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 switchThe same exact job — pre-indexed structural code intelligence over MCP so agents stop grepping. TokenSave is a libSQL semantic graph across 50+ languages; codebase-memory is a zero-dependency C binary betting everything on speed: 158 languages, sub-ms.
Full comparison → 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 switchThe closest fight on the shelf: both are single-binary tree-sitter knowledge graphs served over MCP. codebase-memory-mcp bets on C-core minimalism; gortex on language breadth and cross-service resolution.
Full comparison → MegaMemoryMCP server that lets a coding agent build and query a typed concept graph of the project — SQLite store, in-process embeddings, merge-conflict resolution and a local web graph explorer.
Why switchcodebase-memory-mcp indexes structure with tree-sitter in milliseconds and answers 'what calls this'; MegaMemory holds LLM-authored concepts with embeddings and answers 'why is it like this'. Choose by the question you keep re-asking.
Full comparison → code-review-graphLocal-first code intelligence graph for AI coding tools: Tree-sitter AST graph + blast-radius analysis served over MCP, so agents read ~82x fewer tokens per review question.
Why switchBoth build tree-sitter AST graphs served over MCP with blast-radius answers. code-review-graph is tuned for the review workflow (82x fewer tokens per review question); codebase-memory is the general-purpose engine with LSP-grade types.
Full comparison → serenaThe IDE for your coding agent: an MCP toolkit giving symbol-level retrieval, editing, refactoring and debugging over real language servers — or a JetBrains plugin backend.
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 → 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 switchThe two query styles of agent code context: cocoindex-code embeds AST chunks for semantic 'find the code that does X'; codebase-memory graphs symbols for structural 'who calls this and what breaks'. Different questions — many stacks want both.
Full comparison → code-graph-ragParses a polyglot monorepo with Tree-sitter into a Memgraph knowledge graph: query it in plain English (NL→Cypher), trace data flow, find dead code, edit via AST-surgical patches.
Why switchSame tree-sitter-graph idea at opposite weights: codebase-memory-mcp is a single static C binary indexing 158 languages in milliseconds for cheap agent queries; code-graph-rag runs Memgraph to buy Cypher, taint edges and structural rewriting. Speed and zero deps vs analysis depth.
Full comparison → GraftContext layer for large codebases: a graph of plain-English markdown nodes — no embeddings, no index — agents read like any repo file. Claude Code hooks + MCP; 42% fewer tokens in its bench.
Why switchSame job — spare agents the re-exploration tax — opposite representations: codebase-memory-mcp serves a symbol-level tree-sitter graph over MCP with sub-ms queries; Graft writes plain-English concept nodes as markdown the agent simply reads.
Full comparison →