vibecoded-orchestrator

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SUMMARY

VibeCoded Tools - Orchestrator: improve your Claude Code!

README.md

VibeCoded Orchestrator

CI
Latest release
License: AGPL-3.0
Stability: alpha

A local knowledge graph and code graph for Claude Code, so it stops forgetting your project between sessions.

Status: alpha (v0.2.x). Validated end-to-end on Linux + Windows; macOS is Tier-2 (script-ready, no signed binary). Bugs and rough edges expected — please file Issues. License: AGPL-3.0. Runs entirely on your machine.

What it actually does

Three concrete pains everyone using Claude Code hits, and what VCO does about each:

Pain What VCO does
Claude has no memory between sessions — you re-explain the project every time A persistent Knowledge Graph (markdown nodes + Weaviate vector index) that hooks read on session start and inject into the context window
Claude can't see your codebase structure — it greps blind, misses callers, re-reads the same files A Code Graph of modules, classes, functions, APIs, and cross-service calls, queryable by purpose ("find auth middleware") not just name. Structure is extracted per-language; call edges use Python's ast plus, when the optional codegraph-ts extra is installed, tree-sitter grammars for 10+ other languages
You set up the same .claude/ config and hooks in every new project An installer that drops 36 automation hooks + 53 skills + 44 agents into .claude/, wires the MCP servers, and stays out of your way

You don't change how you use Claude Code. The orchestrator runs in the background through hooks. Open VS Code, talk to Claude, ship code.

Install (≈5 min + first-run downloads)

Primary install guide: vibecodedtools.com/quickstart — same content, more readable, kept in sync with releases.

After cloning, run the entry point for your OS:

OS Install (one-time) Start launcher
Linux bash first-install.sh or double-click first-install.desktop bash start-launcher.sh or start-launcher.desktop
macOS Double-click first-install.command (or bash first-install.sh) Double-click start-launcher.command
Windows Double-click first-install.bat (or .\first-install.bat from a terminal — the leading .\ is required in cmd/PowerShell) Double-click start-launcher.bat

One-liner (Linux / macOS):

git clone https://github.com/hotak92/vibecoded-orchestrator.git && cd vibecoded-orchestrator && bash first-install.sh

first-install.{sh,command,bat} is a thin OS shim (~100 LoC) that runs three steps: (1) detect a usable Python 3.11+ via an OS-aware candidate cascade and prompt to install via the platform package manager if missing (Homebrew on macOS, apt/dnf/pacman/zypper/apk on Linux, winget on Windows); (2) run install.py --bootstrap --json — a read-only system-detection prepass that writes a diagnostic envelope to state/logs/bootstrap-prepass.json (Python/Node/Podman versions, GPU, RAM, OS, package-manager advice); (3) run install.py for the canonical 10-step install. On success, scripts/post-install-launcher.sh auto-spawns the launcher GUI — pass --no-auto-launch to skip.

The bootstrap prepass has no install side effects; failure there does not block the full install. It exists to make failed installs diagnosable without re-running probes by hand.

Tri-OS install smoke CI (install-smoke-tri-os.yml) runs the actual first-install.{sh,command,bat} end-to-end on ubuntu-22.04, ubuntu-24.04, macos-14, windows-latest, and fedora-40 on every PR + push to main + daily at 06:00 UTC; pre-ship gate 22 blocks release tags when this workflow is red on main.

Allow ~5–10 min plus first-run image downloads (~5 GB: Weaviate + Ollama qwen3 weights, +2.5 GB if GPU mode pulls CodeSage-Large-v2).

If anything fails partway through, paste docs/INSTALL_RECOVERY.md into Claude Code — it walks Claude through diagnosing and finishing the build.

Who this is for

If you use VS Code (or any IDE) with Claude Code, this is for you. The orchestrator runs entirely in the background — it indexes your knowledge, your codebase, your tool calls, and feeds Claude richer context every time you talk to it. Your workflow doesn't change.

If you don't use Claude Code: the KG, code graph, MCP servers, and launcher GUI all work standalone, but the value is highest when there's an AI client driving them. See Compatibility.


Features

  • Knowledge Graph — Obsidian-style markdown nodes with typed WikiLinks, indexed in Weaviate via qwen3 embeddings (1024-dim, local). Optional OpenAI embeddings.
  • Code Graph — per-language structural analysis across 10+ languages, populating CodeModule, CodeClass, CodeFunction, CodeAPI, CodeInteraction collections. Call edges (callers / path queries) come from Python's ast; installing the optional codegraph-ts extra (pip install '.[codegraph-ts]', opt out at install with VCT_SKIP_CODEGRAPH_TS=1) adds tree-sitter grammars so call edges extend to rust, go, javascript, typescript, java, c#, c/c++, ruby, lua, and bash. Without the extra those languages simply get no call edges (the rest of the graph is unaffected).
  • 36 automation hooks — context injection on prompt submit, KG/code-graph auto-sync on file edit, credential scans, compaction-preserving context replay, security checks. Linux/macOS use .sh; Windows ships native .ps1. Plus the vct-hub background service that resolves per-project config for hooks, MCPs, and scripts.
  • MCP servers (default install) — 4 registered in ~/.claude.json via launcher/src-tauri/src/mcp_registration.rs::build_default_mcp_entries: weaviate-kg (semantic + graph search + code graph) and search (academic papers via OpenAlex + arXiv) are enabled by default per project; mermaid and excalidraw are registered but default-disabled per project (launcher/src-tauri/vct-launcher-core/src/db/project_mcp_servers.rs::BUNDLED_MCP_DEFAULT_DISABLED) — claude mcp list shows them connected but their tools are not callable until the user opts in via the launcher's Diagrams tab. A fifth MCP — playwright — is enabled by default and invoked separately via npx -y @playwright/mcp@latest (install.py pre-caches at _install_playwright_browsers; opt out with VCT_SKIP_PLAYWRIGHT=1). The vct-coordination MCP is Pro-tier and excluded from the default install. All local, no per-tool API keys. Ollama runs as backend infrastructure (Weaviate vectorizer + code-embed CPU fallback) regardless of MCP wrappers; the CodeSage code-embedding FastAPI service runs locally on port 11440.
  • 44 agents + 53 skills — shipped via install.py templates. Agents handle planning, coding, testing, doc maintenance, KG navigation, code-graph health. Skills cover security review, debugging, architecture, RAG advisory, accessibility, etc.
  • Workflow plumbing — session state tracking (CONTEXT_STATE.md), plan files, memory management, pre-/post-compact context replay so a /compact doesn't lose your thread.

How it works

You type in Claude Code
        |
        v
[UserPromptSubmit hook]
  -> Searches Knowledge Graph for relevant nodes
  -> Searches code graph for relevant entities
  -> Injects results into Claude's context window
        |
        v
Claude generates response, edits files
        |
        v
[PostToolUse hooks]
  -> Auto-syncs edited knowledge / code to KG / Code Graph
  -> Scans for credential leaks
  -> Updates session state

What runs when you add a project

When you click Add project in the launcher GUI, create_project_v2
returns the moment bundle install finishes — but three background tasks
then fan out in parallel so a project with pre-existing
knowledge/**/*.md, docs/**/*.md, or source code lands fully indexed
without manual CLI invocations:

  1. Code graph buildcode-graph-analyze over the project root,
    populating CodeModule / CodeClass / CodeFunction / CodeAPI /
    CodeInteraction collections.
  2. KG sync.claude/scripts/kg-sync --all walks
    knowledge/**/*.md and docs/**/*.md and embeds them into the
    per-project Weaviate collections (<Project>_KnowledgeGraph and
    <Project>_Development). Added in 0.2.2.
  3. KG summaries.claude/scripts/generate-kg-summary.py over
    each knowledge/**/*.md file, producing the
    knowledge/.node_formats.json sidecar consumed by
    hybrid_search's summary tier. Three-tier backend fallback:
    claude CLI on PATH → Ollama at KG_SUMMARY_OLLAMA_URL (default
    http://localhost:11435, model qwen3.5:9b) →
    ANTHROPIC_API_KEY direct → silent skip. Added in 0.2.3.

The project page shows three stacked status banners (KG summaries,
KG sync, code graph build) under the project header — pending /
running / failed always visible, success / skipped auto-hide
30 s after completion. The project header carries three retry
buttons (Re-build code graph, Re-sync KG, Re-build KG summaries).
The launcher's boot sweep marks any running rows left behind by a
prior crash as failed with "launcher crashed mid-run; click Retry to re-run", and re-spawns any pending rows. The summariser
content-hashes each node, so retries on an already-summarised
project are a cheap no-op.

If neither claude CLI nor Ollama is available when the KG-summary
task runs, the third banner goes yellow skipped after the first
node and shows the install hint under Show details; summaries then
backfill incrementally as you edit nodes in Claude Code sessions via
the PostToolUse hook kg-summary-generator.{sh,ps1}.

Where this fits

VCO sits on top of Claude Code rather than replacing your AI assistant. The comparison below is for buyers choosing how to spend their AI-coding attention — VCO + Claude Code, vs. an all-in-one closed product, vs. another open-source extension.

Dimension Cursor GitHub Copilot Augment Devin OpenAI Codex CLI Aider Cline VCO + Claude Code
Open source No No Partial (some components) No Yes (CLI) Yes (MIT) Yes (Apache-2.0) Yes (AGPL-3.0)
Runs locally (no code in vendor cloud) Partial (cloud Composer) No No No Partial (CLI local, API cloud) Yes Yes Yes
Persistent memory across sessions No Yes (Copilot Memory, repo-scoped, 28-day expiry) Partial (team memory) Partial (session-bound) No No No Yes (KG, no expiry)
Code graph (AST, callers, APIs) Partial (file index, opaque) Partial (vector index) Yes (Context Engine) Yes No Yes (repomap) Partial (Tree-sitter, not persisted) Yes (persisted graph)
Bring your own LLM subscription Partial (chat only) No Partial (BYO agent, not LLM) No Yes (OpenAI) Yes (75+ providers) Yes (30+ providers) Yes (Claude)
User-extensible (hooks / agents / skills) Yes (hooks + skills, no marketplace) No Limited (MCP only) No Limited (skills as prompts) Yes (open source) Yes (open source) Yes (36 hooks, 53 skills, 44 agents)
Pricing model $20/mo SaaS $10–20/user/mo BYOA + cloud compute $20/mo + usage Per-token OpenAI Free + your LLM Free + your LLM Free + your Claude sub; €19/mo Pro
Polished v1 product (vs. alpha) Yes Yes Yes Yes Yes Yes Yes No — alpha

Reading the table: Cursor, Copilot, Augment, and Devin are closed all-in-ones — they ship the editor, the AI, the context layer, and the cloud, bundled. Aider, Cline, Continue, and Codex CLI are open-source / BYOL but lack persistent memory and (in most cases) a structural code graph. VCO is the combination that doesn't otherwise exist: open-source, local, BYO-Claude-subscription, and it has both persistent memory and a code graph. The trade you're making for that is product polish — VCO is alpha, the others are stable v1+.

Facts current as of May 2026. Competitor products move fast — verify the row you care about before quoting.

Compatibility

Works with all three Claude Code surfaces. Primary target is the VS Code extension; the Desktop app and standalone CLI are also supported. Hooks fire, agents and skills load, and MCP servers connect regardless of which surface you launch from.

The launcher writes two config files when it creates a project — .claude/settings.json (canonical, read by every Claude Code surface AND propagated to MCP subprocesses) and .claude/env (POSIX shell-sourceable copy) — both carrying the same values, so switching surfaces requires no reconfig. (v0.2.12 / PR-27 / 2026-05-16: a historical third surface, .vscode/settings.json claude-code.env, was removed because it didn't propagate to MCP subprocesses on Linux. See docs/CLAUDE_CODE_COMPATIBILITY.md → "Per-project env files".)

See docs/CLAUDE_CODE_COMPATIBILITY.md for the surface matrix and known caveats.

Downloads (Launcher GUI)

The launcher GUI ships as a per-OS standalone artifact on GitHub Releases.

OS Artifact Notes
Windows 10/11 (x64) vct-launcher-windows-x64.exe Portable, no installer. Unsigned — SmartScreen → "More info" → "Run anyway" on first run. Code signing on backlog.
Linux (x64) *.AppImage (portable) or *.deb AppImage: chmod +x VCT_Launcher_*.AppImage && ./VCT_Launcher_*.AppImage. .deb: sudo dpkg -i vct-launcher_*.deb.
macOS (Apple Silicon) vct-launcher-macos-arm64.zip (dist binary at launcher/dist/macos-arm64/vct-launcher) Tier-2. Ad-hoc codesigned, not notarized — Gatekeeper warns once. See docs/macos-install.md.

No GUI yet? The CLI install path (above) covers all three OSes.

Requirements

Required (install halts and prompts if missing):

  • Python 3.11+ (3.12 is what we develop and CI-test on; 3.13 supported). 3.10 and older are rejected — we depend on stdlib tomllib.
  • Docker or Podman (for Weaviate + Ollama containers).
  • Node.js 18+ with npm — only when building the launcher GUI from source AND/OR installing the Claude Code CLI. Not needed if you use the bundled prebuilt launcher and the VS Code extension.
  • A Claude subscription (Pro / Max / Team / Enterprise) — Claude Code authenticates against your subscription; the orchestrator exists to feed it context. Free Anthropic accounts can browse claude.ai but cannot authenticate Claude Code.

Recommended:

  • VS Code with the Claude Code extension — primary target.
  • The standalone Claude Code CLI (npm install -g @anthropic-ai/claude-code) — VCO uses it to summarize new KG nodes when present, falls back to a local Ollama model otherwise.

Auto-installed when needed:

  • pnpm (via npm install -g, falls back to plain npm).
  • Tauri Linux build deps (apt only) — needed only when building the launcher from source.
  • GPU drivers — detected, not installed; the install prints download URLs if missing.

Optional companions (install asks; default Y):

  • lean-ctx — Rust binary, MIT, zero telemetry. Compresses CLI output by 90–97%, which translates to shorter Claude context windows and lower token costs. Auto-installs via Homebrew / Cargo / AUR when available. Skip with --no-lean-ctx.

Hardware

The installer auto-selects three backends — code embeddings, KG / text embeddings, and KG-summary generation — based on detected VRAM / RAM / CPU cores. All three can be overridden later from the launcher's Preferences panel.

Code embeddings (vectorise functions, classes, modules; populates CodeModule, CodeFunction, CodeClass, etc.):

Tier Backend Model Notes
GPU, VRAM ≥ 12 GB GPU service codesage-large-v2 (2048-dim) Best quality. Code-specialised, served from code_embedding_service.
GPU, VRAM ≥ 6 GB Ollama qwen3-embedding:0.6b (1024-dim) Generalist; runs comfortably alongside other GPU workloads.
GPU, VRAM > 2 GB Ollama unclemusclez/jina-embeddings-v2-base-code:latest (768-dim) Code-specialised, low VRAM footprint.
CPU, RAM > 24 GB & 8+ cores¹ Ollama qwen3-embedding:0.6b Pure-CPU path on capable workstations. Strict > (v0.2.49).
CPU, otherwise Ollama unclemusclez/jina-embeddings-v2-base-code:latest Floor — runs on anything that can run Ollama.
OpenAI API (opt-in) OpenAI text-embedding-3-small (1536-dim) Override only; costs per embedding. Configure via --openai-key.

KG / text embeddings (vectorise knowledge nodes + docs; populates <KG_COLLECTION>, shared KG, <DEVELOPMENT_COLLECTION>):

Tier Backend Model Notes
GPU, VRAM > 8 GB Ollama qwen3-embedding:0.6b (1024-dim) Default; matches the existing KG schema slot qwen3_embed. Strict > (v0.2.49).
GPU, VRAM 4–8 GB¹ Ollama snowflake-arctic-embed2:latest (1024-dim) Same dims as qwen3 → same schema slot, smaller working set. Inclusive at 8 GB (v0.2.49).
GPU, VRAM < 4 GB (or unsupported) Ollama snowflake-arctic-embed2:latest Falls through to CPU treatment.
CPU, RAM > 24 GB & 8+ cores¹ Ollama qwen3-embedding:0.6b Pure-CPU path on capable workstations. Strict > (v0.2.49).
CPU, otherwise Ollama snowflake-arctic-embed2:latest Floor for low-RAM / low-core hosts.
OpenAI API (opt-in) OpenAI text-embedding-3-small (1536-dim) Override only; configure via --openai-key.

KG-summary generation (LLM-written descriptions + per-chunk summaries used by hybrid_search's detail="summary" tier — search still works without these, just with raw KG content):

Tier Backend Model Notes
claude CLI on PATH (authenticated) Claude CLI haiku Always preferred; costs come out of your Claude subscription quota.
GPU, VRAM ≥ 16 GB Ollama qwen3.5:9b Highest local quality.
GPU, VRAM ≥ 6 GB Ollama gemma4:e4b Fast, low VRAM footprint.
CPU, RAM ≥ 12 GB & 6+ cores Ollama gemma4:e4b Pure-CPU fallback.
Anything below those tiers none none Summaries skipped; raw KG content still embedded + searchable.
OpenAI API (opt-in, requires consent) OpenAI gpt-4o-mini Off by default; toggle via Preferences → KG Summaries. Cost warning surfaced on enable.

¹ v0.2.49 boundary fixes (2026-06-07): CPU core thresholds now count physical cores (not logical/SMT threads); RAM/VRAM boundaries moved from >= to > at the 24 GB / 8 GB cutoffs to avoid performance cliffs on boundary hardware. See [[embedding-backend-auto-selection-v0249.md]] for rationale.

Install options & troubleshooting

The full flag list (--gpu, --cpu-only, --low-resource, --openai-key, --update, etc.) and the troubleshooting matrix live in:

Non-interactive / CI: python install.py --quiet --no-containers

Project structure

vibecoded-orchestrator/
├── .claude/
│   ├── hooks/                 # 36 automation hooks (.sh + .ps1 per hook)
│   ├── scripts/               # CLI tools for KG and code graph
│   └── settings.json          # Claude Code configuration
├── claude_mcp_servers/
│   ├── weaviate_mcp/          # Semantic + graph search (default)
│   ├── search_mcp/            # Academic-paper search via OpenAlex + arXiv (default)
│   └── code_embedding_service/ # CodeSage-Large-v2 via FastAPI (default)
│   # Opt-in MCPs (launcher → Modules): vct-ollama (local LLM/embeddings/vision)
├── templates/
│   ├── agents/free/           # 44 bundled agents
│   └── skills/                # 53 bundled skills
├── infrastructure/
│   ├── docker-compose.yml     # Weaviate + Ollama
│   └── docker-compose.gpu.yml # NVIDIA overlay
├── knowledge/                 # Knowledge graph nodes (your persistent memory)
├── docs/                      # Documentation
└── CLAUDE.md                  # Instructions for Claude when opening this repo

Tiers

The whole repository is AGPL-3.0. The codebase you see here is the Free tier — fully functional. Optional paid modules ship as separate signed binaries delivered through the launcher; their source is not in this repo.

Tier Price What you get
Free €0 Full orchestrator: KG, code graph, 36 hooks, 4 default MCP servers in ~/.claude.json (weaviate-kg + search enabled per project; mermaid + excalidraw registered but default-disabled until opt-in) + playwright via npx, 44 agents, 53 skills. AGPL-3.0.
Pro €19/month Free + RL-scored retrieval reranking module + coordination layer (Telegram groups + shared decision/task channels). Modules ship as separate signed binaries via the launcher. Self-host the coordination DB at no extra cost, or use our hosted instance for a small additional fee.
Enterprise Contact us Free + commercial AGPL exemption, priority support, custom SLAs. [email protected]

Pricing finalized at module launch. The Free tier is the whole repository — no source-level dual licensing, no feature gating in the OSS code. RL retrieval falls back to cosine ordering when the Pro module isn't installed.

Telemetry

Telemetry is OPT-IN. Nothing is sent unless you explicitly enable it during install (or later via Settings → Telemetry). When opted in, we collect ONE thing only:

  • Retrieval-chunk embeddings — dense vectors (~2–8 sentences per chunk) produced by the local embedder when it indexes your KG / code graph, paired with which chunks were retrieved and which the user/agent actually used.

These are used exclusively to refine the RL reranker. The training pipeline is open source (claude_mcp_servers/ + state/rl_*).

Never collected, even with telemetry on:

  • Raw text of your KG nodes, code, files, or queries
  • File paths, project names, repo identifiers
  • API keys, secrets, environment variables
  • Personal info, IP, hostname, machine identifiers

Embeddings are aggregated, irreversible representations — we can't reconstruct the source text from the vectors we receive.

Toggle off anytime: Settings → Telemetry → Disable, or VCT_TELEMETRY=0 in .env. Disabling stops collection immediately; previously-collected data isn't deleted retroactively unless you email [email protected].

Licensing

Entire repository is AGPL-3.0-or-later. No source-level dual licensing.

  • Individuals and non-commercial users: use freely under AGPL.
  • Companies running this in a service or product: either open-source your modifications under AGPL, or buy a commercial license / Enterprise subscription. Open the launcher → Settings → Activate License (the OnboardingWizard walks you through the in-product purchase flow).

Commercial-module model: free source under AGPL, plus optional paid binaries delivered via signed-URL CDN. Paid modules ship as pre-compiled, Ed25519-signed artifacts gated by Lemon Squeezy — subscribers receive binaries, not source. The license validator in this repo (VCThelpers/) is AGPL like the rest; the trust root for paid-module access is server-side (Supabase + Lemon Squeezy + signature verification on download).

Contributing

Small fixes and bug reports especially welcome. See CONTRIBUTING.md for workflow and CLA.md for the Contributor License Agreement (accepted via git commit -s).

Documentation

Links


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