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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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.
Similar products
LangSmith is an agent engineering platform to build, test, deploy and observe your agents. 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.
Observability, prompt management and evaluation for LLM applications.
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Langflow is an open-source visual framework for building multi-agent and RAG AI applications. This Lynxroute build is security baked in: authentication enabled, Nginx TLS reverse proxy, unique admin credentials at first boot, and CIS Level 1 hardened Ubuntu 24.04 LTS base.
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Customer reviews
Neelanjana M.
LangChain Makes Building AI Apps Fast with Powerful Integrations
Reviewed on Aug 04, 2026
Review provided by G2
What do you like best about the product?
What I like best about LangChain is how easily it connects language models with external data sources, APIs, databases, and tools. It provides reusable components that make it faster to build AI applications such as chatbots, document assistants, and automated workflows. The wide range of integrations and active community support are also very helpful when developing and testing new use cases.
What do you dislike about the product?
What I dislike about LangChain is that it can become complex when building larger applications. The documentation and framework structure may feel overwhelming for beginners, and frequent updates can sometimes introduce changes that require existing code to be modified. Debugging multi-step chains or agent workflows can also be difficult because it is not always easy to identify where an issue occurred. For simpler AI use cases, the framework may feel heavier than necessary.
What problems is the product solving and how is that benefiting you?
LangChain helps solve the complexity of building AI applications that need to connect language models with documents, databases, APIs, and external tools. Instead of developing every integration and workflow from scratch, it provides reusable components for creating chatbots, document-based question-answering systems, agents, and automated processes.
This benefits me by reducing development time and making it easier to test different AI use cases. It also helps organize multi-step workflows, manage prompts, connect multiple data sources, and build prototypes more efficiently. As a result, I can focus more on the business requirement and user experience rather than spending too much time on basic technical integration.
This benefits me by reducing development time and making it easier to test different AI use cases. It also helps organize multi-step workflows, manage prompts, connect multiple data sources, and build prototypes more efficiently. As a result, I can focus more on the business requirement and user experience rather than spending too much time on basic technical integration.
Shaquashia A.
Brings PDF Sources into Context Effortlessly
Reviewed on Aug 04, 2026
Review provided by G2
What do you like best about the product?
Helps me bring into context from sources such as pdfs.
What do you dislike about the product?
Sometimes I experience difficulty when using through goggle drive.
What problems is the product solving and how is that benefiting you?
Providing the resources I need.
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
Reviewed on Aug 03, 2026
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.
Jeni J.
Modular, Flexible Framework That Speeds Up AI App Development
Reviewed on Aug 03, 2026
Review provided by G2
What do you like best about the product?
What I like most about LangChain is how easy it makes building AI applications that combine LLMs with tools, APIs, databases, and retrieval workflows. The modular design and broad integration ecosystem let me experiment quickly without feeling locked into a single model, and the agent capabilities are surprisingly flexible for real-world use cases. The learning curve can be a bit steep when projects become more complex, but overall it's a powerful framework that significantly speeds up AI development.
What do you dislike about the product?
As projects grow, LangChain can start to feel overly abstract, making debugging and tracing execution across chains or agents more difficult than expected. The documentation and APIs also evolve quickly, so examples from older versions aren't always compatible with the latest release. Despite that, the framework remains highly capable once you get familiar with its patterns.
What problems is the product solving and how is that benefiting you?
LangChain solves the challenge of connecting large language models with external data, APIs, and business workflows, so I don't have to build those integrations from scratch. It's helped me develop AI applications with retrieval, tool calling, and agent-based automation much faster, while making it easier to prototype, test, and iterate on complex use cases.