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LangSmith Agent Engineering Platform (SaaS)
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.
Reviews (75)
Vamshi M.
Easy to use for debugging and monitoring AI applications
Reviewed on Aug 28, 2026
Review provided by G2
What do you like best about the product?
I like how LangSmith helps me trace, debug, and monitor AI applications. It makes it much easier to understand model outputs and spot issues during development, so I can troubleshoot more effectively as I build. LangSmith provides good value for AI development and debugging. Its tracing, monitoring, and evaluation features help save development time and make it easier to identify and fix issues.
What do you dislike about the product?
The interface can feel a bit complex at first, and it may take new users some time to fully understand all of the tracing and monitoring features.
What problems is the product solving and how is that benefiting you?
LangSmith helps me trace and debug AI applications, spot issues in model outputs, and keep an eye on performance. Overall, it makes development and testing smoother, and it saves me time when I need to troubleshoot.
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.
Jeet S.
Excellent Tracing and Performance Debugging of Agentic Workflow
Reviewed on Aug 26, 2026
Review provided by G2
What do you like best about the product?
When I use LangSmith, what I like most is the tracing and debugging I can do at every step—prompts, model responses, tool calls, latency, and errors. The UI/UX makes even complex traces fairly easy to follow, and integrating it into my software or workflow was straightforward by following their support documentation. The biggest benefit for me has been performance debugging: I can quickly identify which step in an agentic workflow is misbehaving when I switch AI models (for example, from Gemini 3.0 to 3.5 Flash). LangSmith helps me see how the model is behaving, where things go wrong, and what I need to fix.
What do you dislike about the product?
Langsmith’s portal is something I’ve been using, and to be honest, even on the free tier I found it hard to navigate the Langsmith web application. For the first few weeks, my main struggle was simply figuring out what to do and where to start. I’d really like to see more practical examples, especially for debugging complex multi-agent workflows and for setting up AI evaluations.
What problems is the product solving and how is that benefiting you?
For me the biggest benefit has been Performance debugging as i mentioned before. I can in depth review whats wrong in the Production , Prompt tweaking or the my all time bug mis behaving of model response is also evaluated here. I am currently on a free-tier and from Pricing perspecitive my stake holder has no issues using it on local deploys and working and evaluating before production.
Information Technology and Services
Really great tool for tracing and debugging LLMs
Reviewed on Aug 25, 2026
Review provided by G2
What do you like best about the product?
The absolute best part about LangSmith is how it handles prompt debugging and real-time tracing. When you are building complex LLM applications, things can get messy pretty fast, but this tool gives you a clear visual breakdown of every single step in the chain. I really love that I don't have to guess where an error or a latency issue is coming from anymore. It literally shows you the exact input, output, and token usage for each run, which has made our testing process so much smoother and saved us countless hours of manual troubleshooting.
What do you dislike about the product?
The pricing model is probably the biggest downside, especially for smaller teams or individual developers who are just experimenting or working on side projects. It can get expensive pretty quickly as your token usage and trace volume grow. Apart from that, because they roll out new features and UI updates so fast, the official documentation sometimes lags behind a bit. There were a couple of times where I had to spend extra time looking through forums or trial-and-error just to figure out how to configure a specific part of the SDK because the online guides weren't fully updated yet.
What problems is the product solving and how is that benefiting you?
It solves a major problem for us by giving full visibility into our LLM application pipelines. Before using this, it was a nightmare to track down why a specific API call failed or why certain prompt were taking too long to respond. Now, we can easily monitor latency, count tokens accurately, and catch bugs before they reach production. It benefits me directly because I don't have to spend hours checking server logs manually anymore, so our workflow is much faster.
Computer Software
Best-in-Class LLM Tracing & Observability for Faster Debugging and Optimization
Reviewed on Aug 24, 2026
Review provided by G2
What do you like best about the product?
The tracing and observability is genuinely best in class for LLM applications. Being able to see every step of a chain or agent run, including the exact prompts sent, model outputs, latencies, and token counts, makes debugging and optimization dramatically easier compared to guessing from logs. For an engineer building complex multi-step pipelines, that level of visibility is invaluable. The ability to capture runs and turn them into evaluation datasets is also a standout feature since it closes the loop between production behavior and offline testing in a way that feels practical rather than theoretical.
What do you dislike about the product?
The pricing model can feel steep once you move beyond experimentation into production-scale tracing, especially for high-volume applications where trace costs add up quickly. The UI, while powerful, has a learning curve and can feel cluttered when navigating deeply nested traces across complex agent runs.
What problems is the product solving and how is that benefiting you?
For my work, the immediate benefit is faster debugging. What used to require adding print statements and mentally reconstructing a chain execution is now a single trace view. Beyond debugging, being able to track latency and token usage per step helps make informed optimization decisions, whether that means caching, shortening prompts, or swapping a model. The dataset and evaluation features also help build more confidence before shipping changes, which matters when LLM behavior can be subtle and hard to catch without systematic testing.
Hrithik Y.
LangSmith makes Debugging and Monitoring Much Easier
Reviewed on Aug 23, 2026
Review provided by G2
What do you like best about the product?
I like LangSmith because it makes debugging AI apps much easier. The tracking is really helpful and quick to without digging tons of logs.
What do you dislike about the product?
The learning curve can be a little steep when you are getting started, especially with all the different tracing and evaluation options. Pricing can also become a concern as usage grows, particularly for smaller teams or personal projects.
What problems is the product solving and how is that benefiting you?
LangSmith helps me understand what is actually happening inside my AI applications, especially when responses do not behave as expected. It saves me time by making debugging, testing, and tracking model performance much easier.