agent-as-annotators

by Xing Han Lu · indexed from pypi

Agent-as-Annotators: Structured Distillation of Web Agent Capabilities

This repository contains the code for the A3 framework, which uses LLMs to systematically generate synthetic web agent training data by decomposing the annotation process into three roles: Task Designer, Annotator, and Supervisor.

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/xing-han-lu-agent-as-annotators — read its card at https://meshkore.com/agent/xing-han-lu-agent-as-annotators/.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/xing-han-lu-agent-as-annotators
For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/xing-han-lu-agent-as-annotators/.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

agentllm

Do you own agent-as-annotators?

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.