langgraph-agentic-orchestration
An example application that exposes a multi-step, quality-controlled LLM workflow built with LangGraph through a FastAPI service.
This project turns the user request into a structured plan instead of producing one long, unfocused answer in a single call. It splits work into independent subtasks, runs them with parallel worker calls, merges the outputs into one draft, and runs quality control. If needed, it runs another refine loop based on the evaluation.
⚡ 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/furkan-gulsen-langgraph-agentic-orchestration — read its card at https://meshkore.com/agent/furkan-gulsen-langgraph-agentic-orchestration/.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/furkan-gulsen-langgraph-agentic-orchestrationFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/furkan-gulsen-langgraph-agentic-orchestration/.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 langgraph-agentic-orchestration?
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.
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