LangSmith vs Strands Evals

LangSmith is a commercial agent-engineering platform for building, evaluating, deploying, monitoring, and improving AI agents. It supports offline evaluations on curated datasets and real-time scoring of production interactions. Evaluation methods include human review, heuristic checks, LLM-as-judge evaluators, pairwise comparisons, and custom Python or TypeScript evaluators. Annotation queues route runs to subject-matter experts for feedback that can calibrate evaluations, improve prompts, or augment datasets. The Playground compares prompt versions or model providers, and LangSmith chat can auto-improve prompts. Observability includes agent and retrieval tracing, token and general cost tracking, trajectory monitoring, online evaluations, and webhook or PagerDuty alerts. Traces can arrive through SDKs or an API from LangGraph, custom Python, or other stacks; SDKs are listed for Python, TypeScript, Go, and Java. Hosting ranges from cloud to hybrid and self-hosted Enterprise options. The Developer plan costs nothing, Plus costs $39 a month, and Enterprise pricing is custom.

Strands Evals is an open-source Python SDK and CLI for assessing AI agent behavior during development and after deployment. It scores outputs and trajectories, helps diagnose failures, probes unsafe behavior, and can simulate users and tools. Evaluators can target an output, tool call, trace, or full session, and several can be combined in an experiment. Built-in groups cover quality, safety, multimodal responses, agent behavior, and skill selection and instruction following. Deterministic checks run without an LLM judge; custom logic can be added by extending the Evaluator class. Install the package with pip as strands-agents-evals to use its Python API or the strands-evals command. The CLI can generate experiments, validate JSON, run evaluations, render reports, and diagnose sessions. Trace providers include AWS CloudWatch Logs, Langfuse, and OpenSearch. Evaluations can use production or staging traces without rerunning their agents. The quickstart uses Amazon Bedrock with Claude as its default judge and requires credentials authorized to invoke Claude. Red-teaming is marked experimental. The SDK is free and supports API, Linux, macOS, self-hosted, and Windows environments.