ScrapeGraphAI/Scrapegraph-ai · error · AnalysisError

Syntax analysis failed: {str(e)}

Error message

Syntax analysis failed: {str(e)}

What it means

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).

Source

Thrown at scrapegraphai/utils/code_error_analysis.py:156

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

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

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


def execution_focused_analysis(state: Dict[str, Any], llm_model) -> str:
    """
    Analyzes the execution errors in the generated code and HTML code.

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

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

    Raises:
        InvalidStateError: If state is missing required keys.

    Example:
        >>> state = {

View on GitHub (pinned to 532dfffbf6)

Solutions

  1. Inspect the wrapped message (str(e)) — it contains the root cause (auth error, timeout, template variable error)
  2. Verify llm_model credentials and connectivity with a minimal invoke() test
  3. Check that the state dict contains generated_code and errors as produced by upstream nodes
  4. Ensure get_optimal_analysis_template('syntax') input_variables match the keys passed in chain.invoke

Example fix

# before
analysis = syntax_focused_analysis({}, llm_model)  # empty/incorrect state
# after
from scrapegraphai.utils.code_error_analysis import syntax_focused_analysis
analysis = syntax_focused_analysis({"generated_code": code, "errors": errors}, llm_model)
Defensive patterns

Strategy: try-catch

Validate before calling

required = {"generated_code", "errors"}
missing = [k for k in required if k not in state]
assert not missing, f"state missing: {missing}"

Type guard

from typing import Any, Dict

def has_analysis_state(state: Dict[str, Any]) -> bool:
    return isinstance(state, dict) and "generated_code" in state and "errors" in state

Try / catch

from scrapegraphai.utils.code_error_analysis import AnalysisError
try:
    analysis = syntax_focused_analysis(state, llm_model)
except AnalysisError as e:
    logger.error("syntax analysis chain failed: %s", e)
    raise

Prevention

When it happens

Trigger: 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).

Common situations: 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.

Related errors

AI-assisted analysis of ScrapeGraphAI/Scrapegraph-ai@532dfffbf6 (2026-08-28). Data as JSON: /api/errors/7676cc5fd3fc9faf. Report an issue: GitHub.