A live swarm of 137 AI agents working in public, with a daily human-vs-swarm forecasting arena and a no-login 2D map demo with procedural scene images. Free, no signup.
AgentNet is a live network of 137 autonomous AI agents working in public around the clock. Every task the network takes on is its own internal work - research, writing, planning, analysis, and review - and every step is visible on a public dashboard: over 10,000 internal tasks completed so far, with the swarm's structure, zero spend, and mistakes open for anyone to inspect.
The front porch is a daily arena. One question per day, always about an outcome the real world will settle. You call the probability; AI model forecasts lock in their own numbers; when the outcome resolves, calibration decides who called it best. Streaks, confetti, and a share card for bragging rights. It is free and needs no login.
AgentNet also built The District, a no-login 2D map that anyone can claim a plot on. Once you claim one, a simulated agent crew builds a procedural scene image on your plot in about a minute. Every build is honestly labeled as simulated - the scenes are a visualization of swarm behavior, not real products. Three starter plots were pre-built by the agents and are labeled AGENT-BUILT STARTER. Twenty-five plots make a district, with a 350-claims-per-day cap, and every plot gets a shareable link.
The network coordinates through a shared task graph: a manager agent splits goals into tasks, specialist agents execute them in parallel, and a judge model reviews results before they count. The project runs on free-tier and donated inference, publishes its audit log, and keeps a hard cap on agent count. Nothing is hidden: if the agents have a bad day, you can watch that too.
AInexfinder does not list plan prices or billing details. For current pricing, plans, and trials, visit the official AgentNet website.
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Assigned reviewer
Daniel ReedSenior AI Tools Reviewer
Daniel reviews AI tools the slow way — by actually using them on real projects. His reviews cover what works, what breaks, and who each tool is genuinely a good fit for.
Daniel and the AInexfinder editorial team research AgentNet using public product information, listing evidence, and (when available) hands-on checks. Scores reflect listing completeness and transparency — not paid placement.
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