nvhive 0.36.0

NVHive — Multi-LLM orchestration platform with intelligent routing, hive consensus, and auto-agent generation

nvHive

A rootless NVIDIA AI lab for students, creators, agents, ComfyUI, and local models.

Official project: https://github.com/thatcooperguy/nvHive

Official PyPI package: https://pypi.org/project/nvhive/

Forks and third-party builds are welcome under the MIT License, but independent redistributions should use distinct project names, package names, release channels, and branding. See NOTICE and TRADEMARKS.

nvHive turns a fresh cloud Linux GPU desktop into a ready-to-use AI workstation without sudo: it finds persistent storage, installs into user-owned paths, opens a setup wizard, recommends models for the detected GPU, and gives students one-click paths for local LLMs, ComfyUI, agents, creative tools, game-dev tools, and music production.

At A Glance

First-time user question	nvHive answer
Where should this install?	AI Wizard auto-detects the writable persistent block volume and keeps models, apps, logs, jobs, and config under NVH_HOME.
What can I make?	Pick AI Starter, Graphics Creator Studio, Game Dev Lab, Music Producer Studio, or Agent Builder.
What models fit this GPU?	The wizard detects GPU/VRAM/CUDA and recommends local LLMs and ComfyUI profiles that fit the machine.
What if the VM image changed?	Boot health checks compare storage, drivers, CUDA, Python, Node, and receipts against the previous session.
What if something breaks?	Fix My Setup runs safe rootless repairs, and support snapshots redact secrets and local paths.
What should AI Wizard remember?	nvHive Vault keeps product memory, pilot notes, decisions, conflicts, and support playbooks as plain Markdown under NVH_HOME. Obsidian is optional.
Do I need root?	No for nvHive, local models, ComfyUI, Blender, Godot helpers, music tools, and OpenClaw. NemoClaw stays blocked until Docker already works without sudo.

The ideal student journey is:

1. Launch the NVIDIA Linux cloud desktop.
2. Run the one-line install or download the Linux binary.
3. Open nvHive AI Studio from the desktop icon.
4. Pick a mission.
5. Let AI Wizard install the right rootless tools into persistent storage.

What you get on the happy path:

• A desktop launcher and WebUI setup wizard.
• Persistent NVH_HOME/NVHIVE_HOME storage for models, ComfyUI, apps, logs, jobs, and config.
• A Workspace Passport for durable storage, boot drift, receipts, jobs, and the rootless policy.
• GPU-aware model recommendations and disk estimates.
• Mission cards for AI Starter, Graphics Creator, Game Dev, Music Producer, and Agent Builder.
• Self-diagnosing checks for storage, Python, Node, CUDA, drivers, boot drift, and install receipts, with one-click rootless repairs in the wizard.
• A product-aware AI Wizard chat helper that works immediately from local setup state, jobs, receipts, redacted logs, and rootless safety rules, then hands off to the local model when Ollama is healthy.
• A rootless Markdown vault for durable memory, known product conflicts, pilot test notes, and optional Obsidian viewing.
• Redacted error reports with request IDs when something needs debugging.

Release status: CI is green across Linux, Windows, and macOS. nvHive is a release candidate and should be treated as pilot-ready until the target NVIDIA Linux VM checklist passes on the actual no-root GPU desktop. PyPI production publishing should wait for that target VM acceptance pass.

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Install

Start with the Linux GPU desktop path if you are on a cloud workstation or GeForce NOW-style session. Other install paths are below for existing Python environments, local laptops, and single-file binary installs. No Docker, no container runtime, and no root access are required for nvHive itself.

For cloud desktops, large downloads should live on the persistent block-backed mount, not the read-only OS disk. The launcher finds the best candidate automatically. If you already know the mount path, set NVH_HOME first:

export NVH_HOME=/mnt/persist/nvhive

Recommended - Launch a Linux GPU desktop lab

This is the easiest path for cloud Linux GPU sessions where only a mounted file volume survives reconnects:

curl -sSL https://raw.githubusercontent.com/thatcooperguy/nvHive/main/start-linux.sh | bash

The launcher auto-detects a likely persistent block-backed mount, sets NVH_HOME, installs nvHive rootlessly if needed, creates the desktop launcher, starts the API/WebUI, and opens the setup wizard. If Python is missing, set NVH_USE_BINARY=1 and the same launcher downloads the single-file Linux binary instead of creating a venv.

curl -sSL https://raw.githubusercontent.com/thatcooperguy/nvHive/main/start-linux.sh | NVH_USE_BINARY=1 bash

General Linux installer

curl -sSL https://raw.githubusercontent.com/thatcooperguy/nvHive/main/install.sh | bash

Works on any Linux box with no root. Installs to NVH_HOME when set, otherwise it looks for writable persistent storage and prints the exact activation path. On Ubuntu cloud desktops where the 200GB+ block volume is mounted as your Linux home directory, the installer chooses /home/$USER/nvhive automatically. It prefers rootless Python from Miniforge/conda when available, uses Python venv + pip by default, bootstraps pip without apt when Debian/Ubuntu images are missing ensurepip, falls back to a managed conda/mamba env under NVH_HOME when needed, pulls the GPU-recommended Ollama model, writes a matching default config, creates ~/.local/bin/nvh, and auto-launches the WebUI when a desktop session is detected.

The WebUI opener is rootless-first: it honors NVH_BROWSER when set, then tries Firefox installed under $NVH_HOME/apps/firefox, then system Firefox, then Chromium/Chrome/open desktop handlers. Firefox launches with an isolated profile under NVH_HOME by default so cloud desktop browser versions cannot corrupt a signed-in/default Firefox profile. Set NVH_BROWSER='firefox --new-window {url}' if you intentionally want to use the VM's default browser profile. On Linux x86_64, if no usable Firefox is found, nvHive can download Firefox into NVH_HOME without sudo. Set NVH_FIREFOX_AUTO_INSTALL=0 to skip that browser bootstrap.

Runtime downloads use latest compatible stable builds by default. For reproducible labs, pin Ollama with NVH_OLLAMA_VERSION=vX.Y.Z or override the exact rootless bundle with NVH_OLLAMA_URL=....

If NVH_HOME is not set, the installer checks $HOME, /mnt, /media/$USER, /workspace, /data, /persistent, and /storage. It also installs a rootless reset helper:

bash "$NVH_HOME/uninstall.sh"          # remove app runtime, keep models/config/projects
bash "$NVH_HOME/uninstall.sh" --purge  # clean from-scratch reset

Set NVH_INSTALL_LAUNCH=0 before running the installer if you want install-only mode without the WebUI auto-launch.

Windows: iwr -useb https://raw.githubusercontent.com/thatcooperguy/nvHive/main/install.ps1 | iex macOS: curl -sSL https://raw.githubusercontent.com/thatcooperguy/nvHive/main/install-mac.sh | bash

Single-file binary (no Python needed)

Fully standalone. No Python install, no pip, no venv. Download the asset, make it executable, and run nvh workstation --launch. Click your OS:

   

Linux terminal path:

mkdir -p "$HOME/.local/bin"
curl -fL https://github.com/thatcooperguy/nvHive/releases/latest/download/nvh-linux-x86_64 -o "$HOME/.local/bin/nvh"
chmod +x "$HOME/.local/bin/nvh"
NVH_HOME=/mnt/persist/nvhive "$HOME/.local/bin/nvh" workstation --launch -y

On Linux/macOS after a browser download: chmod +x nvh-* && NVH_HOME=/mnt/persist/nvhive ./nvh-* workstation --launch -y. Full asset list (wheel, sdist, checksums) lives on the Releases page.

pip from PyPI (for existing Python environments)

The canonical PyPI distribution nvhive is published only from thatcooperguy/nvHive. Forks and third-party builds should use a distinct PyPI distribution name and must not publish under nvhive as an official-looking release.

pip install nvhive              # core
pip install "nvhive[vision]"    # + desktop agent (screenshot, click, type)
pip install "nvhive[browser]"   # + headless browser (playwright)
pip install "nvhive[all]"       # everything

pip install installs the Python package only. For large local models, ComfyUI, and Studio packs, launch the workstation with a persistent NVH_HOME so assets survive reconnects.

First run

nvh                              # guided setup: GPU detect, provider keys, local model pulls
nvh workstation --all -y         # Linux GPU desktop: launcher + WebUI + ComfyUI + studio packs
nvh webui                        # open the dashboard and setup wizard
nvh "your question"              # just ask - nvHive figures out the rest

For a fresh Linux cloud desktop where only a mounted file volume persists, choose that mount before installing large local assets:

export NVH_HOME=/mnt/persist/nvhive
curl -sSL https://raw.githubusercontent.com/thatcooperguy/nvHive/main/install.sh | bash
source "$NVH_HOME/nvh-env.sh"
nvh workstation --home-dir "$NVH_HOME" --all -y

nvh workstation --all -y creates a desktop launcher, starts the WebUI, prepares rootless local model tooling, installs ComfyUI with nvHive starter workflow examples, and adds the beginner AI Starter packs. Creative, game, Claw, and music missions stay one click away in the WebUI or can be installed directly with nvh studio --install creative|game|claw|music -y.

Use packs directly when you want a specific no-root lab:

nvh studio --list
nvh studio --models
nvh studio --install-models recommended -y
nvh studio --install llms -y
nvh studio --install agents -y
nvh studio --install claw -y                  # OpenClaw + NemoClaw when Docker/OpenShell is usable
nvh studio --install comfy -y
nvh studio --install game -y
nvh studio --install creative -y                # Blender LTS + game/asset workspace
nvh studio --install music -y                   # ACE-Step, Demucs, WhisperX, Audacity/LMMS AppImages
nvh studio --install python-runtime-fallback -y  # optional rescue pack, not the default path

Pick Your Mission

The setup wizard starts with one simple question: what do you want to make? Pick a mission and AI Wizard handles storage, GPU checks, Python/Node runtime checks, model recommendations, rootless installers, and background jobs.

The wizard does these checks before heavy installs:

1. Finds the best user-writable persistent storage path.
2. Checks GPU, driver, CUDA, VRAM, Python, Node, and npm health.
3. Writes a Workspace Passport under $NVH_HOME/config/workspace-passport.json.
4. Recommends local models that fit the detected hardware and disk.
5. Installs selected tools into NVH_HOME without root.
6. Tracks long downloads as persistent jobs with logs, receipts, and retry clues.
7. Polls /v1/ready so launchers know whether the workspace is ready, blocked, or only pilot-ready.
8. Offers Fix My Setup and a redacted support snapshot when something breaks.

Mission	What it sets up
AI Starter	Local chat and coding models, Ollama, GitHub helper, and a local agent helper
Graphics Creator Studio	ComfyUI, Blender, image/video workflow examples, and model download plans
Game Dev Lab	Godot helpers, Blender assets, GitHub, Linux game tooling, and Unity/Unreal guidance
Music Producer Studio	AI music generation, stem splitting, transcription, audio apps, and notebooks
Agent Builder	OpenClaw by default, with NemoClaw unlocked only when Docker already works without sudo

Beginner Mode shows one recommended action, a Fix My Setup repair path, and mission cards. Advanced Details stays available for storage, driver, CUDA, Python, Node, logs, receipts, boot drift, and release-readiness diagnostics.

ComfyUI, AI Studio packs, and local model downloads run as persistent jobs under $NVH_HOME/jobs, so setup progress survives browser refreshes and leaves a clear retry trail after cloud desktop reconnects. The model picker shows GPU-fit badges, disk estimates, installed status, and a selected download queue.

When the VM image changes between sessions, AI Wizard compares the new boot fingerprint with $NVH_HOME/config/boot-preflight.json and recommends rootless repairs before launching large installs. If something still fails, Copy Error Report creates a redacted report with request IDs and log locations. See Production Readiness for the release gates and target NVIDIA Linux VM acceptance checklist.

If setup gets stuck:

nvh webui                                      # reopen the wizard
nvh wizard status --home-dir "$NVH_HOME"       # show the Workspace Passport
nvh plan --profile student --home-dir "$NVH_HOME" # preview the no-root install plan
nvh repair --home-dir "$NVH_HOME"              # run safe rootless repairs
nvh wizard support --home-dir "$NVH_HOME"      # create a redacted support snapshot
nvh doctor --storage --home-dir "$NVH_HOME"   # verify the persistent mount
nvh doctor --fix                              # try safe local repairs
tail -n 80 "$NVH_HOME/logs/nvhive.log"        # inspect rootless logs

When the local API is running, Troubleshooting can create the same redacted support snapshot from the UI, or you can call POST /v1/wizard/support-snapshot directly.

On first run, nvh launches a guided setup helper for GPU detection, provider keys, local model pulls, and rootless app installs. Works immediately with local models when available. Every advanced step is skippable. Run nvh setup anytime to reconfigure.

Memory Vault

The WebUI includes Memory Vault, a plain Markdown workspace at $NVH_HOME/vault. It keeps AI Wizard product memory, pilot notes, support playbooks, troubleshooting, decisions, and known product conflicts without requiring root, a database, or a specific editor.

Obsidian support is optional. On Linux x86_64, nvHive can install the Obsidian AppImage under $NVH_HOME/apps/obsidian and create a rootless nvhive-vault launcher. If a VM image cannot run AppImages, the same vault can still be opened in any Markdown editor.

WebUI

nvh webui launches the local dashboard at localhost:3000: setup wizard, chat, council mode, advisor status, analytics, and system health. First run installs frontend dependencies under persistent NVH_HOME; later launches use the production server for faster startup. Use nvh webui --dev only when editing the frontend.

GPU tier model recommendations:

VRAM	Text Model	Vision Model	Behavior
0 GB (no GPU)	Cloud only	Cloud fallback	Free tiers first (Groq, LLM7, GitHub)
4-8 GB	gemma3:4b	moondream	First-run local chat + desktop agent
12-16 GB	qwen2.5-coder:7b	minicpm-v	Coding + vision local
24 GB	llama3.2-vision	qwen3:8b / gemma3:4b	Multimodal first, strong fallbacks
40-48 GB	nemotron	llama3.2-vision / qwen3:8b	Largest fitting local brain first
96+ GB	nemotron + 70B/coder fallbacks	llama3.2-vision	Full local council, $0

Setup auto-detects your VRAM and recommends models that fit concurrently. No root/sudo needed for nvHive packs: tools install under NVH_HOME (bin, models, runtimes, apps, webui, studio, comfyui, cache, and config). Full GPU guide

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Why nvHive

Council scored 68% higher than a single model — at $0 cost. Three free providers running in parallel outperformed a single model on accuracy, completeness, and coherence. Benchmark details below.

• Smart team assembly. nvHive generates expert agents for your question and matches each to the best LLM for their specialty — a "Security Engineer" agent routes to a security-strong provider, a "Database Expert" to one suited for database queries.
• Automatic orchestration. Coding tasks get a planner + coder + reviewer. Complex questions get a council. Simple questions get the fastest advisor. All automatic.
• Scales with what you have. 1 provider → single-model answers. 3+ providers → council on complex questions. Local GPU → free inference alongside cloud. DGX Spark → three 70B models in parallel, fully local.
• 4-layer safety guardrails. Command blocklist, filesystem boundary enforcement, secrets redaction, and resource limits.

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Architecture

9 layers from pip install to GPU inference — install, setup, 4 user interfaces, intent detection, 5 execution modes, smart routing, tool registry, 23+ AI providers, and the hardware stack. Local-first with cloud fallback. Architecture docs

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Features

Desktop Agent

AI that sees your screen, controls mouse/keyboard, installs software, and navigates browsers — powered by local vision models.

nvh "take a screenshot and describe my desktop"
nvh "setup comfyui"                    # agent: git clone → pip install → launch → verify
nvh "open firefox and go to github.com"

Vision pipeline: screenshot → local vision model (llama3.2-vision / minicpm-v) → coordinate estimation → action → verify. Falls back to cloud vision if no local model. Works on Linux (X11), macOS, and Windows. Desktop agent docs

Agentic Coding

Multi-model coding agent with dynamic expert referral, iterative QA, parallel execution, and vision/browser tools.

nvh agent "Fix the streaming timeout bug in council.py"
nvh agent "Add unit tests for auth" --dir ./myproject
nvh agent "Build the notification service" --sandbox     # Docker-isolated
nvh review                     # multi-model code review
nvh test-gen nvh/core/council.py     # AI test generation

Key capabilities: dynamic expert referral, iterative QA refinement, parallel pipeline, Docker sandbox, execution checkpoints with rollback, LLM drift detection, multi-repo workspaces, and VS Code extension. Scales from no-GPU (fully cloud) to DGX Spark (3 local 70B models). Agentic coding docs

Council Mode

Run the same query through multiple providers in parallel, then synthesize. Expert personas generated per query, each assigned to a different model. Responses analyzed for agreement, synthesized by a non-member provider with a confidence score.

nvh convene "Should we use Redis or Postgres for sessions?"   # 3 models → synthesis
nvh throwdown "Review this architecture for scalability"      # 3-pass deep analysis with critique

Different models have different blind spots — council surfaces all perspectives. Council with 3 free providers costs $0. Council docs

Smart Routing

Each request is scored across capability (40%), cost (30%), latency (20%), and health (10%), then routed to the highest-scoring provider. Routing improves over time — after 20 queries per provider, it's fully data-driven.

nvh ask --escalate "Design a distributed lock manager"    # try free first, upgrade if uncertain
nvh ask --verify "Is eval() safe in Python?"              # cross-model verification
nvh routing-stats    # see learned vs static scores
nvh health           # provider resilience dashboard

Local-first with NVIDIA GPUs: simple queries route to your GPU via Ollama — no cloud, no cost, no data leaving your machine. --prefer-nvidia gives a 1.3x routing bonus to NVIDIA hardware. Routing docs

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Providers

23 providers. 63 models. 25 free — no credit card required.

Tier	Providers	Rate Limits
Free (no signup)	Ollama (local), LLM7	Unlimited / 30 RPM
Free (email signup)	Groq, GitHub Models, Cerebras, SambaNova, Cohere, AI21, SiliconFlow, HuggingFace	15-30 RPM
Free (account)	Google Gemini, Mistral, NVIDIA NIM	15-1000 RPM
Paid	OpenAI, Anthropic, DeepSeek, Fireworks, Together, OpenRouter, Grok	Pay per token

Full provider guide

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Integrations

nvHive exposes a CLI (nvh), web dashboard (nvh webui), Python SDK (import nvh), MCP server for Claude Code, and OpenAI/Anthropic-compatible API proxies.

import nvh

response = await nvh.complete([{"role": "user", "content": "Explain quicksort"}])
result = await nvh.convene("Architecture review", cabinet="engineering")

Integration	Setup
Anthropic SDK	ANTHROPIC_BASE_URL=http://localhost:8000/v1/anthropic
OpenAI SDK	OPENAI_BASE_URL=http://localhost:8000/v1/proxy
Claude Code	claude mcp add nvhive -- python -m nvh.mcp_server
NemoClaw	nvh nemoclaw --start — NemoClaw docs

SDK & API reference | Claude Code integration | OpenClaw migration

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Benchmark Results

Real data from NVIDIA DGX Spark (GB10, 120GB). 16 prompts across code generation, debugging, reasoning, math, creative writing, and Q&A. Judged by OpenAI with ground truth verification.

Mode	Accuracy	Completeness	Coherence	Overall	Cost
Single Model (Nemotron Super)	5.5	5.7	5.0	5.1	$0.00
Council (Ollama + Groq + Google)	9.0	8.0	9.0	8.6	$0.00

nvh bench              # GPU speed (tokens/sec)
nvh bench -q           # speed + quality comparison
nvh health             # provider resilience

Results vary by hardware and workload — run nvh bench to measure on your setup.

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Core Commands

Command	What It Does
nvh "question"	Smart route to best available model
nvh convene "question"	Council consensus (3+ models)
nvh throwdown "question"	Three-pass deep analysis with critique
nvh agent "task"	Agentic coding with expert referral + QA
nvh review	Multi-model code review
nvh test-gen file.py	AI test generation with verification
nvh safe "question"	Local only — nothing leaves your machine
nvh serve	Start API server (OpenAI + Anthropic proxy)
nvh webui	Launch web dashboard
nvh health	Provider resilience dashboard
nvh bench	GPU speed test (tokens/sec)
nvh setup	Interactive provider setup
nvh doctor	Full diagnostic dump
nvh plan	Rootless mission install preview
nvh repair	Safe rootless workspace repair
nvh wizard	Workspace Passport, plan, repair, and support snapshot helper

Full command reference (50+ commands)

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Documentation

Guide	Description
Student GPU Cloud / Linux Desktop	No-root NVIDIA Linux workstation and ComfyUI guide
Production Readiness	Release gates and target NVIDIA Linux VM acceptance checklist
GitHub Listing Checklist	Repository description, topics, visual assets, and release-status copy
Deploy Without Root	No-root install on servers
Windows Troubleshooting	Encoding, segfaults, port issues
Getting Started	General CLI/provider setup after the no-root workstation path
Commands	Full CLI reference (50+ commands)
Providers	23 providers, rate limits, free tiers
Council System	Multi-LLM consensus with confidence scoring
Architecture	System design and adaptive routing
GPU Detection	Auto-detection, model selection, OOM protection
SDK & API	Python SDK, REST API, proxies
Agent Tools	Agent tools and capabilities
Configuration	Configuration reference
Web UI	Web dashboard
Releasing	Release runbook

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Important Notes

• Data Privacy: Cloud providers transmit queries to third-party APIs subject to each provider's privacy policy. Use nvh safe or --prefer-nvidia to keep inference local.
• AI Accuracy: AI-generated outputs may contain errors. Review agent-modified files before committing to production.
• Security: Safety guardrails use pattern-matching heuristics. For sensitive environments, use --sandbox with Docker isolation.
• Benchmarks: Results measured on NVIDIA DGX Spark reference hardware. Results vary by hardware, provider, and workload.

License

Source code is licensed under the MIT License. See LICENSE and NOTICE.

The MIT License does not grant rights to use nvHive names, logos, screenshots, release artifacts, package publishing identities, or other branding in a way that causes confusion about the official project. See TRADEMARKS and THIRD_PARTY_NOTICES.