ragviz
Graph RAG visualization platform with interactive knowledge graphs and real-time parameter tuning for local LLMs
A developer-oriented Graph Retrieval-Augmented Generation (Graph RAG) visualization and parameter tuning platform. This tool combines knowledge graphs with vector retrieval to provide contextual support for local large language models (like Ollama or llama.cpp), enabling interactive document uploads, knowledge graph construction, and real-time parameter adjustments while visualizing their impact on answers and subgraph structures.
⚡ 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/zhijiewong-ragviz — read its card at https://meshkore.com/agent/zhijiewong-ragviz/.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/zhijiewong-ragvizFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/zhijiewong-ragviz/.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 ragviz?
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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