{"record":{"id":"2936703debfa14c7","repo":"ScrapeGraphAI/Scrapegraph-ai","slug":"execution-analysis-failed-str-e","errorCode":null,"errorMessage":"Execution analysis failed: {str(e)}","messagePattern":"Execution analysis failed: (.+?)","errorType":"error_code","errorClass":"AnalysisError","httpStatus":null,"severity":"error","filePath":"scrapegraphai/utils/code_error_analysis.py","lineNumber":211,"sourceCode":"            template=get_optimal_analysis_template(\"execution\"),\n            input_variables=[\"generated_code\", \"errors\", \"html_code\", \"html_analysis\"],\n        )\n        chain = prompt | llm_model | StrOutputParser()\n\n        # Execute chain with validated state\n        return chain.invoke(\n            {\n                \"generated_code\": validated_state.generated_code,\n                \"errors\": validated_state.errors[\"execution\"],\n                \"html_code\": validated_state.html_code,\n                \"html_analysis\": validated_state.html_analysis,\n            }\n        )\n\n    except KeyError as e:\n        raise InvalidStateError(f\"Missing required key in state dictionary: {e}\")\n    except Exception as e:\n        raise AnalysisError(f\"Execution analysis failed: {str(e)}\")\n\n\ndef validation_focused_analysis(state: Dict[str, Any], llm_model) -> str:\n    \"\"\"\n    Analyzes the validation errors in the generated code based on a JSON schema.\n\n    Args:\n        state (dict): Contains the 'generated_code', 'errors',\n        'json_schema', and 'execution_result'.\n        llm_model: The language model used for generating the analysis.\n\n    Returns:\n        str: The result of the validation error analysis.\n\n    Raises:\n        InvalidStateError: If state is missing required keys.\n\n    Example:","sourceCodeStart":193,"sourceCodeEnd":229,"githubUrl":"https://github.com/ScrapeGraphAI/Scrapegraph-ai/blob/532dfffbf6ee823a6c9cf8cfedc24a93bf026780/scrapegraphai/utils/code_error_analysis.py#L193-L229","documentation":"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.","triggerScenarios":"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.","commonSituations":"Expired API key, provider outage, proxy/firewall blocking the endpoint, langchain PromptTemplate variable mismatch after upgrading scrapegraphai/langchain.","solutions":["Read the embedded str(e) to identify auth/network/template cause","Test llm_model.invoke('ping') independently","Verify the execution analysis template variables match what the code supplies","Retry after resolving transient provider issues (rate limit, timeout)"],"exampleFix":null,"handlingStrategy":"retry","validationCode":"# smoke-test the model first\ntry:\n    llm_model.invoke(\"ping\")\nexcept Exception:\n    raise RuntimeError(\"llm_model not usable; fix credentials/connectivity\")","typeGuard":null,"tryCatchPattern":"from scrapegraphai.utils.code_error_analysis import AnalysisError\nfor attempt in range(3):\n    try:\n        analysis = execution_focused_analysis(state, llm_model)\n        break\n    except AnalysisError as e:\n        if attempt == 2 or \"auth\" in str(e).lower():\n            raise\n        time.sleep(2 ** attempt)","preventionTips":["Read str(e) to classify auth vs network vs rate limit","Use backoff retries only for transient provider errors","Verify provider API key is set in the environment"],"tags":["llm","execution-analysis","chain-invocation"],"backgroundTag":"llm-chain-invocation-failed","analyzedSha":"532dfffbf6ee823a6c9cf8cfedc24a93bf026780","analyzedAt":"2026-08-28T15:19:38.821Z","schemaVersion":2},"datasetVersion":"2026-08-28T16:17:29.566Z"}