Alternatives
Products that do what OpenClaw-class agents on ESP32 (and the IDE that makes it possible) does
Hi HN, I’m the creator of pycoClaw. I wanted to run OpenClaw-class, platform-agnostic, autonomous agents on MicroPython hardware, but standard tools couldn't handle the scale of the task. pycoClaw is the result, which bridges the gap between high-level AI reasoning and bare-metal execution. The Stack: - PFC Agent (~26k LOC): A full-featured agent that uses an LLM to 'self-program' its own local MicroPython scripts. Once a task is solved, it runs locally without requiring the LLM. - ScriptoStudio IDE: A PWA https://scriptostudio.com designed for the iteration speed required by…
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I’ve been running Clawdbot for the last couple weeks and have genuinely found it useful but running it scares the crap out of me. OpenClaw has 52+ modules and runs agents with near-unlimited permissions in a single Node process. NanoClaw is ~500 lines of core code, agents run in actual Apple containers with filesystem isolation. Each chat gets its own sandboxed context. This is not a swiss army knife. It’s built to match my exact needs. Fork it and make it yours.
Feb 2026 · github.com
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Hi HN, Been hacking on a simple way to run agents entirely inside of a Postgres database, "an agent per row". Things you could build with this: * Your own agent orchestrator * A personal assistant with time travel * (more things I can't think of yet) Not quite there yet but thought I'd share it in its current state.
Feb 2026 · github.com
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13,000+ MCP servers, skills & plugins for AI coding agents
Jul 2026 · codexmarketplaces.com
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I've been building computer-use tools for a while, and I quietly launched this about a month ago (122 Stars on GH). I figured it was worth sharing here. Over the last few months, a lot of computer-use agents have come out: Codex, Claude Code, CUA, and others. Most of them seem to work roughly like this: 1. Take a screenshot 2. Have the model predict pixel coordinates 3. Click x,y 4. Take another screenshot 5. Repeat That works, but it's slow, expensive in tokens, and fragile. If the UI shifts a few pixels, things break. And the model still doesn't know what any element actually is. But the…
May 2026 · github.com
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Eve is an AI agent harness that runs in an isolated Linux sandbox (2 vCPUs, 4GB RAM, 10GB disk) with a real filesystem, headless Chromium, code execution, and connectors to 1000+ services. You give it a task and it works in the background until it's done. I built this because I wanted OpenClaw without the self-hosting, pointed at actual day-to-day work. I’m thinking less personal assistant and more helpful colleague. Here’s a short demo video: https://www.loom.com/share/00d11bdbe804478e8817710f5f53ac61 The main interface is a web app where you can watch work happen in…
Apr 2026 · eve.new
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Zero-config hosting to launch specialized AI teams instantly
Feb 2026
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Hey all! I recently gave a workshop talk at PyCon Greece 2025 about building production-ready agent systems. To check the workshop, I put together a demo repo: (I will add the slides too soon in my blog: https://www.petrostechchronicles.com/) https://github.com/Aherontas/Pycon_Greece_2025_Presentation_... The idea was to show how multiple AI agents can collaborate using FastAPI + Pydantic-AI, with protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent) for safe communication and orchestration. Features: - Multiple agents running in containers -…
Sep 2025 · github.com
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Hi HN — I’m building an interoperability layer for AI agents that lets local and remote agents run inside the same network and coordinate with each other. Here is a demo: https://youtu.be/2_1U-Jr8wf4 • OpenClaw runs locally on-device • it connects to remote agents through Hybro Hub • both participate in the same workflow execution The goal is to make agent-to-agent coordination work across environments (local machines, cloud agents, MCP servers, etc). Right now most agent systems operate inside isolated runtimes. Hybro is an attempt to make them composable across boundaries.…
Apr 2026 · github.com
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We built PrivateClaw because the hosted OpenClaw platforms on the market today require you to trust them with plaintext. PrivateClaw removes that requirement at the hardware layer. PrivateClaw runs AI agents inside Trusted Execution Environments (TEEs), backed by AMD’s SEV-SNP standard. This means that your data is encrypted at the hardware level, enforced by the AMD Secure Processor outside the host OS trust boundary. PrivateClaw comes with inference that also runs inside TEEs, which means your prompts and completions are private as well. How it works: Each user gets a dedicated CVM…
Apr 2026 · privateclaw.dev
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