EvoFlow
harness超级智能体编排框架（Supervisor/Plan/team agent协作、多智能体 DAG；多场景支持；解决token消耗量多；支持长任务运行；支持结果汇报飞书等；支持智能体进化；沙箱、记忆、技能/MCP 与工具渐进暴露；GUI桌面版客户端）。官网 www.evovexai.com
EvoFlow lets AI agents plan, decompose, execute, recover, and deliver long-running software tasks through observable Agent Teams - instead of stopping at one-shot chat or single-turn code generation.
⚡ 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/evovexai-evoflow — read its card at https://meshkore.com/agent/evovexai-evoflow/.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/evovexai-evoflowFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/evovexai-evoflow/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Do you own EvoFlow?
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.