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
Diptesh J.
Makes Connecting LLMs to Tools, APIs, and Data Sources Easy
Reviewed on Aug 12, 2026
Review provided by G2
What do you like best about the product?
It makes it easier to connect LLMs with tools, APIs and data soruces.
What do you dislike about the product?
The learning curve can be a bit steep and the framework feels complex
What problems is the product solving and how is that benefiting you?
It simplifies connecting AI models with APIs, tools that help us to build and test AI applications.
Chirag M.
LangChain Makes Building RAG Pipelines Fast, Simple, and Well-Documented
Reviewed on Aug 12, 2026
Review provided by G2
What do you like best about the product?
What i like best about langchain is that, the core idea behind it, before langchain if i had to create any rag pipeline that is retreival augemented generation pipeline for chatbot kind of applications, it would take more time and efforts in building it from scratch with no framework in place. but with langchain it became much simpler as langchain provided framework for building RAG pipelines, with readily available libraries and components in the framework. So becuase of this the output or endproduct can be built much faster thanks to this so the ROI on this is much better as it takes lesser time to build the end product and the performance too is much better and more accurate as well, especially with the integrations of many components like llms vector search techniques makes it simpler to use. The documentation of langchain is also very direct and hence the support we get from it is very much helpful. Overall building ai products and projects has just gotten simpler thanks to Langchain and its capable intelligence in the vector search techniques. The user experience in using it has been very simple no much complications involved at all.
What do you dislike about the product?
What i dislike about langchain is even though it provides great base for devlopers to builld the RAG pipelines simpler due to its framework, but its not as advanced as other products if one compares, with the advancements in feature what feels is langchain might be a bit lacking compared to Haystack and few other such competitors in this. The reason is that langchain only supports the old or basic llms and vector searches, with advancements in vector searching techniques like in haystack we have hnsw that langchain lacked for long period, even though it has enabled hnsw it is just externally wrapped above the others, its not native as its is in haystack and such, is one of the things i disliked of langchain. it also feels lot heavy to use langchain compared to other light weight frameworks
What problems is the product solving and how is that benefiting you?
Langchain is a framework that comes with lot of addones that saves lot of efforts and time in configuring the pipeline of the agent from scratch. it makes project building like rag chatbots much simpler and faster and infact much better output driven compared to building from scratch, because of the readily available and easy integration that langchain framework provides like the search techniques, llms and tokenizer and chunking methods, connection with open source models too is a plus point that takes very less effort and time of the devloper compared to working on their own to build such end to end products.
Harshul S.
LangChain Makes Structured AI Workflows Simple and Manageable
Reviewed on Aug 11, 2026
Review provided by G2
What do you like best about the product?
What I like best about LangChain is how it simplifies building structured AI workflows. Instead of wiring everything together manually, it gives you clean building blocks for prompts, tools, memory, and agents. It makes complex pipelines feel more manageable and reduces a lot of glue‑code overhead.
What do you dislike about the product?
The only thing I dislike is that some parts of LangChain feel a bit too abstract when you’re trying to build something quickly. Certain components require extra configuration or digging through docs, so simple tasks can end up feeling more complicated than they should be.
What problems is the product solving and how is that benefiting you?
LangChain solves the problem of stitching together different AI components manually. Instead of writing a bunch of glue code for prompts, tools, retrieval, and workflow logic, it gives a structured framework that keeps everything organized. The benefit is faster development, cleaner pipelines, and less time wasted figuring out how pieces should connect.
Ethan J.
LangChain Makes Building AI Apps Easier
Reviewed on Aug 11, 2026
Review provided by G2
What do you like best about the product?
What I like best about LangChain is how it makes it easier to build AI applications by connecting language models with data sources, tools, and workflows.
What do you dislike about the product?
What I dislike about LangChain is that it can feel complex at first, and debugging larger chains or keeping up with frequent updates can sometimes be challenging.
What problems is the product solving and how is that benefiting you?
LangChain simplifies the process of building and connecting AI workflows, helping me integrate models with tools and data more easily while reducing development time.
Sol C.
Really Easy and Fast for Building AI Agents
Reviewed on Aug 11, 2026
Review provided by G2
What do you like best about the product?
Really easy and quick to build AI agents
What do you dislike about the product?
sometimes a bit confusing but overall still easy to use
What problems is the product solving and how is that benefiting you?
automating so many things due to agents