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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Customer reviews
Computer Software
Model-Agnostic Flexibility and Rich Integrations for Building RAG and Agent Workflows
Reviewed on Jul 10, 2026
Review provided by G2
What do you like best about the product?
LangChain's model-agnostic architecture is a game-changer — we can swap LLM providers without rewriting our application logic. The 100+ native integrations for vector databases, search APIs, and data sources dramatically reduce boilerplate when building RAG pipelines. LangGraph has been especially valuable for orchestrating multi-step agentic workflows with conditional logic and stateful execution.
What do you dislike about the product?
The heavy abstractions can make debugging tricky — when something breaks deep in the chain, tracing the root cause takes more time than it should. Documentation often lags behind the frequent updates, and breaking API changes between versions have caused us real headaches mid-project. A steeper learning curve for developers new to LLMs is also worth noting.
What problems is the product solving and how is that benefiting you?
Before LangChain, building AI-powered features meant writing custom integration code for every LLM provider and data source. Now we spin up RAG pipelines and intelligent agents in a fraction of the time. We've cut our AI feature development time by roughly 60% and can serve over 50k monthly requests reliably. It's become the backbone of our AI product development.
Retail
LangChain Brings Structure and Clarity to Complex LLM App Workflows
Reviewed on Jul 09, 2026
Review provided by G2
What do you like best about the product?
What I like most is that LangChain makes it easier to structure LLM application code once things go beyond a basic prompt-response flow. In our case, it’s been useful for handling message types, tool wiring, model integration, and keeping agent-related logic from turning into a mess too quickly. It also plays well with the broader ecosystem around agent workflows, which made it easier to build on top of instead of inventing our own abstractions too early.
What do you dislike about the product?
The main downside is that it can add abstraction faster than it adds clarity. Once you have multiple layers involved, debugging can get harder than it should be, especially when behavior is split across model wrappers, message objects, tools, and orchestration logic. It’s powerful, but you do have to stay fairly close to the framework’s evolution because APIs and best practices shift pretty often.
What problems is the product solving and how is that benefiting you?
LangChain is helping solve the problem of turning LLM features into maintainable application code. Instead of managing prompts, tool calling, structured messages, and integrations in a fully custom way, we can rely on shared patterns that make the system easier to extend. The benefit has been faster iteration and a cleaner path from prototype logic to something that feels more production-ready.
Ayush C.
LangChain: Flexible, Student-Friendly Framework That Speeds AI Development
Reviewed on Jun 30, 2026
Review provided by G2
What do you like best about the product?
As a student, what I like most about LangChain is how it makes building AI applications much easier. Instead of writing a lot of custom code to connect language models, APIs, and databases, I can use its ready-made components and focus on building the actual project. I also like how flexible it is, as it supports different LLMs, vector databases, and tools in one framework. This has helped me complete AI projects faster and understand concepts like RAG, agents, and prompt chaining in a practical way. Even though it takes some time to learn, once I got familiar with it, my development process became much more organized and efficient. Overall, LangChain has been a valuable framework for learning and experimenting with modern AI applications.
What do you dislike about the product?
as a student i found the learning curve a bit steep , especially when working with agents and complex chains. the documentation can feel overwhelming and frequent updates sometimes require changing existing code.
What problems is the product solving and how is that benefiting you?
Before using LangChain, connecting LLMs with external tools,databases and APIs required a lot of custom code.LangChain solved this by providing ready-made components for AI workflows. It helped me build AI applications faster,reduce development time and focus more on project logiv instead of integration.
Ritesh G.
LangChain Speeds Up Building AI Apps with Great Integrations
Reviewed on Jun 26, 2026
Review provided by G2
What do you like best about the product?
As a student, I found LangChain very useful for building AI Applications. The documentation and modular design made it easier to connect LLMs, databases, and APIs. Integration with popular AI models and vector databases is excellent. Performance is good, though debugging complex chains can be difficult. Since it's open source, it offers great value for learning. Overall, it helped me build AI projects much faster
What do you dislike about the product?
I found the learning curve a bit steep, especially when working with agents and complex chains. The documentation can feel overwhelming at beginning, and frequent updates sometimes require changing existing code
What problems is the product solving and how is that benefiting you?
Before using LangChian, connecting LLMs with external tools, databases, and APIs required a lot of custom code. LangChain solved this by providing ready-made components for AI workflows. It helped me build AI applications faster, reduce development time and focus more on project logic instead of integration
Information Technology and Services
Great Abstraction for Prompting, Tool Calls, and LangSmith Observability
Reviewed on May 25, 2026
Review provided by G2
What do you like best about the product?
abstraction over manual prompting, tool calls and also observability via langsmith
What do you dislike about the product?
debugging is painful at times and performance overhead
What problems is the product solving and how is that benefiting you?
it provides me with an enhanced abstracted layer over basic prompting and tool calls