Sold by: LangChainÂ
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.
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
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 minimum $100k 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
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Delivery method
Supported services
Delivery option
LangSmith Helm Deployment
Latest version
Operating system
Linux
Deployed on AWS
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Financing for AWS Marketplace purchases
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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 | Description | Cost/unit |
|---|---|---|
Unit for LangSmith Observability & Evaluation | Per Trace | $0.00625 |
Unit for LangSmith Deployment | Per Agent Run | $0.00625 |
Metered Usage Amount | Metered Usage Amount | $0.01 |
Minimum annual usage commitment, billed in advance | Minimum annual usage commitment, billed in advance | $100,000.00 |
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.
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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)Â .
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 Deployment
Supported services: Learn moreÂ
- 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
This release brings alerting, UI-driven experiment workflows, end-to-end OpenTelemetry support and a host of new capabilities alongside several bug fixes. Under the hood it also rolls out beta Self-Hosted LangGraph Cloud Control Plane and full-text search, plus query performance and ingestion optimizations.
Additional details
Usage instructions
To use your instance follow these instructions:
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Customer reviews
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35 external reviews
Star ratings include only reviews from verified AWS customers. External reviews can also include a star rating, but star ratings from external reviews are not averaged in with the AWS customer star ratings.
Mirian P.
Great for agentic ai programming
Reviewed on Sep 21, 2025
Review provided by G2
What do you like best about the product?
The platform is easy to use, even if you only have a basic understanding of AI concepts. I found that navigating the features didn't require advanced technical knowledge, which made the experience straightforward and accessible.
What do you dislike about the product?
Sometimes, other frameworks appear to be simpler.
What problems is the product solving and how is that benefiting you?
I found that some integrations with cloud services were more straightforward and agnostic when using langchain.
Navdeep S.
Powerful Framework for Building AI Apps Quickly
Reviewed on Aug 13, 2025
Review provided by G2
What do you like best about the product?
I really like how LangChain brings all the moving parts of AI app development together in one place. The integration with different LLMs, vector databases, and APIs is super smooth, so I don’t waste time building connectors from scratch. The documentation is improving, and the community is very active, which makes finding examples and solutions easier. It’s also flexible enough to go from a quick prototype to a production grade application without completely rewriting the code it makes it a powerful tool to have.
What do you dislike about the product?
While LangChain is powerful it can feel overwhelming at first because of how many modules and options it offers. The documentation, though better now, still has gaps for more advanced use cases, and sometimes breaking changes in updates mean I need to adjust my code unexpectedly. It would be nice to have more structured learning paths for newcomers.
What problems is the product solving and how is that benefiting you?
LangChain helps me connect large language models with the right data sources, tools and workflows without having to build everything from scratch. Before using it, I had to manually handle API calls, parse responses, and manage context across different parts of the app, which slowed development. Now I can orchestrate prompts chain multiple steps together, and integrate with vector databases or APIs in a few lines of code. This saves a lot of development time, reduces errors, and lets me focus more on designing better AI experiences for users instead of building low-level infrastructure so its is kind to helpful to me.
Shoaib A.
Langchain Review -MLOps
Reviewed on Aug 12, 2025
Review provided by G2
What do you like best about the product?
Experiment Tracking via prompt templates,
Integration with Vector Database,
Pipeline Composition allowing mw to separate data ingestion, transformation and inference stages,
Reproducibility- it helps me LLM-powered workflows for CI/CD deployment.
Integration with Vector Database,
Pipeline Composition allowing mw to separate data ingestion, transformation and inference stages,
Reproducibility- it helps me LLM-powered workflows for CI/CD deployment.
What do you dislike about the product?
I have been facing complexity in debugging and challenges in scaling.
It has fast-evolving APIs which makes it difficult to track the backward copatibility.
It has fast-evolving APIs which makes it difficult to track the backward copatibility.
What problems is the product solving and how is that benefiting you?
Langchain is solving a set of practical problems around building and deploying applications powered by large language models (LLMs).
Prompt and Memory Management, LLM Orchestration, Data Connectivity
Prompt and Memory Management, LLM Orchestration, Data Connectivity
Fahad S.
Powerful AI orchestration framework with a learning curve
Reviewed on Aug 12, 2025
Review provided by G2
What do you like best about the product?
Comprehensive abstractions for working with LLMs (chains, agents, tools)
Extensive integrations with various AI models and vector databases
Active community and rapid development pace
Flexibility in building complex AI workflows
Good documentation with practical examples
Memory management capabilities for conversational AI
Built-in prompt templates and output parsers
Extensive integrations with various AI models and vector databases
Active community and rapid development pace
Flexibility in building complex AI workflows
Good documentation with practical examples
Memory management capabilities for conversational AI
Built-in prompt templates and output parsers
What do you dislike about the product?
Steep learning curve for beginners
Frequent breaking changes between versions
Can be overly complex for simple use cases
Debugging can be challenging with nested chains
Performance overhead compared to direct API calls
Documentation sometimes lags behind new features
Abstractions can sometimes hide important details
Frequent breaking changes between versions
Can be overly complex for simple use cases
Debugging can be challenging with nested chains
Performance overhead compared to direct API calls
Documentation sometimes lags behind new features
Abstractions can sometimes hide important details
What problems is the product solving and how is that benefiting you?
LangChain significantly reduces the complexity of building production-ready AI applications by providing pre-built components for common patterns like RAG, conversational memory, and agent workflows. It allows our team to switch between different LLM providers without rewriting code, which helps optimize costs and avoid vendor lock-in. The framework handles the complex orchestration of multi-step AI workflows, enabling us to build sophisticated applications that can reason through problems, use external tools, and maintain context across conversations. This has accelerated our development timeline from months to weeks for AI features. The built-in prompt templates and output parsers ensure consistent and reliable responses in production, while the memory management capabilities have been crucial for building stateful AI assistants that remember user context. LangChain's abstractions for vector stores and document loaders have simplified the implementation of RAG systems that query our proprietary data. Overall, it's transformed how quickly we can prototype and deploy AI solutions, though the learning curve was initially steep.
Udith W.
Built advanced LLM apps with LangChain.
Reviewed on Aug 12, 2025
Review provided by G2
What do you like best about the product?
What I like best about LangChain is its flexibility to integrate models, data sources, and tools seamlessly, which made building and scaling complex LLM-powered workflows much faster in my projects.
What do you dislike about the product?
What I dislike about LangChain is that its rapid updates sometimes break existing code or change APIs, which can make maintaining long-term projects a bit challenging.
What problems is the product solving and how is that benefiting you?
LangChain solves the challenge of connecting LLMs with external data, tools, and workflows by providing a modular framework for retrieval, reasoning, and integration. This benefits me by allowing faster development of RAG pipelines, multi-agent systems, and AI applications without reinventing the orchestration logic, so I can focus more on solving domain-specific problems rather than low-level integration.