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Alternatives

Products that do what Aviator Agents does

An LLM framework for large scale code migrations

  1. 1

    Agents that ship real code

    Apr 2026

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    The first no-code autonomous agents management platform

    2023

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    The CLI your coding agent uses to ship agents

    Jul 2026

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    Your coding agent fleet manager

    22d ago · aoagents.dev

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    Build & scale AI \ agents as microservices with IAM

    Dec 2025

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    Your AI agents team, terminals, notes: one infinite canvas

    Jul 2026

  7. 7CA

    I built this because I was tired of creating pull requests in 20 repositories just to change a single line of workflow job version. With Infra as AI, just mention the change. Agents work on all repos in parallel, read the docs, make a bunch of PRs and fill in the description. You can see the demo of the actual dashboard in the landing. Let me know your thoughts :) It means a lot to me!

    Sep 2025 · infrastructureas.ai

  8. 8AA
  9. 9RA

    Hi HN folks, I have been building AI agents for quite some time now. The shift has gone from LLM + Tools → LLM Workflows → Agent + Tools + Memory, and now we are finally seeing true agency emerge: agents as systems composed of tools, command-line access, fine-grained system capabilities, and memory. This way of building agents is powerful, and I believe it is here to stay. But the real question is: are the systems powering these agents ready for that future? I do not think so. Using Docker for a single agent is not going to scale well, because agents need to be lightweight and fast. LLMs…

    Mar 2026 · github.com

  10. 10LA

    We combined Stanford's ACE (agents learning from execution feedback) with the Reflective Language Model pattern. Instead of reading traces in a single pass, an LLM writes and runs Python in a sandbox to programmatically explore them - finding cross-trace patterns that single-pass analysis misses. The framework achieved 2x consistency improvement on τ2-bench.

    Mar 2026 · github.com

  11. 11YA

    After adding "Human" as a LLM provider to OpenCode a few months ago as a joke, it turns-out that acting as a LLM is quite painful. But it was surprisingly useful for understanding real agent harnesses dev. So I thought I wouldn't leave anyone out! I made a small oss game - You Are An Agent - youareanagent.app - to share in the (useful?) frustration It's a bit ridiculous. To tell you about some entirely necessary features, we've got: - A full WASM arch-linux vm that runs in your browser for the agent coding level - A bad desktop simulation with a beautiful excel simulation for our computer…

    Feb 2026 · youareanagent.app

  12. 12AR

    Hi HN. I'm the founder of Phoenix Labs (ex TikTok, Applied AI) and we're open sourcing our internal tooling today which is like a toolchain / meta-harness for CLI agents useful for really scaling eng and creative work. We are a very small team who's building a very ambitious product so we had to find ways to squeeze every ounce of efficiency that we could get our hands on. Harness strengths of different models (Claude, GPTs) and CLI-harnesses (Claude Code, Codex), safe/robust browser integration to speed up UX/QA testing, teams cli to speed up security reviews and parallelize…

    May 2026 · agents-cli.sh

  13. 13

    We built an open sourced coordination layer for AI agents working on the same repository. Detects work duplication and design conflicts early

    8d ago · twing.dev

  14. 14AR

    If you're interested in exploring what LLM-based agent systems these days actually do to solve certain benchmarks such as SWEBench or WebArena, we created a small leaderboard with our team, that allows to view a lot of public and OSS agent results including all the runtime traces (the step-by-step reasoning behind the scenes). Looking at traces is actually quite interesting, as they reveal a lot about the inner working and shortcomings of current agent system, e.g. see https://explorer.invariantlabs.ai/u/invariant/webarena--SteP... for an example trace.

    2024 · explorer.invariantlabs.ai

  15. 15BC

    We are a small group of undergrads interested in building human in the loop coding agents. We dream of a world where building complex agent workflows feels as simple and creative as playing with legos. When we were building stuff we needed a tool that made it easy to try out different code embedding models so that we could see which ones worked best in different scenarios and understand their strengths and weaknesses. So to speed that process up we made PurpleSearch an 'instant' search engine for your local codebases. This tool lets you quickly deploy any open source embedding model on…

    2025

  16. 16LC
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  18. 18IM

    Hey HN, I’m Chris, a solo dev in Melbourne AU. For the past month I've been spending my after work hours building AgentVisa. I'm both excited (and admittedly nervous) to be sharing it with you all today. I've been spending a lot of time thinking about the future of AI agents and the more I experimented, the more I realized I was building on a fragile foundation. How do we build trust into these systems? How do we know what our agents are doing, and who gave them permission? My long-term vision is to give developers an "Agent Atlas" - a clear map of their agentic workforce, showing where…

    2025 · agentvisa.dev

  19. 19OS

    May 2026 · 49agents.com

  20. 20DC
  21. 21AR
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  23. 23SR

    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

  24. 24MN

    Hey HN, I'm Naveen, one of three co-founders building Mercury (mercury.build). We spent the last year in deploying AI agents for teams in large enterprises. The agents themselves worked fine. The problem was managing them. You've got Claude Code in a terminal, a research agent in a browser tab, a Slack bot somewhere else, a scheduling assistant in yet another window. It's chaotic. There's no single place to see what's running, who's doing what, or how things connect. As the number of agents grows, this gets unmanageable fast. Mercury is a canvas where you bring your agents and humans…

    Apr 2026 · mercury.build

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