The new release
On October 12, LightOn released ColGrep-Agent-2B, an open local code-search model. It returns relevant file locations and code to a larger coding agent. Think of it as a specialist helping the main assistant find its way around a repository.
In LightOn’s study of 52 search questions across 13 repositories, the delegated setup used about 34% fewer API tokens than the grep baseline. Average question time increased from 13 to 21.6 seconds. Its observed accuracy was lower, although the difference was not statistically significant; the uncertainty still allows a meaningful decline. These are vendor-reported search results, not proof of faster or better end-to-end software development.
Source: LightOn: ColGrep-Agent: Cutting Frontier Coding Agents’ Tokens by a Third ↗
Your question changes the search
Our practical interpretation: this is most interesting when you understand a behavior but do not know its name in the code. A beginner might ask where an expired upload gets rejected. A maintainer might already know the exact function name. Those are different information problems, even if both end with opening the same file.
Before adding another model, write down what makes your current search frustrating. Is the vocabulary unfamiliar? Does a feature cross several folders? Are you repeatedly feeding irrelevant files into your assistant? A specific complaint gives you something to evaluate. A general feeling that the agent should be smarter does not.
There is also a useful learning angle. When a search tool returns a location, pause before requesting an edit. Read the relevant function, identify its inputs, and follow one caller. You can turn a navigation shortcut into a lesson about the project’s architecture instead of letting the explanation disappear behind an automated patch.
Build a fair comparison
Here is a suggested experiment, not a test we have performed. Choose a public repository you are allowed to use. Prepare six questions: two with exact identifiers, two describing behavior in everyday language, and two requiring a connection between separate modules. Write an expected answer for each before asking the AI, including the files that must be found.
Run your current approach and the local search approach on the same repository revision. Start each question fresh. Record whether the answer found every necessary location, how long it took, how much hosted API usage it incurred, and how many irrelevant files you had to inspect. Keep setup time separate from per-question time.
A result can be useful without winning every column. For occasional study sessions, a longer wait may be fine if the returned evidence is easier to understand. For rapid interactive debugging, latency may matter more. For a large shared project, predictable costs might matter most. Decide your preferred tradeoff before looking at the results so one impressive number does not choose for you.
Finish by checking the returned code yourself. A plausible file path is only a lead. The behavior could depend on configuration, another call site, or a test that contradicts the first explanation. Ask the assistant to show that connection before allowing a change.
Try this, then make it yours.
Write three behavior questions about your own project and identify the evidence a complete answer would need.
Follow the signal.
Our reporting starts here. Practical suggestions are our analysis, and vendor performance statements are claims unless independently verified. We haven’t hands-on tested this release.
