I’m working on creating a SQL agent with LangChain and running into some trouble. My setup includes an SQLite database that’s pretty messy with over 1 million time series records. Right now I’m using the SQLDatabase toolkit paired with a react agent.
The main issue happens when my queries return too many rows. I have data from about 40 different machines, and when I ask the agent to show me all machines, it struggles to process those 40 results even though the SQL query it creates works fine and pulls the right data from the database.
I’m wondering what the best approach would be here. Should I maybe split this into multiple parts where one agent creates the query and another part runs it and sends the raw results directly to the user? Or would it be better to catch the toolkit output before it gets passed back to the language model?
Just so you know, I’m running this with ChatOllama using the qwen3:8b model.
I’m really new to all this stuff so any advice or guides would be really helpful. I can share my code too if that would help.
Your agent’s drowning in unnecessary data. Don’t fix LangChain - build a smarter pipeline around it.
Set up automation that checks query size before processing. When someone asks about machines, automatically check if it’ll return more than your threshold (say 50 rows). If it does, run the query but only pass a summary to the agent.
For your 40 machines scenario: grab all records, format them as “Found 40 machines: [list of names]”, then feed that clean summary to your agent instead of raw database dumps.
You can add progressive detail requests too. User wants more info about Machine_X? Trigger another query for just that machine’s data.
I’ve built similar setups where automation handles database complexity while agents focus on conversation logic. Your qwen3:8b model will perform way better when it’s not parsing massive result sets.
Build the entire workflow - query routing, result processing, conditional formatting, and agent integration - as one automated pipeline. Much cleaner than working around LangChain’s limitations.
Latenode makes this architecture straightforward since you can connect database operations directly to data processing steps and feed clean outputs to your chat agent.
Been there with massive datasets causing agent bottlenecks. The real issue is you’re forcing the LLM to process way more data than it needs to.
Skip wrestling with LangChain limitations - automate this pipeline differently. Set up a workflow that handles SQL execution separately from the chat interface. When someone asks for machine data, trigger a query that pulls results, formats them properly, and delivers exactly what the user needs without overwhelming the model.
I’ve built similar setups where the automation layer sits between the database and chat agent. It runs heavy queries, processes results, and feeds clean summaries back to the conversation. Way more reliable than making agents handle raw database dumps.
You can add smart filtering and formatting rules that adapt based on result size. Small datasets go straight through, large ones get summarized automatically.
Latenode handles this workflow pattern really well since you can chain database operations with data processing and chat interactions seamlessly.
the problem’s your agent processing way too much data at once. add validation that checks result size first - if it’s over 100 rows, just return a count and the first few examples instead of dumping everything to the llm. much easier than rebuilding everything.
totally get it, those large sets can be a pain! definitely using LIMIT or pagination would help. also, make sure your SQLDatabase config has reasonable max_rows. react agents can really lag with this stuff, so breaking it down sounds smart.
Don’t patch this - rebuild your architecture. I had the same problem and fixed it with a two-stage setup. The SQL agent just writes queries, that’s it. A separate service runs them and handles all the business logic for results. For your machine data, add result categorization right in the database. When queries hit your threshold limits, automatically flip to aggregated views or summary tables instead of raw records. The agent never sees the problematic datasets. Here’s the key: treat your agent like a query composer, not a data processor. Let dedicated components handle the heavy lifting - result formatting, user presentation, all that stuff. This separation makes everything way more predictable and kills those memory issues you’re getting with qwen3:8b.
I’ve encountered similar issues with large datasets in LangChain SQL agents. A solution that worked for me was to implement a custom result handler to intercept query results before they reach the LLM. Agents can struggle with processing thousands of rows simultaneously. For your situation, consider adding a preprocessing step to summarize results before sending them to the agent. For example, instead of showing all 40 machine records, you could say, ‘Found 40 machines: Machine1, Machine2…’ and only provide full details when requested. Additionally, setting up result streaming to manage what is presented to the user versus what the agent processes can greatly improve performance. This approach should enhance your qwen model’s handling of large queries.