code-graph-rag vs gortex
Parses 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. — versus — Code-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.
Both build a tree-sitter graph of a polyglot repo; code-graph-rag stands up Memgraph and answers plain-English queries, gortex ships one static Go binary exposing MCP tools. Query surface vs zero infrastructure.
| code-graph-rag | gortex | |
|---|---|---|
| Stars | 4.8k | 1.5k |
| Forks | 637 | 132 |
| Language | Python | Go |
| License | MIT | Apache-2.0 |
| Last activity | today | today |
| Topics | code-intel, rag | code-intel, local |
| Curated connections | 5 | 6 |
code-graph-rag — the curator's take
Deepest of the code-graph tools: not just retrieval — NL→Cypher querying, FLOWS_TO taint tracing across C#/Java/C/Go, dead-code walks from entry points, ast-grep structural search-and-replace, and AST-surgical editing with diff preview, all over mixed languages in one schema. The price is infrastructure: you run Memgraph plus Python tooling. If you only want fast agent context, codebase-memory-mcp (ms indexing, single static binary) or cocoindex-code are far lighter; reach for this when agents need to *query and rewrite* structure, not just find it.
gortex — the curator's take
The coverage play on this shelf: 257 grammars, cross-repo and cross-SERVICE edges (HTTP routes, contracts) with a provenance/confidence tier the single-repo tools don't attempt, and one install that configures 19 coding agents. When NOT: 175 MCP tools is a context bill of its own — prune the toolset or your agent pays the index's price in schema tokens; benchmarks are self-published; and if you want memory as plain files, this is the opposite philosophy.