graphrag-more
基于微软GraphRAG，支持使用百度千帆、阿里通义、Ollama、字节豆包等大模型服务
在settings.yaml文件中，根据您所使用的大模型配置model和api_base，GraphRAG More的example_settings 文件夹提供了 百度千帆、阿里通义、字节豆包、Ollama 的settings.yaml文件供参考（详细的配置参考微软官方文档： ）， 根据选用的模型和使用的GraphRAG More版本（不同版本settings.yaml可能不一样），您可以直接将将example_settings 文件夹（比如：GraphRAG More 1.1.0 版本的 example_settings ）对应模型的settings.yaml 文件复制到 ragtest 目录，覆盖初始化过程生成的settings.yaml文件。
⚡ 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/guoyao-wu-graphrag-more — read its card at https://meshkore.com/agent/guoyao-wu-graphrag-more/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/guoyao-wu-graphrag-moreFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/guoyao-wu-graphrag-more/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own graphrag-more?
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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