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 |
Vendor refund policy
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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Observability, prompt management and evaluation for LLM applications.
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Customer reviews
Program Development
AI applications in natural language – with practical memory modules
Reviewed on Jul 29, 2026
Review provided by G2
What do you like best about the product?
This enables the development of AI applications that can interact with data, tools, and even users in natural language. Built-in memory modules significantly simplify the management of conversation histories.
What do you dislike about the product?
Developers often have to read a lot of code to understand even trivial functions. Additionally, programming skills are required, which can be a real challenge for beginners.
What problems is the product solving and how is that benefiting you?
LangChain primarily solves the major practical hurdles for us that arise when trying to build real, production-ready applications from pure Large Language Models (LLMs). It addresses the typical problems that occur during the development of such applications.
Anand M.
Strong RAG and Agentic Tooling with Helpful Memory Management
Reviewed on Jul 29, 2026
Review provided by G2
What do you like best about the product?
Built in support for RAG and many tools support for agentic platform . there is mechanism for memory management ,which is helpful developing agent memory.
What do you dislike about the product?
A lot of updates tend to break some existing functionality, so it requires continuous changes to keep things working.
What problems is the product solving and how is that benefiting you?
Lang chain helped us develop an agentic platform for data analysis, with memory that keeps track of previous activity and helps improve response quality.
Vibhor J.
LangChain Review
Reviewed on Jul 29, 2026
Review provided by G2
What do you like best about the product?
This tool is built around a developer framework rather than a traditional end-user application interface. Most interactions happen through Python or JavaScript code, APIs, and configuration files, instead of a graphical UI. It also supports integration with more than 1,000 third-party applications.
The LangChain framework itself is free and open source under the MIT License. Any costs typically come from external AI model APIs, vector databases, and the cloud infrastructure you choose to run it on.
LangChain offers tutorials and documentation for self-help. It even provides guided support for subscription-based users.
LangChain is designed to build AI applications that can handle simple as well as large-scale projects including features that make AI workflows more reliable and efficient. However, the overall performance and response speed majorly depends on AI model being used, connected APIs, and how complex the application is, rather than on LangChain itself.
LangChain offers strong AI capabilities, even though it does not have its own AI model. It helps developers build smart AI applications by connecting with popular AI models such as OpenAI, Anthropic, Google Gemini, and others.
The LangChain framework itself is free and open source under the MIT License. Any costs typically come from external AI model APIs, vector databases, and the cloud infrastructure you choose to run it on.
LangChain offers tutorials and documentation for self-help. It even provides guided support for subscription-based users.
LangChain is designed to build AI applications that can handle simple as well as large-scale projects including features that make AI workflows more reliable and efficient. However, the overall performance and response speed majorly depends on AI model being used, connected APIs, and how complex the application is, rather than on LangChain itself.
LangChain offers strong AI capabilities, even though it does not have its own AI model. It helps developers build smart AI applications by connecting with popular AI models such as OpenAI, Anthropic, Google Gemini, and others.
What do you dislike about the product?
This tool may be challenging for beginners, since it requires solid prior knowledge of Python or JavaScript to use effectively. Also, there isn’t a dedicated drag-and-drop or graphical interface for building applications, which can make the overall experience less approachable for new users.
What problems is the product solving and how is that benefiting you?
I’m using this tool to build an AI logic of the application for beta testing. After deploying it in AWS, I review the app’s overall performance to catch any bugs or glitches.
Sourabrata S.
Organized, Encapsulated Libraries Make LangChain Easy to Work With
Reviewed on Jul 28, 2026
Review provided by G2
What do you like best about the product?
I like the way LangChain uses encapsulated code and libraries, which makes it feel more organized and easier to work with.
What do you dislike about the product?
It feels a bit verbose; I prefer Langraph in comparison.
What problems is the product solving and how is that benefiting you?
Langchain helped me create my RAG agent, and the whole process was really easy.
Mohammad A.
Orchestrator Makes Building Multi-Model Agents and RAG Easy
Reviewed on Jul 28, 2026
Review provided by G2
What do you like best about the product?
The orchestrator capability helps us develop multi-model agents and design a RAG system.
What do you dislike about the product?
Nothing which I can say I dont like, but the interface can be improved a little
What problems is the product solving and how is that benefiting you?
Building Agentic AI solution
RAG
Connecting LLM and applications
RAG
Connecting LLM and applications