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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Customer reviews
Veterinary
Natural Language Agent Creation, with learning curve
Reviewed on Aug 24, 2026
Review provided by G2
What do you like best about the product?
I like that I can create agents using natural language, without needing to write any code.
What do you dislike about the product?
Getting started can feel confusing at first, and the cost can be quite high.
What problems is the product solving and how is that benefiting you?
It helps me set up agents to perform tasks automatically, which frees up my time for higher-value work and lets me focus on what matters most.
Computer Software
Huge Ecosystem of Integrations That Speeds Up RAG and Agent Prototyping
Reviewed on Aug 24, 2026
Review provided by G2
What do you like best about the product?
The ecosystem is the biggest strength. LangChain has an enormous library of pre-built integrations covering vector stores, retrievers, document loaders, and tool wrappers, which means most things you want to connect to already have a ready-made abstraction. For an engineer also doing ML work, that breadth saves significant time when prototyping RAG pipelines or agentic systems. The LCEL (LangChain Expression Language) syntax for chaining components is also intuitive once you get used to it, making complex pipelines readable and composable.
What do you dislike about the product?
For simpler use cases, it often feels like overkill and the added complexity is hard to justify compared to just calling model APIs directly.
What problems is the product solving and how is that benefiting you?
For my work, the biggest benefit is speed of integration. When I need to wire up a vector store for RAG or add a retriever to an agentic workflow, I am not writing that from scratch. The unified interface across model providers is also useful since I can swap out the underlying model without rewriting pipeline logic. That flexibility matters when evaluating models or managing cost tradeoffs across different use cases.
Harshini K.
Great for Building Agents for Projects and Hackathons
Reviewed on Aug 24, 2026
Review provided by G2
What do you like best about the product?
I use it to build agents for my projects and hackathons.
What do you dislike about the product?
At first, it’s a little difficult to learn from the docs and tough to implement, but once you get your hands on it, it becomes easier.
What problems is the product solving and how is that benefiting you?
As part of my hackathon, I needed to build an agent that could recommend travel places. Using LangChain, it really worked for me and helped me get the job done.
Aniruddha G.
Comprehensive, Flexible LangChain Platform That Accelerates LLM App Development
Reviewed on Aug 23, 2026
Review provided by G2
What do you like best about the product?
What I like best about LangChain is that it provides a strong overall experience across **UI/UX, integrations, performance, pricing/ROI, support/onboarding, and AI/intelligence**. The framework offers a flexible developer experience with well-structured components and tools that make it easier to build and manage LLM applications. Its **integrations** are one of its biggest strengths, with support for a wide range of models, databases, APIs, and external tools. In terms of **performance**, LangChain provides useful capabilities for creating efficient and scalable AI workflows, although performance can depend on how the framework is implemented. From a **pricing and ROI** perspective, it can reduce development effort by providing reusable components and integrations, helping teams build AI solutions faster. **Support and onboarding** are also strong due to its documentation, examples, and active ecosystem, although beginners may need some time to understand the framework's abstractions. Most importantly, its **AI/intelligence capabilities** make it valuable for building sophisticated applications involving agents, retrieval, tool use, and multi-step reasoning. Overall, LangChain provides a comprehensive platform that can significantly accelerate the development of production-ready AI applications.
What do you dislike about the product?
What I dislike about LangChain is that its flexibility and large number of abstractions can sometimes make the overall experience more complex than necessary. The UI/UX and developer experience can feel overwhelming for beginners, especially when working with multiple components and changing APIs. While the number of integrations is a major strength, managing different integrations can sometimes require additional configuration and troubleshooting. Performance can also be affected by unnecessary abstraction layers or complex chains, particularly in larger workflows. From a pricing/ROI perspective, LangChain itself can help reduce development time, but the overall cost of running LLM applications can still become high depending on model usage and infrastructure. Support and onboarding could be more straightforward for new users, as the ecosystem can take time to understand. Finally, although its AI/intelligence capabilities are powerful, building reliable agents and complex workflows can require significant effort, testing, and monitoring to achieve consistent results.
What problems is the product solving and how is that benefiting you?
LangChain helps address the complexity of building and connecting AI/LLM applications by offering ready-to-use components, integrations, and workflows. It simplifies integration work by connecting different LLM providers, databases, APIs, and external tools. Its AI/intelligence capabilities also make it easier to build agents, retrieval-based applications, and multi-step AI workflows.
From a UI/UX and overall developer experience perspective, the reusable components reduce the amount of custom development needed and make projects easier to maintain as they grow. It can also support better performance and scalability by providing more structured ways to manage complex workflows. Overall, LangChain benefits us by reducing development time and effort, simplifying integrations, and enabling the team to build and iterate on AI solutions faster, which ultimately improves productivity and ROI.
From a UI/UX and overall developer experience perspective, the reusable components reduce the amount of custom development needed and make projects easier to maintain as they grow. It can also support better performance and scalability by providing more structured ways to manage complex workflows. Overall, LangChain benefits us by reducing development time and effort, simplifying integrations, and enabling the team to build and iterate on AI solutions faster, which ultimately improves productivity and ROI.
Vivek S.
Allowed us to explore AI assisted automation use cases within business process.
Reviewed on Aug 23, 2026
Review provided by G2
What do you like best about the product?
As I was working on various BPM / process improvement projects, I wanted to dive into how AI can tie into regular business processes. LangChain allowed me a framework to start playing with AI implementations without having to start from scratch. The biggest win is the flexibility of it. We can connect different models, data sources, etc. and build out workflows that our specific business needs. This is super helpful when trying to prototype ideas for automation / digital transformation initiatives.
What do you dislike about the product?
The challenge I faced was initially understanding all of the pieces. There are a lot of different concepts at play and can be overwhelming if you don't have a deep technical background (as I don't).
What problems is the product solving and how is that benefiting you?
Allowed us to explore AI assisted automation use cases within business process.