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    Enables AI agents to perform semantic code search across entire codebases using natural language queries. Provides fast indexing and ranked search results with line numbers and file paths through the Seroost search engine.
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    Local semantic recall MCP server for citation-integrity workflows. Enables semantic search over a text corpus, returning file paths and page numbers for verification, with 100% local processing using ONNX embeddings and LanceDB.
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    MIT
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    Local offline semantic search over documents (txt, md, pdf, docx, pptx, csv). Indexes folders into a LanceDB vector database with multilingual embeddings and supports hybrid vector + keyword search via Reciprocal Rank Fusion. No API keys, no cloud, no Docker required.
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    AGPL 3.0
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    Enables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.
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    BSD 2-Clause "Simplified"
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    Reduces token consumption by over 80% through intelligent file caching, returning only diffs for modified files and suppressing unchanged content. It features a suite of 12 tools for semantic search, batch reading, and efficient file editing to optimize LLM interactions with large codebases.
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    MIT
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    A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.
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    Drives a real Chromium browser to extract a page's DOM into compact Semantic JSON with Playwright-native locators, enabling AI agents to generate consistent, accurate Playwright tests.
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    MIT
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    Downloads your entire Search Console dataset into a local SQLite database, then gives your LLM a pre-built SQL query library for every standard SEO analysis type, with context available for your LLM to perform any SQL query to answer your questions and analyse for you.
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    Apache 2.0
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    Federated, local-first search for AI agents: one query fans across transcripts, files, a knowledge graph, a vector store, the live web, and YouTube, fused by trust-weighted RRF into one ranked answer. Five modes (grep/lexical/semantic/hybrid/rerank), runs entirely on your machine over MCP.
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    Apache 2.0
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    Enables natural language search of ANSA Python API documentation, allowing AI to find relevant functions, signatures, and code examples for writing ANSA scripts.
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    MIT