LangChain Agent Producing Unreliable Pandas DataFrame Query Results

[Neo_Movies]

I have a LangChain AI agent that queries pandas DataFrames but it’s giving me inconsistent results when processing user input.

The agent works with three datasets and can create plotly visualizations. Most of the time it performs well, but sometimes it misinterprets user queries.

For example, when users ask about Puerto Rico, the agent should look for “PRI” but sometimes searches for “PR” instead, which returns nothing. Same issue happens with Mexico - users might type “MX” but the agent doesn’t find matches even though “MEX” and “Mexico” exist in the data.

I’ve added column metadata to the pre-prompt and enabled chat history memory. The temperature is set to 0. Sometimes capitalization in user questions also causes problems despite having instructions to handle this.

How can I make the agent more reliable at interpreting different ways users refer to the same data without hardcoding every possible variation?

[sophialee92]

Had the same issue with a financial data agent that kept confusing ticker symbols and company names. Fixed it by adding a preprocessing layer that cleans up user input before it reaches the LangChain agent. I built a simple mapping dictionary for common variations like country codes, then wrote a custom function to check queries against known aliases before sending them to the agent. Kept this mapping separate from the main logic so I could easily add new edge cases. Also helped to put concrete examples in the system prompt showing how to handle ambiguous terms. Instead of just saying “be flexible with country names,” I showed actual query examples with the right transformations. Cut false negatives by about 70% without retraining anything.