LangSmith vs LangChain: Key Differences & Best Use Cases

TL;DR: LangChain and LangSmith play different roles in your LLM dev stack. LangChain is a framework for building applications, while LangSmith is a platform for debugging, evaluating, deploying, and monitoring them. 

The LLM development ecosystem has expanded quickly in recent years. Teams building with models such as OpenAI’s GPT-5, Anthropic’s Claude models, or open-source alternatives need tools that simplify both development and monitoring. LangChain became a leading framework for orchestrating LLM workflows, while LangSmith followed to fill important gaps in debugging, evaluation, and production readiness.

Both tools come from the same team at LangChain Inc. but focus on different stages of the workflow.

Let’s look at what each framework and platform does, how they differ, and when to use one or both.

What is LangChain?

LangChain is an open-source framework for building agents and LLM-powered applications. Launched in October 2022, it has grown to over 119,000 stars on GitHub, making it one of the most popular tools in the LLM space.

The framework lets you chain together models, embeddings, vector stores, and tools, simplifying the development of complex LLM applications. It supports real-time data augmentation, connects to external systems, and allows model swapping without rewriting your logic.

You can install LangChain with:

pip install langchain

Core Features and Capabilities

LangChain provides building blocks for common patterns. You can implement RAG by connecting document loaders, vector stores, and language models, while the framework handles orchestration between these components.

Its integration library includes chat models, embedding models, vector stores, retrievers, and tools, all accessible through a unified interface.

LangChain supports streaming responses, batch processing, and async execution, which is useful for responsive UIs or processing large datasets. It also includes memory components that maintain conversation history across turns.

For advanced agent orchestration, LangGraph provides additional features for building controllable workflows, long-term memory, and human-in-the-loop capabilities.

When to Use LangChain?

LangChain works well for prototyping LLM applications quickly. The abstractions let you test ideas without implementing infrastructure from scratch. Research teams and early-stage startups use it to validate concepts before investing in production architecture.

Applications requiring real-time data augmentation benefit from LangChain's integrations. If you need to connect LLMs to databases, APIs, or document stores, the framework provides pre-built connectors that handle common patterns.

Model interoperability matters when you're experimenting with different providers or want to avoid vendor lock-in. LangChain's abstractions let you swap between models without rewriting core logic.

However, simple applications that just call an LLM and return the response don't need the framework's complexity. Teams with strong opinions about architecture may prefer building directly on provider APIs.

What is LangSmith?

LangSmith is a platform for developing, debugging, evaluating, deploying, and monitoring LLM applications. It launched as a commercial product from the LangChain team to address production challenges that the open-source framework doesn’t cover.

The platform is framework-agnostic. You can use it with LangChain, LangGraph, or any other setup, including custom code that calls LLM APIs directly. 

LangSmith provides tools for tracing requests, evaluating outputs, testing prompts, and managing deployments in one place. You can prototype locally and then move to production with integrated monitoring and evaluation, combining observability, testing, and deployment in a single workflow.

Core Features and Capabilities

LangSmith's tracing system records every step in your LLM workflow. Each trace shows the full execution graph: which functions ran, their inputs and outputs, latency for each step, and total tokens used. You can drill into any trace to debug failures or understand unexpected behavior.

Evaluation lets you run your application against test datasets, measure performance, and compare runs. It supports automatic evaluation with LLMs as judges and human feedback collection.

Dataset management helps turn production traces into test cases, tracking which pass or fail across versions.

Deployment allows agents to run as scalable servers, handling long-running, stateful workflows with discovery, reuse, configuration, and team sharing.

LangSmith Studio provides a visual interface to design, test, and refine applications before coding, speeding up iteration for complex workflows.

Monitoring dashboards display error rates, latency, token usage, and cost, with alerts for threshold breaches or recurring errors.

Compliance covers HIPAA, SOC 2 Type 2, and GDPR standards for enterprise deployments.

When to Use LangSmith

LangSmith becomes valuable when you need visibility into production applications. If you're debugging why certain prompts fail or trying to reduce latency, the tracing data shows exactly what's happening.

Evaluation workflows benefit from dataset testing. Teams comparing models, testing prompt variations, or measuring accuracy improvements can use LangSmith to make A/B testing systematic instead of ad-hoc.

Production deployment and monitoring requires infrastructure. LangSmith provides the deployment platform, metrics, and alerting needed to maintain reliable services. Enterprise teams often mandate this level of observability and compliance for production systems.

The platform works with any LLM application, not just those built with LangChain. If you're using LlamaIndex, Haystack, or custom orchestration code, you can still use LangSmith for observability and evaluation.

LangChain vs LangSmith: Side-by-Side Comparison

These two play complementary roles rather than competing directly.

Key Differences

LangChain is a runtime framework. It executes your application logic, manages state, and orchestrates LLM calls. You write code using LangChain's abstractions, and that code runs in your infrastructure.

LangSmith is a development and deployment platform. It observes your application through tracing, provides evaluation tools, and hosts your deployments. Your application sends data to LangSmith's API, but you can also use it independently of any specific framework.

The relationship: LangChain builds applications. LangSmith debugs, evaluates, deploys, and monitors them. 

Performance & Scalability

LangChain performance depends on usage. Its abstraction layers add minimal overhead for single LLM calls, but complex chains with many steps accumulate more due to serialization and state management.

LangSmith doesn’t impact runtime performance directly. Traces are sent asynchronously, adding negligible latency, though detailed tracing slightly increases memory and network usage. Its deployment platform manages scaling for long-running, stateful workflows without manual server or load balancer setup.

Ease of Use & Learning Curve

LangChain has a steeper learning curve. Chains, agents, tools, and memory abstractions take time to understand and compose. Documentation is extensive, but the breadth of features can be overwhelming.

LangSmith is simpler to start. Enabling tracing, running your application, and viewing results in the UI is straightforward. Evaluation features require more setup, but the core workflow - run tests, measure results, compare versions - is easy to grasp.

Integration Capabilities

LangChain integrates across the LLM ecosystem, including model providers, vector stores, retrievers, tools, and data sources, making experimentation straightforward.

LangSmith integrates via SDK and API.

It works automatically with LangChain and LangGraph, and can be manually instrumented for other frameworks or custom code. It supports LlamaIndex, Haystack, or custom pipelines, allowing you to wrap any LLM call with tracing decorators for observability.

Strengths and Weaknesses

LangChain

Strengths	Weaknesses
Fast prototyping with built-in components	Steep learning curve
Broad integrations with LLM tools	Some performance overhead
Active open-source community	Too heavy for simple apps
Flexible model interoperability	Complex flows better handled in LangGraph

LangSmith

Strengths	Weaknesses
Framework-agnostic observability	Usage-based pricing
Built-in evaluation and testing	Requires sending data to external service
Integrated deployment and monitoring	Manual setup for non-LangChain apps
Enterprise-grade compliance	Limited value without production traffic

When to Use LangChain or LangSmith

LangChain works well for quick prototyping and experimentation. It lets you connect models, memory, and tools without building infrastructure first. Use LangChain when testing ideas or building early versions.

LangSmith becomes useful as you move toward production. It helps trace executions, debug issues, and evaluate prompts or models. Use LangSmith when you need visibility and consistent evaluation.

For deployments that need to handle ongoing or stateful workloads, LangSmith’s platform manages scaling and monitoring. Use it when uptime and reliability matter.

When building complex agents that involve human feedback, combine LangGraph for orchestration with LangSmith for deployment and tracking. This setup keeps development and production in sync.

Smaller teams can start with LangChain, then bring in LangSmith later. Larger teams often use both to cover the full lifecycle. Together, they support fast development and dependable production.

Getting Started

Start with LangChain to build and test your LLM applications. When you need to debug, evaluate, or monitor them in production, bring in LangSmith.

For more complex agent workflows, LangGraph gives you extra control. LangSmith connects with any framework, so you can use it wherever you need visibility or testing.

You can also connect with our experts to discuss your stack, compare tooling options, or plan a smooth move from prototyping to production.

Frequently Asked Questions

Can you use LangSmith without LangChain?

Yes. LangSmith is framework-agnostic and works with any LLM application. It supports LangChain, LangGraph, LlamaIndex, Haystack, and custom code that calls LLM APIs directly. 

You instrument your application using LangSmith's Python or JavaScript SDK to send traces and evaluation data. The integration requires manual instrumentation for non-LangChain apps, but the observability, evaluation, and deployment features work the same way.

How do you integrate LangSmith with an existing LangChain project?

Set environment variables: LANGSMITH_TRACING=true and LANGSMITH_API_KEY=your_api_key. LangChain will automatically send traces to LangSmith. You can optionally set LANGSMITH_WORKSPACE_ID for organization-scoped keys and LANGSMITH_PROJECT to organize traces by project. No code changes are required. Visit the LangSmith dashboard to view your traces after running your application.

Does LangSmith work with frameworks other than LangChain?

Yes. LangSmith provides SDKs for Python and JavaScript that let you manually instrument any application. You can use the @traceable decorator or wrapOpenAI wrapper to trace calls to OpenAI, Anthropic, or any LLM provider. 

The platform works with LlamaIndex, custom orchestration code, or direct API calls. Manual instrumentation requires more code than LangChain's automatic tracing, but you get the same observability, evaluation, and deployment features.

Do I need both LangChain AND LangSmith for production?

Not required, but recommended for production applications. LangChain handles execution and orchestration. LangSmith provides monitoring, debugging, evaluation, and deployment infrastructure. 

You can use LangChain without LangSmith by implementing your own logging and monitoring. You can use LangSmith without LangChain by manually instrumenting your application. Using both gives you a complete solution: LangChain for building, LangSmith for operating in production.

What specific problems does LangSmith solve that LangChain doesn't?

LangSmith addresses debugging, evaluation, deployment, and observability. When your application produces incorrect outputs, LangSmith's traces show exactly what happened at each step. When you want to test if a change improves your application, LangSmith's datasets and evaluation features measure the impact quantitatively. 

When you need to deploy and scale agents, LangSmith provides the infrastructure for long-running, stateful workflows. When you need to monitor production performance, LangSmith provides metrics, alerts, and error tracking. LangChain focuses on execution, not observation or deployment, so it doesn't provide these capabilities.