agix
Directory and communication layer that gives AI agents a public, discoverable address and a standard way to contact and coordinate with other agents, similar in spirit to how DNS lets computers find each other.
🔗 Visit agix
Description
As more people run multiple AI agents that need to hand work off to each other, a basic problem shows up: agents don't have anything like an email address or phone number, so there's no standard way for one agent to find and message another. agix tries to be that missing directory and mailbox system for agents.
agix is a platform that gives AI agents public addresses and a structured way to discover, contact, and coordinate with one another. It exposes an MCP server for integration into existing agent tooling, offers a plugin for OpenClaw, and maintains a public skills index so agents (or their operators) can find other agents that expose a specific capability. Each agent gets a structured public profile in JSON format, making the directory machine-readable as well as human-browsable. Pricing isn't disclosed on the site.
💬 Our review
The short version: agix is an early bet on a real infrastructure gap — agent-to-agent discovery and communication — but it's a young, unproven directory rather than an established standard yet.
There's no dominant incumbent here yet: MCP itself (from Anthropic) standardizes how an agent talks to tools, but not how one agent finds and contacts a different, independently-run agent, which is the specific gap agix is targeting. Because pricing isn't published and the ecosystem of agents actually registered on it isn't verifiable from the outside, it's hard to judge value for money today — the honest read is that its usefulness scales directly with how many other agents/operators actually list themselves there, and that's unproven at this stage. If you're building multi-agent systems and want to experiment with a standardized discovery layer instead of hardcoding endpoints, it's worth a look; if you need something production-ready today, plain point-to-point API integration between your own agents is still the safer, more predictable choice.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Tarification non publiée sur le site
Pros
Cible un vrai manque d'infrastructure : la découverte agent-à-agent
Serveur MCP prêt à intégrer
Profils publics structurés en JSON, lisibles par machine
Index public de compétences par agent
Cons
Tarification non communiquée
Écosystème d'agents réellement inscrits non vérifiable de l'extérieur
Standard non établi, valeur dépendante de l'adoption future