LangSmith vs LangFuse: Which AI Evaluation Tool Fits Your Stack

LangSmith is a commercial AI evaluation and tracing platform developed by the LangChain team. It integrates with LangChain and LangGraph applications, providing detailed trace analysis, prompt versioning, evaluation workflows, and developer-focused dashboards.

LangFuse is an open-source observability and evaluation tool for LLM applications. It supports any framework, enables tracing and prompt management, and can be self-hosted or accessed via managed cloud services.

Core Capabilities

Both tools capture detailed traces of AI interactions, including inputs, outputs, and internal steps, enabling teams to inspect and debug their systems. However, there are key differences between them:

• LangSmith emphasizes evLangSmith focuses on evaluation workflows, like managing datasets, creating custom scoring functions, and comparing model outputs side by side. Its connection with LangChain makes setup easier for teams already using that ecosystem.
• LangFuse focuses on framework-agnostic observability and detailed trace logging. It supports prompt versioning and analytics across multiple frameworks (e.g., LangChain, LlamaIndex, raw provider APIs) and is ideal for teams seeking open, extensible tooling.

Use Case Fit

LangSmith is ideal when:

• Your stack is primarily built on LangChain or LangGraph.
• You want tightly integrated evaluation tools (LLM-as-a-judge, custom metrics, prompt canvas).
• You prefer a managed SaaS experience with built-in dashboards and alerting.

LangFuse is ideal when:

• You need open-source flexibility and self-hosting.
• Your environment uses multiple frameworks or custom APIs.
• Cost control and extensibility are priorities, with the ability to export data for further analysis.

In practice, teams working within the LangChain ecosystem often choose LangSmith for its seamless integration and robust evaluation tools. Projects that use multiple frameworks or require full self-hosting may prefer LangFuse, especially for open and flexible AI testing workflows.

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