("max_tokens"), None)
        temperature_value = _coerce_float(kwargs.get("temperature"), 0.7)
        return await _anthropic_complete(
            model=model,
            prompt=prompt,
            system_prompt=system_prompt,
            api_key=api_key,
            base_url=base_url,
            max_tokens=max_tokens_value,
            temperature=temperature_value,
        )

    if binding_lower == "cohere":
        max_tokens_value = _coerce_int(kwargs.get("max_tokens"), None)
        temperature_value = _coerce_float(kwargs.get("temperature"), 0.7)
        return await _cohere_complete(

View on GitHub (pinned to 3e82f13042)