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Related Servers

Alternatives to codemogger

No user-submitted related servers found.

    Related Servers

    • A
      license
      A
      quality
      B
      maintenance
      Enables semantic and keyword search over a local codebase by chunking source files along function and class boundaries, embedding them offline, and fusing vector and BM25 results. Runs entirely on your machine in a single SQLite file, so AI coding agents can find relevant code with no API keys, telemetry, or cloud calls.
      7
      368 npm
      MIT
    • F
      license
      Not graded
      quality
      D
      maintenance
      A minimalist indexing tool that provides AI agents with semantic search and structural AST parsing for deep codebase understanding. It enables autonomous agents to navigate large codebases predictably using vector embeddings and native language server capabilities like definition and reference tracking.
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    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI coding agents to quickly search and navigate large codebases by indexing symbols and references, offering tools for fuzzy symbol lookup, file outlining, and reference discovery without a language server.
      1
      MIT
    • F
      license
      Not graded
      quality
      D
      maintenance
      Enables AI agents to semantically search and navigate code repositories using natural language, with support for multiple repos, incremental indexing, and no local install needed.
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    • A
      license
      Not graded
      quality
      C
      maintenance
      A graph-powered code intelligence engine that indexes codebases into a structural knowledge graph to provide AI agents with deep context on function calls, types, and execution flows. It offers local, zero-dependency tools for hybrid search, impact analysis, and dead code detection across Python, JavaScript, and TypeScript projects.
      885 PyPI
      814
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI agents to explore a repository by querying a pre-built structural knowledge graph of symbols and their relationships — definitions, callers, callees, blast radius, and file-level impact — instead of grepping and reading files. This cuts the tokens spent on code exploration by roughly 96–99% while running entirely on local SQLite with tree-sitter parsing.
      1
      MIT