Sold by: LangChain
Deployed on AWS
LangSmith is an agent engineering platform to build, test, deploy and observe your agents. 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.
4.4
Overview
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LangSmith Observability
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LangSmith Deployments
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LangSmith Evaluations
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LangSmith Fleet
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Agent Development Lifecycle
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 is the framework agnostic agent engineering platform for observing, evaluating, and deploying agents. It's hard to build agents because you can't plan for every input, and LLMs decide every output on the fly at runtime.
- LangSmith Fleet is a no-code platform for creating and managing AI agents. It allows you to create agents from templates, connect your accounts, and let the agent handle routine work while you stay in control.
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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 | Description | Cost/unit |
|---|---|---|
Usage Fee | Units of metered usage as calculated based on platform activity | $0.01 |
Dimensions summary
You pay through a single usage-based dimension: metered Units tied to platform activity. There are no fixed tiers or seat charges on this listing. Instead, your bill scales with how much work your agents perform and how much data you store. Activity across services like observability tracing, deployments, engine runs, and sandboxes converts into normalized compute and storage units. The more you run and store, the more Units you consume. This keeps costs aligned with actual use, so you pay only for the platform activity you generate each billing period.
Top-of-mind questions for buyers
Your bill uses two normalized units. A Compute Unit measures work done — compute, memory, and model usage across services like engine runs, deployments, and sandboxes. A Storage Unit measures data stored or managed, including traces. Each service meters at its own rate, then rolls up into these units.
Charges accrue only while resources run. Serverless deployments scale to zero when idle, so you pay only during active runtime. Sandboxes are billed per second and shut down automatically using configurable time-to-live settings. Stopping or deleting these resources ends the metered charges tied to them.
Both compute and storage charges apply at the same time. Trace volume and storage consume Storage Units for observability workloads. Engine runs, deployments, fleet activity, and sandboxes consume Compute Units. One engine run can consume roughly 5–30 Compute Units, depending on trace volume and application complexity.
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Customer reviews
Daniel R.
Helpful tracing and debugging tools for LLM workflows
Reviewed on Aug 27, 2026
Review provided by G2
What do you like best about the product?
The tracing view is the strongest part. Being able to see every step of a chain or agent run, prompts, tool calls, intermediate outputs, and latency, makes debugging much faster. I also like the dataset and evaluation features; they help me test prompt changes more systematically instead of guessing.
What do you dislike about the product?
The UI can feel heavy when you have a lot of traces open. Filtering and finding specific runs sometimes takes longer than it should. Pricing also adds up quickly once you move beyond light usage, especially if multiple people on the team need access.
What problems is the product solving and how is that benefiting you?
Before LangSmith, debugging LLM workflows meant digging through logs or print statements. Now I can quickly see where a prompt or a tool call is failing and fix it. It has reduced the time I spend investigating issues and made it easier to improve the reliability of our AI features.
Shubhamm D.
Clear Observability and Evaluation Tools That Improve LLM App Quality
Reviewed on Aug 27, 2026
Review provided by G2
What do you like best about the product?
LangSmith makes it easy to trace, debug, evaluate, and monitor LLM and agent applications. I especially like its clear observability and evaluation tools, which help identify issues and improve application quality.
What do you dislike about the product?
Some advanced features can take time to learn, and the platform may feel a bit complex for beginners. I’d also like to see simpler setup and more straightforward pricing options.
What problems is the product solving and how is that benefiting you?
LangSmith helps me evaluate and monitor LLM and agent applications by providing clear traces, debugging insights, and evaluation metrics. This makes it easier to identify issues, improve reliability, and deliver better-performing AI applications
Information Technology and Services
Easy to Use and Helpful for AI Development
Reviewed on Aug 27, 2026
Review provided by G2
What do you like best about the product?
I like that it’s easy to use and helps me see what’s happening with my AI apps.
What do you dislike about the product?
Sometimes it feels a little confusing because there are many features and it takes some time to understand everything. Once you get used to it, it’s easier to use.
What problems is the product solving and how is that benefiting you?
It helps me find and understand issues in my AI apps. I can see what went wrong and fix problems faster, which saves me a lot of time.
Mahika S.
Easy AI App Tracking and Debugging, but a Steep Learning Curve
Reviewed on Aug 26, 2026
Review provided by G2
What do you like best about the product?
what i like about langsmith is how easy it makes it to track, test, and improve ai applications. the interface is simple, and seeing what's working or going wrong helps me fix issues much faster. the value is good for the price. it saves a lot of time when debugging and monitoring ai workflows, and the visibility it provides makes it easier to catch issues before they become bigger problems.
What do you dislike about the product?
the learning curve can be a little steep at first especially with the more advanced features. there's also a lot of information to go through, which can feel overwhelming when you're just getting started.
What problems is the product solving and how is that benefiting you?
Langsmith helps me understand whats happening inside my ai workflow, spot errors and track performance. it saves time when debugging and makes it easier to improve responses, test changes and keep the overall system reliable.
Computer Software
End-to-End Visibility That Makes AI Debugging Concrete
Reviewed on Aug 26, 2026
Review provided by G2
What do you like best about the product?
I like best about langsmith its the end to end visibility into what an AI application is actually doing. Langsmith lets you inspect the whole execution trace-LLM calls tool calls retrieval steps intermediate output latency and error so debugging become much more concrete. You can turn production traces into datasets and use offline/online evals to measure improvement rather than replying on intuition.
What do you dislike about the product?
Langsmith is very good but I dislike a few things about is langsmith works with other framework but it's smoothest experience is clearly with langchain/langGraph. If you later move to a costom agent loop a another framework you lose same of that it just works advantage. If you need is show me the LLM calls latency tokens and and errors langsmith datasets evaluation prompts management experiments etc can be more machinery than you actually need.
What problems is the product solving and how is that benefiting you?
Langsmith is trying to make AI application observable testable and improvable more like traditional software. A normal software program is relatively deterministic if a function breaks you can inspect the input code stack trace and output. LLM application are much messier an agent might. Receive a user question. Decide to search the web. Retrieve several documents.