semble

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.

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!

lity 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!

e, Cursor, Codex, OpenCode, and any other MCP-compatible CLI/agent. Repos are cloned and indexed on demand. - Zero setup: runs on CPU with no API keys, GPU, or external services. Install as an MCP server for Claude Code: claude mcp add semble -s user -- uvx --from "semble[mcp]" semble Or check our README for install instructions for Codex, OpenCode, Cursor, and other agents. Semble: https://github.com/MinishLab/semble Benchmarks: https://github.com/MinishLab/semble/tree/main/benchmarks How it works: https://github.com/MinishLab/semble#how-it-works Model: https://huggingface.co/minishlab/potion-code-16M