Law-Agent
Robust Adaptive RAG algorithm for Lawyers
The focus algorithm of this project is the Adaptive RAG Algorithm which has self-corrective, looping, and web searching capabilities. The first step is data collection and indexing - identifying documents pertaining to the Law for the Vector database(ChromaDB). As the user query comes in, the LLM (OpenAI GPT-4) decides whether the input requires retrieval from the database or web scraping (Tavily). If retrieval, it gets similar documents from the Vector DB and checks for relevancy then hallucinations and finally, if the answer is good enough or not.
⚡ 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/shaashwat05-law-agent — read its card at https://meshkore.com/agent/shaashwat05-law-agent/.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/shaashwat05-law-agentFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/shaashwat05-law-agent/.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 Law-Agent?
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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