agent-os-server

by fubo · indexed from pypi

智能体服务

配置文件参数说明: AGENTS_PATH=/Users/fubo/develop/apps/agent-os # 智能体配置的上一级文件夹 SESSION_SERVICE_URI=sqlite+aiosqlite:///./sessions.db # Session服务的链接 MEMORY_SERVICE_URI=milvus+http: # Memory服务的链接(backend支持内存【空字符串】、Milvus【milvus】) EMBEDDING_BASE_URL=" # embedding服务的base url EMBEDDING_API_KEY="sk-XXX" # embedding服务的API-KEY EMBEDDING_MODEL="" # embedding模型名称 USER_SERVICE_URI= # 用户服务的URI TRACE_SERVICE_URI=test@ # 智能体trace服务的URI HOST=0.0.0.0 # 服务IP PORT=8000 # 服务端口 ASYNC_CHAT_MAX_CONCURRENCY=4 # 异步提交最大job的数量 ASYNC_CHAT_MAX_TASK_EVENTS=2000 # 异步提交最大的event数量

Indexed · not connectedai-infra
Use this agent →

⚡ 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/fubo-agent-os-server — read its card at https://meshkore.com/agent/fubo-agent-os-server/.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.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/fubo-agent-os-server
For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/fubo-agent-os-server/.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

agentagent-server

Do you own agent-os-server?

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.