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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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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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
Chaitrali M.
Powerful Framework for Building AI Applications with Great Flexibility
Reviewed on Aug 26, 2026
Review provided by G2
What do you like best about the product?
LangChain makes it easier to build AI-powered applications through reusable components for prompts, agents, tools, memory, and RAG workflows. I particularly like its wide range of integrations with LLM providers, vector databases, APIs, and other tools, which makes experimentation and switching between technologies more flexible. The ecosystem around LangChain, including LangSmith, also improves the overall development experience by helping with debugging, tracing, and monitoring. Its active community, documentation, and onboarding resources make it easier to get started once the core concepts are understood. Daily use has been smooth and intuitive overall. The interface and workflow are easy to navigate once the basic concepts are understood, and integrating AI models into applications feels straightforward. I found the overall experience reliable with minimal frustration, although some advanced features and configurations can take a little time to understand. Overall, I’d rate the day-to-day experience 9/10.
What do you dislike about the product?
The main drawback is the learning curve, especially for beginners, because the framework has many concepts and components to understand. Frequent updates can also introduce changes that require developers to modify existing code. Performance can depend heavily on the design of the application, particularly in complex agent or multi-step workflows. Although the documentation has improved, some advanced topics could still benefit from clearer explanations and more practical examples. The overall cost can also increase depending on the LLM, vector database, and external services used alongside LangChain.
What problems is the product solving and how is that benefiting you?
LangChain simplifies the development of LLM-based applications by providing structured ways to handle prompt orchestration, tool usage, document retrieval, memory, and multi-step workflows. I have used it for AI chatbots and RAG-based applications that interact with external knowledge sources. Instead of building every integration from scratch, LangChain provides reusable components that reduce development time and improve maintainability. This helps me prototype AI applications faster and experiment with different models and tools with fewer major code changes.
Akshay S.
LangChain Saves Time Connecting Prompts and Workflows
Reviewed on Aug 25, 2026
Review provided by G2
What do you like best about the product?
I use Langchain for genrative task when making a generative ai pipeline using llms langchain really helps me to connect the prompt template and diff workflows through chains and saves me a ton of time to write the glue code
What do you dislike about the product?
it cant orchestrate complex workflows like LangGraph in which we can use graphs and make complex workflows with built in observability of LangSmith
What problems is the product solving and how is that benefiting you?
The main problem i face using making genrative ai workflows is when using diff models of chatgpt claude or gemini or an open-source model langchain provides inbuilt calling of diff models by just using the api key with built in type of model i wanna use helps me same time in debugging and writing glue code
Soumyaranjan N.
Makes Connecting LLMs to Docs, APIs, and Databases Easy
Reviewed on Aug 25, 2026
Review provided by G2
What do you like best about the product?
What I like most about LangChain is that it makes it easier to connect an LLM with documents, APIs, databases and other tools.The best part is it gives what documents or trained with database it fetch from there not give from its own research. From my point of view it is worthit to buy but some conditions there like if your development is based on chatbot or anything where Customers some questions answered get from chatbot instead call the your support it is more help full to you and any kind of data you need you just train them they will provide accurate data what you teach them not there own data.
What do you dislike about the product?
Crafting well-designed abstractions is hard even when the requirements are well understood and stableCode quality isn't great and structure is pretty iffy. Docs are way outdated, deprecation warnings implemented poorly. And when you need to dig under the surface to fix something, you see the ugly. But it gets the job done. I can see what they want to do, but it's bloated very quickly, probably because of its popularity
What problems is the product solving and how is that benefiting you?
It help to build real chatbot means what documents we teach it gives from that only.AI gives more accurate facts because it reads real documents instead of guessing.AI models only know what they learned during training. LangChain connects them to files, databases, and the internet
RISHABH K.
Makes AI Workflows Easier to Manage
Reviewed on Aug 25, 2026
Review provided by G2
What do you like best about the product?
Easy way to build and test AI workflows.
What do you dislike about the product?
Can feel complex for beginners at first.
What problems is the product solving and how is that benefiting you?
For me, LangChain mainly helps reduce the effort of connecting different parts of an AI application. Instead of handling prompts, models, external data, and other tools completely separately, I can structure them into one workflow.
This saves time when I am experimenting or building something because I can focus more on how the workflow should work rather than writing everything from scratch. I also find it useful when an application involves multiple steps or needs to work with external data.
That said, it was a little confusing at the beginning because there are many concepts to understand. But after spending some time with it, the workflow became easier to follow and manage.
This saves time when I am experimenting or building something because I can focus more on how the workflow should work rather than writing everything from scratch. I also find it useful when an application involves multiple steps or needs to work with external data.
That said, it was a little confusing at the beginning because there are many concepts to understand. But after spending some time with it, the workflow became easier to follow and manage.
Vamshi M.
LangChain Streamlines Building AI Apps with Powerful LLM Workflows
Reviewed on Aug 25, 2026
Review provided by G2
What do you like best about the product?
LangChain makes it easier to build AI applications by offering practical tools for managing LLMs, prompts, chains, agents, and integrations. What I like most is how it streamlines otherwise complex workflows, making it simpler for developers to prototype quickly and then turn those prototypes into real AI-powered applications.
What do you dislike about the product?
The biggest downside is that LangChain can feel overwhelming for beginners, particularly when trying to grasp its many abstractions and components. Even relatively simple tasks can end up requiring more code and setup than you might expect.
What problems is the product solving and how is that benefiting you?
LangChain simplifies building AI applications by connecting LLMs with prompts, tools, data sources, and APIs. It saves me development time, makes complex AI workflows easier to manage, and helps me build, test, and iterate on AI applications more efficiently.