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.
4.6
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 based on what you use across five independent metrics. Per Trace bills you for each application execution you record and store. Per Agent Run and Per Agent Builder Run charge for agent executions and agent-building activity. Metered Usage Amount captures compute and storage consumed by platform services. These usage metrics accumulate as your work grows, so costs scale with activity rather than fixed seats. The Minimum annual usage commitment is billed in advance, setting a prepaid baseline of usage for the year. Actual charges depend on the work your agents perform.
Top-of-mind questions for buyers
A trace records a single execution of your application, such as an agent, evaluator, or playground session. One trace can hold many steps, including model calls and other tracked events. Base traces keep data for 14 days. You can upgrade base traces to extended traces with 400-day retention for an added fee.
Metered Usage Amount captures compute and storage used by platform services. Compute is measured in LangChain Compute Units and storage in LangChain Storage Units. Different services meter at different rates. Deployments and sandboxes bill per second while running, so idle serverless deployments that scale to zero stop accruing compute charges.
Per Trace, Per Agent Run, Per Agent Builder Run, and Metered Usage Amount all bill independently and add together on one invoice. Your prepaid annual commitment sets a baseline of usage bought in advance. As your work grows, charges accumulate against that baseline, then continue as pay-as-you-go once it is consumed.
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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AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
Drew B.
LangChain Powers Our Content Engine Without Losing Brand Voice
Reviewed on Aug 03, 2026
Review provided by G2
What do you like best about the product?
With LangChain's Generative AI Infrastructure, I can distribute our content on social media, email, and the web without sacrificing our brand voice. I integrate our guidelines, tone documents, and product catalog in one retrieval system so that all the AI-generated copy matches our voice. I am able to generate variations of campaigns and localizations without sacrificing creative consistency that previously needed hours of manual checking.
What do you dislike about the product?
The setup process requires some Python skills which many marketers lack, and some integrations seem to be developer-oriented rather than marketer-friendly.
What problems is the product solving and how is that benefiting you?
LangChain saves time on routine work with content creation and localization, letting my team concentrate on the creative side of work.
Piyush R.
Langchain SDK: Descriptive Docs and Connectors Make Building Agents Easy
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Review provided by G2
What do you like best about the product?
I am a regular user of langchain SDK, since 2023, I have been primarily building chatbots and multi workflow agents using langchain. It is a got to tool now, because of the descriptive documentation support and the chain connecters that makes connecting the embedding and inference models at ease.
What do you dislike about the product?
While the implementation is very simple but when it comes to debugging any failure in the chains, the error logs does not help a lot. Without any observation tool like Langsmith. It's very hard to debug failures. Also, the documentation and implementation has evolved since the years so deprecation of methods was frequent during the usage.
What problems is the product solving and how is that benefiting you?
Itegrating AI usecases and RAG with memory, and multiple output parser, has been a piece of cake with langchain, the methods are short and the parameters are limited, which help deploying AI agents at ease.
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