Open-source coding agent. Local-first. Zero cost. Zero cloud.
_{Built to democratize AI. Powered by .NET.}

Quickstart · Free OMA Course · What's shipping · How it compares · What's inside · Hardware · Docs · Roadmap · Contributing

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OpenMono is a coding agent that runs entirely on your hardware — no subscriptions, no data leaving your network, no per-token billing. It pairs a .NET 10 CLI with its own llama.cpp inference server, giving you a full agentic loop with 21 built-in tools, Docker sandboxing, and deep code intelligence. NVIDIA GPU, AMD APU (integrated Radeon iGPU, unified memory), or Apple Silicon (Metal) — it auto-configures itself. You own the model, the compute, and the data.

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Free Course — Get Started with OMA

_{Need help? Contact us — we'll help you get set up.}

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Quickstart

One command. Auto-detects GPU · APU/iGPU · Apple Silicon. Installs model, runtime, and Docker containers.

bash <(curl -fsSL https://raw.githubusercontent.com/StartupHakk/OpenMonoAgent.ai/refs/heads/main/get-openmono.sh)

Then from any project:

openmono agent            # TUI mode (default)
openmono agent --classic  # classic scrolling terminal

_{→ Full command reference — daily commands, setup flags, GPU/iGPU options · Need help? Book a call}

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What's shipping

_{Get them: openmono setup search · OPENMONO_VISION_ENABLED=1 · App Store · Google Play · VS Code Marketplace}

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How it compares

Most coding agents are cloud products wearing an open-source label. Your prompts, your code, and your context hit someone else's servers on every keystroke. OpenMono runs the model on your hardware — after the one-time setup, inference costs nothing. Your code never leaves the machine. No account. No usage dashboard. No API key.

→ Full architecture + diagram · 5 provider backends · runs at ~45 tok/s on GPU, ~20 tok/s on AMD iGPU (APU)

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What's inside

01 · Bundled inference — zero config, zero cost llama.cpp ships inside Docker. Installer detects your hardware and picks the right model. After setup, every token is free. GPU Qwen3.8-27B dense · ~60 tok/s iGPU Qwen3.6-35B-A3B MoE · APU unified memory · ~20 tok/s Mac Qwen3.6-35B-A3B MoE · Metal · ~45–48 tok/s → Models & reasoning mode	02 · Agentic loop that earns its name Up to 1,000 iterations per turn. Doom-loop detection nudges on repeated tool batches, escalates the nudge at 3 consecutive detections, and ends the turn at 5. Checkpoints at 65% context fill, compacts at 80%. Runs until done — then stops.
03 · 21 tools, guarded pipeline Every call: parse → schema validate → path sanity → plan-mode gate → host-mode gate → permission check → cache → pre-hook → execute → post-hook → artifact store → cache write → invalidate. Read-only tools run in parallel. Nothing bypasses the pipeline.	04 · 5 specialist sub-agents Isolated sessions with locked tool sets and turn budgets: Explore · read-only discovery · 100 turns Plan · architecture, no writes · 100 turns Coder · full file access · 300 turns Verify · adversarial + Roslyn · 150 turns general-purpose · everything · 200 turns
05 · Docker sandbox In sandbox mode the project mounts as /workspace. The agent reads and writes your real files — that's the blast radius; nothing outside that mount is visible. Host mode is the explicit exception: machine-level actions run only through approved HostExec calls.	06 · Deep code intelligence Roslyn: type hierarchy, blast-radius, cross-file symbol search, callers, diagnostics — 5-min compilation cache. LSP for TypeScript, Python, Go, Rust, lazy-started on first use. Auto-detects graphify (semantic concept graph, 25+ languages) and code-review-graph (structural call graph via MCP, ~22 tools) if installed — no config needed.
07 · Playbooks YAML workflows with typed parameters, conditional gates, and checkpoint/resume.	08 · 5 provider backends Local llama.cpp is the default; Strata is the supported Linux high-end backend. OpenAI, Anthropic, and Ollama are available but WIP — see Models for details. Providers are selected in settings.json at startup; /model switches the model on the active provider.
09 · Distributed inference Agent on your laptop, inference on a separate GPU machine. No port forwarding — tunnel is established outbound from the inference box. Free relay at app.openmonoagent.ai. → Dual-box setup guide	10 · Vision Attach images in chat with @screenshot.png or ask the agent to read any image file. The multimodal projector (mmproj) is downloaded automatically at setup. Supported formats: PNG, JPG, GIF, WebP. Large images are auto-resized to fit within the image budget. Enable vision in settings.json (inference.vision_enabled) or set OPENMONO_VISION_ENABLED=1. → Vision setup & usage
11 · Private web search & scraping Self-hosted search via SearXNG — your queries never leave the machine. Anti-bot scraping via Scrapling + Camoufox (real browser, Cloudflare bypass). Single Caddy gateway, auto-detected. Falls back to DuckDuckGo / direct fetch when the gateway is absent. openmono setup search · openmono setup scraper → Web services architecture	12 · VS Code extension The full agent loop in your editor sidebar — streaming responses, live Markdown, file edits, bash, and permission prompts without leaving VS Code. Connects to the local agent over ACP on port 7475. Also works in Cursor. code --install-extension StartupHakk.openmono-agent → Extension docs · Marketplace

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Supported Hardware

_{Setup auto-detects GPU · iGPU/APU · no config needed and picks the best model your hardware can run — no prompts. Pass --options to choose the model, vision, and parallel slots yourself, or --model=<name> to pin one. The pick depends on VRAM and system RAM: on Linux a 24 GB+ card gets Qwen3.8-Flash-Next (Strata) on a 48 GB-class box and Qwen3.8-27B below that, a 16 GB card keeps Qwen3.8-27B, a 12 GB card and AMD 780M iGPUs (32 GB+ RAM) get Ternary-Bonsai-2-27B via Prism. Requires Ubuntu 26.04 LTS (recommended) or 25.10. On macOS, the full and inference roles require Apple Silicon (M1+); 48 GB+ unified memory is the recommended, tested configuration. 24–47 GB and 16–23 GB Macs run Ternary-Bonsai (PQ2_0 / PTQ1_0) with a tighter context window. Intel Macs: agent-only mode. macOS 14+ (Sonoma/Sequoia) recommended.}

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Need Help?

Stuck on setup, or want OpenMono on your own servers? We install and support private OpenMono deployments — contact us, book a call, and we'll walk you through getting it running on your hardware. Pick your hardware. We handle the rest.

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Docs

Roadmap	What's next
Setup & commands	Daily commands, TUI vs classic, flags
Deployment modes	Single-box vs split-box, server-ops runbook
Architecture	.NET CLI + llama.cpp + Docker, full diagram
Models & reasoning	Model tiers, reasoning mode, provider config
Configuration	settings.json, providers, permissions, MCP servers
Playbooks	YAML workflows, typed params, checkpoint/resume
graphify	Semantic code graph, 25+ languages
code-review-graph	Structural call graph via MCP
VS Code extension	Chat panel for VS Code 1.85+ · also works in Cursor · Marketplace
Contributing	How to contribute

PUBLIC BETA  Early access is open — we're shipping updates fast. Try it out and tell us what you'd like to see next.

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Contributing

OpenMono is early and moving fast. Contributions are welcome — new tools, providers, LSP servers, playbooks, bug fixes, or docs.

→ Read the contributing guide before opening a PR

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"AI shouldn't be a subscription you rent. It should be infrastructure you own —
sitting on your desk, serving your code, answering only to you."

_{— Startup Hakk}

Need Help? · Contributing

_{GNU AFFERO GENERAL PUBLIC LICENSE v3.0 · © 2026 StartupHakk}