Is Grep All You Need? How Agent Harnesses Reshape Agentic Search
78 points by Anon84 6 hours ago | 34 comments

quinncom 2 hours ago
Don’t presume this study has anything to do with programming. They measured an agent’s ability to search long conversations, not code.

> We evaluate on a 116-question representative subset of the LongMemEval benchmark (Wu et al., 2025), which tests an agent’s ability to answer questions over long conversations spanning multiple sessions.

reply
schipperai 16 minutes ago
I get a sense that I was click-baited by article's title with the classic trope of "X is all you need". This research is a solid contribution, but is far from all we need to understand grep vs semantic search in agent retrieval.
reply
alexrigler 3 hours ago
Combining regex filtering with semantic ranking using multi-vector embeddings has yielded good results for me. I use ColGREP from the LightOn team asa daily driver - https://github.com/lightonai/next-plaid/blob/main/colgrep/RE...
reply
piekvorst 3 hours ago
I have always used traditional grep to search codebases. It serves me better than an IDE when there’re lots of scattered and frequent queries.

grep’s design is surprisingly winning, exceeding expectations to this day.

reply
weaksauce 2 hours ago
you might be interested in https://github.com/boyter/cs

pretty fast and neat project to search code interactively with a lot of optimizations on finding the right thing

reply
piker 4 hours ago
I recently watched the new Palantir + Kirkland & Ellis fund formation platform demo, and I was surprised to see how effective the union of structured data was in an agent harness. We're used to dealing with flat files and comparing here basic ways of searching, essentially, long strings, but using Palantir's "Ontology" graph framework, I think Kirkland is going to be able to achieve some exception and differentiating outcomes in legal tech. The whole idea assumes that they've got great structured data already, and perhaps that's the real valuable unknown, but giving an agent those tools is super powerful.

I wrote about it[1] and came away with a different view on both Palantir and the future of agentic workflows personally.

[1] sorry, LinkedIn: https://www.linkedin.com/pulse/fund-managements-killer-app-d...

reply
darkteflon 21 minutes ago
That was great, thanks for the write-up. It’s rare to get a peek into Palantir’s ontology-forward approach. I’ve certainly been curious.

> But it would make no sense to have an LLM regurgitate an existing form document token-by-token rather than call a piece of 1994 software like Hotdocs to populate some placeholders.

This is a real “oof”, isn’t it. Very difficult to understand what they were going for here. Perhaps they just assumed no one in the intended audience would pick it up. But it certainly is enough of a red flag that it made me go back to the top of your write-up for a re-read, thinking about their whole pipeline in much more sceptical terms.

reply
gbacon 3 hours ago
This is a surprising result. With structured inputs like source code, I’d expect grep to outperform semantic search, but natural language’s errors and inconsistencies seem to leave so many cracks for information to fall through.
reply
sdesol 3 hours ago
This paper is based on quality so I don't think it should be that surprising if you take loops into consideration. What the agent finds in the first pass, can help if formulate the next grep if needed.
reply
jeffchuber 3 hours ago
If you are truly bitter-lesson pilled - give the agent all the tools and let it decide which to use.

- regex (grep) - hybrid search (bm25+vector)

this X vs Y is uninteresting when the answer can be both.

reply
budududuroiu 44 minutes ago
Both is usually the right answer, since you can use LLMs to do query expansion and effectively increase the recall performance of your retrieval algo
reply
bachittle 3 hours ago
Exactly this, and this tool called qmd is what I use for the hybrid search portion. It also uses local LLMs to provide summaries on your own markdown data too. My agents use both depending on what type of search they are doing, and both provide good results.

https://github.com/tobi/qmd

reply
pastel8739 3 hours ago
That assumes that the agent knows which one is better. And to bake in which one is better via post-training would require a study like this to establish where each one works well
reply
fnordpiglet 3 hours ago
I’ve got a custom ultra high performance streaming semantic search I exposed as a tool and the RL bias in Claude is almost insurmountable without copious and consistent steering. Codex will follow instructions and use the tools I ask it to but for gods sake between Claude asking to take a nap because it’s getting late in the session and it regressing to RL biased tools like grep it’s maddening. When I can get it to use my compositional tools tool calls drop from like 20-50 to 3-4, but it’s almost impossible to steer.
reply
dominotw 3 hours ago
it will only use tools it was trained on? what's the benfit of givig it all the tools.
reply
worthless-trash 3 hours ago
I'm still disappointed that ai can't use ctags, its used for finding strings and patterns, its right there.
reply
sdesol 3 hours ago
> I'm still disappointed that ai can't use ctags,

What do you mean by this? Do you mean not automatically build the index?

reply
worthless-trash 3 hours ago
it inspects a project, finds the ctags files, then goes on to use grep.
reply
worthless-trash 2 hours ago
[flagged]
reply
stephantul 2 hours ago
This paper oversells on the title. Like, what is chronos, which embedding model was used, which reranker, how was the reranking done, why is chronos much better than claude code
reply
hmokiguess 4 hours ago
Tangential, I have a hook that rewriters grep to rg but lately I wonder if this is actually wasteful as the model is so biased to grep, is there a way to shim/alias perhaps?
reply
sdesol 3 hours ago
My CLI does something close to this:

https://github.com/gitsense/gsc-cli

`gsc grep` is just an alias for `gsc rg`, mostly because agents are much more likely to reach for “grep” than “rg”.

It works pretty well, but it is not a perfect drop-in replacement. `grep` and `ripgrep` differ in a few details, especially around glob/wildcard behaviour and flags. What I found works is to not use `grep` in search examples, and have the CLI spit out an error message for the AI saying this is `ripgrep`, so it needs to use `ripgrep` syntax.

reply
celrod 3 hours ago
If performance is the concern, ugrep will get you most of the way there relative to gnu grep, and should be fully grep compatible in terms of syntax:

https://github.com/Genivia/ugrep#aliases

Claude Code may ship with ugrep already.

reply
verdverm 4 hours ago
Many harnesses are doing this already, "Grep" is the tool name, ripgrep is the implementation

It depends on if it is using Grep the harness tool or Grep from the bash tool

reply
hmokiguess 3 hours ago
I see it using the Bash tool infrequently though sometimes Grep. I'm on Claude Code for now due to subscription lock-in, been contemplating moving to pi though
reply
joelfried 3 hours ago
My experience here (also Claude user) is that the model uses different tools in different contexts. I see rg more on frontend and grep more on backend work. I imagine it defaults to using the tool it has more learning around within the contexts it's reaching for and since for the most part it's 6 of one or half a dozen of the other you'll see environment specific usages for these tools in claude for now. I imagine eventually it'll standardize but we're early yet on such things.

If you'd told me a decade ago I'd finally learn some sed in 26 because I'd want to understand what the AI was doing I'd have told you you were crazy . . .

reply
Analemma_ 2 hours ago
Why do you have subscription lock-in? Even if you pay for a yearly subscription, Anthropic will refund you pro rata if you cancel early.
reply
cyanydeez 3 hours ago
I've been on a look out for any harness that properly secures a protocol to the LLM, but they're all just "here's some tools, hopefully you don't use bash for everything".
reply
liminal 2 hours ago
Is <blank> the only ML paper title?
reply
yodon 4 hours ago
Feels important, but I wish they also had compared against something like MeiliSearch or Algolia.
reply
verdverm 4 hours ago
100%, there's even Typesense, open source Algolia, which can do hybrid search and a number of other fancy things

I'm currently working on a markdown kb / search tool for my agents, in part built on TS

reply
kwillets 3 hours ago
I'm curious to see what patterns it's grepping.
reply
sys_64738 4 hours ago
Surely 'strings' would be even better?
reply
greenavocado 3 hours ago
This has been posted before, but a dead-simple pattern that helps enormously with steering the model to the right code area is a DESIGN.md that it creates, updates, and references periodically.
reply
nibbleyou 47 minutes ago
What does it contain?
reply
tailor_gunjan93 2 hours ago
[flagged]
reply
gauravvij137 49 minutes ago
[flagged]
reply
sdesol 3 hours ago
[flagged]
reply
wseadowntown 4 hours ago
[dead]
reply