Who is Iron-Jarvis for?

Suits developers and power users who want agents with auditable, approval-gated actions on their own machine. Windows users get an installer, and others need to be comfortable running from source.

What can you build with Iron-Jarvis?

Delegate a coding task to subagents and review the resulting git diff before merging. Run an AI assistant fully on your own GPUs using Ollama or vLLM. Build a visual workflow that chains agent steps and runs on a schedule. Redact personal data from PDFs and Office files while keeping the original format. Keep separate per-client memory by binding a notes folder to each project.

RealDealCPA-VR /

Iron-Jarvis

Local-first desktop app where AI agents plan, build and review code you approve

63 stars
4 stars since 3 Oct
Python
Updated today
Some experience
AI agents

Open on GitHub (opens in a new tab) Website (opens in a new tab)

Iron Jarvis tries to be an operating system for AI agents rather than a chatbot. You type into one chat surface. Quick questions get a fast answer, and requests that need real tool work are handed to a full agent, with a visible reason.

A supervisor splits a goal across subagents such as Planner, Builder, Reviewer and Researcher. Each works in a disposable git worktree. Code lands on a branch and is never merged automatically, so you read the diff and approve it. Every tool call passes a permission engine that denies by default.

It can use cloud models through an API key or an existing claude/codex CLI login, or local servers such as Ollama, vLLM and LM Studio. A deterministic mock model lets it run offline. The README says the packaged installer is Windows-only. On macOS and Linux you run it from source.

What it does

Supervisor and subagents
A supervisor delegates to specialist subagents with isolated context. Code changes go to a git branch and wait for your approval before merging.
Fail-closed permissions and vault
Each tool is set to allow, ask or deny, and the shell stays locked down. API keys and tokens live in an encrypted vault that agents never see.
Layered, visible memory
Memory has session, project, user and org layers. You can bind an Obsidian vault, Notion, a folder or an MCP memory server per project, and view it as a 3D graph.
Workflow canvas and scheduling
Drag step nodes into a workflow and run it. Agents can build workflows too. Scheduled tasks use repeat presets or a set date and time.
Local models and phone chat
Point it at any OpenAI-compatible local endpoint, and make a Telegram bot two-way to chat with the same agent from your phone, behind a sender allowlist.

What you could build

Delegate a coding task to subagents and review the resulting git diff before merging.
Run an AI assistant fully on your own GPUs using Ollama or vLLM.
Build a visual workflow that chains agent steps and runs on a schedule.
Redact personal data from PDFs and Office files while keeping the original format.
Keep separate per-client memory by binding a notes folder to each project.

Get started

On Windows, download Iron-Jarvis-Setup-<version>.exe from the Releases page and run it. Click More info, then Run anyway, on the SmartScreen warning, since the app is not code-signed yet. On macOS or Linux, the README mentions running the daemon and dashboard from source, but the excerpt gives no full steps.

Commands are copied from the project's README. Read it on GitHub for the full setup.