LangFlow vs LangSmith: Which One Should You Use?

The LangChain ecosystem has grown a lot since its early days, and now there are tools for different stages of building LLM applications. LangFlow makes it easy to prototype quickly with a visual interface and even deploy your flows. 

LangSmith, on the other hand, is all about tracking, evaluating, and monitoring your applications once they’re running. They overlap a bit, but each has its main focus, and this comparison will help you figure out which one makes sense for your project.

In this guide, we’ll look at what each tool does, how they differ, and when to use LangFlow, LangSmith, or both.

What Is LangFlow?

LangFlow is a platform for building and deploying AI workflows visually. You drag and drop components to create chains, test them in an interactive playground, and deploy as APIs or export as code. The tool provides both a visual authoring experience and production deployment options.

LangFlow runs as a web application or desktop app. You build flows in your browser, test them immediately, and deploy them as API endpoints or MCP servers. The platform works with all major LLMs, vector databases, and AI tools.

Key Features of LangFlow

1. Drag-and-Drop Visual Builder

The canvas-based interface lets you add components by dragging them onto the workspace. Connect outputs to inputs with lines. Configure each component through a sidebar panel. No need to remember syntax or import statements initially.

This visual approach helps non-engineers understand AI workflows. Product managers and designers can prototype ideas alongside developers.

2. Source Code Access & Customization

LangFlow provides access to the underlying Python code for every component. You can customize any part of the workflow using Python when the visual interface becomes limiting. This bridges visual prototyping and code-level control.

3. Interactive Playground

Test flows immediately with step-by-step control. Input sample data and see results in real-time. The playground shows execution flow and intermediate outputs, making debugging straightforward.

4. Multi-Agent Orchestration

LangFlow supports building multi-agent systems with conversation management and retrieval. You can coordinate multiple AI agents working together on complex tasks.

5. Deployment Options

Deploy flows as REST APIs with one click. Export workflows as JSON for Python applications. Deploy as MCP (Model Context Protocol) servers to turn flows into tools for MCP clients. These options cover different integration patterns.

6. Built-in Observability

LangFlow integrates with LangSmith, LangFuse, and other observability platforms. You can trace execution and monitor performance without setting up separate infrastructure.

7. Enterprise Features

The platform includes security and scalability features for production use. Desktop app available for Windows and macOS with dependency management and automatic updates.

Typical Use Cases & When to Use LangFlow

Use LangFlow when:

• Prototyping new AI features rapidly.

• Building and deploying production workflows visually.

• Teaching LangChain concepts to team members.

• Demonstrating AI workflows to stakeholders.

• Creating API endpoints without backend infrastructure.

• Deploying MCP servers for tool integration.

Product managers use it to build working prototypes. Developers use it to deploy production APIs quickly. Teams use it to collaborate on AI workflows without everyone needing deep coding experience.

Constraints and Considerations

Complex custom logic may require dropping to Python code. While LangFlow supports production deployment, very large-scale systems with specific infrastructure requirements might need custom implementations.

The visual interface works best for standard patterns. Highly specialized or unusual architectures may be easier to implement in pure code.

What Is LangSmith?

LangSmith is a platform for developing, debugging, and deploying LLM applications. It provides observability through tracing, evaluation tools for measuring quality, deployment infrastructure, and prompt testing capabilities. The platform works with any LLM application, regardless of framework.

LangSmith helps you trace every request, evaluate outputs, test prompts, and manage deployments in one place. You can prototype locally, then move to production with integrated monitoring.

Key Features of LangSmith

1. Observability & Tracing

LangSmith traces execution through your entire application. For nested chains or agent workflows, you see each step with timing information. The trace shows which LLM was called, what prompt was sent, what response came back, and how long it took.

This visibility works with any LLM application, including native integration with LangChain Python and JavaScript libraries.

2. Evaluation Tools

Build test sets from production data. Score performance with automated evaluators or expert feedback. Track quality metrics over time to ensure consistency. Annotation queues let team members review outputs and provide feedback.

3. Deployment Infrastructure

LangSmith Deployment (formerly LangGraph Platform) lets you deploy agents as production-ready services. The infrastructure handles memory, auto-scaling, and enterprise-grade security. Deploy in one click with APIs built for long-running agent workloads.

4. Studio Visual Interface

Studio provides a visual interface to design, test, and refine applications end-to-end. This combines visual development with LangSmith's observability and evaluation features.

5. Prompt Testing

Iterate on prompts with built-in versioning and collaboration. Test different prompt variations and track which ones perform best. Ship improvements faster with organized prompt management.

5. Performance Metrics & Cost Tracking

LangSmith breaks down latency by component and tracks costs per LLM call. You can identify slow steps and optimize expensive operations. These metrics matter for production systems where performance affects user experience.

6. Framework Independence

LangSmith works with any LLM application - OpenAI, Anthropic, custom models, or any framework. The SDK integrates with Python and JavaScript applications. You're not locked into LangChain.

Collaboration Features

Teams can share traces, debugging sessions, and evaluations. Versioning tracks changes over time. Monitoring dashboards show production health metrics.

When to Use LangSmith

Use LangSmith when:

• Debugging complex LLM applications.

• Monitoring production systems.

• Evaluating output quality systematically.

• Deploying agents at scale.

• Testing and versioning prompts.

• Tracking costs and performance

• Collaborating on LLM development.

It fits projects where reliability, performance, and quality require systematic measurement and improvement.

Limitations & Considerations

LangSmith requires instrumentation and setup. For very simple prototypes, the overhead might exceed the value. Teams need to understand observability concepts to use the platform effectively.

The evaluation and monitoring features shine with production systems. Early prototyping may not need this level of tooling immediately.

LangFlow vs LangSmith: Side-by-Side Comparison

Purpose & Primary Use Cases

LangFlow focuses on visual workflow building and quick deployment. You can create AI workflows visually, test them interactively, and deploy them as APIs or MCP servers.

LangSmith focuses on observability, evaluation, and production monitoring. You can trace applications, evaluate quality, test prompts, and deploy agents with managed infrastructure.

Both now include deployment capabilities. LangFlow deploys workflows you build visually, while LangSmith deploys agents with full observability and scalable infrastructure.

Technical Approach & Architecture

LangFlow provides a visual UI for building workflows with source code access. It's a low-code platform that generates deployable APIs and code exports. The focus is on accessibility and rapid development.

LangSmith is an observability and deployment platform. It instruments your code to capture execution traces and provides infrastructure for running agents. The focus is on visibility, quality, and production reliability.

LangFlow works primarily with LangChain abstractions. LangSmith is framework-agnostic and works with any LLM application.

Feature Comparison:

Feature	LangFlow	LangSmith
Primary Purpose	Visual building & deployment	Observability & evaluation
Visual Interface	Drag-and-drop builder	Studio (design interface)
Code Access	Python customization	Framework-agnostic SDKs
Deployment	API, JSON export, MCP server	Agent servers with scaling
Observability	Integrates with LangSmith/LangFuse	Native comprehensive tracing
Evaluation Tools	Basic	Advanced evaluation suite
Prompt Testing	In playground	Dedicated prompt hub
Learning Curve	Low	Moderate
Framework Requirement	LangChain-focused	Framework-agnostic
Multi-Agent Support	Built-in orchestration	Deployment infrastructure
Collaboration	Visual sharing	Traces, evals, versioning

User Experience / Ease of Use

LangFlow prioritizes visual development. Non-technical users can build and deploy workflows. The interactive playground makes testing immediate. Source code access provides an escape hatch when needed.

LangSmith requires technical knowledge to instrument applications and interpret traces. The power comes from comprehensive observability and evaluation capabilities. Studio provides visual design, but the platform assumes development expertise.

Overlapping Features

Both platforms now include:

• Deployment capabilities (different approaches).

• Visual interfaces (different purposes).

• Observability (LangFlow integrates, LangSmith provides),

• Production features (different scopes).

The overlap means you can choose based on your primary workflow preference - visual-first building (LangFlow) or code-first with deep observability (LangSmith).

Using Them Together

LangFlow integrates with LangSmith for observability. You can build workflows in LangFlow and trace them with LangSmith. This combines visual development with comprehensive monitoring.

Alternatively, prototype in LangFlow, then move to code-based development with LangSmith instrumentation for production systems requiring custom infrastructure.

Which Should You Use: LangFlow, LangSmith, or Both?

Choosing Based on Development Style

1. Visual-first development: Use LangFlow. Build workflows visually, customize with Python when needed, and deploy as APIs. The integrated observability covers monitoring needs.

2. Code-first development: Use LangSmith. Write your application in any framework, add tracing, evaluate quality, and deploy with managed infrastructure.

3. Hybrid approach: Use both. Build in LangFlow with LangSmith observability, or prototype in LangFlow and implement in code with LangSmith monitoring.

Choosing Based on Requirements

1. Need quick deployment of standard workflows: LangFlow provides the fastest path. Build visually and deploy as API in one platform.

2. Need deep observability and evaluation: LangSmith provides comprehensive tracing, evaluation tools, and production monitoring regardless of how you build.

3. Need multi-agent orchestration: LangFlow includes built-in orchestration features. LangSmith provides deployment infrastructure for agents built in any framework.

4. Need framework flexibility: LangSmith works with any LLM application. LangFlow works primarily with LangChain patterns.

Getting Started

LangFlow lets you build AI workflows visually and deploy them with minimal code. LangSmith provides observability, evaluation, and production monitoring for any LLM stack.

You can test LangFlow via its open-source desktop app and explore LangSmith’s free tier to see how it handles monitoring and evaluation.

You can also connect with our experts for guidance on setting up workflows, integrating LangFlow and LangSmith, or optimizing your LLM applications for production.

Frequently Asked Questions

What is the difference between LangFlow and LangSmith?

LangFlow is a visual platform for building and deploying AI workflows. You drag and drop components, test in a playground, and deploy as APIs or MCP servers. LangSmith is an observability and evaluation platform for debugging, monitoring, and deploying LLM applications. LangFlow focuses on visual development. LangSmith focuses on production reliability and quality measurement.

Can I use LangFlow and LangSmith together?

Yes. LangFlow integrates with LangSmith for observability. You can build workflows visually in LangFlow and trace execution with LangSmith. This combines rapid visual development with comprehensive monitoring and evaluation. The platforms complement each other well.

Is LangSmith only for LangChain apps?

No. LangSmith works with any LLM application regardless of framework. While it integrates natively with LangChain Python and JavaScript libraries, you can trace calls to OpenAI, Anthropic, or custom models directly. The platform is framework-agnostic with SDKs for Python and JavaScript.

Is LangFlow open-source?

Yes. LangFlow is open source and available on GitHub. You can self-host it or use the hosted version. The platform includes a desktop app for Windows and macOS with dependency management and automatic updates. You can customize any component using Python and contribute improvements back to the community.