Society
The TCP/IP for AI agent swarms. Open source.
What it does
The open protocol for AI agent swarms. Build AI systems where autonomous agents collaborate through peer-to-peer networks, structured workflows, and shared knowledge. No central server required.
Does the same job
all alternatives →- ASAgent Swarm – Multi-agent self-learning teams (OSS)Feb 2026 · github.com · ▲63
- TPTrust Protocols for Anthropic/OpenAI/GeminiFeb 2026 · mnemom.ai · ▲40
Much of my work right now involves complex, long-running, multi-agentic teams of agents. I kept running into the same problem: “How do I keep these guys in line?” Rules weren’t cutting it, and we needed a scalable, agentic-native STANDARD I could count on. There wasn’t one. So I built one. Here are two open-source protocols that extend A2A, granting AI agents behavioral contracts and runtime integrity monitoring: - Agent Alignment Protocol (AAP): What an agent can do / has done. - Agent Integrity Protocol (AIP): What an agent is thinking about doing / is allowed to do. The problem:…
- DRDecentralized robots (and things) orchestration system2025 · docs.p2p.industries · ▲69
Hi HN, we build an open-source operating system extension for orchestrating robot swarms fully decentralized. This first beta version allows you to create fully decentralized robot swarms. The system will set up a wireless mesh network and run a p2p networking stack on top of it, such that nodes can interact with each other through various abstractions using our SDKs (Rust, Python, TypeScript) or a CLI. We hope this is a step toward better inter-robot communication (and a fun project if you have a few Raspberry Pis lying around). Our mesh network is created by B.A.T.M.A.N.-adv and we’ve…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Launched alongside, March 2026
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