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
Details
Sold by
Categories
Delivery method
Supported services
Delivery option
LangSmith Helm Chart
Latest version
Operating system
Linux
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Trust Center
Access real-time vendor security and compliance information through their Trust Center powered by Drata or Vanta. Review certifications and security standards before purchase.
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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
Helpful?
Vendor refund policy
https://www.langchain.com/terms-of-service#:~:text=Customer%20will%20pay%20LangChain%20all ,Fees%20paid%20are%20non%2Drefundable.
Custom pricing options
Request a private offer to receive a custom quote.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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.
Resources
Vendor resources
Support
Vendor support
AWS infrastructure support
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 a framework-agnostic platform for building production AI agents. Trace, evaluate, and deploy agents with integrated observability and one-click deployment.
LangSmith BYOC deploys a fully managed agent observability, evaluation, and deployment platform inside your own AWS account and VPC, keeping sensitive data in your cloud boundary.
Enable your software teams to orchestrate AI agents across the entire SDLC with GitLab Duo Agent Platform Self-Hosted for offline licensing customers.
This is a repackaged open source software product wherein additional charges apply for Code Creator integration of Dify and Langfuse, Docker based deployment, private AWS runtime configuration, browser based administrator onboarding, model provider connectivity, and integrated AI observability for traces metrics evaluations latency and cost. Build and monitor AI applications agents and workflows inside your own AWS account. Self hosted Dify and Langfuse AI app studio on Ubuntu that lets you build AI workflows and agents in the browser and observe every request in your own AWS account using your own LLM provider keys.
Customer reviews
Shashank S.
LangChain Simplifies Building and Connecting LLM Applications
Reviewed on Oct 15, 2026
Review provided by G2
What do you like best about the product?
I like LangChain because it makes building LLM-powered applications more flexible and organized. Its prompt templates, model integration, documents loaders, text splitters, retrievers, and vector store integrations help connect different components into a single workflow. I also find its RAG capabilities useful for working with external data, while tool calling helps connect language models with other services. The documentations and integration options make it easier to experiment with different approaches when developing AI applications.
What do you dislike about the product?
One downside of LangChain is that the learning curve can be steep when working with complex chains, agents, and integration. Debugging workflows sometimes takes extra time, especially when multiple components are connected. Frequent updates and changes between versions can also require code adjustments. Clearer error messages and more consistent documentation across integrations would make the development experience smoother.
What problems is the product solving and how is that benefiting you?
LanChain helps me build and experiment with LLM-powered application by connecting language models with prompts, external data, and tools. It is useful for organizing multi-step workflows, working with document retrieval, and building retrieval-augmented generation (RAG) pipelines. It also makes it easier to integrate different model providers and manage chains of operations, reducing the amount of custom code needed for common tasks.
Roshan P.
LangChain Makes Building Flexible AI Workflows Fast and Straightforward
Reviewed on Oct 14, 2026
Review provided by G2
What do you like best about the product?
What I like most about LangChain is how straightforward it makes building AI applications and connecting different language models to external tools and data sources. I also appreciate the flexibility it provides for creating workflows and trying out different models as I iterate. Overall, it saves me development time and makes it easier to manage and improve AI projects without having to build everything from scratch.
What do you dislike about the product?
One thing I dislike about LangChain is that it can feel a bit complicated to get started, especially when you’re dealing with different integrations and configuration options. At times, debugging takes longer than I expect, partly because the framework is updated frequently and things change from version to version. The documentation is helpful overall, but I think some examples could be simpler and more beginner-friendly, with clearer steps that are easier to follow.
What problems is the product solving and how is that benefiting you?
LangChain helps address the challenge of building AI applications that need to connect language models with external data, APIs, and other tools. It makes it easier to design AI-powered workflows without having to write everything from scratch. For me, that means less development time spent on boilerplate, smoother integration across components, and a more efficient way to build and test AI solutions.
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.
Shobha D.
Build AI Apps Fast with Less Glue Code
Reviewed on Oct 12, 2026
Review provided by G2
What do you like best about the product?
What I like most is how fast you can build things. It handles all the messy glue work between the AI model, prompts, and external data. Instead of writing tons of code from scratch, you can connect the pieces together and test a working app in hours.
What do you dislike about the product?
It feels too bloated and complicated for simple tasks. They hide basic things under too many layers, so when something breaks, debugging is painful. Plus, the library changes so fast that old code breaks constantly, and finding clear, up-to-date documentation can be a real headache.
What problems is the product solving and how is that benefiting you?
Before LangChain, connecting an AI model to your own files and databases took endless repetitive code. LangChain solves that by providing ready-made building blocks. It benefits me by cutting down setup time, letting me focus on the actual app logic rather than wiring everything manually.
RaHul K.
Great modular framework for RAG and agents, though documentation can be confusing
Reviewed on Oct 10, 2026
Review provided by G2
What do you like best about the product?
What I Like (Pros):
Massive collection of integrations with various vector stores, models, and APIs.
Speeds up prototyping and building complex LLM-powered applications significantly.
Strong community support and pre-built components for standard AI tasks.
Massive collection of integrations with various vector stores, models, and APIs.
Speeds up prototyping and building complex LLM-powered applications significantly.
Strong community support and pre-built components for standard AI tasks.
What do you dislike about the product?
What I Dislike
Rapid updates and shifting abstractions mean that breaking changes can happen frequently, requiring constant code maintenance.
Documentation can sometimes be tricky to navigate due to the fast-paced evolution of the framework.
Rapid updates and shifting abstractions mean that breaking changes can happen frequently, requiring constant code maintenance.
Documentation can sometimes be tricky to navigate due to the fast-paced evolution of the framework.
What problems is the product solving and how is that benefiting you?
The Problem It Solves:
Our development team needed a structured, modular way to connect large language models with external data sources, prompt templates, and memory components without writing complex boilerplate code from scratch.
How We Are Using It (How it's solving the problem):
We use LangChain at our workplace to orchestrate our LLM workflows, build retrieval-augmented generation (RAG) pipelines, and manage multi-step agent interactions efficiently
Our development team needed a structured, modular way to connect large language models with external data sources, prompt templates, and memory components without writing complex boilerplate code from scratch.
How We Are Using It (How it's solving the problem):
We use LangChain at our workplace to orchestrate our LLM workflows, build retrieval-augmented generation (RAG) pipelines, and manage multi-step agent interactions efficiently