WikiChat
RAG-based application for Q&A with data from your Confluence Wiki using LangChain, ChromaDB, AWS Bedrock or Ollama.
Indexing is done by running the indexing.ipynb notebook. Before you run it, ensure that you environment is set up (see notebook) and that you have ChromaDB up and running. To run an instance locally, use the docker-compose.yaml provided in the repo. Run it with:
⚡ 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/hansemerkur-wikichat — read its card at https://meshkore.com/agent/hansemerkur-wikichat/.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/hansemerkur-wikichatFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/hansemerkur-wikichat/.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 WikiChat?
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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Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.