ScrapeGraphAI/Scrapegraph-ai · error · AnalysisError

Execution analysis failed: {str(e)}

Error message

Execution analysis failed: {str(e)}

What it means

Raised by execution_focused_analysis when the LLM chain fails for any non-KeyError reason; the original exception text is embedded in 'Execution analysis failed: ...'. Root cause is almost always the llm_model invocation or prompt rendering, not the state.

Source

Thrown at scrapegraphai/utils/code_error_analysis.py:211

            template=get_optimal_analysis_template("execution"),
            input_variables=["generated_code", "errors", "html_code", "html_analysis"],
        )
        chain = prompt | llm_model | StrOutputParser()

        # Execute chain with validated state
        return chain.invoke(
            {
                "generated_code": validated_state.generated_code,
                "errors": validated_state.errors["execution"],
                "html_code": validated_state.html_code,
                "html_analysis": validated_state.html_analysis,
            }
        )

    except KeyError as e:
        raise InvalidStateError(f"Missing required key in state dictionary: {e}")
    except Exception as e:
        raise AnalysisError(f"Execution analysis failed: {str(e)}")


def validation_focused_analysis(state: Dict[str, Any], llm_model) -> str:
    """
    Analyzes the validation errors in the generated code based on a JSON schema.

    Args:
        state (dict): Contains the 'generated_code', 'errors',
        'json_schema', and 'execution_result'.
        llm_model: The language model used for generating the analysis.

    Returns:
        str: The result of the validation error analysis.

    Raises:
        InvalidStateError: If state is missing required keys.

    Example:

View on GitHub (pinned to 532dfffbf6)

Solutions

  1. Read the embedded str(e) to identify auth/network/template cause
  2. Test llm_model.invoke('ping') independently
  3. Verify the execution analysis template variables match what the code supplies
  4. Retry after resolving transient provider issues (rate limit, timeout)
Defensive patterns

Strategy: retry

Validate before calling

# smoke-test the model first
try:
    llm_model.invoke("ping")
except Exception:
    raise RuntimeError("llm_model not usable; fix credentials/connectivity")

Try / catch

from scrapegraphai.utils.code_error_analysis import AnalysisError
for attempt in range(3):
    try:
        analysis = execution_focused_analysis(state, llm_model)
        break
    except AnalysisError as e:
        if attempt == 2 or "auth" in str(e).lower():
            raise
        time.sleep(2 ** attempt)

Prevention

When it happens

Trigger: execution_reasoning_loop / execution_focused_analysis(state, llm_model) with invalid API credentials, network failure to the LLM provider, rate limiting, or a prompt template/input_variables mismatch for the 'execution' template.

Common situations: Expired API key, provider outage, proxy/firewall blocking the endpoint, langchain PromptTemplate variable mismatch after upgrading scrapegraphai/langchain.

Related errors

AI-assisted analysis of ScrapeGraphAI/Scrapegraph-ai@532dfffbf6 (2026-08-28). Data as JSON: /api/errors/2936703debfa14c7. Report an issue: GitHub.