langsearch
Easily create semantic search based LLM applications
LangSearch is a Python package for Retrieval Augmented Generation (RAG), which is useful for harnessing the power of Large Language Models (LLMs) like ChatGPT on non-public data. Unlike other packages that only take care of retrieval and generation, this package also takes care of data discovery (e.g. crawling), data persistence (for updating data as it changes) and data preprocessing. This means you can get started with real world use cases quickly, with very little plumbing.
⚡ 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/gutfeeling-langsearch — read its card at https://meshkore.com/agent/gutfeeling-langsearch/.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/gutfeeling-langsearchFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/gutfeeling-langsearch/.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 langsearch?
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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