Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep

hckrnws

Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep

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by Bibabomas

Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.

Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.

Main features:

- Token-efficient: 98% fewer tokens than grep+read

- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)

- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested

- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode

- Zero config: no API keys, no GPU, no external services

Install in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" semble

Or check our README for other installation instructions, benchmarks, and methodology:

Semble: https://github.com/MinishLab/semble

Benchmarks: https://github.com/MinishLab/semble/tree/main/benchmarks

Model: https://huggingface.co/minishlab/potion-code-16M

Let us know if you have any feedback or questions!

freakynit
186d
What I have personally observed with such tools is that they make the AI's dumb, similar to how it makes coders dumb when relying more on AI tools.
These agentic AI's are already smart enough to figure out a highly optimized path to code exploration or search. But, with these tools, they just go very aggressive, partly because the search results from these tools almost in 100% of the cases do not furnish full details, but, just the pointers.
To confirm this behaviour, I did a small test run. This is in no way conclusive, but, the results do align with what I been observing:
---
Task: trace full ingestion and search paths in some okayish complex project. Harness is Pi.
1. With "codebase-memory-mcp": 85k/4.4k (input/output tokens).
2. With my own regular setup: 67k/3.2k.
3. Without any of these: 80k/3.2k.
As we see, such a tool made it worse (not by much, but, still). The outputs were same in quality and informational content.
---
Now, what my "regular setup" mentioned above is?:
Just one line in AGENTS.md and CLAUDE.md: "Start by reading PROJECT.md" .
And PROJECT.md contains just following: 2-3 line description of the project, all relevant files and their one-line description, any nuiances, and finally, ends with this line:
```
    ## To LLM
    Update this file if the changes you have done are worth updating here. The intent of this file is to give you a rough idea of the project, from where you can explore further, if needed.
```
1. parasti
  35d
  > These agentic AI's are already smart enough to figure out a highly optimized path to code exploration or search.
  Hasn't been my experience. We used to use Augment Code at work which has a thing called Context Engine - basically an MCP that can answer natural language queries about pre-indexed code. Then we switched to Claude Code, which for some reason prefers to use sed to read from files using line ranges from its own memory (this despite having a range-capable read tool). I don't know, does that really mean that sed is the highly optimized path?
  1. spoiler
    25d
boyter
36d
Interesting. I too have been working in this space, though I took a different approach. Rather than building an index, I worked on making a "smarter grep" by offering search over codebases (and any text content really) with ranking and some structural awareness of the code. Most of my time was spend dealing with performance, and as a result it runs extremely quickly.
I will have to add this as a comparison to https://github.com/boyter/cs and see what my LLMs prefer for the sort of questions I ask. It too ships with MCP, but does NOT build an index for its search. I am very curious to see how it would rank seeing as it does not do basic BM25 but a code semantic variant of it.
This seems to work better for the "how does auth work" style of queries, while cs does "authenticate --only-declarations" and then weighs results based on content of the files, IE where matches are, in code, comments and the overall complexity of the file.
Have starred and will be watching.
1. zikani_03
jerezzprime
236d
I'd be interested in seeing actual agent benchmarks (eg CC or Copilot CLI with grep removed and this tool instead).
For example, I have explored RTK and various LSP implementations and find that the models are so heavily RL'd with grep that they do not trust results in other forms and will continually retry or reread, and all token savings are lost because the model does not trust the results of the other tools.
1. AussieWog93
  46d
  I just put something in my global CLAUDE.md (under ~/.Claude) asking it to use the LSP instead of grep and have never had this issue since.
esperent
86d
I did some evals with pi and GPT 5.5. I tested RTK on / headroom on / both on / both off (all with the standard pi system instructions and no AGENTS.md).
I forget the exact tests I used (a couple of the standard agent evals that people use, one python and one typescript because those are what I use).
I don't claim it was an exhaustive test, or even a good one. It's possible I could have spent a day or so tuning my AGENTS.md and the pi system prompt/tool instructions and gotten better results, because if there's one thing running evals taught me it's that subtle differences there can change the results a lot.
However, I got clearly better results with both off, enough to convince me to stop the tests immediately after 3 rounds.
The problem was that while context use did go down (sometimes), the number of turns to complete went up so the overall cost of the conversation was higher.
It's made me very aware of one thing: so many people are sharing these kind of tools, but either with zero evals (or suspiciously hard to reproduce), or in the case of this one, extensive benchmarks testing the wrong thing.
I'm sure this tool does use fewer tokens than grep, and the benchmarks prove it, but that's not what matters here. What matters is, does an agent using it get the same quality of work done more quickly and for lower cost?
aadishv
16d
Seems like a cool idea so I decided to play with it a bit. The test I ran was in the browsercode (https://github.com/browser-use/browsercode) repo with the following prompt:
"Answer this question by only using the `semble` CLI (docs below):
> What tools does Browsercode provide to the agent other than the base OpenCode tools? Provide the exact schema for tool input and tool output and briefly summarize what they do and how they work
---
[the AGENTS.md snippet provided from https://github.com/MinishLab/semble#bash-integration]"
And the equivalent for the non-Semble test:
"Answer this question by only using the `rg` and `fd` CLIs:
> What tools does Browsercode provide to the agent other than the base OpenCode tools? Provide the exact schema for tool input and tool output and briefly summarize what they do and how they work"
In both cases, I used Pi with gpt-5.4 medium and a very minimal setup otherwise. (And yes, I did verify that either instance only used rg & fd, or only used semble.)
Without Semble, it used 10.9% of the model context and used $0.144 of API credits (or, at least, that's what Pi reported - I used this with a Codex sub so cannot be sure). With Semble, it used 9.8% of the model context and $0.172 of API credits. The resulting responses were also about the same. Very close!
I tried one more test in the OpenCode repo. The question was > Trace the path from 1) the OPENCODE_EXPERIMENTAL_EXA env var being set to to 1 to 2) the resulting effects in the system prompt or tool provided to the OpenCode agent.
And I included the same instructions/docs as above. The non-Semble version was a bit more detailed -- it went into whether the tool call path invoked Exa based on whether Exa or Parallel was enabled for the web search provider -- but w.r.t. actually answering the question, both versions were accurate. The Semble version used 14.7% context / $0.282 API cost, while the non-Semble version used 19.0% / $0.352. Clearly a win for Semble for context efficiency, but note that the non-Semble version finished about twice as fast as the Semble version.
Of course this is just me messing around. ymmv.
adelks
05d
I know this tool was meant for AI, but I am more interested in using it myself when exploring new code bases or even my own, when I want to refactor something and want an overview of where to change stuff. LSPs do that, but this tool sounds like it can go one step further.
andai
46d
Nice, this sounds great. I want to mention a related issue here, which is that on small codebases, Claude spends a lot of time looking for stuff when it could have just dumped the whole codebase into the context in one go and used very little tokens.
I found a nice workaround which is that you can just dump the whole directory into context, as a startup hook. So then Claude skips the "fumble around blindly in the dark" portion of every task. (I've also seen a great project that worked on bigger repos where it'll give the model an outline with stubs, though I forget what it was called.)
1. mackenney
  26d
singpolyma3
16d
Semantic code search seems like a useful tool for a human too. Not just for agents.
1. stephantul
  06d
  Yeah I agree. I have used semble to quickly index a large monorepo and just ask a question about it, it surfaced the right files pretty quickly. Although without an IDE, it's difficult to display them in nice way
AussieWog93
56d
Better than grep obviously, but how does this compare to existing LSPs?
1. CharlesW
  46d
  Or tools like `ck`: https://beaconbay.github.io/ck/
kurtextrem
03d
Related read (not from me): https://entire.io/blog/improving-agentic-search-in-coding-ag...
> The clearest result was that faster search alone only modestly helps, while better-ranked results improve first-query retrieval and help agents find the right code sooner.
Their tool "pgr" is a research preview only, so it'd be interesting to see semble vs pgr.
I'm also collecting other tools that are similar, most notably is probably Morph's WarpGrep (has a free tier too). Apart from that, there is codemogger (https://github.com/glommer/codemogger), cs (the author also commented in this HN post).
In the similar area, but not fully related, the author of fff is also pretty involved in any thread that goes into that direction (see e.g. https://x.com/neogoose_btw/status/2052161471296225710). Similar to colGREP is also mgrep (by mixedbread) and osgrep (but they seem to predate colGREP). I also found codedb on X (https://codegraff.com/blog/codedb-code-intelligence), the post reads well, but haven't tried.
jelder
05d
LSPs are already miles better than grep-like tools. This was true for humans as much as it is for LLMs. It's a shame that Claude still treats it as a second-class citizen (both in the app itself and in the training). A simple "remember to use LSP instead of grep" is usually enough to get it on the right track.
wrxd
16d
I also like the index feature form https://maki.sh Source code has a lot of structure, using a real parser instead of grepping and reading files can potentially save a lot of tokens
1. Bibabomas • OP
  05d
  Interesting, will have a look at this, thanks for sharing.
[deleted]
06d
[deleted]
nextaccountic
16d
How does this compare with colgrep?
https://github.com/lightonai/next-plaid/tree/main/colgrep
1. stephantul
  06d
  The comparison is in the benchmarks, see the README
abcdefg12
56d
Shouldn’t it be a part of the harness at least for local codebase? I wonder how many harnesses are doing that already.
1. dopidopHN2
  16d
  I'm playing with PI as a custom harness ( for Claude code because that what is provided to me )
  I will try that ! It make sense and I'm curious to see results, for this or any similar projects mentioned in the thread
bluejay2387
15d
So about a year ago I wrote my own attempt at something like this using vector indexing and BM25 (the latest version uses CocoIndex, I had a custom coded solution using ChromaDB before). I wrote a comprehensive enough test set that showed performance increases on the quality of search results and reduction in token usage versus grep and rg. I haven't had time to really polish it but it worked well enough, particularly for one project where I have around 250k documentation files and docs out number code files 1000 to 1 (about 50% reduction in tokens and 30% increase in successful searches). Yesterday for grins I tried this project and was fairly disappointed to see it blow away my kludged solution particularly given that it doesn't have a lengthy indexing process. I haven't tested it on the 250k doc project yet, but in another project that I have a test suite for semantic search on it outperformed my solution by about 20% even on documentation in terms of successful search results (which I didn't expect given that it seems to only be tuned for code). I haven't gone through the code to see what its doing differently than what I tried, but what ever its doing it seems to have potential.
cagz
15d
Would be nice to see the actual % token/time saving across end-to-end coding sessions over these time periods https://github.com/MinishLab/semble#savings
My observation is that greps and the processing of grep outputs account for only a small portion of overall consumption; I haven't measured this scientifically though.
1. Bibabomas • OP
  05d
  Yeah this is a good point, but it's complicated to do well since semble then has to be aware of everything else that happens in a session, which would then make it more intrusive (and it's deliberately designed as a local, non-intrusive alternative to other solutions). I'm thinking that perhaps we can do an isolated opt-in benchmark for this perhaps.
te0006
04d
Related: https://github.com/jahala/tilth Edit: recent HN discussion: https://news.ycombinator.com/item?id=46952321
sonink
15d
The bigger problem with solutions like these is that most AI already know how to use grep and search really well because of their training. Any such new tool that you handle to the AI, takes away from the cognitive capability of the AI. Humans would normally 'learn' how to operate tools like this - but the learning in LLM's is frozen and they already with a very strong depth in existing tools like grep.
For example, an AI would already use linux commands like tree to traverse the code base. And again it already has good training in this.
The other problem is that it is easy to cook up examples which demonstrate the efficacy of tools like these - but actually proving that the cognitive deficit that such tools result it, is surmounted by their efficacy in long horizon runs. My first contact instinct is that this will result in a net negative 'deployable intelligence' over long horizon runs - make the agent perform worse than using existing tools.
Proving the opposite is a non-trivial problem - but maybe it might be something you want to take up.
1. Bibabomas • OP
digitaltrees
05d
Cool project. I built a custome IDE and coding agent harness and will integrate this into it. If you’re interested in a collaboration, I’d be happy to share revenue to sponsor your open source repo.
https://calendly.com/ryanwmartin/open-office-hours
luodaint
05d
Metric that measures the quality beyond simple tokens count: correction loop frequency.
When grep does not find a file of interest, the agent does not fail; it will continue working on an incomplete context. For a monolingual code base, the miss rate is okay. In case of polylingual code (Python backend code and TypeScript frontend code), the problems emerge when it comes to querying for cross-file dependencies. Grep will return a route from the backend API. However, there is an interface in TypeScript that needs to be matched. Agent generates a response that does not fit the type. Correction cycle is one; two if the type conflict is ambiguous.
Combining grep with the understanding of semantic relations between files is a solution. Number of tokens saved is real but underestimates the actual benefit since fewer correction cycles are more valuable than tokens themselves.
pu_pe
45d
> uses 98% fewer tokens than grep
So are we supposed to believe that grep is so wasteful that models are reading 98% useless garbage every time they call it? Either this claim is not representative, or you're missing something else when you throw away the vast majority of context for the model.
1. Bibabomas • OP
  15d
  The 98% is vs the grep+read loop, not grep output alone. When an agent hits an unfamiliar codebase it typically does "cat file" or reads the whole thing first, at least in my experience. If you're reliably getting agents to do "grep -C N" and stop there I'd genuinely be curious what your setup looks like, because I think the quality of the results is just too low to serve as useful context.
jrflo
25d
Interesting for sure, but I think I need to be convinced this is necessary. How many tokens does Grep use on the average response anyways? Does this reduce input token usage from 100k to 2k per query or from 1k to 20? How does this effect output code quality?
1. Bibabomas • OP
  14d
  There's a "semble savings" command you can run to see token savings. It varies heavily based on the repo: in general, the larger the project the larger the savings. Output code quality is something we're still trying to measure, but it's much harder than measuring retrieval quality.
cityofdelusion
15d
Feedback:
codex-cli hangs when calling this through the MCP. The semble process even sticks around as a zombie, forever stalled out. No idea why, logs have nothing.
When called through a skill via CLI style calling, GPT 5.5 loves to give a ton of search terms like it is used to doing with ripgrep. Not sure how effective this is, the short docs in the github and the instructions the agent has isn't clear on what is optimal.
Lastly, I got some errors with external connections to github when I was installing it for bash use. Maybe its related to the hanging? No idea.
edit: My agent also loves to follow-on with ripgrep, which seems redundant. Acts like it has trust issues. I think a more extensive agent skill description could guide the agent into proper use.
1. Bibabomas • OP
  05d
_ink_
16d
Would this replace something like codebase-memory-mcp[1] or improve when both is being used?
[1] - https://github.com/DeusData/codebase-memory-mcp
1. Bibabomas • OP
  05d
  [dead]
esafranchik
56d
Is the benchmark measuring one-shot retrieval accuracy, or Coding agent response accuracy?
1. stephantul
  46d
  Hey! Co-author here. The benchmark currently only measures retrieval accuracy.
  We’re interested in measuring it end to end and also optimizing, e.g. the prompt and tools, for this, but we just haven’t gotten around to it.
mrpf1ster
16d
Does this work well for non-coding documents as well? Say api docs or AI memory files?
1. stephantul
  06d
  Hey, this is something we're actively investigating. We recently added a flag, `--include-text-files`, which, when set, also makes Semble index regular documents (i.e., markdown, text, json). This should also work relatively well.
smcleod
16d
How does it compare to context-mode or serina that are both well established now?
1. Bibabomas • OP
  05d
  Serena does a lot more than semble (I actually used serena before building this and didn't like how much it does by default). That also made it hard to see if it was actually working well with how many moving parts there are. Semble only does 1 thing: very quick code search, that's it. Context-mode I have not used before though, I will have a look at that, thanks for sharing!
jahala
26d
This looks great! I built a tool in the same space- and I found that the biggest challenge was often to get the agent to prefer to use the tool over bash tools. What’s your experience with that?
1. Bibabomas • OP
  05d
  This is mostly done with the prompt. To be fair, we are still evaluating and improving this as well!
onoesworkacct
16d
fantastic token savings and performance... but unlike grep it's probabilistic search on search terms.
is that an issue? the tiny model might not surface something important
1. stephantul
  05d
  It's not probabilistic, and exact matches will always be preferred over non-exact. So if you search for a function name this will surface it.
zhxiaoliang
15d
The instructions on how to install and use it could use some work. I did eventually install it. Will try it later and report back.
1. stephantul
  05d
  Oh sorry that happened. Feel free to open an issue or report it here
luodaint
05d
"The problem is that there are several bottlenecks internally," which include the requirements, specs, and testing. Another one not mentioned in the article.
Before you had faster implementation times, something would take six weeks to implement. Feedback from the client about how far off target you were came through in the same amount of time: a help desk ticket, a post-call check-in, a quarter end review. The price you paid for being off target was proportional to how long it took to figure out.
Now, when you can ship features in an afternoon, the customer feedback loop remains the same speed. Surveys, help desk tickets, and churn analysis come back days, even weeks later, by which point you've shipped five new features going the same way.
You can fix the internal bottlenecks easily enough: write better specs, have faster test cycles, deploy continuously. The customer feedback loop bottleneck is built into the system. It won't get any faster just because implementation did.
Today most organizations are busy fixing the internal bottleneck, but not the external one.
PufPufPuf
15d
The savings are calculated against the assumption that agents read the matched files in their entirety. In my experience, they are smart enough to use grep to bring up a few lines of surrounding context, then read in full only the files that look relevant.
1. Bibabomas • OP
  05d
  In practice this rarely happens though, at least in practice I rarely see agents "grep -C N" or something like that on files it didn't read yet. I use Claude Code and OpenCode extensively, and especially during the first pass through a codebase that is not well understood the agent often just does "cat file" or something similar and gets the entire file in context first, and only then starts doing more finegrained searches, but at that point you already have a lot of irrelevant context in memory. I think the whole value proposition of semble is that you don't have to do that initial read at all and can instead get the right (small) context bits. If you experience is different, would you mind sharing what your setup is like, e.g. how do you get the agent to read less?
florians
05d
“> Claude: Semble surfaced things grep missed — here are the additions to the earlier answer.”
Nice!
handonam
15d
The one thing I'm a bit nervous about: security. Thoughts of supply-chain "what-ifs" gives me a bit of pause here. Would like to hear security-minded folks give their thoughts on this.
1. Bibabomas • OP
  05d
  Hey, we do a couple of things specifically to prevent supply-chain attacks. We use trusted publishing on PyPI, and --exclude newer for uv's package resolution. We also try to use the least amount of dependencies possible. A transitive dependency could in theory still be problematic though, e.g. if there's a supply-chain attack on numpy.
  The tool itself is fully local though, so there's no real security risks there, there are no outbound network calls or anything like that.
derrickrburns
15d
Have you considered solving a different problem? What are agents trying to achieve when searching a code base? Finding seams. How about indexing seams instead?
1. Bibabomas • OP
  05d
  What do you mean by this exactly?
therealdrag0
15d
How does that compare to Cursors’s workspace indexing?
Also curious what the authors think about Claude team explicitly trying out indexing and deciding against it.
1. Bibabomas • OP
  05d
  The first is hard to test for us unfortunately since we don't use Cursor. But the Claude thing is interesting. I think that providers (especially the ones that directly sell LLM calls like Anthropic) are not incentivised per se to think about token efficiency vs performance, so if you're chasing pure performance, just loading the full codebase into memory might still be the "benchmark topping" way to go. I think the dust hasn't really settled yet and we'll likely see a lot of changes in the coming year about what's the "correct" way to solve it. It might be different based on your harness/budget/model as well.
shermantanktop
15d
Do inefficient grep/sed behaviors have a secondary benefit in seeding the context with breadcrumbs of code from irrelevant matches?
1. Bibabomas • OP
  05d
  In theory maybe, but in practice it hurts more than it helps I think. Irrelevant context makes the model more likely to reason from the wrong code (and it's slower and more expensive).
[deleted]
05d
[deleted]
porker
16d
Congratulations on the release!
Could you add fff to the benchmarks?
1. stephantul
  06d
  We hadn't found that one yet. Will do!
michal_lola2
05d
Exciting. I've been playing with AI dev pipelines and the "give the agent the full codebase vs. let it search" trade-off is what I keep running into - both have pros and cons depending on the task. This looks like the latter pushed harder than I've seen before. Looking forward to trying it
gslepak
16d
Does this support any language or is it limited to a specific set of languages?
1. stephantul
  06d
  For chunking Semble supports all languages supported by tree-sitter-language-pack. The models we train are trained on 6 languages, but can handle way more.
cush
15d
These almost always come with the cost of worse adherence
1. Bibabomas • OP
  04d
  Fair, many tools trade off cost for performance/adherence. So far our experience is good with Semble (at least with Anthropic/OpenAI models), but if you have any feedback feel free to reach out. We're working on evaluations for this as well, though it will take some time as this is much harder than benchmarking retrieval quality.
paolomon
05d
Interesting approach, gonna leave a gh star clap
ludicrousdispla
66d
grep doesn't need tokens, so what is 98% fewer than zero?
1. mrweasel
  05d
  We might not be AI/agent enough for this product. I wondered about that as well, but it's not actually a grep problem, it's an ingest problem for agents. Apparently some agents use grep to navigate code and it's this "operation" that consume tokens, grep does in fact consume zero tokens.
flossly
15d
I feel this should be a mode on ripgrep. a disk search-lib in python? really?
Burntsushi (author of ripgrep), please chime in!
1. Bibabomas • OP
  05d
  Hey, what's the issue with a disk search-lib in python specifically? The library is extremely fast. Yes, we could probably squeeze some more performance in Rust, but that's not our native programming language, so we opted for doing it correctly rather than use a language that we don't understand well enough.
jasonli0226
06d
thanks for sharing!
JasonBuildAI
05d
amazing jobs
vemulasukrit
06d
Nice!
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ramsono
06d
Very useful thanks for sharing!
vikeri
26d
very curious to give it a spin but why write a cli in python? would surely be faster and more portable with go or rust?
1. skeledrew
  16d
  Perhaps Python is their main language (they seem to be ML peeps, which would make that most likely), which means it's easier for them to do manual reviews even if they're using AI for implementing, etc.

## To LLM Update this file if the changes you have done are worth updating here. The intent of this file is to give you a rough idea of the project, from where you can explore further, if needed.