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 switchSame job — a code knowledge graph served to agents over MCP: codegraph-mcp bets on compliance (hash-chained audit of every read, cross-language HTTP edges); tokensave bets on breadth and token savings (50+ langs, 80+ tools, 12 agent integrations).
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 switchTwo ways to hand an agent a graph of the repo: codegraph-mcp derives symbols, call edges and blast radius mechanically from source, MegaMemory stores the concepts and decisions an agent wrote down. Mechanical facts vs. curated intent.
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 switchBoth build code knowledge graphs for AI agents. codegraph-mcp is a read-side, on-prem MCP service with audit logging; code-graph-rag adds NL→Cypher queries, data-flow tracing and AST-based editing on Memgraph. Compliance-friendly retrieval vs full query-and-rewrite.
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 pre-index a semantic code graph and serve it to agents over MCP; code-review-graph adds blast-radius/risk scoring and a CI review action, codegraph-mcp stays a leaner symbol/caller index.
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 switchBoth serve codebase context to agents over MCP. codegraph-mcp builds a cross-language knowledge graph with audit logging for on-prem rigor; cocoindex-code trades graph depth for zero-config semantic search that installs in a minute.
Full comparison → codeflowPaste a GitHub URL or drop a local folder → interactive architecture map in the browser: dependency graph, blast radius, health grade, security scan. Single index.html, zero install.
Why switchSame 'understand the codebase structure' job: codegraph-mcp is an on-prem symbol/call-edge knowledge graph consumed by agents; codeflow is a zero-install visual map consumed by people.
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 switchBoth serve on-prem code graphs with call edges and cross-language links over MCP; codegraph-mcp differentiates on the hash-chained audit log, gortex on coverage and agent integrations.
Full comparison → repowiseCodebase intelligence for AI and humans: deterministic code-health scores calibrated on real defects, graph-aware refactoring plans agents can execute, auto-docs and git analytics over 9 MCP tools.
Why switchBoth serve a code knowledge graph to agents over MCP; codegraph-mcp goes deep on cross-language symbol/call/blast-radius queries, repowise trades some of that depth for defect-risk scores and refactoring plans.
Full comparison → Understand-AnythingPlugin for Claude Code and 16 other hosts that turns any codebase into an interactive knowledge graph — multi-agent analysis, layered dashboard, guided tours, diff-impact view, domain mapping.
Why switchBoth build knowledge graphs of your codebase, for opposite consumers: codegraph-mcp serves symbols, call edges and blast-radius queries to AI agents over MCP with an audit chain; Understand Anything renders an interactive dashboard for human comprehension and onboarding.
Full comparison →