BerriAI/litellm · error · Exception

Model needs to be set for bedrock

Error message

Model needs to be set for bedrock

What it means

The bedrock image route delegates to bedrock_image_generation.image_generation, which must build a signed boto3 request against a concrete Bedrock inference endpoint, so the model id (e.g. 'stability.sd3-large-v1:0', 'amazon.titan-image-generator-v1') is mandatory. As with other providers, litellm's dall-e-2 fallback only applies when neither model nor custom_llm_provider is given, so provider-without-model reaches this guard.

Solutions

  1. Use the provider-prefixed form: litellm.image_generation(model='bedrock/stability.sd3-large-v1:0', prompt='...') with AWS credentials in the environment.
  2. Or pass model='amazon.titan-image-generator-v2:0' together with custom_llm_provider='bedrock'.
  3. Check your proxy/Router yaml: every bedrock deployment needs a concrete model id, not just model_name.

Example fix

# before
resp = litellm.image_generation(prompt="a cat", custom_llm_provider="bedrock")
# Exception: Model needs to be set for bedrock

# after
resp = litellm.image_generation(model="bedrock/stability.sd3-large-v1:0", prompt="a cat")
Defensive patterns

Strategy: validation

Validate before calling

def call_bedrock_image(prompt: str, model: str | None = None):
    model = model or "bedrock/stability.sd3-large-v1:0"
    if not isinstance(model, str) or not model.strip():
        raise ValueError("model is required for bedrock image generation (e.g. 'bedrock/stability.sd3-large-v1:0')")
    return litellm.image_generation(model=model, prompt=prompt)

Type guard

def is_bedrock_image_ready(model: str | None, custom_llm_provider: str | None) -> bool:
    if custom_llm_provider == "bedrock":
        return isinstance(model, str) and model.strip() != ""
    return True

Try / catch

try:
    resp = litellm.image_generation(prompt=p, custom_llm_provider="bedrock")
except Exception as e:
    if "Model needs to be set for bedrock" in str(e):
        raise ValueError("set model='bedrock/<model-id>' and AWS credentials") from e
    raise

Prevention

When it happens

Trigger: litellm.image_generation(prompt='...', custom_llm_provider='bedrock') with no model; proxy Router model entry with custom_llm_provider='bedrock' and an empty model field; passing model=None explicitly with the bedrock provider kwarg.

Common situations: AWS proxy config where model_name is set for routing but the actual Bedrock model id under litellm_params is blank; assuming env credentials (AWS_ACCESS_KEY_ID etc.) are enough and model is optional; migrating an azure/openai config and forgetting the model line.

Understand the failure class

Background: "missing required argument" and "the following required arguments were not provided": what required-argument errors mean and how to fix them — this error's family across 20 libraries.

Related errors

AI-assisted analysis of BerriAI/litellm@77b7c6c40c (2026-08-18). Data as JSON: /api/errors/b9615d2fa30f1626. Report an issue: GitHub.

Appendix: source

Thrown at litellm/images/main.py:486

            # Forward OpenAI organization if present (set by proxy pre-call utils)
            organization: Final[str | None] = kwargs.get("organization", None)
            model_response = openai_chat_completions.image_generation(
                model=model,
                prompt=prompt,
                timeout=timeout,
                api_key=api_key or dynamic_api_key,
                api_base=api_base,
                logging_obj=litellm_logging_obj,
                optional_params=optional_params,
                model_response=model_response,
                organization=organization,
                aimg_generation=aimg_generation,
                client=client,
                headers=headers,
            )
        elif custom_llm_provider == "bedrock":
            if model is None:
                raise Exception("Model needs to be set for bedrock")
            model_response = bedrock_image_generation.image_generation(
                model=model,
                prompt=prompt,
                timeout=timeout,
                logging_obj=litellm_logging_obj,
                optional_params=optional_params,
                model_response=model_response,
                aimg_generation=aimg_generation,
                client=client,
                api_base=api_base,
                api_key=api_key,
            )
        elif custom_llm_provider in litellm._custom_providers:  # Assume custom LLM provider
            # Get the Custom Handler
            custom_handler: CustomLLM | None = None
            for item in litellm.custom_provider_map:
                if item["provider"] == custom_llm_provider:
                    custom_handler = item["custom_handler"]

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