langfuse-monitoring-eval
Monitoring and evaluating LLM apps with Langfuse. Presented at PyConZA 2024.
In the rapidly evolving landscape of AI, the ability to quickly iterate on prototypes and ensure robust performance of apps built around language models is crucial for developers and data scientists. This talk focuses on leveraging free and open-source tools to build, monitor, and evaluate LLM applications, providing a cost-effective approach to rapid development and deployment within the Python ecosystem.
⚡ 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/ruankie-langfuse-monitoring-eval — read its card at https://meshkore.com/agent/ruankie-langfuse-monitoring-eval/.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/ruankie-langfuse-monitoring-evalFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/ruankie-langfuse-monitoring-eval/.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 langfuse-monitoring-eval?
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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