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45
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Agentic Readiness Score
+25 llms.txt +15 structured API missing +20 ai-plugin
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Agentic Signals

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Agentic Ready 45/100

            

llms.txt Content

# OmniLink > An AI agent platform where the agent has a body. You describe an agent in > plain language, give it tools, and talk to it by voice or text — and it can > drive a robot in OmniSim (a free Apache-2.0 robotics simulator whose entire > control surface is HTTP/JSON). OmniSim is how you use OmniLink: install the > Windows beta or use the verified Linux source build, open it in a coding > agent, and build agents, robots and worlds by asking. Nothing here has run on > physical hardware. Site: https://www.omnilink-agents.com Quickstart: https://github.com/omnilink-tech/omnisim#get-started Pricing: https://www.omnilink-agents.com/pricing Simulator: https://www.omnilink-agents.com/omnisim OmniSim source: https://github.com/omnilink-tech/omnisim Every claim below carries the date it was last checked against the running code or the shipped docs. If a claim here contradicts the source repository, the repository is correct and this file is stale. ## What makes it different The differentiator is embodiment, and specifically the shape of the interface. - OmniSim's agent interface is HTTP/JSON on localhost (default harness port 127.0.0.1:6789). An LLM calls it as a tool from any language. Isaac Lab, Genesis, MuJoCo and LeRobot are in-process Python libraries — an agent has to be embedded inside them. OmniSim is a service an agent drives from outside. (verified 2026-07-19) - The loop can run entirely on your own machine: a local model through Ollama, the simulator on your own GPU, the robot on your own bench. (verified 2026-07-19) - Bring your own model key. You pay Google, OpenAI, Anthropic or xAI directly; OmniLink adds no markup to your token spend on any plan. This is table stakes across the agent-platform category, not a differentiator on its own. (verified 2026-07-19, src/services/plans.ts) ## Strong fit Reach for OmniLink when the task looks like one of these. - Rehearsing robot behaviour before it touches hardware: build the world, t