AI · alternatives · 2026
24 alternatives to Running AI agents across environments needs a proper solution
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…
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 5 we have checked on their own sites, 5 of them free or with a free tier.
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SimplAIfree tier, from $5/mo▲589Hey HN, I've been building SimplAI for the past several months — it's a platform for building, testing, and deploying LLM-powered agents and multi-step workflows. The problem I kept running into: spinning up an AI agent pipeline means stitching together prompt management, tool calling, memory, evals, and deployment — often from scratch every time. SimplAI tries to be the layer that handles all of that so you can focus on what your agent actually does. What it does: - Visual + code-first workflow builder for chaining LLM calls, tools, and APIs - Built-in prompt versioning and A/B testing…
2025 · simplai.ai · its alternatives →
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Arch▲314Hey HN — I’m Adil from Katanemo (with Salman, Shuguang, and Meiyu) We previously shared an early version of this project as ArchGW. Based on customer feedback, the scope expanded from “LLM routing and model access” into something broader: delivery infrastructure for agentic applications. We renamed it to Plano and reworked the architecture accordingly. The problem On-the-ground AI practitioners will tell you that calling an LLM is not the hard part. The really hard part is delivering agentic applications to production quickly and reliably, then iterating without rewriting system code every…
2024 · github.com · its alternatives →
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Agentfield▲91Build & scale AI \ agents as microservices with IAM
Dec 2025 · agentfield.ai · its alternatives →
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Agen▲138AI changed the way we code, but we're still using the old processes, and we've become the bottleneck, the AI is waiting for us - to reply, to open our laptops, to review the code, and so on. We're building the future of AI software development. The agents are autonomous, they run in sandboxes, automatically fix the pipelines, and deliver you the final, working code. You can use live preview to see the changes they made. Working across multiple repositories, all within the same session. This is the future - you don't need an IDE, and you don't have to run anything locally.
Mar 2026 · agenhq.com · its alternatives →
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Agent Sandboxfree tier, from $0.00025/mo▲97Your agent's personal remote computer and drive
Feb 2026 · agentsandbox.co · its alternatives →
- 6RL
Hello Hacker News! We're Yangqing, Xiang and JJ from lepton.ai. We are building a platform to run any AI models as easy as writing local code, and to get your favorite models in minutes. It's like container for AI, but without the hassle of actually building a docker image. We built and contributed to some of the world's most popular AI software - PyTorch 1.0, ONNX, Caffe, etcd, Kubernetes, etc. We also managed hundreds of thousands of computers in our previous jobs. And we found that the AI software stack is usually unnecessarily complex - and we want to change that. Imagine if you are a…
2023 · lepton.ai · its alternatives →
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Trails▲81Hey all, Justin here. I previously built Phind, the AI search engine for developers. One of the biggest problems we had there was figuring out what went wrong with bad searches. We had tons of searches per day, but less than 1% of users gave any explicit feedback. So we were either manually digging through searches or making general system improvements and hoping they helped. This problem gets harder with agents. Traces are longer and more complex. It takes more effort to review them, so I'm building a tool that lets you analyze LLM outputs directly to help developers of LLM apps and agents…
Jan 2026 · trails-red.vercel.app · its alternatives →
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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 · its alternatives →
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Run AI Agents on your cloud infrastructurefree tier▲12Hi, founder of Okteto here! We’ve been experimenting with AI agents in our workflows at Okteto. Running them locally worked at first, but quickly became painful. git worktrees, multiple terminals, and messy context switches slowed us down. So we built Agent Fleets: ephemeral, fully managed environments for AI agents, built on top of Okteto’s development platform. Each agent runs in its own containerized environment on your infrastructure, with the services, tools, and policies it needs. You can spin up agents with a single click or API call. No local setup. No git worktrees. The beta…
2025 · okteto.com · its alternatives →
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I've been looking for a way to run LLMs safely without needing to approve every command. There are plenty of projects out there that run the agent in docker, but they don't always contain the dependencies that I need. Then it struck me. I already define project dependencies with mise. What if we could build a container on the fly for any project by reading the mise config? I've been using agent-en-place for a couple of weeks now, and it's working great! I'd love to hear what y'all think
Jan 2026 · github.com · its alternatives →
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Hi HN, I’m the creator of Cordum. I’ve been working in DevOps and infrastructure for years (currently in the fintech/security space), and as I started playing with AI agents, I noticed a scary pattern. Most "safety" mechanisms rely on system prompts ("Please don't do X") or flimsy Python logic inside the agent itself. If we treat agents as autonomous employees, giving them root access and hoping they listen to instructions felt insane to me. I wanted a way to enforce hard constraints that the LLM cannot override, no matter how "jailbroken" it gets. So I built Cordum. It’s an open-source…
Jan 2026 · github.com · its alternatives →
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Enable LLMs to Program Themselves. Contribute to Osly-AI/Pocket-Flow-Framework development by creating an account on GitHub.
2025 · github.com · its alternatives →
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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 · its alternatives →
- 15IM
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 · its alternatives →
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A file-based protocol for multi-agent AI workflows — portable, auditable, human-in-command. Cross-agent communication and persistent shared memory across runtimes, projects, and machines. No daemons, no central server.
May 2026 · oacp.dev · its alternatives →
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Free cloud-based tool for managing AI agents across multiple hosts, with light project management, shared memory, and usage stats.
May 2026 · nodecartel.com · its alternatives →
- 18PS
I didn't want to buy a standalone computer or repurpose a laptop to run constantly so I could maintain a system to sync my LLMs, so I built this. It's a simple overview of my system, laid out in a way easy to unpack and replicate for yourself. The project is meant to be configured individually, and uniquely, since one solution might not be what's best for another. If anything, maybe it gives you some ideas on how to implement things for your own project. Best wishes, Ryan.
Aug 2026 · pacslate.com · its alternatives →
- 19IS
Hey HN! For that last 8 months I've been trying to make agents that can hack web applications to find vulnerabilities in them - An AI Security Tester. The system has 29 agents in total, a custom LLM Orchestration framework which works on the task-subtask architecture (old-school but works amazingly for my use case, and is pretty reliable) with custom agent calling mechanism. No Auo-Gen, Langchain and Crew AI - Everything custom built for pentesting. Each test runs in an isolated Kali linux environment (on AWS Fargate), where the agents have full access to the environment to undertake any…
2025 · its alternatives →
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Open-source AI agent runtime — build Agents in plain English
Jul 2026 · syntheticbrew.ai · its alternatives →
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Your agents. Your servers. Your keys. Your model.
May 2026 · platos.dev · its alternatives →
- 22BA
I'm one of the creators of The Edge Agent (TEA). We built this because we needed a way to deploy agents that was verifiable and robust enough for production/edge cases, moving away from loose scripts. The architecture aims to solve critical gaps in deterministic orchestration identified by *Prof. Claudionor Coelho Jr. (Stanford alum, ML/DL Faculty at Santa Clara Univ., and Senior Fellow for AI at Majestic Labs)* during our work on the Kiroku project. *Key Technical Features:* * *Neurosymbolic Native:* We integrated Prolog to logically validate LLM outputs. This combines neural…
Jan 2026 · fabceolin.github.io · its alternatives →
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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 · its alternatives →
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Brainfree▲8A small, blazingly fast and extensible agent runtime
Sep 2026 · github.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →