An open-source AI observability platform for tracing, evaluating, and debugging LLM applications and agentic workflows.
### Key Features
– **Standardized Agent Tracing:** Implements the OpenInference standard to seamlessly capture spans, tool calls, and execution steps in complex agent chains.
– **Evals at Scale:** Run automated, model-graded evaluations for latency, token spend, hallucinations, and toxicity directly within your pipeline.
### Use Cases
– Profiling and debugging latency bottlenecks in agentic workflows, especially when running local agent backends optimized with Unsloth or lightweight execution setups like GGML and llama.cpp.
### Developer Pros & Cons
– **Pro:** Fully open-source and easy to run locally with zero-config initialization via a single Python command.
– **Con:** Storing and query-indexing heavy production trace volumes locally requires additional database configuration overhead.