{"record":{"id":"7676cc5fd3fc9faf","repo":"ScrapeGraphAI/Scrapegraph-ai","slug":"syntax-analysis-failed-str-e","errorCode":null,"errorMessage":"Syntax analysis failed: {str(e)}","messagePattern":"Syntax analysis failed: (.+?)","errorType":"error_code","errorClass":"AnalysisError","httpStatus":null,"severity":"error","filePath":"scrapegraphai/utils/code_error_analysis.py","lineNumber":156,"sourceCode":"        # Create prompt template and chain\n        prompt = PromptTemplate(\n            template=get_optimal_analysis_template(\"syntax\"),\n            input_variables=[\"generated_code\", \"errors\"],\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[\"syntax\"],\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\"Syntax analysis failed: {str(e)}\")\n\n\ndef execution_focused_analysis(state: Dict[str, Any], llm_model) -> str:\n    \"\"\"\n    Analyzes the execution errors in the generated code and HTML code.\n\n    Args:\n        state (dict): Contains the 'generated_code', 'errors', 'html_code', and 'html_analysis'.\n        llm_model: The language model used for generating the analysis.\n\n    Returns:\n        str: The result of the execution error analysis.\n\n    Raises:\n        InvalidStateError: If state is missing required keys.\n\n    Example:\n        >>> state = {","sourceCodeStart":138,"sourceCodeEnd":174,"githubUrl":"https://github.com/ScrapeGraphAI/Scrapegraph-ai/blob/532dfffbf6ee823a6c9cf8cfedc24a93bf026780/scrapegraphai/utils/code_error_analysis.py#L138-L174","documentation":"Raised by syntax_focused_analysis when the underlying LLM chain (prompt construction + invoke) fails for any reason other than a missing state key. It wraps the original exception text, so the real cause (LLM auth, network, prompt template mismatch) is in str(e).","triggerScenarios":"Calling syntax_reasoning_loop / syntax_focused_analysis(state, llm_model) where llm_model is misconfigured (invalid API key, wrong base_url), the invoke() network call fails, or the analysis prompt template expects variables not provided (e.g. state missing keys used by the template like generated_code/errors).","commonSituations":"Missing or wrong OPENAI_APIKEY env var; pointing llm_model at an unreachable endpoint; state dict passed manually without keys produced by upstream nodes; langchain version changes breaking invoke() signature.","solutions":["Inspect the wrapped message (str(e)) — it contains the root cause (auth error, timeout, template variable error)","Verify llm_model credentials and connectivity with a minimal invoke() test","Check that the state dict contains generated_code and errors as produced by upstream nodes","Ensure get_optimal_analysis_template('syntax') input_variables match the keys passed in chain.invoke"],"exampleFix":"# before\nanalysis = syntax_focused_analysis({}, llm_model)  # empty/incorrect state\n# after\nfrom scrapegraphai.utils.code_error_analysis import syntax_focused_analysis\nanalysis = syntax_focused_analysis({\"generated_code\": code, \"errors\": errors}, llm_model)","handlingStrategy":"try-catch","validationCode":"required = {\"generated_code\", \"errors\"}\nmissing = [k for k in required if k not in state]\nassert not missing, f\"state missing: {missing}\"","typeGuard":"from typing import Any, Dict\n\ndef has_analysis_state(state: Dict[str, Any]) -> bool:\n    return isinstance(state, dict) and \"generated_code\" in state and \"errors\" in state","tryCatchPattern":"from scrapegraphai.utils.code_error_analysis import AnalysisError\ntry:\n    analysis = syntax_focused_analysis(state, llm_model)\nexcept AnalysisError as e:\n    logger.error(\"syntax analysis chain failed: %s\", e)\n    raise","preventionTips":["Log str(e) — it carries the root cause (auth/network/template)","Test llm_model with a direct invoke before running the loop","Keep state keys in sync with upstream node outputs"],"tags":["llm","state","syntax-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"}