Gortex brings cross-repo code intelligence to MCP with 94% token reduction
Andrew Kumanyaev built Gortex, a Go-based tool that constructs an in-memory knowledge graph from multi-repo codebases and exposes it via 47 MCP tools, reporting a 94% reduction in token usage during complex investigations.
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Andrew Kumanyaev built Gortex after existing code indexers and graph-based retrievers failed to support cross-repository workflows. The tool constructs an in-memory knowledge graph capturing files, symbols, imports, call chains, and type relationships across multiple repos, then exposes this graph via 47 MCP tools that AI agents can query directly. A single `smart_context` call replaces the need to read 5–10 files to trace a call chain. Kumanyaev reports a 94% reduction in token usage — roughly 3,051,768 tokens saved across 618 calls — and cost avoidance ranging from ~$3 (claude-haiku-4.5) to ~$45 (claude-opus-4) based on built-in telemetry.
Andrew Kumanyaev describes building Gortex out of frustration with token-heavy AI coding sessions across multi-repository codebases. Existing tools required per-repo installation and re-indexing each time he switched between codebases, creating a fragmented workflow with constant context management overhead. Gortex addresses this with a workspace config that points at multiple repositories simultaneously, resolving symbols across all of them — the capability he found missing in every alternative he evaluated.
The knowledge graph Gortex builds goes beyond raw file contents, capturing files, symbols, imports, call chains, type relationships, and cross-repo dependencies.
The knowledge graph Gortex builds goes beyond raw file contents, capturing files, symbols, imports, call chains, type relationships, and cross-repo dependencies. It persists the graph to disk between sessions and restores it incrementally on startup. Key features include confidence-tiered call graph edges (flagging whether each edge was resolved by LSP, inferred from AST analysis, or matched via text patterns), community-scoped `SKILL.md` files auto-generated via Louvain clustering, and hybrid BM25 + vector semantic search powered by a bundled pure-Go ONNX runtime — no Python or external model server required. Indexing VS Code's ~10,700-file codebase takes about one minute on Apple Silicon; the Linux kernel (70K files, 1.69M nodes) takes around three minutes.
Kumanyaev reports that across 618 calls, Gortex returned 228,102 tokens while saving 3,051,768 — a 14.4x efficiency ratio. The tool exposes 47 MCP tools to AI agents, with `smart_context` as the primary interface for graph-derived answers. Gortex is written in Go, source-available under the PolyForm Small Business license, and free for individuals, open source projects, small businesses under 50 employees or $500K revenue, education, and government.
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