gortexCode-intelligence engine in one static Go binary: tree-sitter graph over 257 languages, compiler-grade resolution for 17, multi-repo, 175 configurable MCP tools — up to 50x fewer tokens. 100% local.
Why switchBoth give agents symbol-level code intelligence over MCP. Gortex ships one static Go binary with a tree-sitter graph across 257 languages and compiler-grade resolution for 17; Serena leans on real language servers, so its depth follows the LSP ecosystem rather than its own parsers.
Full comparison → codebase-memory-mcpCode intelligence MCP in pure C: tree-sitter knowledge graph over 158 languages, average repo indexed in milliseconds, sub-ms queries, 10x fewer tokens. Single static binary, zero deps.
Why switchSame job, opposite engineering bet: codebase-memory-mcp indexes a tree-sitter knowledge graph in pure C for millisecond indexing and sub-millisecond queries; Serena skips the index and asks a language server live, trading startup cost for semantic accuracy.
Full comparison → tokensaveCode-intelligence MCP server for coding agents — a pre-indexed semantic graph (libSQL + FTS5) they query instead of grepping: symbols, callers, impact radius in one call. 100% local, 50+ languages.
Why switchBoth replace grepping with a queryable code model. tokensave pre-indexes a semantic graph in libSQL and answers symbols, callers and impact radius in one call; Serena offers IDE-grade editing and refactoring on top of retrieval, not just lookup.
Full comparison → cocoindex-codeAST-based semantic code search for coding agents: pipx install, zero config, local embeddings out of the box — a CLI/skill/MCP that cuts agent context ~70% vs grepping. Built on CocoIndex.
Why switchRetrieval by meaning versus retrieval by structure: cocoindex-code does AST-aware semantic search with local embeddings and zero config, Serena resolves actual symbols and references through the language server. Search when you don't know the name, Serena when you do.
Full comparison →