ScrapeGraphAI/Scrapegraph-ai · error · CodeGenerationError

Syntax code generation failed

Error message

Syntax code generation failed: {str(e)}

What it means

Generic failure of the syntax-focused code-generation LLM chain; the original exception text is appended. The state was valid enough to reach invoke(), so the failure is in the LLM call or prompt rendering.

Solutions

1. Read str(e) from the message to get the root cause
2. Sanity-test llm_model with a direct invoke
3. Trim/limit generated_code length if context overflow is indicated
4. Retry with backoff for rate limit / transient network errors

llm_model.invoke("ping")  # fail fast on auth/network issues

from scrapegraphai.utils.code_error_correction import CodeGenerationError
for attempt in range(3):
    try:
        new_code = syntax_focused_code_generation(state, analysis, llm_model)
        break
    except CodeGenerationError as e:
        if attempt == 2:
            raise
        time.sleep(2 ** attempt)

When it happens

Trigger: syntax_focused_code_generation / syntax_reasoning_loop with misconfigured llm_model (auth, base_url), provider timeout/rate limit, or PromptTemplate rendering failure for the 'syntax' correction template.

Common situations: Expired API key, local LLM server down, long generated_code exceeding context window, langchain version incompatibility.

Related errors

• Execution analysis failed
• Execution code generation failed
• Semantic analysis failed
• Syntax analysis failed
• Validation analysis failed

AI-assisted analysis of ScrapeGraphAI/Scrapegraph-ai@532dfffbf6 (2026-08-28). Data as JSON: /api/errors/b99ff3b3467341a7. Report an issue: GitHub.

Appendix: source

Thrown at scrapegraphai/utils/code_error_correction.py:136

        # Create prompt template and chain
        prompt = PromptTemplate(
            template=get_optimal_correction_template("syntax"),
            input_variables=["analysis", "generated_code"],
        )
        chain = prompt | llm_model | StrOutputParser()

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

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


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

    Args:
        state (dict): Contains the 'generated_code'.
        analysis (str): The analysis of the execution errors.
        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.

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