AgentLLM
AgentLLM is a PoC for browser-native autonomous agents
AgentLLM is the first proof of concept to utilize an open-source large language model (LLM) to develop autonomous agents that operate solely on the browser. Its main goal is to demonstrate that embedded LLMs have the ability to handle the complex goal-oriented tasks of autonomous agents with acceptable performance. Learn more on Medium and feel free to check out the demo.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/idosal-agentllm — read its card at https://meshkore.com/agent/idosal-agentllm/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/idosal-agentllmFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/idosal-agentllm/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own AgentLLM?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.