What is LangSmith?

One of the biggest challenges in building reliable large language model (LLM) applications is understanding why an artificial intelligence (AI) system fails or behaves unexpectedly, once deployed. Developers often struggle to trace bugs, fine-tune prompts, evaluate performance across edge cases or debug tool use and memory issues in complex agent workflows. LangSmith, developed by the team behind LangChain, offers a robust solution for addressing these challenges. It serves as a dedicated platform for monitoring, debugging and evaluating applications built with large language models. It lets developers inspect traces, monitor performance, test different prompt versions and track how external tools and memory are used in real-time, all within a unified interface designed to make LLM apps more robust and production ready. 

LangChain and LangSmith are tools to support LLM development, but the purpose of each tool varies.  

LangChain is an open source Python framework that simplifies the building and deployment of LLM applications. It connects multiple LLM components into structured workflows by using modular building blocks such as chains, agents and memory. These components enable the integration of LLMs with external tools, application programming interfaces (APIs) and data sources to build complex applications. Instead of relying on a single model, it supports chaining together models for tasks such as text understanding, response generation and reasoning, allowing each step to build on the last. LangChain supports prompt engineering through reusable templates and integrates with LangGraph for visually designing workflows. This ability makes it especially powerful for building conversational agents and AI systems that require context handling and logical progression.  

Moreover, LangSmith is the operational backbone to LangChain’s development capabilities. While LangChain helps you build workflows, LangSmith helps ensure that they run smoothly by offering tools for debugging, monitoring and managing complex AI systems. LangSmith provides deep visibility into model behavior, making it easier to identify performance issues, trace errors and optimize responses in real time. It also supports orchestration across multiple models and pipelines, allowing seamless deployment and coordination. LangSmith offers seamless integration with external tools such as TensorFlow, Kubernetes. It can also be integrated with major cloud providers like AWS, GCP and Azure, while also providing robust support for hybrid setups and on-premises deployments. LangSmith supports real-world AI application development, including chatbots and other interactive systems such as AI agents, virtual assistants and conversational interfaces. This capability helps developers streamline their workflows. 

Together, LangChain and LangSmith simplify the entire development process from prototyping to production.

LangSmith operates by embedding itself into the LLM application stack, whether you're using LangChain or building custom pipelines to provide visibility, traceability and control at every stage of development and production. It captures granular data from each LLM interaction and visualizes it, helping developers pinpoint problems, test solutions and optimize performance.  

The major functions of LangSmith are:

1. Debugging

2. Testing

3. Evaluating

4. Monitoring

LLM applications often involve complex reasoning paths, dynamic tool usage and multistep chains. When errors occur, such as infinite loops, incorrect outputs or tool invocation failures, traditional debugging methods fall short. LangSmith offers detailed, sequential visibility into each interaction with LLMs, helping ensure clear traceability throughout the process. Trace, track and display the step-by-step flow of data through the application by using LangChain Expression Language (LCEL). This visibility helps troubleshoot long response times, errors or unexpected behavior. LangSmith provides rich visualization tools to display LLM call traces, helping developers understand and debug complex workflows easily. Developers can inspect individual prompts and responses, intermediate steps within chains and agents, and tool calls and their corresponding outputs. This fine-grained visibility enables rapid identification and resolution of issues, significantly reducing development time and improving application stability.

LLM applications require frequent updates, whether optimizing prompts, adjusting chain logic or changing model parameters. Helping ensure these changes do not introduce regressions is essential. LangSmith supports dataset-driven testing, allowing developers to run predefined or custom test suites across application versions, compare outputs visually and semantically and identify changes in behavior before deploying to production. This testing facilitates rigorous quality assurance and promotes safe, iterative development. LangSmith’s support for automated evaluations enables teams to quickly iterate on prompt designs and model parameters to ensure consistent quality.

Beyond functional correctness, the quality of LLM-generated outputs must be continuously evaluated against business and user expectations. LangSmith offers both built-in and customizable evaluators to assess performance across various dimensions such as accuracy, relevance and coherence. With LangSmith’s evaluation capabilities, teams can benchmark performance across datasets and prompt variations, surface edge cases that degrade user experience and track improvements or regressions with clear metrics. This structured evaluation process helps ensure that LLM systems remain effective, accurate and aligned with intended outcomes.

Deploying LLM applications into production requires robust monitoring to help ensure consistent performance and immediate incident response. LangSmith delivers end-to-end observability for LLM workflows such as real-time logging of executions, latency and error rates, integration with alerting systems for prompt incident reporting and dashboards that provide insights into usage patterns and system health. This operational intelligence allows engineering teams to proactively manage application behavior, helping ensure reliability and responsiveness in live environments. Real-world deployment monitoring with LangSmith helps teams streamline incident response and maintain robust system health. 

LangSmith works through a simple Python SDK that helps developers build and manage AI applications easily. It connects with AI models like OpenAI’s GPT and uses techniques such as retrieval-augmented generation (RAG) to improve how these models work. By using an API key, developers can track and debug AI agents, including those based on ChatGPT, making sure everything runs smoothly and performs well in generative AI projects. 

For example, this research presents a LangSmith editor that assists non-native researchers in writing academic papers in English, particularly in the NLP domain. The system offers three main features: text revision suggestions based on rough drafts, text completion conditioned on context and grammatical or spelling error correction.^[1] Results demonstrated that LangSmith improves the quality of draft revisions, especially when human and machine collaboration is involved, enabling non-native writers to produce more fluent and stylistically appropriate academic texts. The system enhances diversity and inclusion by lowering language barriers in scientific communication. This example highlights a real-world use case where LangSmith facilitates data science research by improving collaboration between humans and AI in academic writing. Such use cases demonstrate LangSmith’s ability to enhance inclusivity and productivity in various AI-driven fields. 

Factory, a company building AI agents to automate the Software Development Lifecycle (SDLC), uses LangSmith to help ensure secure, reliable LLM operations in enterprise environments.^[2] They integrated LangSmith with AWS CloudWatch and gained full traceability across its LLM pipelines, enabling faster debugging and better context management. Using LangSmith’s Feedback API, they automated prompt evaluation and refinement based on real user input. This helped double iteration speed and reduced open-to-merge time by 20%, making LangSmith a critical part of their AI development and observability workflow. 

All-in-one platform: LangSmith consolidates all core functions—debugging, testing, deployment, monitoring—into a single cohesive platform. Real-world deployment monitoring with LangSmith helps teams streamline incident response and maintain robust system health. Its clean, developer-friendly interface makes it easy to navigate complex workflows and manage projects efficiently without switching between multiple tools. 

Robust debugging and evaluation: Provides detailed trace analysis, prompt testing and dataset management tools that help pinpoint issues, measure performance and refine LLM behavior with precision. 

Enterprise-ready scalability: Designed to support high-volume, production-grade applications, making it a strong fit for enterprise teams building and maintaining complex AI systems.

Steep learning curve for beginners: LangSmith can be challenging for beginners, as it demands a solid understanding of LLM tools and DevOps processes, which can limit its accessibility for newcomers. 

Heavy dependence on LangChain ecosystem: LangSmith is deeply tied to LangChain. While this is great for users of that framework, it might not be as helpful for those using other orchestration tools or custom stacks. 

Scalability and cost for large-scale projects: For enterprise use, costs can grow with scale, especially when dealing with frequent evaluations, large trace storage or advanced analytics. 

The choice between LangChain, LangSmith or a combination of both depends on the specific requirements of your LLM application. LangChain is well suited for designing and prototyping complex language model workflows, enabling seamless integration with external tools and APIs. Use LangSmith when you're ready to move into production and need robust tools for debugging, testing, monitoring and maintaining LLM applications at scale. When used together, these platforms provide a comprehensive and scalable solution for building, deploying and maintaining high-quality LLM applications.