Phoenix: AI Observability and LLM Evaluation Traces for LangChain and LlamaIndex

AI observability and evaluation data from Arize-ai's Phoenix framework, updated in March 2026, includes traces, spans, and evaluation metrics. It captures prompt inputs and model outputs from LLM frameworks such as LangChain, LlamaIndex, and smolagents.

Use Cases

• Monitoring LLM response quality using 'evals' labels for accuracy and relevance
• Debugging agentic logic by tracing execution 'spans' across LangChain or LlamaIndex components
• Optimizing prompt engineering by comparing 'prompt' versions against model output text

Strengths

• Integration with OpenAI and Anthropic APIs
• Support for LangChain and LlamaIndex trace formats
• Open-source framework for AI engineering

Limitations

• Data content is dependent on user-specific LLM implementations
• No fixed record count as it is a dynamic observability tool

Provenance

Source

Arize-ai

Collection Method

Instrumented tracing of LLM applications

Freshness

Updated as of March 2026.

Geography

Global