semble
★ 4.5k
vs
mcp-omnisearch
★ 313
semble vs mcp-omnisearch
semble: 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.; mcp-omnisearch: mcp-omnisearch is an MCP server that unifies multiple search and extraction APIs (Tavily, Brave, Kagi, Exa AI, GitHub, Linkup, Firecrawl) into four tools: web_search, ai_search, github_search, and web_extract. It supports flexible provider selection and key management, and can be deployed on any platform with Node.js.
01
TL;DR
Choose semble if…Enhancing AI agents (e.g., Claude Code, Cursor, Codex) with fast and accurate code search capabilities
Choose mcp-omnisearch if…Perform comprehensive web searches using multiple search engines in one request
02
Side-by-Side Comparison
03
Features
semble01Fast 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
mcp-omnisearch01Unified web search across multiple providers (Tavily, Brave, Kagi, Exa)
02AI-powered search with sourced answers (Kagi FastGPT, Exa Answer, Linkup)
03GitHub search for code, repositories, and users
04Web content extraction with modes like crawl, scrape, summarize, and find similar
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Use Cases
semble↳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
mcp-omnisearch↳Perform comprehensive web searches using multiple search engines in one request
↳Obtain AI-generated answers with citations for complex queries
↳Extract and summarize content from web pages for research or data collection
05
Best For
sembleCode AssistantRAG / Knowledge Base
mcp-omnisearchAPI IntegrationData Processing
FAQ
FAQ
What is the difference between semble and mcp-omnisearch?
Both semble and mcp-omnisearch are in the RAG / Knowledge Base category. semble has 4.5k stars, while mcp-omnisearch has 313 stars.
Which is better, semble or mcp-omnisearch?
The best choice depends on your use case. Choose semble if Enhancing AI agents (e.g., Claude Code, Cursor, Codex) with fast and accurate code search capabilities, and mcp-omnisearch if Perform comprehensive web searches using multiple search engines in one request.
Is semble free or open source?
Yes, semble is open source on GitHub (MIT).
Is mcp-omnisearch free or open source?
Yes, mcp-omnisearch is open source on GitHub (MIT).
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