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.
4.6
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 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.
This product has charges associated with it for deployment, configuration, and ongoing support. Self-hosted LangChain application server. Deploy chains, agents, and RAG pipelines as production REST APIs with playground and monitoring.
We help customers design and implement Agentic applications on AWS using our modular GenAI Building Block approach. Our experts support rapid PoCs and production deployments, guiding you in selecting and integrating the right LLM for your use case. This ensures flexibility, scalability, and cost-efficiency in your AWS environment.
Customer reviews
LOKESH G.
Flexible, Well-Documented Framework for Building and Scaling AI Apps Fast
Reviewed on Jul 23, 2026
Review provided by G2
What do you like best about the product?
It makes it easy to connect large language models to external tools, APIs, databases, and vector stores, which helps you build sophisticated AI applications without having to create everything from scratch. The framework also provides strong support for retrieval-augmented generation (RAG), agent workflows, memory, and prompt management, and it’s backed by comprehensive documentation and an active open-source community. Overall, this flexibility lets developers prototype quickly and then scale their applications as requirements evolve.
What do you dislike about the product?
One drawback of LangChain is its complexity and steep learning curve, particularly for beginners. The framework also evolves quickly, which can result in frequent breaking changes and a constant need to revisit and update existing code. For simpler AI applications, it may feel overly abstract, adding unnecessary overhead compared with using a model provider’s SDK directly. On top of that, debugging more complex agent workflows can be difficult because there are so many layers and moving parts involved.
What problems is the product solving and how is that benefiting you?
LangChain addresses the challenge of connecting large language models with external data sources, APIs, databases, and business workflows. It offers reusable building blocks for prompt management, retrieval-augmented generation (RAG), agent orchestration, memory, and tool integration, which reduces how much custom infrastructure developers need to create from scratch. As a result, it’s quicker to prototype AI applications, streamline development, and build more capable, context-aware solutions that can pull in real-time information and help automate complex tasks.
Carlos Abel B.
Flexibility and time savings with a very active open source community
Reviewed on Jul 23, 2026
Review provided by G2
What do you like best about the product?
What I like the most is the flexibility it offers to connect language models with databases and external tools. It saves you a lot of time by not having to program the integration logic from scratch, and the fact that it's open source with such an active community makes it easy to find solutions quickly if you get stuck.
What do you dislike about the product?
What I like least is that the learning curve can be a bit steep at first if you're not very familiar with the framework's structure. Additionally, as the AI ecosystem advances so quickly, they constantly release updates and sometimes change the syntax of some functions, so you have to review the documentation frequently.
What problems is the product solving and how is that benefiting you?
LangChain solves the problem of having to manually connect an AI model with local files, databases, or external services, simplifying the entire process through ready-to-use components. This benefits me because it saves weeks of development when creating applications like chatbots or search assistants, allowing for rapid prototyping and scaling projects without reinventing the wheel.
Sukanya N.
Modular, Flexible, and Powerful for Building Scalable LLM Apps
Reviewed on Jul 22, 2026
Review provided by G2
What do you like best about the product?
What I like most about LangChain is its modular architecture. It makes it easy to build, test, and scale LLM applications by combining prompts, tools, memory, and retrieval. Its flexibility and integrations help developers create powerful AI workflows quickly while keeping the code organized and maintainable.
What do you dislike about the product?
One drawback of LangChain is its complexity. It has a steep learning curve, frequent API changes, and can add unnecessary abstraction for simple projects. Debugging multi-step workflows can also be challenging, making development and maintenance more difficult for beginners.
What problems is the product solving and how is that benefiting you?
LangChain solves the challenge of building end-to-end LLM applications by providing tools for prompt management, retrieval, memory, and workflow orchestration. It benefits me by simplifying development, reducing repetitive coding, and enabling faster creation of scalable, maintainable, and feature-rich AI applications.
Research
LangChain Makes Building Real-World LLM Workflows Effortless
Reviewed on Jul 22, 2026
Review provided by G2
What do you like best about the product?
What I like the most about langchain is how it helps connect LLMs with real application workflows, like RAG, document retrieval, memory and tool calling.
What do you dislike about the product?
I think langchain have lot of concepts and the best way to structure a project is not always obvious at first.
What problems is the product solving and how is that benefiting you?
It reduces complexity of connecting different parts of the llm application manually so instead of separately managing retrieval, prompt tools and model calls this launching gives a common framework that makes the whole framework easier to build and itstate on
Asif A.
Open Source and Actively Maintained
Reviewed on Jul 22, 2026
Review provided by G2
What do you like best about the product?
That it’s open sources and being actively maintained.
What do you dislike about the product?
If it could support a rate smoother config as well in its agent config then it would better as Gemini calls are rate limited
What problems is the product solving and how is that benefiting you?
Building our agentic infrastructure around it for non deterministic things