I’m pretty new to working with Langchain and I’m trying to build a Python application that leverages search agents (like serpapi) to gather information from the web. The main issue I’m running into is that the search results I get back are often not relevant to what I actually need. When I try to get references or sources from the agent, it keeps returning stuff that doesn’t match my requirements.
I want to have better control over the search process itself. Is there a way to customize or filter the search queries before they get sent out? I’ve looked into custom tools but those didn’t really solve my problem.
What’s the best approach to manage and refine search behavior in Langchain agents? Are there any specific parameters or methods I should be using to get more targeted results?
Had this same issue last year building a research tool. Query rewriting at the agent level totally fixed it.
Write a custom function that takes your original query and splits it into 3-5 focused versions, each hitting different angles. Run searches on all of them.
For filtering, use embedding similarity checks on the results. I compare each snippet against the original intent with sentence transformers and toss anything under 0.7 similarity.
Keep a blacklist of domains that always return junk. Filter those out before your agent even sees them.
One thing that really worked - add a validation step where the agent ranks each result 1-10 based on your criteria before returning anything. Makes it actually evaluate what it found instead of dumping everything back at you.
try messin with the prompt template! really does help. lower max_results to cut out the junk & def check out serpapi filters, they r super handy with langchain.
I built a pre-processing layer that sits before queries hit the agent - worked really well. Created a custom SerpAPI wrapper that validates and cleans up queries using keyword extraction and semantic filtering. Catches broad or irrelevant terms before they mess up your results. I also chain multiple small, specific searches instead of throwing one massive query at it. Set up result scoring based on how similar results are to what you’re targeting, then auto-dump anything below your threshold. Treat search like a multi-step process, not just a single agent action.
Try custom agent prompts with specific constraints about what counts as a relevant result. I’ve tweaked the agent’s system message to explicitly reject certain sources or focus on particular domains - works pretty well. Another thing that helped me: use a two-stage process. First agent takes your query and generates better search terms, then feeds those to the search tool. Here’s what I learned - SerpAPI has solid built-in parameters like date ranges, site restrictions, and result types. You can access these through the Langchain wrapper. Also try running the same question with different phrasings, then use semantic similarity to grab the best results from all attempts.
I’ve been fighting this same problem for years. Most solutions just react to bad results after the damage is done.
Here’s what actually works: build an automation pipeline that handles query optimization and filters results before your agents touch anything. I create workflows that take your original intent, generate multiple targeted search variations, run them simultaneously, then cross-reference and score everything.
The magic happens with feedback loops. Mark results as good or bad, and the system learns to adjust future queries. No more manual prompt tweaking or crossing your fingers that agents get it right.
I built this for our research team. The automation rewrites queries, filters domains, scores similarity, and ranks results - completely hands-off. Our search accuracy went from 60% to over 90%.
You don’t need custom wrappers or to mess with agent internals. Just chain the logic together and let automation do the work.
Latenode makes this ridiculously easy to set up and maintain: https://latenode.com