Shikigami, run AI coding agents in parallel, each in a Git worktree
Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly spotted two main problems. The first is that I couldn't easily tell which agent was stuck waiting on me and which was still working, so I'd cycle through sessions and checking on them. The second one: agents sharing a single branch step on each other. Two of them could be editing…
Shikigami, run AI coding agents in parallel, each in a Git worktree launched on July 19, 2026 with 7 votes, #2,664 of 24,623 launches that month and more than 75% of that year's launches.
In plain words
Shikigami is a desktop application that runs multiple AI coding agents in parallel, each isolated in its own Git worktree to prevent conflicts. It works with Claude, OpenAI Codex, and local models via Ollama. The app provides a built-in editor, terminals, Git tools, and database viewing to review agent work side by side. Sessions are saved and resumable. It's available free for macOS, Linux, and Windows.
written from the facts on this page · September 2026
From the sources
An agentic IDE for running a pack of AI coding agents (Claude Code, OpenAI Codex, and opencode, with local models via Ollama) in parallel, each in its own isolated git worktree, each resumable. A desktop app to review their work: built-in editor, terminals, git, databases, and Docker. Free. macOS, Linux & Windows.
Run a pack of agents (on Claude, Codex, or opencode), each in its own isolated git worktree, so they don’t step on each other. Then review it all in the editor: diffs, git, databases, Docker. Sessions are saved, so you can quit and pick any of them up later. Shikigami beta 0.44.0 tab.offsetParent !== null); const target = tabs[(tabs.indexOf($event.target) - 1 + tabs.length) % tabs.length]; target.focus(); setWorkspace(target.dataset.workspaceId); } " @keydown.right=" if ($event.target.matches('[role=tab]')) { $event.preventDefault(); const tabs = Array.from($el.querySelectorAll('[role=tab]')).filter((tab) => tab.offsetParent !== null); const target = tabs[(tabs.indexOf($event.target)…from shikigami.dev
In the maker’s words, at launch
Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly…
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1CodeJan 2026 · github.com · ▲569Just to clarify the background a bit. This project wasn’t planned as a big standalone release at first. On January 16, Ollama added support for an Anthropic-compatible API, and I was curious how far this could be pushed in practice. I decided to try plugging local Ollama models directly into a Claude Code-style workflow and see if it would actually work end to end. Here is the release note from Ollama that made this possible: https://ollama.com/blog/claude Technically, what I do is pretty straightforward: - Detect which local models are available in Ollama. - When…
Claude Code Desktop App RedesignedApr 2026 · claude.com · ▲564Run parallel coding agents from one desktop workspace
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Hi HN! We just launched Verdent, an AI coding agent that actually plans before it codes, tests its own work, and delivers production-ready results without you having to clean up after it. tl;dr Check out the website [1] and read the story [2] - The problem: Current AI tools dump code and leave you to debug. You ask for "build a login endpoint" and get a passwordless JWT mess that doesn't fit your project. We've talked to hundreds of engineers who are frustrated with having to micromanage AI output. - What we built: Verdent works more like a senior engineer. It asks clarifying questions…
Parallel CodeMar 2026 · parallelcode.app · ▲159I kept running into a structural problem with AI coding tools. You try one agent. It makes changes. You try another. Now you're manually diffing branches and cleaning up experimental commits. The tools are powerful, but the workflow isn’t designed for comparison. So I built Parallel Code. It runs Claude Code, Codex, and Gemini CLI natively (no API wrapper, no feature abstraction), but isolates each agent in: its own terminal its own Git worktree its own feature branch The workflow becomes: Spawn N worktrees Assign one AI agent per worktree Let them implement independently Compare diffs Merge…
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