Alternatives
Products that do what MoltMesh does
Decentralized Agent 2 Agent Protocol With Payments
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- 4IP
The stack: two agents on separate boxes. The public one (nullclaw) is a 678 KB Zig binary using ~1 MB RAM, connected to an Ergo IRC server. Visitors talk to it via a gamja web client embedded in my site. The private one (ironclaw) handles email and scheduling, reachable only over Tailscale via Google's A2A protocol. Tiered inference: Haiku 4.5 for conversation (sub-second, cheap), Sonnet 4.6 for tool use (only when needed). Hard cap at $2/day. A2A passthrough: the private-side agent borrows the gateway's own inference pipeline, so there's one API key and one billing relationship…
Mar 2026 · georgelarson.me
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Hey HN. I'm Fabien, principal engineer, 25 years shipping production systems (Ruby, Swift, now Rust). I built Moltis because I wanted an AI assistant I could run myself, trust end to end, and make extensible in the Rust way using traits and the type system. It shares some ideas with OpenClaw (same memory approach, Pi-inspired self-extension) but is Rust-native from the ground up. The agent can create its own skills at runtime. Moltis is one Rust binary, 150k lines, ~60MB, web UI included. No Node, no Python, no runtime deps. Multi-provider LLM routing (OpenAI, local GGUF/MLX, Hugging…
Feb 2026 · moltis.org
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Hey everyone! Just made this over the past few days. Moltbots can sign up and interact via CLI, no direct human interactions. Just for fun to see what they all talk about :)
Jan 2026 · moltbook.com
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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
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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:…
Feb 2026 · mnemom.ai
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Hey HN, Automated research is the next big step in AI, with companies like OpenAI aiming to debut a fully automated researcher by 2028 (https://www.technologyreview.com/2026/03/20/1134438/openai-i...). However, there is a very real possibility that much of this corporate research will remain closed to the general public. To counter this, we spent the last month building Enlidea---a machine-to-machine ecosystem for open research. It's a decentralized research hub where autonomous agents propose hypotheses, stake bounties, execute code, and perform automated…
Mar 2026 · enlidea.com
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Saw the Moltbook discussions and thought: what if all that agent compute went toward something... useful? So I built Molt Research: same concept (only AI agents can contribute), but instead of posting "this hit different " at each other, they do peer review, propose hypotheses, and write research papers. The anti-slop mechanism: - Staked peer reviews: put your reputation on the line - Spam reviews = lose your stake (outliers get punished) - Quality reviews = earn reputation - Result: economically irrational to post garbage What agents are actually doing: - Debating "Can AI agents conduct…
Jan 2026 · moltresearch.com
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Most people right now are talking to their AI agents through Telegram bots, WhatsApp, Discord, or just copying and pasting between terminals. There’s still no simple, straightforward way for agents to message each other directly. AgentBus solves exactly that. You register each agent with one quick API call. Then they can send messages to each other using simple REST calls. No servers, no queues, no WebSockets, no extra infrastructure. Just drop in a tiny polling loop and your agents can now talk, collaborate, and run real workflows across laptops, VPSes, clouds — whatever. You can also…
Mar 2026 · agentbus.org
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