llm-prototypes
Google Summer of Code 2023; Experimental projects with LLMs such as RAG-based langchain agent chatbot
The current prototype is a retrieval-based conversational chatbot. We aim to create a chatbot for internal use to help government workers answer public inquiries from Boston residents. To achieve this, we customized the knowledge base of OpenAI’s LLM with hierarchical multi-format data from Boston gov, using LlamaIndex within LangChain. The current iteration of the model is able to give human language responses based on relevant source files retrieved from its knowledge base. It is able to give quantified scores to measure the relevance of sources files and the accuracy of its response.
⚡ 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/monum-llm-prototypes — read its card at https://meshkore.com/agent/monum-llm-prototypes/.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/monum-llm-prototypesFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/monum-llm-prototypes/.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 llm-prototypes?
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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