code-graph-rag vs codegraph-mcp
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 — No-train, on-prem code knowledge graph served to AI agents over MCP — symbols, call edges, cross-language links and blast-radius queries, with a hash-chained audit log of every read.
Both 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.
| code-graph-rag | codegraph-mcp | |
|---|---|---|
| Stars | 2.5k | 7 |
| Forks | 408 | 0 |
| Language | Python | Python |
| License | MIT | NOASSERTION |
| Last activity | yesterday | 1 months ago |
| Topics | coding, rag | coding, rag |
| Curated connections | 4 | 8 |
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.
codegraph-mcp — the curator's take
Niche but real: if agents must understand code that cannot leave your building AND compliance asks 'what exactly did the agent read', the tamper-evident audit chain is the only game in town; cross-language call edges (TS fetch → Go/Python handler) catch what one-file context misses. NOT for most teams yet: 5 stars, single vendor, and — deal-breaker until fixed — no clear open-source license (NOASSERTION on GitHub). Treat it as an evaluation candidate, not a dependency.