I’ve built a system that uses a LangChain AI agent for analyzing pandas DataFrames. The agent gets comprehensive instructions through pre-prompts, along with conversation history and table metadata. My main issue is with how the agent processes user text input and converts it into proper pandas operations.
The agent works well most of the time. It can answer data questions and generate plotly charts as instructed in the pre-prompt setup. But I keep running into problems with inconsistent behavior.
When users search for country information, results vary unpredictably. Puerto Rico should match using “PRI” code, but sometimes the agent tries “PR” instead and finds nothing. Mexico works fine when users type “Mexico” or “MEX” since both appear in different DataFrame columns. But if someone enters “MX” for Mexico, the agent often searches unsuccessfully and returns empty results with no charts.
Case sensitivity also causes issues despite adding instructions in the pre-prompt to handle this. I’ve provided detailed column metadata for all three datasets and enabled chat history with memory. The AI temperature is set to 0. I need a solution that doesn’t require hardcoding every possible country variation or user input format.
Any suggestions for improving this reliability issue?
totally! fuzzy matching could really sort this out. clean up inputs and try using fuzzywuzzy for those variations, might make your agent more consistent for sure. best of luck!
Had the same problem building a data analysis tool last year. LangChain agents are pretty literal with matching, even with solid prompts. What worked for me was adding a preprocessing layer before the data reaches the agent. I built a mapping dictionary that normalizes country code variations and feeds the clean version to the agent instead of raw user input. For Puerto Rico, I’d map both “PRI” and “PR” to whatever format your DataFrame uses. Users can still type naturally since the preprocessing runs behind the scenes. This scales way better than cramming every variation into prompts, and it fixed most of my consistency issues without overhauling the LangChain setup.
Here’s what saved me tons of headaches: build a two-step query strategy right into your prompts. Don’t let the agent jump straight into pandas operations. Make it first figure out what the user wants, then search across all relevant columns.
I’d tweak your pre-prompt like this: “Before querying, check if the input could match country names, codes, or abbreviations in columns X, Y, Z. Try multiple column searches before giving up.”
With Puerto Rico, the agent would automatically check both the country name column AND country code column. Same with Mexico/MEX/MX - it searches multiple spots instead of picking one and failing.
This stays within LangChain without external preprocessing. The agent gets more thorough instead of more literal. Works really well when you tell the agent to be comprehensive in its search strategy rather than trying to guess every input variation.