Observability and Evaluation

How each AI framework supports tracing, debugging, and evaluating LLM application quality.

Vercel AI SDK

Provides telemetry hooks and integrates with OpenTelemetry, with deeper tracing available through Vercel’s own platform tooling, like AI Gateway observability, when deployed there.

TanStack AI

As a young, library-first project, leaves most observability and evaluation tooling to external libraries rather than shipping its own, consistent with its “compose what you need” philosophy.

LangChain

Pairs closely with LangSmith, its companion platform for tracing, debugging, and evaluating LLM applications, which has become a widely used observability layer even for non-LangChain apps.

LangGraph

Integrates with LangSmith for tracing graph execution step by step, particularly useful for debugging complex, multi-node agent workflows.

LlamaIndex

Integrates with its own observability tooling as well as third-party providers, focused on tracing retrieval and query pipelines.

Mastra

Treats evaluation as a first-class, code-based concept — eval suites run in CI and can gate deploys on metrics like faithfulness and answer relevance, alongside built-in OpenTelemetry tracing.

CrewAI

Offers basic execution logging and integrates with external observability platforms, without as mature a first-party evaluation story as some newer frameworks.

Semantic Kernel (Microsoft Agent Framework)

Integrates with standard .NET/Python telemetry conventions and Azure Monitor/Application Insights for enterprises already on Azure.

Haystack

Provides pipeline-level logging and integrates with observability tools, with evaluation components purpose-built for measuring RAG quality, like retrieval relevance and answer correctness.