Alternatives
Products that do what Agent Loop does
Unlimited Cloud AI Coding Agents — Free Beta
- 1

- 2

- 3

- 4

- 5

- 6

- 7

- 8

- 9

- 102C
Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
Feb 2026 · github.com
- 11

- 12

- 13

- 14

- 15

- 16
- 17LL
Some time ago I built a simple app to run swarms of coding agents — I call it fleet (https://news.ycombinator.com/item?id=48256389). It's based on centralized beads with a Python orchestrator and can run any coder (Claude, agy, Codex). Recently I added a UI to manage the whole agent lifecycle: adding new tasks, monitoring running ones, and a chat interface built on MCP with a centralized SQLite DB. From the UI I can spawn agents to run in any directory, define dependencies on other tasks, and specify which coder/model should do the job. Today I can run 10–15 agents…
Jun 2026
- 18

Design safe, token-aware AI agent loops — free, no sign-up
Jul 2026 · loopengineering.app
- 19

- 20OA
Hi HN. I've been running AI coding agents (Claude Code, Codex, etc.) on real repos for a while now. The dirty secret of "autonomous coding" is that agents stop all the time — quota limits, test failures, policy violations, bad judgement calls. You end up babysitting them. So I asked a different question: what if the system was designed around the assumption that agents WILL fail, and the job of the infrastructure is to never let that failure become a dead end? openTiger is a "non-human-first" orchestration system that runs multiple AI agents in parallel — planner, workers, testers, judge —…
Feb 2026 · github.com
- 21

- 22RT
Now that AI is capable of writing large volumes of production-quality code, our role as developers is changing. Our primary job is no longer writing code. It’s planning and communicating software design and architecture. We have to do this collaboratively with agents and then review and iterate on their implementations. IDEs were not built for this workflow. So about three months ago I decided to try to build what I thought this new interface should look like. Runner is a coding agent purpose-built for this new “plan and review” workflow. It’s not for vibe coding. It’s for professional…
Sep 2025 · runnercode.com
- 23EY
I built an open-source AI agent for security testing to find and fix vulnerabilities in your code. I’ve noticed how bad security vulnerabilities have gotten with everyone shipping AI code slop, so I wanted to build something that allows for vibe-coding at full speed without compromising security. Traditional security tools aren’t effective, and manual pen-testing can’t keep up with the rapidly growing AI code This tool runs your code dynamically, finds vulnerabilities, and validates them through actual exploitation. You can either run it against your codebase or enter your (or someone…
2025 · github.com
- 24SR
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…
Jul 2026 · shikigami.dev
Ranked by how close each launch is in meaning, then by votes. Refine with a description →