semble
Active·★ 4.5k·MIT·Updated 2026-05-29
★ Code Assistant★ RAG / Knowledge Base
Fast and Accurate Code Search for Agents
Semble is a high-performance code search library designed for AI agents, providing instant access to precise code snippets. It offers significantly faster indexing and querying compared to transformer models, achieving 99% of their retrieval quality while running entirely on CPU without external dependencies.
#agents#code-search#embeddings#mcp#mcp-server#mcp-servers#model-context-protocol#retrieval
01
Features
01Fast performance on CPU (indexes in ~250ms, queries in ~1.5ms)
02High accuracy (NDCG@10 of 0.854), comparable to transformer models
03Supports indexing local paths and remote Git repositories
04Functions as an MCP server for various AI agents
05Zero setup, no API keys, GPU, or external services required
02
Compatibility
Claude Code
Claude Code
Verified via docs
Codex
Codex
Verified via docs
OpenCode
OpenCode
Verified via docs
Cursor
Cursor
Verified via docs
03
Quick start
1
$ pip install semble
04
Use cases
↳Enhancing AI agents (e.g., Claude Code, Cursor, Codex) with fast and accurate code search capabilities
↳Searching local or remote codebases for specific code snippets based on natural language or code queries
↳Finding semantically similar code sections related to a given file path and line number
05
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Comments
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- SSkyler BrownMay 25, 2026
Good for agents that need to find relevant code across many files quickly
- LLogan BrownMay 5, 2026
The accuracy on semantic code queries is better than pure keyword search approaches
- RRowan ZhangMar 11, 2026
Fast and accurate code search for agents is exactly what large codebase navigation needs
- CCasey RiveraMar 9, 2026
Used for codebase exploration in large repos, the speed makes real-time agent queries viable