LLM Output Parsing Error in n8n AI Template Workflow

Below is some guidance on troubleshooting the LLM output parsing error:

Understanding the Error

The error

[ERROR: Could not parse LLM output: I don’t know.

indicates that the language model’s response did not follow the expected structured format. In many LangChain workflows (including the SQLite database agent), the AI Agent node or Basic LLM Chain node is set up to expect a specific output format (for example, valid JSON or a schema defined by a prompt). If the model simply returns a free-form answer like “I don’t know.” then the parser cannot extract the needed information.

Recommended Troubleshooting Steps

1. Verify the Prompt and Output Format
   • If your node is set to “Define below” for the prompt, ensure that the prompt explicitly tells the LLM to use a specific format.
   • Use the Require Specific Output Format option to force responses into valid JSON (or another expected format).
   • Review the prompt instructions in the workflow and adjust them so the model knows to return a structured output.

2. Examine the Chat Messages Configuration
   • Double-check that your Chat messages (if used) are properly defined.
   • If you are using sample inputs in the prompt (like system or user messages), ensure that these messages clearly guide the model to produce responses in your expected format.

3. Review the Workflow Template Settings
   • Since you downloaded the workflow from the Talk to your SQLite database with a LangChain AI Agent template, verify that none of the node parameters have been unintentionally modified.
   • Compare your prompt setup with that provided in the template. There might be a note or a comment in the template explaining the strict output formatting requirements.

4. Test with Alternative Output Expectations
   • As a diagnostic step, try changing the Response Format option (in your OpenAI Chat Model node) to text temporarily. While this may not solve the parsing issue, it can help clarify if the issue is purely the output format versus the prompt instructions.

5. Check Data Flow from Previous Nodes
   • Make sure that any input data required by the prompt is available and correctly formatted. Missing or null fields could also cause unexpected outputs.

Final Thoughts

It appears that neither the database connection nor the OpenAI key is at fault—the error centers on the LLM’s response formatting. Adjusting your prompt to clearly specify the expected format (or ensuring the Require Specific Output Format option is active) should help in making the LLM output parsable by the node.

If you need further assistance tweaking your prompt configuration, feel free to share additional details on how you’ve configured your node parameters.

Below is some guidance based on the template and known issues when working with LangChain AI Agent workflows on n8n:

What's Happening

The error

  [ERROR: Could not parse LLM output: I don’t know.]

indicates that the LLM returned a message (i.e. "I don't know") that doesn’t match the expected format (typically a valid SQL query). In other words, although your OpenAI key is valid and the database appears to be loaded, the AI Agent isn’t receiving output it can parse and use for the SQL operations.

Possible Causes & Troubleshooting Steps

1. Prompt Configuration and Response Format

   • The template is set up to convert natural language into a SQL query. If the prompt isn’t specific enough or lacks the necessary context (for example, details about the database schema), the LLM may reply with a fallback text such as “I don’t know.”
   • Action: Review the prompt settings in your AI Agent node. If possible, switch the Prompt parameter from “Take from previous node automatically” to “Define below” so you can adjust and customize the prompt. Ensure the prompt clearly instructs the LLM to output a syntactically valid SQL query.

2. Sub-node Behavior

   • Remember that sub-nodes in n8n (such as those integrated into the AI Agent workflow) process expressions differently than root nodes. They only convert the first item in a list when using expressions.
   • Action: Verify that any expressions in your workflow are correctly referencing the intended values. This is especially important if you’re relying on dynamic data from previous nodes.

3. LLM Response Validation

   • The error suggests that the parsing step expects a query but receives a plain-text answer instead. This might happen when the LLM can’t derive a SQL command from the input question.
   • Action: Test the LLM output separately (e.g. using a simple script with your ChatOpenAI instance) with your current prompt. This helps ensure that the model is returning a valid SQL command rather than a default “I don’t know” response.

4. Additional Debugging

   • Double-check that all other parts of your workflow are configured as in the template—for example, confirming that the SQLite database schema is recognized and that connections between nodes are correctly set up.
   • Ensure that there hasn’t been any inadvertent change in the prompt template as it drives the conversion of natural language to SQL.

Next Steps

• Adjust and test your prompt: Revise the prompt text in the AI Agent node to be more specific about what SQL query is expected. You might include example formats or reminders about the database schema.

• Isolate the issue: If practical, isolate the LLM conversation by sending in a test question via a simple node setup. Check if it returns a valid SQL or still outputs “I don’t know.”

Following these adjustments should help the agent produce an output that can be parsed successfully by n8n. If you continue to encounter issues, consider rechecking the node configurations using the template instructions and refer to any additional troubleshooting tips provided in the n8n documentation for LangChain integrations.

Hope this helps resolve the error!