LangSmithObservability, evaluation, and debugging platform for LLM applications from the LangChain team

LangSmith is a developer platform built by the team behind LangChain to help teams trace, test, evaluate, and monitor applications powered by large language models. While it integrates tightly with the LangChain and LangGraph frameworks, it is framework-agnostic and can instrument any LLM application through its SDKs and APIs. Its core purpose is to address the inherent unpredictability of LLM-based systems, where outputs are non-deterministic and failures can be subtle, by giving developers visibility into what their chains, agents, and prompts are actually doing at runtime. The platform centers on tracing: each run of an application produces a detailed, nested trace showing every step, including prompts sent, model responses, token usage, latency, tool calls, and intermediate outputs. This makes it easier to debug complex multi-step agents and retrieval-augmented generation pipelines where the source of a bad answer might be buried several layers deep. Developers can inspect individual traces, filter and search across runs, and drill into the exact inputs and outputs at each node. LangSmith also provides evaluation tooling for measuring application quality. Teams can build datasets from production traces or curated examples, run their application against those datasets, and score outputs using built-in evaluators, custom code-based checks, or LLM-as-judge approaches. This supports regression testing when prompts or models change and helps quantify whether changes actually improve results rather than relying on intuition. For production use, it offers monitoring dashboards that track metrics such as latency, cost, error rates, and feedback over time, along with the ability to collect human feedback and user annotations. A prompt management and playground component lets teams iterate on and version prompts, and compare model outputs side by side. LangSmith is aimed primarily at developers and teams shipping LLM features who need to move beyond ad hoc print-statement debugging toward systematic observability and evaluation. Its main strength is the depth of integration with the LangChain ecosystem and the unified workflow connecting tracing, datasets, and evaluation. Honest trade-offs include that the richest experience assumes you are comfortable in the LangChain/LangGraph world, that LLM-based evaluation is itself imperfect and requires careful design, and that it is a hosted commercial product with usage-based pricing, though self-hosting options exist for some plans. It competes with other LLM observability tools such as Langfuse, Helicone, Arize Phoenix, and Weights & Biases Weave.