SIFS
Extremely fast hybrid code search for agents.
What it does
Coding agents waste too much context before they understand a repo. They grep around the codebase, read large chunks, and make guesses. SIFS gives them fast local search. It runs as a CLI or MCP server. The default mode is hybrid search: BM25/semantic retrieval. Current benchmark: 63 large repos 19 languages 1,251 annotated search tasks index: 6.5 ms search: 0.376 ms SIFS is ahead of any comparable tool on this benchmark. Very open to feedback and issues/PRs! Let me know if you try it.
Does the same job
all alternatives →- SCSemble – Code search for agents that uses 98% fewer tokens than grepMay 2026 · github.com · ▲445
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…
- MSMantic.sh – A structural code search engine for AI agentsJan 2026 · github.com · ▲81
Author here! Some context: I published this 48 hours ago and it was auto-listed on MCPMarket (the MCP tools directory). Got 700+ organic downloads with zero marketing—developers were actively searching for exactly this solution. The "Git Accelerator" optimization story: Initially used a file walker that took 6.6s on Chromium. Profiling showed 90% was filesystem I/O. The fix: git ls-files returns 480k paths in ~200ms. Added smart heuristics for untracked files (only scan dirs <50k files), bringing total to 0.46s. Why this matters: Agents can't wait 10 seconds for search. Sub-500ms makes…
- SGSemantic grep with local embeddingsSep 2025 · github.com · ▲179
- CACodebased, an AI Search Engine for Code2024 · codebased.sh · ▲17
Codebased combines Tree Sitter for code awareness (find functions, data structures, constants, etc. not just lines of code), full-text search using SQLite, and semantic search using OpenAI embeddings + FAISS. Despite being implemented in Python, supporting semantic search, making multiple API calls for embedding and re-ranking, it is faster than ripgrep for runng searches against the Linux kernel (takes ~1 second vs. ~2 seconds, obviously depends on system, temperature, time of day, tidal forces, etc.) Up next: - A Perplexity-like agent for interpreting results, making multiple follow-up…
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