arjunkshah/loopy — Free, local-first agent operating system: assign work on a board and a fleet of AI coding agents shi…

Description and use

**Brief Summary (2‑3 sentences)**
Loopy is an open‑source, local‑first “agent operating system” that lets you assign work on a board and have a fleet of AI coding agents (Codex, Claude Code, Cursor, Gemini, etc.) execute it under configurable policies. It orchestrates up to 43 different coding‑agent CLIs, turning ad‑hoc prompts and tools into repeatable, version‑controlled workflows that run on your own keys.

**Value**

• **From isolated prompts to reusable pipelines** – Loopy abstracts the glue code needed to chain multiple LLM‑based coding agents, giving you a single place to define, monitor, and version‑control complex development tasks.
• **Policy‑driven execution** – You can enforce security, cost, or quality policies (e.g., max token usage, approved models) before any agent runs, which is crucial for internal compliance.
• **Tool‑agnostic orchestration** – By supporting a wide range of agent CLIs, Loopy lets teams experiment with the best‑in‑class model for each sub‑task without rewriting integration code.

**Practical Adoption Path**

1. **Explore the API/CLI** – Clone the repo, install the Python package, and run the sample board to see how tasks are defined and dispatched.
2. **Pilot on a small team** – Connect your own API keys for the supported agents, create a simple “code‑review → refactor → test” pipeline, and iterate on policy settings.
3. **Integrate with existing CI/CD** – Wrap Loopy commands in your build scripts or GitHub Actions to automate code generation steps as part of the normal development flow.
4. **Scale and customize** – Add custom agents or external tools via the provided SDK, and store board state in a version‑controlled datastore for reproducibility.

**Production Readiness**

• **Maturity**: Medium. The project is functional and actively updated (last commit 2026‑06‑24) with 26 stars and 6 forks, indicating modest community interest.
• **Dependencies**: Relies on many external CLI tools; you’ll need to manage their versions and ensure they remain available.
• **Security & Licensing**: No immediate red flags, but the license and long‑term maintainer commitment require a final review before a production rollout.
• **Fit for production**: Suitable for prototypes, internal tooling, or as a “glue” layer in a controlled environment. For mission‑critical services, add thorough testing, dependency pinning, and a fallback plan in case an upstream agent CLI is deprecated.

Problem

arjunkshah/loopy helps turn isolated prompts and tools into repeatable agent workflows.

Outcome

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.

Workflow

• coordinate multi-agent workflows
• add tool-use pipelines
• standardize agent memory

Evidence

Free, local-first agent operating system: assign work on a board and a fleet of AI coding agents ships it under policy. Orchestrates 43 coding-agent CLIs (Codex, Claude Code, Cursor, Gemini). Open source, agentic, runs on your keys.

Source