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I've been skeptical of these semantic search tools. Not only are agents already great with grep, the problem imo is these search tools treat your specific code like a destination, but your codebase is actually a graph, and your agent needs more context around your search term in order to make changes.

Luckily, graph traversal of your code has been solved for a long time, by LSP. But LSP is so extremely memory inefficient.

I created cx[0] to strip away the bloat from LSP into a lightweight navigation tool for agents, using only tree-sitter. I never got around to sharing on HN but might be time for a post.

[0] https://github.com/ind-igo/cx

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.

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?

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.)

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.

Better than grep obviously, but how does this compare to existing LSPs?

Semantic code search seems like a useful tool for a human too. Not just for agents.

How does this compare with colgrep?

https://github.com/lightonai/next-plaid/tree/main/colgrep

Shouldn’t it be a part of the harness at least for local codebase? I wonder how many harnesses are doing that already.

Does this support any language or is it limited to a specific set of languages?

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

Would this replace something like codebase-memory-mcp[1] or improve when both is being used?

[1] - https://github.com/DeusData/codebase-memory-mcp

How does it compare to context-mode or serina that are both well established now?

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

Is the benchmark measuring one-shot retrieval accuracy, or Coding agent response accuracy?

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?

Congratulations on the release!

Could you add fff to the benchmarks?

Does this work well for non-coding documents as well? Say api docs or AI memory files?

Very useful thanks for sharing!

Nice!

thanks for sharing!

grep doesn't need tokens, so what is 98% fewer than zero?

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?