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
Sushma K.
A Flexible Framework for Building LLM - Powered Applications
Reviewed on Aug 29, 2026
Review provided by G2
What do you like best about the product?
Makes LLM development easier, with simple integrations and flexible workflows.
What do you dislike about the product?
The setup can feel confusing, and debugging sometimes takes more time than expected.
What problems is the product solving and how is that benefiting you?
LangChain helps solve the challenge of connecting different parts of an AI application in one place. Instead of handling model calls, prompts, data retrieval, tools, and workflows separately, I can manage them through a more structured framework. This saves development time and makes experimentation much easier. I also find it useful when working with external APIs, databases, and retrieval-based applications. Overall, it reduces repetitive integration work and gives me a cleaner way to build, test, and maintain LLM-based applications without creating every component from scratch.
Banking
Great for building AI apps
Reviewed on Aug 29, 2026
Review provided by G2
What do you like best about the product?
The thing i like about it is that it very easily connects me to different databases and ai models .
What do you dislike about the product?
I don't like when our app breaks we have to search and dig through multiple layers of hidden code to find error , that is very frustrating
What problems is the product solving and how is that benefiting you?
Anytime i can easily change or swap my AI model because of this with one line of code whenever there are changes in price or performance
Shubh J.
LangChain Streamlines Building Scalable AI Apps with Reusable Components
Reviewed on Aug 29, 2026
Review provided by G2
What do you like best about the product?
What I like most about LangChain is how it simplifies building AI applications by connecting models with tools, data sources, and custom workflows. It gives me a flexible structure for experimenting with different approaches without having to build every integration from scratch.
What do you dislike about the product?
One thing I dislike about LangChain is that the framework can feel overly complex for smaller projects. There are many abstractions and components to understand, and sometimes figuring out the right way to implement a simple workflow takes more effort than expected.
What problems is the product solving and how is that benefiting you?
LangChain helps solve the challenge of turning standalone language models into useful, connected applications. It makes it easier to manage prompts, tool calls, external data, and multi-step workflows in one framework. For me, this reduces development time and makes it easier to test and improve AI features without building the underlying connections from scratch.
Ashish R.
Ideal for rapid prototyping of AI, just be wary of breaking updates.
Reviewed on Aug 28, 2026
Review provided by G2
What do you like best about the product?
To be frankly it really saves an enormous amount of time with boilerplate coding. I am using it for implementing AI functionalities in my web applications, and it makes my life so much simpler when dealing with API calls and prompt memory. There is no need for me to write wrappers each time because I can simply use the modules available to me
What do you dislike about the product?
So sometimes it can be really annoying to work with documentation at times, particularly considering how often the library changes. I have had some cases where the update caused problems with my code, or where the tutorial found online was already out of date. Another challenge is debugging complex chains in case of failure.
What problems is the product solving and how is that benefiting you?
Frankly speaking, it addresses the problem of integrating the LLMs into third-party tools and databases. Most importantly, I needed to write quite a bit of messy custom code to enable the AI to access its conversation history or retrieve data from documents. The biggest advantage for me personally is rapid development. I can develop prototypes and test out new ideas regarding AI integration much faster since I don’t need to create all the backend logic from scratch.
Dheeraj M.
Powerful for LLM Apps, but a Steeper Learning Curve and Evolving APIs
Reviewed on Aug 27, 2026
Review provided by G2
What do you like best about the product?
What I like most about LangChain is how much it simplifies building LLM-powered applications. I particularly appreciate its support for prompt management, chains, agents, tool integration, and retrieval workflows. Overall, it makes it much easier to connect LLMs with APIs, databases, and other external tools without having to build all the orchestration from scratch. It has also helped me structure my AI workflows more clearly and iterate on them faster. LangChain provides strong value because the core framework is open source and gives me useful tools for building LLM applications without a direct software license cost. The integrations, agent workflows, and retrieval capabilities can save development time compared with building these components from scratch. Overall, I find the value very good for the functionality it provides.
What do you dislike about the product?
One downside is that LangChain can feel complex when you’re building larger or more advanced workflows. The framework includes many abstractions and components, so it can take time to understand how everything fits together and how to use it effectively. The documentation and APIs can also shift as the ecosystem evolves, which makes it harder to keep up. A clearer, simpler learning path and more stable interfaces would make it much easier for new developers to get started and stay productive.
What problems is the product solving and how is that benefiting you?
LangChain helps me simplify the development of LLM-powered applications by offering a structured approach to building chains, agents, retrieval workflows, and tool integrations. It cuts down on the amount of custom orchestration code I have to write and makes it easier to connect models to APIs, databases, and other services. As a result, I can prototype and iterate on AI features more quickly while keeping the overall application logic cleaner, better organized, and easier to maintain.