Alternatives
Products that do what Desktop app to run Python agents over TCP with live server geolocation does
I built an open-source desktop app for running and monitoring Python agents that talk over TCP. It works on macOS, Linux, and Windows. The basic idea is simple: if a repo has an `agent.py` entrypoint, the app can import it from GitHub, install `requirements.txt` if needed, connect it to a TCP server, and show what it is doing in one UI. Current features: * import agent repos from GitHub, including private repos * run agents through `agent.py` * optional `requirements.txt` support * optional `id.json` for agent metadata * connect agents to TCP servers * inspect message flow in one place *…
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Hello HN, Pietro here! I've been really excited to see the recent buzz around MCP and all the cool things people are building with it. Though, the fact that you can use it only through desktop apps really seemed wrong and prevented me for trying most examples, so I wrote a simple client, then I wrapped into some class, and I ended up creating a python package that abstracts some of the async uglyness. You need: * one of those MCPconfig JSONs * 6 lines of code and you can have an agent use the MCP tools from python. The structure is simple: an MCP client creates and manages the connection and…
2025 · github.com
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Built an AI code reviewer using Letta (Python) that I can call natively from Rust applications. The interesting part: real-time streaming works perfectly across the language boundary with zero hassle using RunAgent. The agent runs in Python with persistent memory, leverages the best in house agentic memory management with Letta (Pythonic AI agent framework), and my rust code just uses it (kinda) natively, though Letta has no Rust bindings. And, streaming works like magic. No FFI, no complex bridges - just native async/streaming that feels like calling any Rust librar, but without…
2025 · medium.com
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Hi, A few months ago, I shared a tool I built for personal use to easily trace HTTP client requests in any Python app. It links requests to the corresponding Python code and comes with a nice UI. By easily, I mean: no external dependencies, no setup, no superuser rights, and no code modifications — just do "pip install httpdbg" and then launch your code using the "pyhttpdbg" command. That’s it. I received a lot of positive feedback — not just here, but also on social media, in podcasts, and beyond. So, thank you! Since I last introduced httpdbg, I’ve made many improvements — based on both my…
2025 · httpdbg.readthedocs.io
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Jun 2026 · github.com
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2015 · workerbee.tonkworks.com
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Hey HN, In the months since we initially released Burr (https://news.ycombinator.com/item?id=39917364), we have been hard at work. We wanted to share some of the most exciting changes we’ve made to build Burr out as a full-stack development framework for AI agents. In case you don’t recall, Burr is an open-source python library that makes it easier to build and debug GenAI applications & agents by representing them as graphs of simple python objects/functions. Burr only abstracts away system-level concerns (state persistence, debugging, observability), and does not…
2024 · burr.dagworks.io
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Hey HN, Over the past 10 months I've been using Claude Code heavily, and one limitation kept coming up: you can really only run one coding agent at a time. While one agent is refactoring something, the rest of the repo is basically blocked unless you start manually juggling branches and working directories. The core issue is that AI coding agents operate directly in your filesystem. If two agents run in the same working directory they quickly start stepping on each other’s changes. Git worktrees turned out to be a surprisingly good primitive for solving this. So I built ChatML, a Desktop app…
Mar 2026 · github.com
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Read an article about analyzing Garmin data with AI. Sounded great — except I didn't want to send my health data to any cloud service. So I asked Claude to write me 2-3 scripts and a dashboard. This escalated a bit. 30 days and 20$ later I have this: A local-first Garmin archive with interactive HTML dashboards, Excel exports, weather and pollen context, AES-256 encrypted token storage, and a self-healing data pipeline with 515 automated tests. Windows desktop app, no terminal needed. Nothing leaves your machine. I never wrote a line of Python. I understood the problems and made the…
Apr 2026 · 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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2022 · pythonjob.xyz
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Hi! I’ve always wanted a simple way to throw my Python projects online. Just to try things out, share them, or test a webhook with a real endpoint. But every time I hit the same wall: Buy a server, Set up HTTPS, Configure DNS, Get a domain. Write boilerplate to glue it together. It always felt like too much for something so small that I wanted to test. So Ive been hacking on Thread4 (currently in alpha): a tiny platform where you can write a Python function and go live instantly at yourname.thread4.eu. It’s not for production use, just quick testing, experimenting, or sharing your idea…
2025 · thread4.eu
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Hey HN, I built SuperHQ, an app that lets you run coding agents in local sandboxes (powered by Shuru). No custom UI wrapping the agents, they run as CLI/TUI like they were designed to. It just provides you the tools most of us (okay, maybe just me?) needed for running multiple coding agents in parallel without worrying about breaking your system or work environment. Each agent runs in its own microVM. You mount your projects in, writes go to a tmpfs overlay so your host is never touched, and you get a unified diff view to accept or discard changes. API keys never enter the sandbox, they…
Apr 2026 · superhq.ai
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I have built many AI agents, and all frameworks felt so bloated, slow, and unpredictable. Therefore, I hacked together a minimal library that works with JSON/dict/kwargs definitions for each step, allowing you a simpler way to define reproducible agents. It supports concurrency for up to 1000 calls/min, giving you speed and predictability in your workflows. Install pip install flashlearn Input is a list of dictionaries Simply take user inputs, API responses, and calculations from other tools and feed them to FlashLearn. user_inputs = [{"query": "When was python launched?"}]…
2025 · github.com
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RunKit is a browser based tmux manager that works great on a phone over Tailscale. I created it to be able to run several AI agents in parallel, after trying out several existing tools. There's no database (state is read live from tmux). Right now, its the first thing I install on any server, and from there on RunKit becomes the management cock-pit for that server. Also, for running several agents in parallel, RunKit has been a lifesaver. It shows the status of all running agent on the left panel, and sends you a notification anytime an agent is stuck. Please try it out, and let me know if…
Jul 2026 · github.com
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Lazyagent a simple way to see what your coding agents were actually doing across Claude, Codex, and OpenCode. Once you have more than 1 agent running, its really hard to answer the simple question: what is it doing right now and why? It is a local terminal UI that collects your agents runtime events and shows their activity in one place. You can inspect: - agents and subagents - tool calls - prompts - outputs - status changes Sessions and projects are there to give that activity some context, so it is easier to follow what happened over time and which run belongs to which repo. And make it…
Apr 2026 · github.com
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