Sold by: LangChain
Deployed on AWS
LangSmith provides tools for developing, debugging, and deploying LLM applications. It helps you trace requests, evaluate outputs, test prompts, and manage deployments in one place.
LangSmith is framework agnostic, so you can use it with or without LangChain open-source libraries langchain and langgraph.
Prototype locally, then move to production with integrated monitoring and evaluation to build more reliable AI systems.
LangSmith provides:
- Observability to see exactly how your agent thinks and acts with detailed tracing and aggregate trend metrics.
- Evaluation to test and score agent behavior on production data and offline datasets for continuous improvement.
- Deployment to ship your agent in one click, using scalable infrastructure built for long-running tasks.
4.5
Overview
LangSmith Observability and Evals is a unified observability & evals platform where teams can debug, test, and monitor AI app performance - whether building with LangChain or not.
Find failures fast with agent observability. Quickly debug and understand non-deterministic LLM app behavior with tracing. See what your agent is doing step by step, then fix issues to improve latency and response quality.
Evaluate your agent's performance. Evaluate your app by saving production traces to datasets, then score performance with LLM-as-Judge evaluators. Gather human feedback from subject-matter experts to assess response relevance, correctness, harmfulness, and other criteria.
Experiment with models and prompts in the Playground, and compare outputs across different prompt versions. Any teammate can use the Prompt Canvas UI to directly recommend and improve prompts.
Track business-critical metrics like costs, latency, and response quality with live dashboards, then get alerted when problems arise and drill into root cause.
LangSmith Deployments is a purpose-built infrastructure and management layer for deploying and scaling long-running, stateful agents -- offering:
- 1-click deployment to go live in minutes,
- 30 API endpoints for designing custom user experiences that fit any interaction pattern
- Horizontal scaling to handle bursty, long-running traffic
- A persistence layer to support memory, conversational history, and async collaboration with human-in-the-loop or multi-agent workflows
- Native LangSmith Studio, the agent IDE, for easy debugging, visibility, and iteration
LangSmith Agent Builder: Give every team the ability to build, use, and improve AI agents with the security your org requires.
Highlights
- LangSmith Observability and Evals is a unified observability & evals platform where teams can debug, test, and monitor AI app performance - whether building with LangChain or not. Quickly debug and understand non-deterministic LLM app behavior with tracing. See what your agent is doing step by step, then fix issues to improve latency and response quality.
- LangSmith Deployments is a purpose-built infrastructure and management layer for deploying and scaling long-running, stateful agents offering 1/1-click deployment to go live in minutes, 2/Horizontal scaling to handle bursty, long-running traffic 3/A persistence layer to support memory, conversational history, and async collaboration with human-in-the-loop or multi-agent workflows.
- Please note: there is a $150k annual Platform License plus a minimum $150k annual usage commitment to access this package. To discuss enterprise pricing or to activate your commitment and obtain your license key after signup, please contact us at https://www.langchain.com/contact-sales - alternatively, our self-serve cloud-based products are available at https://www.langchain.com
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LangSmith Helm Chart
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Linux
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/unit |
|---|---|
Per Trace | $0.01 |
Per Agent Run | $0.01 |
Metered Usage Amount | $0.01 |
Minimum annual usage commitment, billed in advance | $150,000.00 |
Per Agent Builder Run | $0.10 |
Dimensions summary
You pay for what you use across five independent metering dimensions. Per Trace charges cover observability data captured when your agents run. Per Agent Run and Per Agent Builder Run charge for agent executions on the platform. Metered Usage Amount tracks normalized compute and storage consumption from deployments, engine analysis, and related services. The Minimum annual usage commitment is billed in advance, setting a baseline you draw down against as usage accrues. These dimensions add together based on actual consumption, so your total scales with trace volume, agent activity, and resources used.
Top-of-mind questions for buyers
A trace is a single execution of your application—an agent run, evaluator, or playground session. One trace can include many steps, such as model calls and other tracked events. All those steps roll up into the single trace you are billed for.
It tracks normalized units of work and storage across services. Compute-related work is measured in LangChain Compute Units, and data stored or managed is measured in LangChain Storage Units. Deployments, engine analysis, and sandboxes all consume these units at different rates based on the resources they use.
It depends on your workload. High trace volume from heavy observability pushes Per Trace charges up. Running agents in production drives Per Agent Run and Metered Usage Amount through deployment uptime and engine analysis. All dimensions bill independently and add together on one invoice based on actual consumption.
www.langchain.com
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https://www.langchain.com/terms-of-service#:~:text=Customer%20will%20pay%20LangChain%20all ,Fees%20paid%20are%20non%2Drefundable.
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Delivery details
LangSmith Helm Chart
Supported services: Learn more
- Amazon EKS
Helm chart
Helm charts are Kubernetes YAML manifests combined into a single package that can be installed on Kubernetes clusters. The containerized application is deployed on a cluster by running a single Helm install command to install the seller-provided Helm chart.
Version release notes
LangSmith 0.13.14 release
Additional details
Usage instructions
See https://docs.smith.langchain.com/self_hosting for full installation and configuration instructions.
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Customer reviews
Deepak A.
Easy Model Switching Across Providers
Reviewed on Sep 04, 2026
Review provided by G2
What do you like best about the product?
I can use it with any model provider. Switching between models is easy.
What do you dislike about the product?
I find it very unstable, with every new release my project breaks.
What problems is the product solving and how is that benefiting you?
I’m using it to build an autonomous API testing framework that can detect backend APIs and generate automation code within the automation framework.
Abhishek S.
LangChain Makes Building Flexible AI Workflows Easy
Reviewed on Sep 02, 2026
Review provided by G2
What do you like best about the product?
What I love most about LangChain is how it makes building AI applications so much easier. It connects large language models with tools, APIs, databases, and external data in a really intuitive way. Its modular approach, along with great integrations and support for agents and retrieval-augmented generation (RAG), gives you the flexibility to design effective AI workflows.
What do you dislike about the product?
What I really don’t like about LangChain is that it often feels unnecessarily complicated for simple tasks. The sheer number of abstractions and components can make it challenging to debug and fully understand the workflow. On top of that, it changes quite frequently, so keeping up with updates and compatibility can take a lot of extra work.
What problems is the product solving and how is that benefiting you?
LangChain makes it easy to build and connect LLM-powered applications by offering a collection of ready-made components for prompts, agents, tools, memory, retrieval, and workflows. This really helps me save time on development since I don’t have to create these integrations from scratch. It also simplifies the creation, testing, and scaling of AI applications, all while keeping the workflow nice and tidy.
Saurabh Z.
LangChain Makes Working with LLMs Easier and More Flexible
Reviewed on Sep 02, 2026
Review provided by G2
What do you like best about the product?
I like best about langchain is that it makes working with LLM much easier I like the flexibility it provides for connecting models with tools,data and API
What do you dislike about the product?
It can feel a little complex first especially with number of concepts and available components.
What problems is the product solving and how is that benefiting you?
Langchain solves the hassle of managing different part of an LLM application in one place.It makes it easier to connect models with data,tools and API.
Sindhu S.
Flexible Framework for AI Application Development
Reviewed on Sep 02, 2026
Review provided by G2
What do you like best about the product?
It makes building LLM apps easier by handling chains, tools, prompts, and integrations. For me, Lang chain provides good value considering how much development time it can save. I don't have to build the whole LLM workflow, prompt handling, retrieval, and tool integration from scratch. There is still some overhead when projects get more complex, and debugging can take time, but I think the flexibility and integrations make it worth the cost, especially when working on multiple AI features or prototypes.
What do you dislike about the product?
Some abstractions feel heavy, and debugging chains can get tricky when workflows become complex.
What problems is the product solving and how is that benefiting you?
LangChain helps me speed up the development of LLM-based features without building everything from scratch. I mainly use it for managing prompts, connecting models with tools, handling retrieval, and building multi-step workflows. It also makes it easier to experiment with different models and integrations. From a development point of view, it saves time during prototyping and lets me focus more on the actual application logic instead of writing a lot of boilerplate code.
Akshay R.
LangChain Makes Model Swaps Effortless While You’re Still Experimenting
Reviewed on Aug 31, 2026
Review provided by G2
What do you like best about the product?
What I like most about LangChain is that it lowers the barrier to just trying something. You want to swap GPT-4 for Claude to see which handles your use case better — that's like a two-line change instead of rewriting your whole app. When you're still figuring out what you're building, that flexibility is worth a lot.
What do you dislike about the product?
It's smooth right up until you need to bend it a little — then you're suddenly wrestling with the framework to make it do something it wasn't quite built for, when honestly, just writing those fifteen lines yourself would've taken less time and less heartache.
What problems is the product solving and how is that benefiting you?
Provider lock-in — swap between OpenAI, Anthropic, or a local model without rewriting your whole app. Big win when you're still shopping around for what works best. Prototyping speed — going from idea to working demo is genuinely faster, since a lot of the scaffolding already exists.
Recommendations to others considering the product:
Provider lock-in — swap between OpenAI, Anthropic, or a local model without rewriting your whole app. Big win when you're still shopping around for what works best. Prototyping speed — going from idea to working demo is genuinely faster, since a lot of the scaffolding already exists.