ScrapeGraphAI/Scrapegraph-ai · error · CodeGenerationError

Validation code generation failed: {str(e)}

Error message

Validation code generation failed: {str(e)}

What it means

Generic failure of the validation-focused code-generation LLM chain; the underlying exception is appended to the message. Inputs passed validation, so the LLM call or prompt rendering is at fault.

Source

Thrown at scrapegraphai/utils/code_error_correction.py:248

            input_variables=["analysis", "generated_code", "json_schema"],
        )
        chain = prompt | llm_model | StrOutputParser()

        # Execute chain with validated state
        return chain.invoke(
            {
                "analysis": analysis,
                "generated_code": validated_state.generated_code,
                "json_schema": validated_state.json_schema,
            }
        )

    except KeyError as e:
        raise InvalidCorrectionStateError(
            f"Missing required key in state dictionary: {e}"
        )
    except Exception as e:
        raise CodeGenerationError(f"Validation code generation failed: {str(e)}")


def semantic_focused_code_generation(
    state: Dict[str, Any], analysis: str, llm_model
) -> str:
    """
    Generates corrected code based on semantic error analysis.

    Args:
        state (dict): Contains the 'generated_code', 'execution_result', and 'reference_answer'.
        analysis (str): The analysis of the semantic differences.
        llm_model: The language model used for generating the corrected code.

    Returns:
        str: The corrected code.

    Raises:
        InvalidCorrectionStateError: If state is missing required keys or analysis is invalid.

View on GitHub (pinned to 532dfffbf6)

Solutions

  1. Read str(e) to identify the provider/prompt cause
  2. Convert json_schema to a plain dict (e.g. schema.model_dump() or json.loads(schema.model_dump_json())) before passing state
  3. Verify API key and retry transient errors with backoff

Example fix

# before
state["json_schema"] = my_pydantic_model
# after
state["json_schema"] = my_pydantic_model.model_dump()
Defensive patterns

Strategy: retry

Validate before calling

import json
json.dumps(state["json_schema"])  # schema must be JSON-serializable
llm_model.invoke("ping")

Try / catch

from scrapegraphai.utils.code_error_correction import CodeGenerationError
try:
    new_code = validation_focused_code_generation(state, analysis, llm_model)
except CodeGenerationError as e:
    logger.error("validation correction failed: %s", e)
    raise

Prevention

When it happens

Trigger: validation_focused_code_generation / validation_reasoning_loop with auth/network-failing llm_model, rate limits, or json_schema that cannot be serialized into the prompt.

Common situations: Bad provider credentials, quota exhausted, non-JSON-serializable schema objects (Pydantic models passed raw) blowing up template rendering.

Related errors

AI-assisted analysis of ScrapeGraphAI/Scrapegraph-ai@532dfffbf6 (2026-08-28). Data as JSON: /api/errors/23ae5afd8aae48b4. Report an issue: GitHub.