🔭 Task

AI for LLM Monitoring (2026)

LLM monitoring (tracking prompts, responses, latency, cost, and quality across production AI applications) became essential as teams shipped LLM features and discovered that quality regressions, cost spikes, and latency drift happen invisibly without telemetry. AI-augmented LLM observability platforms now capture every model call into searchable trace UIs, surface quality regressions across model versions, and run evaluation suites against production traces. Langfuse leads open-source LLM observability with strong LangChain integration; LangSmith ships LangChain-native observability from the LangChain team; Helicone offers proxy-based one-line setup with strong cost-tracking.