codegraph-mcp vs gortex
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. — 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 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.
| codegraph-mcp | gortex | |
|---|---|---|
| Stars | 7 | 1.1k |
| Forks | 0 | 98 |
| Language | Python | Go |
| License | NOASSERTION | Apache-2.0 |
| Last activity | 1 months ago | yesterday |
| Topics | code-intel, rag | code-intel, local |
| Curated connections | 9 | 3 |
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.
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.