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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Operating system
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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Support
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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
Mahwish Q.
Speeds Up RAG App Builds, but Abstractions and Breaking Changes Can Frustrate
Reviewed on Oct 12, 2026
Review provided by G2
What do you like best about the product?
Langchain's greatest strength is how dramatically it speeds up AI application development through its flexible, model-agnostic framework
What I like best is its rich ecosystem of pre-built integrations, which makes connecting LLMs to vector database and external data sources for RAG workflows virtually effortless. While the high level of abstraction cam occasionally make debugging tricky, the massive amount of boilerplate code it saves makes Langchain an essential toolkit for building scalable AI applications.
What I like best is its rich ecosystem of pre-built integrations, which makes connecting LLMs to vector database and external data sources for RAG workflows virtually effortless. While the high level of abstraction cam occasionally make debugging tricky, the massive amount of boilerplate code it saves makes Langchain an essential toolkit for building scalable AI applications.
What do you dislike about the product?
What developers typically dislike about LangChain is its heavy reliance on over-engineered abstractions, which often wraps straightforward API calls in multiple layers of complex classes and makes debugging stack traces unnecessarily difficult. Additionally, the fast-moving nature of the framework frequently introduces breaking changes and outdated documentation, while adding dependency bloat and friction for simple use cases that would otherwise be handled in a few lines of raw python.
What problems is the product solving and how is that benefiting you?
Langchain directly addresses the critical bottleneck of context, engineering and operational friction when bridging raw Large language Models with production software. By providing unified abstractions, it solves the fragmentation of model APIs, the complexity of data ingestion for Retrieval-Augmented Generation (RAG), and the tedious state management required for conversational memory and multi-step agent workflows.
Anonymous
Streamlined Multi-Agent AI with Langchain
Reviewed on Oct 10, 2026
Review provided by G2
What do you like best about the product?
I really like Langchain's standardized abstractions and stateful orchestration management. It makes building agentic AI applications and multi-agent AI workflows so much easier. Its lightweight API calling and the flexibility it provides are fantastic, allowing me to switch or compare LLM providers and vector databases with minimal code changes. Langchain streamlines API calls and speeds up development from initial client demo to production-ready applications. As an AI engineer who's used many frameworks, the predictable execution loops and complex multi-agent workflows of Langchain, without having to build custom state management, make my work simpler and more flexible than other frameworks.
What do you dislike about the product?
I don't have any significant dislikes at the moment, but I think Langchain could improve its documentation clarity. Since the ecosystem changes so quickly, breaking changes and deprecated methods can lead to extra maintenance overhead when upgrading packages. Better documentation and smoother version upgrades would help, especially with highlighting changed methods.
What problems is the product solving and how is that benefiting you?
Langchain simplifies building production-grade agentic applications. It standardizes abstractions and offers stateful orchestration with Langgraph, tackling complexity, debugging, and workload management without custom state code. These features streamline API calls, speed up development, and provide flexibility.
Rohit K.
Saves time while creating multi agent system
Reviewed on Oct 04, 2026
Review provided by G2
What do you like best about the product?
It's the best among the other applications for the role of making advanced multi agent systems and to easily create a loops. And if you want to switch from one model to another model then it only needs 1 to 2 lines of code depending on the model
What do you dislike about the product?
It's good for the advance but not for the basic things like it requires the unnecessary code for it and also important the framework takes a massive third party integration which you even not use and which leads to the increase in the size of the project and as we are using the third party so it has the risk also
What problems is the product solving and how is that benefiting you?
It's helping me in like I am working the side by side on the AI and machine learning so it is useful for me when I am learning about the LLM, API creation etc and while creating the advance code and working on a project it's a lot of time and also they gave us a very high security like they don't have any access to our private data like files if you are doing something on our private project so they don't interfere there
Chaitanya T.
Simplifies Building LLM Apps with Reusable Prompt, Model, and Tool Components
Reviewed on Sep 30, 2026
Review provided by G2
What do you like best about the product?
I like it because it simplifies building LLM-powered applications by providing reusable components for prompts, models and tools.
What do you dislike about the product?
It can sometimes feel complex because it has many abstractions and components and its API changes frequently.
What problems is the product solving and how is that benefiting you?
It simplifies the development of LLM-powered applications by providing reusable components for prompts, model integration and retrieval. It helps me connect different AI components into a complex workflow more easily.
Jai Y.
Eases AI Pipeline Building, Needs Better Debugging
Reviewed on Sep 29, 2026
Review provided by G2
What do you like best about the product?
I use LangChain to create agent and workflow chains. I find its modular architecture and standard interfaces really useful as they make it incredibly easy to string together LLMs, vector data, and memory into scalable, production-ready AI pipelines. With its modular design, I can swap models or databases with a single line of code without having to rewrite my app. It's very efficient in providing me with reusable session handling, prompt, output, input tool calling, so I don't have to create these things manually when creating an agent. Also, the initial setup was fairly easy for someone with knowledge about agent frameworks.
What do you dislike about the product?
LangChain's complex abstractions hide the underlying logic, making it difficult to debug, customize, and maintain as your project grows. It could simplify its abstractions, improve transparency into execution flow, and provide better debugging tools so developers can easily customize and troubleshoot complex workflows.
What problems is the product solving and how is that benefiting you?
Langchain provides reusable session handling and standard interfaces, so I don't have to manually create them, making it easy to build scalable AI pipelines.