Sema

What it does

Stop wasting tokens on file navigation. Speed up Claude Code and OpenAI Codex on large codebases. Sema is a semantic code indexer and MCP server. It indexes your entire codebase locally — every function, class, and method — and gives your AI assistant a search API so it never has to read files blindly again. Index once. Your AI searches forever.

Sema is a semantic code indexer and MCP server. Index your codebase once, your AI assistant searches it forever — 4–9× fewer tokens per question. Works with Claude Code and OpenAI Codex.

Sema indexes your codebase once. Your AI assistant searches it forever — 4–9× fewer tokens per question , no API keys, runs locally. Numbers measured via tiktoken (cl100k_base) on the real hoppscotch repo. Run sema against fastapi-users and the ratio holds at 9×. Sema parses every function, class, and method, embeds them locally with SBERT, and stores them in a ChromaDB index on disk. Your AI assistant connects over MCP and queries the index instead of reading files. Every function, class, method, and section becomes a Chunk — with its full source, signature, line range, and call list. SBERT runs locally — no API key, no network. ~80MB model downloaded once and cached globally. ChromaDB…from sema-landing-one.vercel.app

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  Semble – 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…

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  CodeRLM – Tree-sitter-backed code indexing for LLM agentsFeb 2026 · github.com · ▲81

  I've been building a tool that changes how LLM coding agents explore codebases, and I wanted to share it along with some early observations. Typically claude code globs directories, greps for patterns, and reads files with minimal guidance. It works in kind of the same way you'd learn to navigate a city by walking every street. You'll eventually build a mental map, but claude never does - at least not any that persists across different contexts. The Recursive Language Models paper from Zhang, Kraska, and Khattab at MIT CSAIL introduced a cleaner framing. Instead of cramming everything into…

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