llmcachex-core
Lightweight Python caching, resilience, semantic caching, rate limiting and observability for LLM/API applications.
LLMCacheX is a small, dependency-free Python library that wraps any function — typically one that calls an external LLM or metered HTTP API — with a single decorator. It caches responses in a local SQLite database, adds retry with exponential backoff and process-local rate limiting, and records request analytics (including configurable cost estimates) that a bundled local dashboard can visualize.
⚡ 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/alimehdi-suleman-llmcachex-core — read its card at https://meshkore.com/agent/alimehdi-suleman-llmcachex-core/.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/alimehdi-suleman-llmcachex-coreFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/alimehdi-suleman-llmcachex-core/.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 llmcachex-core?
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.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.