LoopTroop

A smart local engine that automates big coding tasks from start to finish. LLM councils plan it. Ralph loops perfect it. OpenCode worktrees ship it.

LoopTroop helps you turn a coding ticket into a planned, reviewable, agent-executed pull request.

Instead of trusting a single, endless AI chat session - where the conversation history gets bloated, the AI gets confused, and code quality falls off a cliff - LoopTroop breaks the job into clean, separate stages. Planning turns an interview into a PRD, which is then split into the smallest manageable milestones, called "beads." Execution runs each bead through multiple targeted auto-fix loops. A final review ties it all together.

Architectural Layer	Core	Technical Lifecycle
1. Planning	LLM Councils Plan It	Human Input ➔ AI Interview ➔ PRD ➔ Atomic Beads
2. Execution	Ralph Loops Perfect It	Isolated Bead Work ➔ Multi-Loop Automated Testing & Fixing
3. Shipping	OpenCode Worktrees Ship It	Code Isolation ➔ Final Verification Pass ➔ Main Branch Handoff

An animated walkthrough of a ticket lifecycle and the configuration menu.

📸 Screenshots

Click to expand the screenshot gallery

Manage attached repositories, review ticket counts, and add new projects from the dashboard.

Choose the main implementer model, council members, and effort levels for local orchestration.

Answer focused planning questions before specs and implementation plans are approved.

Track council progress, generated artifacts, and live execution logs inside a ticket.

Review bead completion, commits, changes, and final implementation details before closing the workflow.

Inspect bead-level progress, task status, and live execution logs while an implementation bead runs.

Review the focused workspace view shown when an implementation bead is blocked by an error.

Compare a different bead's error state, diagnostics, and recovery context before deciding whether to continue or retry.

🎬 16 Min Deep Dive - Presentation & Full Ticket Demo

Click to expand the presentation and demo

Watch the full 16-minute presentation and ticket demo.

Quick start

curl --proto "=https" --proto-redir "=https" --tlsv1.2 -fsSL https://www.looptroop.ovh/install | sh
looptroop open

open starts LoopTroop in the background if it is not already running. Use looptroop start if you want the service without a browser.

Then configure your settings and models (from providers already added in OpenCode), attach a local repository with a GitHub origin, create a ticket, and start it.

Every way to install it

curl / PowerShell — the one-line installer (shown above)

curl --proto "=https" --proto-redir "=https" --tlsv1.2 -fsSL https://www.looptroop.ovh/install | sh

$script = curl.exe --proto "=https" --proto-redir "=https" --tlsv1.2 -fsSL https://www.looptroop.ovh/install.ps1; if ($LASTEXITCODE -ne 0 -or !$script) { throw "Installer download failed" }; & ([scriptblock]::Create(($script -join "`n")))

Resolves the newest release, checks the download against the checksum that release published, and hands it to npm. Because it installs through npm, npm install -g looptroop@latest and npm uninstall -g looptroop keep working exactly as they would have. Pin a version with --version X.Y.Z (-Version on Windows). It installs wherever npm's global prefix points; change that with npm config set prefix.

The PowerShell command also needs curl.exe, included in current Windows 10 and Windows 11. If it is unavailable, use the npm channel below. It downloads the complete script over HTTPS and stops if the download fails.

Needs Node and npm already installed. It never installs Node for you, never asks for sudo, and writes nothing outside npm's global prefix.

There is also a standalone executable that carries its own Node runtime — see Installation.

npm — everywhere

npm install -g looptroop
npm install -g looptroop@latest   # upgrade

Needs Node 24.18.1 or newer and npm 12.0.2 or newer, plus git and gh.

Homebrew — macOS and Linux

brew install looptroop-ai/tap/looptroop
brew upgrade looptroop            # upgrade

Needs nothing else. The formula pulls in node@24 and gh, and takes git from the OS. It installs a locked bundle built once per release with every dependency resolved at build time, so everyone on this channel runs the exact versions the release was tested against.

Scoop — Windows

scoop bucket add looptroop https://github.com/looptroop-ai/scoop-bucket
scoop install looptroop
scoop update looptroop            # upgrade

Needs nothing else. The manifest depends on nodejs-lts, git and gh. Like Homebrew, it installs the locked bundle.

bun — everywhere

bun add -g looptroop
bun add -g looptroop@latest       # upgrade

Needs Node 24.18.1 or newer as well as bun — the launcher is a Node program, so bun installs it but Node runs it — plus git and gh.

pnpm — everywhere

pnpm add -g looptroop
pnpm add -g looptroop@latest      # upgrade

Needs Node 24.18.1 or newer as well as pnpm, plus git and gh.

pnpm holds a new version back for about a day: it will not resolve a tag to a version published in the last 24 hours — a supply-chain protection, on by default — so @latest installs the newest release older than that window. Asking for an exact version bypasses it.

Yarn Classic — everywhere

yarn global add looptroop
export PATH="$(yarn global bin):$PATH"   # Yarn does not do this for you
yarn global upgrade looptroop@latest     # upgrade

Needs Node 24.18.1 or newer as well as Yarn, plus git and gh.

Yarn does not put its global binaries on PATH. This looks like a failed install and is not: the add reports success, and then looptroop is not a command. Add the line above to your shell profile, or the next terminal will have forgotten it. npm, bun and pnpm all install somewhere already on PATH, which is why this catches people out on Yarn alone.

Yarn Classic (1.x) only. Yarn 2 removed yarn global and never replaced it, so modern Yarn cannot install a CLI globally at all — and it does not say so cleanly: yarn global add looptroop on Yarn 4 reads global as a package name and fails with a lockfile error. On modern Yarn, run it without installing with yarn dlx looptroop, or install it with one of the other channels.

Docker — linux/amd64 and linux/arm64

docker pull looptroopai/looptroop:latest

Needs only Docker. Node, git and gh are all in the image. Two things it still needs from you, both deliberately not baked in: an OpenCode server it can reach, and a project mounted at its own absolute path. Both are covered on the Installation page.

The Installation page is the one place that tracks which channels are live, and covers upgrading, uninstalling, verifying a download against the checksums each release publishes, and running in a container.

What you need besides LoopTroop

git, and gh authenticated, for the pull-request step at the end of a ticket. Installed for you via Homebrew, Scoop, Chocolatey, WinGet and the AUR; not installed if you used npm, bun, pnpm, Yarn or the standalone executable, which have no way to declare a dependency.
OpenCode, with at least one configured model provider. LoopTroop will start it if it is already installed, but it will not work without it and it will not install it for you.

What is LoopTroop?

LoopTroop is a local GUI orchestrator for long-running, high-correctness AI software delivery - taking you from a raw idea to merged code. Free and fully open-source.

Unlike high-speed coding tools that optimize for immediate chat responses, LoopTroop is built for complex, multi-file feature work where alignment and correctness are paramount. It optimizes for a "slow and perfect" paradigm, intentionally sacrificing raw speed to deliver a final result that matches exactly how you envisioned it.

Great Context Engineering = Zero AI Slop: LoopTroop employs precise context curation at every stage, feeding the agent only the absolute minimum context it needs. See Context Engineering below for details.

How it works

flowchart LR
    A["🎫 Ticket Input"] --> B["🔍 Codebase Discovery"]
    B --> C["🏛️ LLM Council Planning<br/>(Interview, PRD & Beads)"]
    C --> D["🛑 Human Approval Gate<br/>(optional in future releases)"]
    D --> E["🧪 Isolated OpenCode Bead Execution<br/>(Git Worktree)"]
    E --> F["✅ Final Tests"]
    F --> H["🧭 Optional Manual QA<br/>(user runs the app)"]
    H --> I["📦 Integration & PR Review"]
    H -.->|"Failures become QA fix beads"| E
    E -.->|"On Failure"| G["🔄 Ralph-Style Recovery Loop"]
    G -.->|"Retry"| E

LoopTroop keeps workflow state outside the model, stores durable artifacts, and asks for approval at important boundaries. Optional Manual QA runs after final tests: you complete the checklist while manually controlling the app, then the ticket continues to integration. Failed checks create QA-fix beads, while improvements creates new tickets.

Core ideas

Context Engineering

Context rot is the enemy of autonomous agents. Traditional agent loops suffer from it-excessive conversational history and irrelevant files overwhelm the model, causing code quality to degrade. Performance can drop severely when reaching just 40% of the maximum context window, resulting in missing files, broken imports, and "AI slop." [note]

LoopTroop solves this through precise context curation. Instead of sending full conversational transcripts, the engine isolates payloads to the active status. During execution, the agent only sees the specific active bead, its immediate file target, and the test file. During planning phases, it receives only the minimum context relevant to the current step.

This eliminates conversation pollution from previous execution attempts, prevents LLM drift and performance degradation, and keeps model focus high. Keeping the working context fresh is what makes multi-hour, multi-step engineering cycles actually work.

Read more: Context Engineering

LLM Council

The LLM Council is LoopTroop's planning system. Instead of relying on a single model run, LoopTroop orchestrates multiple independent model instances that draft plans, score each other using a weighted rubric, and vote on proposals. The winner then refines its draft by synthesizing the strongest ideas from the losing drafts and verifies coverage before any execution begins.

This multi-role process (draft → vote → refine → verify) is utilized for:

Interview questions
PRD/Specs generation
Bead/blueprint generation

Read more: LLM Council

Interview

Before writing a spec, the LLM Council compiles a list of targeted questions to resolve any ambiguities. This interactive session gathers requirements and clarifies intent-because matching your vision is the goal, this phase can take over an hour by design.

You answer these questions directly in the Interview workspace to clarify edge cases, design decisions, and requirements, ensuring the model never operates on false assumptions. Although a final interview is created after the council's draft-vote-refine cycle is complete, the user still receives questions in batches that can adapt based on previous answers.

Read more: Interview

PRD (Product Requirements Document)

Once the interview phase is complete, the LLM Council translates your initial ticket and your interview answers into a structured Product Requirements Document consisting of Epics and User Stories, complete with highly decomposed implementation steps. This spec serves as the single source of truth for the implementation, detailing the technical approach, edge cases, scope, and expected validation steps before any coding starts. The PRD is stored as a durable artifact for later reference during bead execution.

Read more: PRD

Beads

LoopTroop implements only the Beads methodology-not the full external Beads Project-extracting just the lightweight planning structure needed to bring immediate value to your repository.

Using Steve Yegge's Beads Project methodology, epics are split into "beads"-the smallest, independently implementable units of work. Each bead contains:

Clear purpose and objective
Measurable acceptance criteria
Necessary dependencies and prerequisite context
Specific target files
Expected validation and testing steps

A bead acts as a small, isolated implementation unit, allowing the execution agent to complete concrete tasks sequentially rather than attempting a massive, single-pass code rewrite.

Read more: Beads

Execution & Ralph-style recovery

The actual implementation is carried out by an AI coding agent (OpenCode) running in an isolated workspace. If the agent struggles, continuing the same conversation can make things worse. LoopTroop's retry mechanism (the "Ralph Loop") preserves a highly compact error trace from the failure, resets the worktree, discards the contaminated session, and begins a fresh run with clean context-plus a note from previous failures.

fail ──> log failure trace ──> reset worktree ──> retry fresh

This cycle repeats until all tests pass or retry limits are reached. This can take hours (sometimes 10+ hours) by design. It is built to run unattended (e.g., overnight).

Read more: Beads & Execution

Worktree isolation

LoopTroop runs execution steps inside isolated Git worktrees rather than modifying your active branch. This keeps your working copy clean and ensures reliable, inspectable diffs. Note that worktrees provide workspace isolation, not sandboxed host security.

Read more: System Architecture

Human approval gates

LoopTroop keeps you in control of critical state transitions. You actively review and sign off on planning specs, execution blueprints, and final pull request deliverables. (Note: Human approval gates will become optional in future releases).

For tickets with Manual QA enabled, LoopTroop prepares a checklist while you manually control the app and accept/reject/skip/create new tickets from the items.

Read more: Ticket Flow

Run it in a VM

Beyond the install itself — covered per channel above — LoopTroop needs a local repository with a GitHub origin, and strongly wants a VM or sandboxed development environment.

Why a VM?

LoopTroop is designed for serious agentic coding work that runs unattended. To make this possible, the orchestrator runs OpenCode in dangerously-skip-permissions (YOLO) mode, granting the agent full local execution rights without prompting for confirmation.

While this makes long-running autonomous tasks possible, it introduces real risks. AI agents are not perfect. If a generation goes wrong, the agent can execute commands that delete critical system folders, corrupt active configurations, or break your workspace. Git worktrees isolate your code changes, but they do not sandbox the command execution process itself. The agent runs with your local user privileges.

Recommended setup: run LoopTroop inside a disposable VM, cloud dev machine, or sandboxed development environment.

Git worktrees protect your attached repository checkout
Logs and artifacts help you inspect what happened
A VM protects the rest of your computer

Why not just use a coding agent directly?

Direct coding-agent loops are highly useful, but they degrade rapidly when task complexity or repository scale increases.

Core Challenge	Direct Agent Behavior	LoopTroop's Structural Fix
Flawed Planning	A single model attempts to draft a multi-step plan in one pass, frequently missing structural edge cases.	LLM Council Consensus: Competing models draft, vote on, and synthesize a single, rigorous implementation plan.
Monolithic Overload	Direct agents try to solve a complex feature in a single massive prompt, leaving incomplete files or "TODO" placeholders.	Atomic Bead Decompositions: Automatically breaks down the feature into independent, test-backed "beads" to focus on smallest changes at a time.
Single-Provider Bias	Relying on one model makes your pipeline highly vulnerable to that specific model's logical blind spots and systemic failures.	Cross-Model Councils: Harnesses diverse providers and architectures (e.g., Anthropic, OpenAI, NVIDIA NIM) to critique and align code drafts.
Context Rot	Long-running chats suffer from token bloat and context degradation, leading to broken imports or forgotten criteria.	Modern Context Engineering: The environment strictly isolates context, feeding the agent only the absolute minimum context it needs at each step.
Degenerate Retries	When a command fails, the agent tries to fix it within the same polluted chat session, compounding previous errors.	Ralph-Style Retries: Discards the broken chat session entirely and retries the exact bead with a fresh context window (plus notes from previous failures).
Risky Edits	Code modifications are made directly in your active checkout, potentially leaving your main branch in an unstable state.	Isolated Git Worktrees: Executes all changes in dedicated, isolated worktrees away from your primary working branch.
Opaque Execution	Internal states, planning notes, and test outputs are lost inside unstructured chat history.	Structured Durability: Maintains state locally inside SQLite, JSONL logs, and easily inspectable `.ticket/**` YAML artifacts.

What LoopTroop is not

LoopTroop is not a magic autopilot. It does not remove the need to review code, inspect diffs, protect secrets, or run work in a safe environment. It is best understood as an orchestration layer around coding agents: planning, state, approvals, execution boundaries, retries, and delivery.

Cost-Sensitive Budgets: Orchestrating multi-model councils and long retry loops uses a high volume of API tokens, though costs can be mitigated by leveraging subscription plans via providers in OpenCode.
Urgent or Quick Fixes: If you need a trivial change completed in seconds, LoopTroop's overhead will feel slow.
Simple Tasks: For quick edits or trivial apps, standard IDE chat tools or tools like Replit, Bolt, or Lovable are better fits.

Documentation

The README gives a first-glance overview. The full docs are maintained in the public LoopTroop-Website repository and published at:

https://www.looptroop.ovh/docs/

Useful pages:

Page	What it explains
Installation	Every channel, what each installs, upgrading, uninstalling, and verifying a download
CLI Reference	Every command and option, the `--json` output, and what running as a service means
Getting Started	Setup, startup, ports, and first project attach
Configuration	All profile settings with defaults, ranges, and trade-offs
Ticket Lifecycle Screenshots	Visual walkthrough of every workflow status with screenshots and action summaries
LLM Council	Multi-model draft, vote, refine, and coverage planning
Context Engineering	Why prompts are built from minimal per-status context and what each status receives
Beads & Execution	Bead execution, retries, resets, and context wipe notes

When the app is running, the same docs are also available from the dashboard.

Project status

LoopTroop is early alpha software, but it is usable for real work. The full ticket lifecycle is implemented, but some bugs are still likely. The core primitives (planning, execution, retries) are functional.

Configured limitations: In LoopTroop alpha, LLM Councils support 2–10 distinct models, including the main implementer. Each project may have only one active ticket in the execution band at a time; additional tickets must wait until it finishes or is canceled.

Roadmap: Roadmap

Contributing

Contributions, ideas, bug reports, and workflow feedback are welcome.

See CONTRIBUTING.md for setup, issue, pull request, documentation, and changelog guidance. Please also follow the Code of Conduct.