Features
Continuous high-resolution profiling timelines
Operation duration and resource utilization tracking
LLM generation tracing with per-step timing
Token throughput and latency breakdowns
System-level metrics for CPU, GPU, accelerators
Error monitoring for device-level failures
Inference telemetry for AI agents (autodebug)
Low-overhead CUDA kernel attribution
Claude Code integration for AI debugging
Profiler CLI and Python API
Support for vLLM, SGLang, PyTorch, TensorRT-LLM
Hierarchical LLM traces with cost/latency filtering
LLM-as-a-judge evaluation and heuristic functions
One-click prompt deployment and rollback
Playground for side-by-side model/input testing
Experiments with test case comparison
Human annotation and golden dataset creation
Cost and latency dashboards with alerts
Monitors and alerts (Slack, webhooks, GitHub Actions)
Full-text search (Cloud rollout)
Code evaluators (Python/TypeScript)
Langfuse Assistant (natural-language queries)
Multi-modal datasets (images, audio, video, documents)
OpenTelemetry-native instrumentation
Python and TypeScript native SDKs
REST APIs and S3 blob storage export
