Andrey Kumanyaev 275780299a fix(mcp): expose the disk-commit boundary when a mutating tool is abandoned
A tool call is bounded by a transport deadline; when it fires, boundHandler
answers the client and lets the handler keep running. agents.AtomicWriteFile
takes no context, so a handler that had already reached its rename committed to
disk anyway — and the caller was told only "the work may still complete in the
background, so treat any side effect as unknown".

That single message covered two opposite outcomes. A client reading it as
"nothing happened" retried, or fell back to its own editor, and applied the same
logical change twice.

Split the two states and make both observable:

- Every disk commit registers in a durable ledger BEFORE the write and is
  stamped terminal immediately after it, so a receipt exists for any mutation
  that is still reachable.
- commitFileMutation adds a cancellation gate between registration and the
  write. Stopping there is a guarantee that nothing was written, not a guess;
  the remaining window is one atomic rename wide and is covered by an
  in_flight receipt.
- The abandoned-call error now reports what actually landed — committed with
  path and new_sha, not_applied, or genuinely unknown — plus a machine-readable
  mutation_commit={...} tail. Read tools keep the original wording.
- Edit responses carry disk_status and graph_status as independent fields:
  bytes reaching disk and the graph catching up are different questions with
  different failure modes.
- mutation_status (facade: change/receipt) queries a receipt by id,
  mutation_id, or path for 30 minutes, refreshing a still-pending graph state
  rather than answering from a stale snapshot.
- edit_file / write_file / edit_symbol take an optional mutation_id
  idempotency key, mirroring batch_edit's transaction_id: the same key with the
  identical edit replays the original result instead of writing twice, and a
  different edit under the same key is refused.

rename_symbol and the batch symbol/file edits register in the same ledger; a
coordinated rename now stops at its first cancelled write instead of attempting
every remaining file.

The agent preset's tools/list ceiling is re-based 28200 -> 28400 for the
mutation_id parameter on the two edit tools (148 bytes measured); the preset
itself did not grow.

Fixes #548
2026-08-19 23:01:25 +02:00
2026-06-06 22:29:36 +02:00
2026-08-19 22:58:13 +02:00

Gortex

High-performance and efficient code-intelligence engine for AI agents and IDE

Indexes code into graph and exposes it via CLI, MCP Server, and web UI. Multi-repository support by default.

Single static binary for macOS, Linux, and Windows — no dependency chain, simple installation and use.


CI Latest release Sigstore signed SLSA 3 VirusTotal macOS Linux Windows

MCP Toplist OpenSSF Scorecard Go Reference Discord
zzet%2Fgortex | Trendshift

High-quality parsing 257 languages/grammars through tree-sitter AST analysis, in-process resolvers, enhanced with compiler-grade resolution for Python, TypeScript / JavaScript, PHP, C#, Go, C, C++, Java, Kotlin, Swift, Zig, Rust, Ruby, Elixir, Ocaml, Haskell, and others - producing a persistent provenance-tiered knowledge graph of functions, classes, call chains, HTTP routes, and cross-service contracts and calls with a strong confidence model. 175 (configurable) MCP tools - use only what you need. Zero dependencies. Plug and play across 19 coding agents. Up to 50× fewer tokens per response. Reproducible benchmarks.

19 AI coding agents (Claude Code, Kiro, Cursor, Windsurf, VS Code / Copilot, Continue.dev, Cline, OpenCode, Antigravity, Codex CLI, Gemini CLI, Zed, Aider, Kilo Code, OpenClaw, Hermes, Oh My Pi, Pi, Kimi) supported out of the box.

One install configures every one detected on your machine — see docs/agents.md.

Gortex Web UI — force-directed knowledge graph visualization

Gortex Web UI — force-directed knowledge graph visualization

Why it matters

  • 50× fewer tokens per response — graph-native lookups beat naive file reads. Agents read just what they need, not the full file, not the 500-line file around it.
  • Full development cycle - no 'read file before edit'. Agents ask for sources, tell what to change, and don't waste context of reading noise.
  • 257 languages/grammars - every file in the repository reachable; no mix of tools and bloating/hallycinating agents. Three tiers (bespoke tree-sitter, regex, forest-backed signatures) plus Jupyter and Databricks notebooks → docs/languages.md
  • Cross-repo by default — N repos in one graph; contracts, references, and call chains span repo boundaries with evidence-gated resolution, contract matching, impact analysis, per-session isolation → docs/multi-repo.md
  • Extreamly fast analysis — a precomputed depth-3 reach index turns blast-radius queries into O(seeds × reach) map lookups. Safe to ask "what breaks if I change this?" on every edit. No dozens of tool calls to grasp context.
  • Zero external dependencies — single binary, everything in-process. No network, no model download to get started. Install, start daemon, use.
  • Agent integrations (20)gortex init configures every detected coding assistant on the machine → docs/agents.md
  • 100+ MCP tools, 16 resources, 3 prompts — symbol lookup, call chains, blast radius, dataflow, clone detection, refactoring, code actions → docs/mcp.md
  • Semantic search default-on — baked GloVe-50d (3.8 MB embedded), store-native FTS5/BM25 + vector with adaptive alpha fusion, zero deps; opt-in MiniLM / Ollama / OpenAI → docs/semantic-search.md
  • Speculative executionpreview_edit / simulate_chain answer "what would change if I applied this WorkspaceEdit?" without touching disk
  • Live editor overlays — push unsaved buffers as a shadow graph; tools read through it. Branching for parallel speculative sessions
  • GCX1 wire format — published, round-trippable. An additional 27% tokens vs JSON at same fidelity → docs/wire-format.md
  • Long-living daemon — one process serves every IDE window; live fsnotify, an on-disk SQLite graph store, incremental restart, OS-supervised lifecycle
  • 9 LLM providers (optional) — local llama.cpp, Anthropic, OpenAI, Ollama, Claude / Codex CLI subprocess, Gemini, Bedrock, DeepSeek → docs/llm.md
  • Composable safetyverify_change, check_guards, audit_agent_config flag broken callers, guard violations, stale docs before they ship
  • PR review, end to endgortex prs triages open PRs (per-PR blast radius, merge-order conflicts via shared communities, AI-ranked queue, reviewer suggestions); gortex review emits line-anchored findings with a BLOCK/REVIEW/APPROVE verdict from a graph-grounded rulepack; MCP tools (pr_risk, get_pr_impact, review, review_pack, post_review, …) expose it to agents → docs/cli.md
  • HTTP server + Web UI — versioned /v1/* API + MCP 2026 Streamable HTTP; standalone Next.js 15 UI with five 3D graph modes → docs/server.md
  • Telemetry off by default — opt-in anonymous tool/command counts only (no code, paths, names, or exact counts); nothing transmitted unless you configure an endpoint. gortex telemetry on|off|status; honours DO_NOT_TRACKdocs/telemetry.md

Full catalog of features: docs/features.md. Complete CLI reference: docs/cli.md.

Install

# macOS / Linux
curl -fsSL https://get.gortex.dev | sh

# Windows (PowerShell)
irm https://get.gortex.dev/install.ps1 | iex

Detects OS/arch, verifies SHA256 + cosign, installs to PATH. Re-run to upgrade. Homebrew, .deb / .rpm / .apk, scoop, signed binaries, and from-source builds: docs/installation.md.

Quick Start

gortex install                          # one-time machine setup (MCP, skills, slash commands)
gortex daemon start --detach            # background daemon
gortex track ~/projects/myapp           # add a repo
cd ~/projects/myapp && gortex init      # per-repo: .mcp.json, hooks, community routing

Your AI assistant now uses graph queries. Full 15-minute walkthrough: docs/onboarding.md.

Cross-Repo API Contracts

Gortex auto-detects API contracts across repos and matches providers to consumers, surfaced via the contracts MCP tool and the web UI Contracts page.

Contract type Detection Provider Consumer
HTTP routes Framework annotations (gin, Express, FastAPI, Spring, …) Route handler HTTP client calls (fetch, http.Get)
gRPC Proto service definitions Service RPC Client stub calls
GraphQL Schema type/field definitions Schema Query/mutation strings
Message topics Kafka / RabbitMQ / NATS / Redis pub/sub Publish calls Subscribe calls
WebSocket Event emit/listen patterns emit() on()
Env vars os.Getenv, process.env, .env files Setenv / .env Getenv / process.env
OpenAPI Swagger / OpenAPI spec files Spec paths (linked to HTTP routes)
Temporal workflows Go / Java SDK annotations Activity / workflow function ExecuteActivity / ExecuteChildWorkflow

Contracts are normalised to canonical IDs (e.g. http::GET::/api/users/{id}) and matched across repos to detect orphan providers / consumers and mismatches. See docs/contracts.md.

Scale — battle-tested on large repos

Measured on an Apple Silicon laptop with the default CGO build.

Repository Files Nodes Edges Index time Throughput Peak heap
torvalds/linux 70,333 1,690,174 6,239,570 ~3 min 300 files/s 5.07 GB
microsoft/vscode 10,762 204,501 808,902 ~1 min 143 files/s 580 MB
zzet/gortex (self) 430 5,583 53,830 3.4s 127 files/s 52 MB

Parsing dominates wall time (6580 %); reference resolution and search-index build scale sub-linearly.

Token savings dashboard

gortex savings reports tokens saved vs naive file reads — per-call, per-session, and cumulative across restarts, priced in USD against the headline model.

Gortex Token Savings
====================
Cost avoided:   $168.69 (claude-opus-4) across 1,878 calls · 11,246,094 tokens saved

Today       ████████░░░░░░░░   50.0%  saved 9,200 / 18,400 tokens   $0.14
Last 7 days ██████████░░░░░░   62.5%  saved 60,100 / 96,200 tokens  $0.90
All time    ███████████████░   93.3%  saved 11,246,094 / 12,050,716 tokens  $168.69

--verbose adds the per-tool breakdown; --json is machine-readable. Full reference: docs/savings.md.

Architecture

gortex binary
  CLI (cobra)    ──> MultiIndexer ──> Graph store (SQLite, shared, per-repo indexed)
  MCP (stdio)    ──────────────────> Query Engine (repo/project/ref scoping)
  HTTP /v1/*     ──────────────────> same tools + /v1/graph + /v1/events (SSE)
  Daemon (unix)  ──────────────────> shared graph for every MCP client, session isolation
                  MultiWatcher    <── filesystem events (fsnotify, per-repo)
                  CrossRepoResolver ──> cross-repo edge creation (type-aware)
                  Persistence     ──> the same SQLite store, written as it indexes

Data flow, graph schema (node and edge kinds, multi-repo fields, test taxonomy), persistence model: docs/architecture.md.

Documentation

Topic Reference
First-time walkthrough onboarding.md
Installation & supply-chain verification installation.md
Full feature catalog features.md
CLI reference cli.md
MCP tools, resources, prompts mcp.md
Multi-repo workspaces multi-repo.md
HTTP server + Web UI + MCP 2026 transport server.md
Cross-repo API contracts contracts.md
Semantic search semantic-search.md
Optional LLM features llm.md
LSP integration lsp.md
Per-community skills & agent usage skills.md
AI agent adapters (20) agents.md
Supported languages (257) languages.md
Token savings savings.md
GCX1 wire format wire-format.md
Architecture & graph schema architecture.md
Evaluation methodology 04-evaluation/
Telemetry & privacy telemetry.md
Versioning policy versioning.md

Building from source

make build          # binary with version stamping
make test           # go test -race ./...
make lint           # golangci-lint

Requires Go 1.26+ and CGO (for tree-sitter C bindings).

License

Apache License 2.0. See LICENSE.md. Copyright 2024-2026 Andrey Kumanyaev me@zzet.org.

Contributing

See CONTRIBUTING.md for guidelines on adding features, language extractors, and submitting PRs.

New contributors are very welcome — join us on Discord and introduce yourself. It's the easiest place to ask questions, check whether someone is already working on something, and get feedback on a design before you write the code.

S
Description
High-performance code-intelligence engine for AI agents and IDE, supports 257 languages, multi repositories, based on graph, with access via CLI, MCP Server, and API. AI coding agents teammate - expose only needed information, cutting token usage up to 50x. 100% local. Discord: https://discord.gg/39MFHu3J5d|GitHub 镜像 1.5k · 🍴 134
https://github.com/zzet/gortex Readme 39 MiB
Languages
Go 99%
Python 0.5%
Shell 0.2%
TypeScript 0.1%