--- title: LangSmith Reviews meta_title: 'LangSmith Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 80 reviews by the users' company size, role or industry to find out how LangSmith works for a business like yours. aggregate_rating: rating_value: 4.4 review_count: 80 scale: '5' date_modified: '2026-09-15' parent_category: name: Monitoring url: https://www.g2.com/categories/monitoring --- # LangSmith Reviews **Vendor:** Langchain **Category:** [AI Agent Observability Software](https://www.g2.com/categories/ai-agent-observability) **Average Rating:** 4.4/5.0 **Total Reviews:** 80 **AI Verified:** At least 10 G2 reviewers have confirmed using this product's AI features and functionality. ## About LangSmith LangSmith Observability gives you complete visibility into agent behavior. ‍ Trace your preferred framework or integrate LangSmith with any agent stack using our Python, Typescript, Go, or Java SDKs. ## LangSmith Reviews ### 1. Saved me from hours of blind debugging on my AI backend **Rating:** 4.0/5.0 stars **Reviewed by:** Ashish R. | Student Developer, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** September 02, 2026 **What do you like best about LangSmith?** Honestly the most important thing is being able to look at what my prompts are actually doing behind the scenes. As I was integrating AI capabilities into my web app backend, I was blindly making too many attempts with APIs. LangSmith offers an excellent trace of the whole process in a very visually appealing way. This is going to save me lots of time because I am going to know exactly which particular part went wrong instead of looking at some normal errors from the console. **What do you dislike about LangSmith?** On the other side, the dashboard can get a bit messy type at the first time around logging into it. There is simply too much happening with all the traces, and I had trouble finding my token usage stats at first. Also, configuring the environment variables to properly connect to my local web application was a bit time taken or bothered me on the first day around. It seems that the UI was designed for large enterprises and not individuals trying to prototype and debug things. **What problems is LangSmith solving and how is that benefiting you?** I can say it majorly solves the 'black box' problem that comes with using LLMs. Whenever creating the backend of Marketplus(My Major Project) and any AI chain didn't work or produced an undesired output, identifying the error used to be a pain. But with the help of LangSmith, I can get a clear view of all steps taken in the execution of the prompt. The first advantage here is saving the time during debugging. Another benefit is keeping track of the tokens, especially during the development of the web application. ### 2. Makes AI agent debugging much easier **Rating:** 4.5/5.0 stars **Reviewed by:** Varun S. | Software Developer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** September 02, 2026 **Describe the project or task LangSmith helped with:** LangSmith is a comprehensive platform designed to streamline AI agent development by integrating observability, debugging, and evaluation tools into a single interface. It offers robust integrations with popular frameworks like LangChain and LangGraph, as well as support for various model providers and APIs, making it adaptable to existing AI stacks. The platform excels in providing detailed execution traces that enhance the debugging process, allowing developers to inspect every aspect of an agent's workflow, from high-level traces to individual LLM calls and tool interactions. This level of detail aids in identifying performance bottlenecks and understanding the root causes of issues. LangSmith also connects tracing with evaluation and feedback mechanisms, enabling systematic experimentation and quality monitoring. Users can leverage traces to evaluate changes, compare outputs, and incorporate human feedback, ensuring continuous improvement of AI agents. The user interface is designed to facilitate a seamless debugging experience, with intuitive navigation that allows users to drill down into specific steps and inspect inputs, outputs, and performance metrics. This comprehensive approach reduces the need for custom observability infrastructure and simplifies the process of reproducing and investigating production issues. Overall, LangSmith stands out as a valuable tool for AI agent development, offering a unified platform that combines observability, debugging, and evaluation capabilities to enhance the efficiency and effectiveness of AI workflows. **What do you like best about LangSmith?** What I like best about LangSmith is that it brings most of the AI development and observability workflow into one place. The UI/UX is especially useful once you get familiar with the different views, because I can go from a high-level trace down into individual LLM calls, prompts, tool calls, errors, latency and token usage when debugging an agent. The integrations are another strong point, particularly with LangChain and LangGraph, while the available SDKs and integrations with providers such as OpenAI and Anthropic make it easier to fit into an existing stack. From a performance perspective, the trace search and filtering experience is fast enough to work with large amounts of agent activity without feeling like the observability layer is getting in the way. The AI and evaluation side is also valuable because LangSmith goes beyond simply collecting logs — you can use traces for evaluations, compare outputs, add human feedback and monitor quality in production. For pricing/ROI, the biggest value is the time saved when diagnosing agent issues and evaluating changes instead of having to build and maintain that tooling internally. The free tier also makes it relatively easy to start small before committing further. Onboarding and documentation are another positive; getting basic tracing running is straightforward, although the platform has enough concepts and features that there is some learning involved. Overall, the combination of observability, debugging, integrations, evaluations and performance is what makes LangSmith stand out for AI agent development. **What do you dislike about LangSmith?** The main thing I dislike about LangSmith is that there is a lot going on, so the UI/UX can feel overwhelming at first. There are projects, runs, traces, datasets, evaluators, prompts and other concepts to understand, and it takes some time before the workflow feels natural. I would like to see a simpler default experience for teams that mainly need tracing and debugging. Pricing/ROI is probably the other area I would improve. The basic pricing is easy enough to understand, but once you start looking at trace volume, retention and usage-based services, it becomes harder to estimate the real monthly cost. The current plans combine seat pricing with usage-based charges, so having stronger cost forecasting and clearer usage warnings would make budgeting easier. For integrations, LangSmith supports a broad range of frameworks and providers, which is good, but the experience is naturally strongest when your stack fits closely with the LangChain ecosystem. I would like more equally polished plug-and-play integrations and examples for teams using completely different agent stacks. Performance is generally strong, but very detailed traces can become harder to navigate simply because there is so much information on screen. Better ways to summarize large traces and surface the important events first would improve the experience. For AI/intelligence, the evaluation and debugging capabilities are powerful, but there is still some configuration involved before they become really useful for a specific application. More opinionated recommendations for evaluators, thresholds and common failure patterns would make the platform more approachable. Finally, support/onboarding could be more guided for first-time users. The documentation is useful, but because LangSmith has a broad feature set, a more structured “start here” path for different use cases would reduce the initial learning curve. **Recommendations to others considering LangSmith:** To improve LangSmith, I recommend simplifying the initial user experience to make it less overwhelming for new users. This could involve creating a more streamlined default interface that focuses on the core functionalities of tracing and debugging, which are essential for most teams. Additionally, enhancing the pricing model to provide clearer cost forecasting and usage warnings would be beneficial. This would help users better understand the potential costs associated with trace volume, retention, and usage-based services, making budgeting more straightforward. For integrations, expanding the range of polished plug-and-play options and providing more examples for diverse agent stacks would be advantageous. This would ensure that teams using different frameworks can seamlessly integrate LangSmith into their existing workflows. Improving the navigation of detailed traces by summarizing large traces and highlighting important events first would enhance the user experience. This would make it easier for users to focus on the most critical information without being overwhelmed by the volume of data. In terms of AI/intelligence, offering more opinionated recommendations for evaluators, thresholds, and common failure patterns would make the platform more approachable. This would help users configure the evaluation and debugging capabilities more effectively for their specific applications. Finally, providing more guided support and onboarding for first-time users would be beneficial. A structured "start here" path for different use cases would reduce the initial learning curve and help users quickly become proficient with LangSmith. **What problems is LangSmith solving and how is that benefiting you?** Before using LangSmith, it can be difficult to understand what is happening inside an AI agent once a request moves through multiple prompts, LLM calls, tools and intermediate steps. Basic application logs usually show that something failed, but not why. LangSmith solves that by giving a much clearer view of the complete execution flow through traces, which makes debugging and troubleshooting much faster. It also helps with AI quality and intelligence by connecting tracing with evaluations, datasets and feedback. Instead of checking outputs manually every time a prompt or agent workflow changes, I can use the same traces to evaluate changes and identify regressions. That makes experimentation more systematic and gives a better picture of whether an agent is actually improving. The UI/UX makes the debugging workflow easier because I can drill from a high-level run into individual steps and inspect inputs, outputs, latency and token usage. From a performance perspective, having this information in one observability layer makes it easier to identify slow or expensive parts of an agent workflow. The integrations are also important because LangSmith can fit into an existing AI stack rather than requiring a completely separate development workflow. It works particularly well with LangChain/LangGraph, while support for different model providers and APIs makes it useful as the application evolves. The biggest ROI comes from reducing the amount of custom observability and evaluation infrastructure that would otherwise have to be built and maintained internally. It also makes production issues easier to reproduce and investigate, which can reduce the time spent debugging. For support and onboarding, the documentation and examples help get the basic tracing workflow running fairly quickly. There is still a learning curve because the platform covers a lot of functionality, but once the core concepts are understood, it becomes a useful part of the development workflow. Overall, the main benefit is having one place to understand, debug, evaluate and monitor AI applications instead of piecing those capabilities together from separate logging, testing and monitoring tools. ### 3. Powerful Debugging and Token Cost Insights for Multi-Agent Workflows **Rating:** 4.5/5.0 stars **Reviewed by:** Dibyaranjan N. | CAD ENGINEERING, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 30, 2026 **What do you like best about LangSmith?** What I like most about Langsmith is its debugging features. In my application, I connect with more than one agent, so it helps me distinguish between the AI agent and the agent’s internal mechanisms, and check whether they are returning the correct documents or not. It also estimates the token cost that the agent uses. **What do you dislike about LangSmith?** What I dislike about LangSmith is that it can feel overwhelming when you first start using it. There are a lot of concepts—projects, traces, runs, datasets, experiments, and evaluations—so it takes time to understand how everything fits together. Sometimes it also creates problems when the agent workflow gets more complex, and I’m trying to track down a particular answer. **What problems is LangSmith solving and how is that benefiting you?** The best part is that it tracks all my agents connected to my application. It also helps with debugging when something goes wrong or incorrect data comes in whether the issue is from the LLM or from my documents—so I can easily find out the cause. After I deploy the project, it keeps monitoring my application, estimates what cost it takes, and shows the running status of the project. ### 4. Intuitive End-to-End Tracing That Makes LLM Debugging Much Easier **Rating:** 4.0/5.0 stars **Reviewed by:** Bhumil J. | Senior Software Engineer, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 29, 2026 **What do you like best about LangSmith?** What I like most about LangSmith is how much easier it makes debugging AI applications. With traditional apps, you can usually check the logs and quickly understand what went wrong, but with LLM workflows it’s often hard to tell whether the problem is in the prompt, retrieval, the model’s response, or some intermediate step. LangSmith makes that much clearer by letting you trace the entire flow end to end. The UI is also very good and intuitive, which makes it easier to navigate and use while you’re troubleshooting. **What do you dislike about LangSmith?** One thing I don’t love is that LangSmith can feel a bit overwhelming at first, especially when I’m juggling multiple traces, datasets, and evaluations. There’s definitely a learning curve before it becomes clear how to organize everything in a way that feels efficient and consistent. As an application grows, I also wish there were simpler ways to quickly surface the most important issues or bottlenecks, without having to dig through several traces to find what matters. That said, these are relatively minor concerns overall, and the observability LangSmith provides still makes it very useful for building and debugging LLM applications. **What problems is LangSmith solving and how is that benefiting you?** LangSmith helps address the challenge of understanding and debugging complex LLM workflows. It provides clear visibility into prompts, model calls, retrieval, and intermediate steps, which makes it much easier to pinpoint where things go wrong. As a result, I save development time and can experiment with, iterate on, and improve LLM applications with more confidence. ### 5. Excellent Tracing and Performance Debugging of Agentic Workflow **Rating:** 4.0/5.0 stars **Reviewed by:** Jeet S. | Data Engineer, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 25, 2026 **What do you like best about LangSmith?** 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 LangSmith?** 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 LangSmith 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. ### 6. Per-Step Trace Waterfall Makes Debugging Fast and Clear **Rating:** 4.5/5.0 stars **Reviewed by:** Nishant S. | Director of product, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google using a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 20, 2026 **What do you like best about LangSmith?** The per-step trace waterfall — planning, semantic context, SQL execution, response generation each show up as separate spans with their own inputs/outputs, so debugging a bad answer means looking at exactly which step failed instead of guessing from the final output. It also plugs in cleanly via OTLP even for a non-LangChain agent stack, and keeping dev/eval traces in a separate project from prod kept iteration risk-free. **What do you dislike about LangSmith?** Span deduplication is global by span identity, even across project deletes, so re-forwarding a trace means remapping every span ID or it silently gets dropped. The attribute schema is also under-documented — keys like input.value/output.value work but the seemingly equivalent langsmith.span.inputs/outputs silently don't, which took trial and error to figure out. **What problems is LangSmith solving and how is that benefiting you?** It gives us a way to inspect exactly what an agent did at each step without touching the live production agent or its UI — critical when iterating on prompts/config against a dev clone. That per-step breakdown turned "why did this answer come out wrong" from a guessing exercise into a quick lookup, cutting iteration time on agent tuning significantly. ### 7. Excellent Observability for Building Production AI Applications ⭐ **Rating:** 5.0/5.0 stars **Reviewed by:** Srinivas P. | Engineering Manager, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google using a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 07, 2026 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about LangSmith?** We've had a very positive experience using LangSmith to develop and monitor our AI workflows. Our primary use case is analyzing product page images, identifying UI controls, and generating structured content using LLMs. LangSmith gives us excellent visibility into every request, making it much easier to debug prompts, inspect model outputs, and understand where issues occur in the pipeline. The UI is intuitive and well organized, allowing us to quickly navigate traces, compare executions, and investigate failures without digging through application logs. Integration with our existing LLM stack was straightforward, and we were able to start instrumenting our workflows with minimal effort. Performance has been reliable even as our workload has grown, and the detailed observability has significantly reduced the time spent debugging and tuning prompts. From an ROI perspective, the time savings in development and troubleshooting have easily justified adopting the platform. The AI-focused tooling—such as prompt tracing, execution history, and evaluation capabilities—helps us iterate faster and improve output quality with confidence. Documentation and onboarding were clear enough to get started quickly, and the available resources made it easy to adopt best practices. Overall, LangSmith has become an essential part of our AI development workflow by providing the transparency and tooling needed to build, test, and maintain production LLM applications. **What do you dislike about LangSmith?** One area that could be improved is the learning curve around some of the more advanced features, such as evaluations and experiment management. While the core tracing experience is straightforward, it takes some time to fully understand and make the most of the broader feature set. It would also be helpful to have more built-in dashboards and customizable reporting for monitoring production metrics at a higher level, instead of relying primarily on individual trace analysis. Other than that, we've had a solid experience using the platform. **What problems is LangSmith solving and how is that benefiting you?** LangSmith helps us solve one of the biggest challenges in building AI applications: understanding why an LLM produced a particular output. We use it for workflows that analyze product page images, identify UI controls, and generate structured content. The detailed traces allow us to inspect prompts, model responses, latency, and execution steps, making it much easier to debug issues and improve prompt quality. Before using LangSmith, troubleshooting often meant piecing together information from application logs. Now, we have a centralized view of each request, which has significantly reduced debugging time and accelerated development. It also gives us greater confidence when deploying prompt changes because we can evaluate and compare results more systematically. Overall, it has improved the reliability of our AI workflows while reducing the engineering effort required to maintain them. ### 8. Crystal-Clear LLM Tracing and Evaluation That Takes the Guesswork Out of Debugging **Rating:** 4.5/5.0 stars **Reviewed by:** Harshul S. | Sr tech support, Information Services, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** August 17, 2026 **What do you like best about LangSmith?** What I like best about LangSmith is how clearly it shows what’s happening inside an LLM workflow. The tracing and evaluation tools make it easy to understand where things break, why a model behaved a certain way, and how to improve it. It removes a lot of guesswork from debugging. **What do you dislike about LangSmith?** The only thing I dislike is that some parts of LangSmith feel a bit heavy when you’re trying to move fast. The tracing is great, but occasionally the interface feels cluttered, and finding specific runs or datasets takes more clicks than expected. It’s powerful, but not always the quickest to navigate. **What problems is LangSmith solving and how is that benefiting you?** LangSmith solves the problem of not knowing what’s actually happening inside an LLM workflow. Instead of guessing why a model responded a certain way or where a chain broke, it gives clear traces, evaluations, and dataset visibility. The benefit is faster debugging, more predictable behavior, and a much smoother iteration cycle. ### 9. Excellent observability for debugging LLM and agent workflows **Rating:** 4.5/5.0 stars **Reviewed by:** Md A. | Lead Consultant, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Source: Organic:** Organic review. This review was written entirely without invitation or incentive from G2, a seller, or an affiliate. **Reviewed Date:** August 17, 2026 **What do you like best about LangSmith?** What I like best about LangSmith is the visibility it provides into what is happening inside an LLM or agent workflow. Instead of only looking at the final response, I can inspect the execution and understand where a particular step went wrong. This is especially useful when a workflow involves multiple model calls or tool interactions, because debugging those issues from the final output alone can be difficult. I also like that it gives a more structured way to evaluate and troubleshoot AI applications rather than relying entirely on application logs and manual testing. **What do you dislike about LangSmith?** The main thing I dislike about LangSmith is that it can take some time to get comfortable with all the tracing, evaluation, and observability concepts. When a workflow becomes more complex, there can be a lot of information to go through, and finding the exact issue is not always immediately obvious. I also think the experience could be simpler for developers who just want quick debugging without having to spend much time configuring or understanding the observability side of the platform. **What problems is LangSmith solving and how is that benefiting you?** LangSmith solves one of the harder parts of building LLM and agent applications: understanding what is actually happening during execution. With multi-step workflows, looking only at the final response makes it difficult to identify whether the problem came from the model, a prompt, a tool call, or another step in the workflow. The tracing and observability capabilities give me a clearer view of individual runs, which makes debugging and troubleshooting much easier. It also helps when testing changes to prompts or workflows because I can inspect executions rather than relying only on the final output. This saves time when trying to understand unexpected agent behavior and improve the workflow. ### 10. Excellent AI Debugging and Evaluation Platform **Rating:** 4.5/5.0 stars **Reviewed by:** Chaitrali M. | Student btech AI&DS, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 13, 2026 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about LangSmith?** LangSmith offers excellent tracing and debugging tools, making it much easier to build, test, and refine LLM applications. The interface is well organized, and the detailed execution traces help quickly identify issues in prompts, tools, and workflows. It integrates smoothly with LangChain and other AI frameworks, while the evaluation and monitoring features make it easy to compare prompt versions and improve model performance over time. The documentation and onboarding resources are also helpful, allowing teams to become productive quickly and build more reliable AI applications. **What do you dislike about LangSmith?** Initial setup and configuration can take time, especially for users who are new to LLM development workflows. The platform has a learning curve because of its advanced tracing, evaluation, and monitoring features, and the interface can feel overwhelming at first. More beginner-focused tutorials and a simplified onboarding experience would make adoption easier. **What problems is LangSmith solving and how is that benefiting you?** LangSmith helps me identify issues in LLM applications by offering tracing, debugging, prompt evaluation, and performance monitoring in one place. It cuts down development time, improves response quality, and makes it easier to test and optimize AI workflows before deployment. ### 11. Helpful tracing and debugging tools for LLM workflows **Rating:** 4.0/5.0 stars **Reviewed by:** Daniel R. | Data analyst, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Source: G2 invite:** Invitation from G2. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** August 27, 2026 **What do you like best about LangSmith?** 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 LangSmith?** 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 LangSmith 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. ### 12. Clean Interface and Powerful Tracing for Understanding LLM Apps **Rating:** 4.5/5.0 stars **Reviewed by:** Anson D. | Software QA, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 30, 2026 **What do you like best about LangSmith?** I like that LangSmith makes it easier to track and understand what is happening with an LLM application. The tracing feature is useful when I need to check individual steps and find where an issue is coming from. The interface is fairly clean, and it doesn't take too long to get familiar with the main features. **What do you dislike about LangSmith?** There are quite a few features and settings, so it can take some time to understand everything when you are new to the platform. The pricing can also become a consideration as usage increases. **What problems is LangSmith solving and how is that benefiting you?** It helps with debugging and monitoring AI workflows by giving more visibility into requests, responses, and individual steps. Instead of trying to understand an issue only from application logs, I can use the traces to get a clearer picture of what happened. This makes troubleshooting and testing AI applications more manageable. ### 13. Easy LLM Tracing and Debugging with LangSmith **Rating:** 4.0/5.0 stars **Reviewed by:** Muhammad O. | Salesforce Business Analyst, Information Technology and Services, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** August 06, 2026 **What do you like best about LangSmith?** What I like most about LangSmith is how straightforward it makes tracing and debugging LLM workflows. The interface feels clean and well organized, and it’s easy to inspect prompts, responses, and each execution step all in one place. This saves me time when I’m testing different prompt variations, and it helps me spot problems quickly. I also appreciate how well it integrates with the LangChain ecosystem, which makes it a practical tool for building, refining, and improving AI applications. **What do you dislike about LangSmith?** What I dislike most about LangSmith is that some of its advanced features can feel a bit overwhelming when you’re just getting started. It took me a while to wrap my head around the tracing and evaluation workflow, and I ended up leaning on the documentation more than I expected. I also feel that a few workflows could be streamlined to cut down on the number of steps involved. That said, once I got familiar with how everything fits together, the overall experience became much smoother. **What problems is LangSmith solving and how is that benefiting you?** LangSmith helps me troubleshoot and improve my LLM applications by making it easier to trace requests, inspect outputs, and pinpoint where issues occur. Rather than spending a lot of time manually debugging prompts or model responses, I can quickly see what happened in a given run and adjust accordingly. Overall, it has reduced my development time, improved response quality, and made it much more efficient to test and iterate on new prompt variations. ### 14. Excellent Observability and Evaluation Tools for Modern AI Workflows **Rating:** 4.5/5.0 stars **Reviewed by:** Chris B. | Client Services Director, Marketing and Advertising, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 03, 2026 **What do you like best about LangSmith?** LangSmith provides the visibility and control that modern AI workflows sorely need. The observability tools are great – every agent action, every model call, every retrieval step is logged in a nice, searchable trace. This has drastically reduced our debugging time, and improved our ability to diagnose issues such as hallucinations, latency spikes, or unexpected cost behavior.The evaluation workflows are just as impressive. With automated LLM-as-judge testing, side-by-side comparisons, and human review pipelines, it’s easy to keep quality high as you iterate quickly. **What do you dislike about LangSmith?** LangSmith is powerful and the learning curve is a little steep at first. If your team lacks experience with structured observability, then it may take some time to fully adopt the workflow. The cost might be a concern for smaller organizations, but the value becomes evident when operating at scale. A more beginner friendly onboarding content would make the ramp up smoother. **What problems is LangSmith solving and how is that benefiting you?** LangSmith solves the biggest operational challenges in AI deployment: lack of transparency, inconsistent quality, and unpredictable costs. With SmithDB, we can query millions of traces instantly, which is essential for compliance, debugging, and client reporting. The platform ensures our AI systems are reliable, measurable, and continuously improving — which directly strengthens the service quality we deliver to clients. ### 15. Excellent Agent Observability and Evaluations with Clear, User-Friendly Dashboards **Rating:** 4.5/5.0 stars **Reviewed by:** Robert S. | Chief Financial Officer, Sports, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 01, 2026 **What do you like best about LangSmith?** LangSmith provides excellent visibility into how AI agents act in production. The observability tools make debugging easy. The evaluation workflows ensure our models keep improving. I particularly appreciate the ability to integrate with different frameworks, whether it’s LangChain, OpenAI, or Anthropic. The platform fits well with all of them. The dashboards are clear and user-friendly. They help us feel confident in both performance and cost management. **What do you dislike about LangSmith?** The platform is powerful, but it can take some getting used to. New users might need some time to fully understand the structured observability and evaluation workflows. Pricing can seem high for smaller teams – but the ROI is clear when you scale. Would be nice to see a few more beginner friendly onboarding resources. **What problems is LangSmith solving and how is that benefiting you?** LangSmith helps us solve the hardest problems in deploying AI: hallucinations, unpredictable costs, and lack of transparency. With SmithDB, we can query millions of traces in seconds, which is critical for debugging and compliance. This has directly improved reliability, reduced downtime and strengthened customer trust in our AI-driven offerings. ### 16. LangSmith Transformed Our AI Workflow with Best-in-Class Observability **Rating:** 4.5/5.0 stars **Reviewed by:** Lincoln T. | Enterprise, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 01, 2026 **What do you like best about LangSmith?** LangSmith has revolutionized our approach to developing and operating AI applications. The observability capabilities of LangSmith cannot be beat being able to see the execution process of each agent run step by step has saved us many hours of troubleshooting. The evaluation functionality is impressive and allows us to perform automated testing and human-in-the-loop testing. I also love the smooth integration with different platforms such as LangChain, OpenAI, and Anthropic. **What do you dislike about LangSmith?** Learning curve can be quite steep when working with new teams. It will take some time before you can fully grasp how to make use of the observability process in its fullest. Prices can also be considered quite high for startups, although they are very valuable once you scale. **What problems is LangSmith solving and how is that benefiting you?** LangSmith assists us in overcoming some of the most difficult issues involved in deploying AI models: hallucination, unpredictable pricing, and lack of visibility. The SmithDB allows us to search through millions of traces in seconds, which is vital for debugging and compliance. LangSmith guarantees the reliability, cost-effectiveness, and consistent improvement of our agents. ### 17. Comprehensive Tracing and Monitoring That Makes LLM Debugging Easy **Rating:** 5.0/5.0 stars **Reviewed by:** jamsheed I. | Senior Civil Engineer, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Source: G2 invite:** Invitation from G2. This reviewer was not provided any incentive by G2 for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** July 30, 2026 **What do you like best about LangSmith?** Comprehensive tracing: View prompts, model responses, tool calls, and execution paths in one place, making debugging much faster. Built-in evaluation: Compare prompts, models, and application versions using automated and human evaluations to improve quality over time. Production monitoring: Track latency, errors, costs, and application performance to quickly identify issues after deployment. Dataset management: Create and reuse test datasets to validate changes before releasing updates. Developer-friendly integrations: Works seamlessly with LangChain and can also be used with custom LLM applications through its SDKs. **What do you dislike about LangSmith?** No dislike from my side. It is very helpful. **What problems is LangSmith solving and how is that benefiting you?** Difficult debugging: Instead of guessing why an AI response was incorrect, LangSmith provides detailed execution traces showing prompts, model outputs, tool calls, and intermediate steps. This makes it much faster to identify and fix issues. Inconsistent AI performance: Its evaluation framework lets you test prompts and models against benchmark datasets, helping ensure changes actually improve quality before deployment. Limited production visibility: LangSmith monitors latency, errors, token usage, and application behavior in production, making it easier to detect problems early and maintain reliability. Regression risk: By comparing application versions and running repeatable evaluations, it helps prevent updates from introducing new bugs or reducing response quality ### 18. Powerful LLM Tracing and Debugging with Smooth LangChain Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Muhammed A. | Technical Project Manager , Information Technology and Services, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** July 28, 2026 **What do you like best about LangSmith?** LangSmith has been valuable for tracing and debugging the LLM pipeline behind a customer support assistant for a logistics platform. It makes it much easier to see exactly what’s happening at each step of a conversation with cargo owners or drivers, instead of treating the model like a black box. The ability to log and inspect individual runs—prompts, intermediate steps, and final outputs—has helped us pinpoint where the model produces inaccurate or unhelpful responses, especially on shipment- and trip-related questions. The evaluation tools for comparing different prompt versions or model configurations side by side have also made it far more systematic to iterate on the assistant’s behavior, rather than relying on manual testing every time we make a change. Finally, it integrates smoothly with LangChain, which we used to build the underlying pipeline, so there was very little friction in setting up tracing across our existing codebase. **What do you dislike about LangSmith?** Pricing can get expensive as trace volume scales with a production support assistant handling real conversations daily. The UI feels a bit overwhelming when filtering through large volumes of trace data to find specific problematic conversations. Advanced features like custom evaluators are also underdocumented, requiring some trial and error to configure properly. **What problems is LangSmith solving and how is that benefiting you?** LangSmith has given us visibility into how our customer support assistant actually behaves in real conversations, instead of guessing why certain responses go wrong. This has made it much faster to catch and fix issues in how the model handles shipment and trip-related queries, improving the assistant's reliability over time without relying purely on manual testing. ### 19. LangSmith Makes LLM Debugging and Evaluation Fast, Clear, and Reliable **Rating:** 5.0/5.0 stars **Reviewed by:** Piyush P. | Senior Software Engineer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 28, 2026 **What do you like best about LangSmith?** What I like most about LangSmith is that it makes building LLM applications much easier to understand and improve. Instead of wondering why an AI response was incorrect, I can see the complete execution flow, including prompts, model calls, tool usage, and retrieved context. This makes debugging much faster and more efficient. The evaluation features are another highlight for me. I can compare different prompts or models, run experiments, and measure performance using the same set of test cases. It removes a lot of manual work and helps me make decisions based on actual results rather than assumptions. I also find the dataset management very useful. Real user conversations can be converted into evaluation datasets, making it easier to test improvements and ensure the application gets better over time. Since I work with LangChain and LangGraph, the integration feels natural. It gives a clear picture of how agents move through different steps, which helps identify performance issues and optimize workflows. Overall, LangSmith saves time, simplifies debugging, and provides the tools needed to build reliable AI applications. It has become an important part of my development workflow because it helps me improve quality with confidence instead of relying on trial and error. **What do you dislike about LangSmith?** Nothing as of now, as i have started using from 6 months only. **What problems is LangSmith solving and how is that benefiting you?** LangSmith solves one of the biggest challenges in LLM development, which is understanding why an application behaves the way it does. When working with AI agents, there are many moving parts such as prompts, retrieval, tool calls, and model responses. Without proper visibility, debugging becomes slow and frustrating. LangSmith provides detailed traces of every execution, making it easy to identify where things went wrong. It also solves the problem of evaluating changes. Earlier, testing prompt updates or switching models often depended on manual checks and personal judgment. With LangSmith, I can run structured evaluations on the same dataset and compare results objectively. This makes it easier to improve accuracy while avoiding regressions. Another benefit is monitoring production applications. By reviewing real user interactions, I can identify recurring issues, collect useful examples, and turn them into evaluation datasets for future testing. This creates a continuous improvement cycle instead of fixing problems one by one. For me, LangSmith has reduced debugging time, made experimentation more reliable, and increased confidence when deploying updates. Instead of relying on trial and error, I can use real execution data and measurable results to improve the quality, reliability, and performance of LLM applications. ### 20. Clean, Intuitive LLM Observability with Reliable Tracing and Evaluation **Rating:** 4.5/5.0 stars **Reviewed by:** Manav B. | QA Associate, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 07, 2026 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about LangSmith?** What I like best about LangSmith is its excellent visibility into the entire LLM application lifecycle. The UI is clean and intuitive, making it easy to inspect traces, debug issues, and compare prompt or model versions without feeling overwhelmed. It integrates seamlessly with the LangChain ecosystem and popular LLM providers, so getting started is straightforward. Performance has been reliable even when working with larger volumes of traces, and the built-in evaluation and monitoring capabilities make it much easier to improve AI application quality over time. The onboarding experience is well documented, and the platform provides the tools needed to quickly diagnose problems and iterate faster. While it represents an additional cost, the time saved on debugging, testing, and evaluation delivers strong ROI for teams building production AI applications. **What do you dislike about LangSmith?** While LangSmith is a powerful platform, there are a few areas that could be improved. The pricing can become expensive as projects scale, especially for smaller teams or startups with high trace volumes. Some advanced features have a learning curve, and first-time users may need more guided onboarding or in-product tutorials to take full advantage of the platform. Although the UI is generally well designed, navigating complex traces or large datasets can occasionally feel overwhelming. I would also like to see broader integrations with third-party observability and DevOps tools, along with more customizable dashboards and reporting options. Overall, these are areas for refinement rather than major drawbacks, and the platform continues to improve. **What problems is LangSmith solving and how is that benefiting you?** LangSmith solves one of the biggest challenges in developing AI applications: understanding, debugging, and improving LLM behavior. It provides detailed tracing, prompt evaluation, and monitoring, making it much easier to identify failures, optimize prompts, and measure the impact of changes before deploying them to production. This has significantly reduced the time spent troubleshooting complex workflows and increased confidence in application quality. The platform's integrations fit well into existing development workflows, while its evaluation capabilities help ensure AI responses are more accurate and consistent. Overall, LangSmith has improved our team's productivity, accelerated iteration cycles, and delivered a strong return on investment by reducing development time and helping us build more reliable AI applications. ### 21. All-in-One LLM Tracing, Debugging, and Evaluation with LangSmith **Rating:** 5.0/5.0 stars **Reviewed by:** Ishita S. | Student, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** July 22, 2026 **What do you like best about LangSmith?** What I like most about LangSmith is that it lets me trace, debug, and evaluate LLM applications all in one place. It offers detailed visibility into model behavior, which makes it easier to spot issues, refine prompts, and keep an eye on overall application performance. I also appreciate its smooth integration with LangChain, along with its support for experiment tracking. **What do you dislike about LangSmith?** One thing I dislike is that there’s a learning curve, especially for users who are new to LLM development and observability tools. Also, some advanced features are only available on paid plans, and setting up evaluations and tracing for more complex applications can take a while. **What problems is LangSmith solving and how is that benefiting you?** LangSmith addresses the challenge of debugging and evaluating LLM applications, which can be especially difficult because of the non-deterministic nature of AI models. It lets me track execution traces, compare prompts with model outputs, spot where failures occur, and evaluate overall application performance. For me, this makes it easier to build more reliable AI applications, spend less time debugging, and improve the quality and accuracy of my LLM-based solutions. ### 22. All-in-One AI Execution Tracking with Smooth Onboarding and Strong Analytics **Rating:** 4.5/5.0 stars **Reviewed by:** Jayanth C. | Software intern, Internet, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google using a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 17, 2026 **What do you like best about LangSmith?** LangSmith is a platform where we can see all of our AI executions and performance in one place. It has a lightweight theme but still comes with strong capabilities. It provides great support and smooth onboarding for my team. It supports AI/models, and it helps us understand how many tokens are used and how much time it takes for execution. LangSmith also makes it easy to trace errors during execution, and it offers better analytics tools to review what’s happening. The UI of LangSmith is pretty responsive, and it has flexible pricing for normal startups; it supports the best plans. We can quickly integrate in our app if we use Python. **What do you dislike about LangSmith?** Support for other programming languages isn’t very effective compared to Python. **What problems is LangSmith solving and how is that benefiting you?** Langsmith solves the problem of AI observability and tracing AI execution in a detailed way. It can clearly track what our input is and what output we get. It has been very helpful in my projects for understanding token usage for each request, as well as the error rate, success rate, and execution details. These metrics are useful for calculating and evaluating my performance. ### 23. Incredible observability tool that saves days of troubleshooting **Rating:** 4.0/5.0 stars **Reviewed by:** Arjun D. | QA Engineer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google One Tap using a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 16, 2026 **What do you like best about LangSmith?** The absolute best thing about LangSmith is the end-to-end trace visibility, especially when you are dealing with complex agent workflows or nested chains. Before using it, trying to figure out where a model hallucinated or why a response took ten seconds felt like total guesswork. With LangSmith, you get a clean visual tree that shows you every single step. You can see the exact prompt that went in, the raw token output, how a tool was called, and how much it cost. Another huge lifesaver is the testing and evaluation setup. Being able to turn production errors into test datasets with one click means you can run regression tests and immediately see if tweaking a prompt is going to break your system before you push it live. It completely takes the anxiety out of updating prompts and swapping models. **What do you dislike about LangSmith?** The biggest issue is definitely the price tag once you move past the hobby stage. The cost jumps up quickly because they charge you per user seat on top of your actual usage fees. If you want your product managers or QA team to log in and look at how prompts are performing, the bill gets expensive fast just based on headcount. Also, the data retention limit on the base plan is pretty short, only keeping your logs for 14 days. If you want to look back at old errors from a month ago, you have to pay a lot more. Lastly, it is clearly built to work best with LangChain. If you are using a different framework or just writing raw Python code, getting everything set up and hooked into their tracking system takes a lot more manual work. **What problems is LangSmith solving and how is that benefiting you?** The main problem LangSmith solves is the total black box nature of building AI apps. When you build complex AI agents, you are basically throwing prompts into the dark. When a user gets a bad answer or the app takes forever to reply, it is incredibly hard to pinpoint exactly where things went wrong without building your own massive logging infrastructure. LangSmith brings complete visibility to that mess by tracking every single step, prompt, tool call, token cost, and latency metric in a clean visual tree. This benefits me by completely removing the guesswork from debugging and upgrading our system. Instead of constantly worrying that changing a prompt will silently break twenty other things, I can use their evaluation tools to run tests against real production datasets before we push any updates live. It acts like a safety net that saves us days of manual troubleshooting and keeps our token costs from spiraling out of control. ### 24. Easy to use for debugging and monitoring AI applications **Rating:** 5.0/5.0 stars **Reviewed by:** Vamshi M. | Student, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** August 28, 2026 **What do you like best about LangSmith?** 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 LangSmith?** 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 LangSmith 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. ### 25. Informative UI and a Single Platform for Observability and Prompt Storage **Rating:** 5.0/5.0 stars **Reviewed by:** Piyush R. | Software Development Engineer-1, Information Technology and Services, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 29, 2026 **What do you like best about LangSmith?** I have been using Langsmith as my primary observability tool for years now. The UI is very informative. It's a single platform to view observe and even store prompts for my AI Agents. The datasets and experiments also help me better train my agents. **What do you dislike about LangSmith?** According to the pricing plans, rather than having role restrictions, Langsmith have number of collaborators as restriction, which becomes a little troublesome working with a bigger team **What problems is LangSmith solving and how is that benefiting you?** The main issue I run into when working with AI right now is observability—especially deep observability, where I can see how every step in my LangGraph or LangChain flow is behaving. Being able to store metadata so I can map it to my platform’s resources, and to pull prompts in real time, makes things much easier. The evaluation criteria available also help me test my agents using basic datasets. ### 26. Great tracing and debugging for AI agents, made my Claude SDK project much easier to work with. **Rating:** 4.5/5.0 stars **Reviewed by:** Williams P. | Data analytics, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 08, 2026 **What do you like best about LangSmith?** I really like how easy it is to trace and debug agents. I created a project called "Williams project," plugged in the Claude Agent SDK using their quickstart code, and right away I had full visibility into every call, token usage, and the complete flow. It turned confusing agent behavior into something I could actually understand and fix quickly. **What do you dislike about LangSmith?** The dashboard throws a lot at you when you're new, there are many tabs and options, so it took me a couple of sessions to feel comfortable. The free tier is helpful for getting started, but the costs add up if you do a lot of heavy testing. **What problems is LangSmith solving and how is that benefiting you?** It solves the headache of not knowing what's happening inside your AI agents. Before LangSmith, we'd run agents, something would fail or loop, and we'd spend forever trying to reproduce it. Now I can trace everything, see exactly where it broke, and use the evaluators to improve outputs. For our small team this means we iterate much faster, ship more reliable stuff, and waste way less time on debugging. It's been a real productivity boost for us. ### 27. End-to-End Agent Tracing with an Intuitive UI and Great Performance **Rating:** 4.0/5.0 stars **Reviewed by:** Rakshit A. | AI Application Engineer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Organic Review from User Profile:** Invitation from G2. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** July 07, 2026 **What do you like best about LangSmith?** The best thing about LangSmith is how it helps us trace the entire agent orchestration we built for our use case. We can trace and review every step of a run prompts, intermediate outputs, tool-call outputs, and latencies which has saved us a lot of time when tracking down issues in applications like a RAG pipeline that returned a bad answer, or a voice agent that produced a hallucinated response. The integration between LangChain and LangGraph also makes it very easy to adopt: I don’t have to make many changes to my existing code, and I can get started quickly with full observability. The UI is easy to understand yet very detailed, so I can track everything in one place, and the overall performance is really great. **What do you dislike about LangSmith?** The main thing I dislike about LangSmith is its pricing model. Once you scale your usage, trace volume adds up very fast—especially when you’re running multiple agents or doing any high-frequency voice bot sessions in production—so the costs can climb very quickly. Also, onboarding for advanced features like custom evaluators isn’t that good out of the box. **What problems is LangSmith solving and how is that benefiting you?** Before using LangSmith, we had a lot of trouble debugging our RAG pipelines and even voice agents. We had to manually sift through logs and print statements to figure out why an LLM gave a wrong answer or why a tool call failed. This was especially painful when running multi-step agent workflows, where a single bad output could be the result of four or five upstream steps. Now, with LangSmith tracing, I can pinpoint the exact prompt, retriever result, or tool call that caused the issue, instead of wasting hours trying to track it down. It’s saved us a lot of time and frustration during debugging. Another important problem it has solved is silent regressions, where a prompt tweak or even a single model swap quietly breaks everything downstream. We used to struggle with shipping changes and only finding out something broke through user complaints. Now we run those changes against saved datasets and eval suites in LangSmith before deploying, which has resulted in far fewer production incidents. It has also helped non-engineering people review agent behavior: product and QA folks can annotate traces directly. ### 28. LangSmith Makes Tracing and Evaluation AI Workflows Simple **Rating:** 4.5/5.0 stars **Reviewed by:** Shubh J. | Developer, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** September 02, 2026 **What do you like best about LangSmith?** What I like most about LangSmith is how it makes it easier to understand and improve AI applications. The tracing and evaluation features help me quickly identify where a model or workflow is going wrong and make improvements based on real results. **What do you dislike about LangSmith?** One thing I dislike about LangSmith is that it can feel a little overwhelming at first. There are many monitoring, tracing, and evaluation features, so understanding how everything fits together takes some time, especially for beginners. **What problems is LangSmith solving and how is that benefiting you?** LangSmith helps solve the difficulty of debugging and evaluating LLM-based applications. It gives me visibility into how prompts, models, and chains perform, making it easier to spot errors, compare results, and improve the overall reliability of my AI workflows. ### 29. LangSmith Makes AI Testing, Tracing, and Debugging Effortless **Rating:** 4.0/5.0 stars **Reviewed by:** Abdullah S. | Account Manager, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** September 03, 2026 **What do you like best about LangSmith?** I also like that LangSmith helps with testing AI applications. Language models can sometimes give different answers to the same type of question, so normal software testing isn’t always enough. LangSmith makes it easier to create test cases and compare results, which helps me verify whether changes to prompts, models, or application logic are actually improving the final result. One of the best features is its tracing and debugging capability: I can see the inputs, output, model response, and the other steps involved in a request. **What do you dislike about LangSmith?** One thing I dislike about Langsmith is that it can feel a bit hard to understand the first time you use it. The platform offers many useful features, but as a new user you may need some time to figure out where everything is and how each feature works. The interface presents a lot of information at once, and it’s not always obvious what I should do next. A simpler design, along with clearer guidance for beginners, would make the overall experience much easier. **What problems is LangSmith solving and how is that benefiting you?** LangSmith is solving an important problem for teams that build applications using large language models and AI. When an AI application gives a wrong, unclear, or unexpected answer, it can be difficult to understand what happened. LangSmith helps me see what is happening inside our AI application, so we can identify issues and improve the system. One of the main problems LangSmith solves is debugging. An AI application can have many steps, such as receiving a user question, searching for information, calling another tool, processing the information, and creating a final answer. ### 30. Clean Interface for Getting Started with LLM Tracing **Rating:** 4.5/5.0 stars **Reviewed by:** Caneel M. | Sr. Software Engineer , Computer Software, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 28, 2026 **What do you like best about LangSmith?** What I liked most is how organized the interface is. The setup guides are easy to follow, and I like that it supports different AI frameworks in one place. It makes it easier to understand how tracing works before integrating it into a project. **What do you dislike about LangSmith?** Since there are many features, it takes some time to understand everything if you're using it for the first time. I also felt that some concepts are easier to understand after trying them in a real project rather than just reading the documentation. **What problems is LangSmith solving and how is that benefiting you?** It gives me a structured way to inspect and debug LLM workflows instead of trying to understand everything from application logs. Having tracing and evaluation tools in one place makes it easier to prepare AI applications for testing and future development. ### 31. Really great tool for tracing and debugging LLMs **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 25, 2026 **What do you like best about LangSmith?** 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 LangSmith?** 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 LangSmith 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. ### 32. Powerful LLM Tracing and Evaluation, but Onboarding and Navigation Need Simplifying **Rating:** 3.5/5.0 stars **Reviewed by:** Bhumika R. | AI/ML Developer Intern, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 16, 2026 **What do you like best about LangSmith?** What i like best about LangSmith is how easy it makes it to trace and debug LLM applications, can quickly see each step of a workflow,inspect prompts and responses, and understand where something went wrong.The evaluation and monitoring features are also useful because they help me compare results and improve output quality without spending much time testing everything manually. **What do you dislike about LangSmith?** One thing is dislike about LangSmith is that the interface can feel a little overwhelming at first,especially when working with detailed traces and evaluations.Some features take time to understand,and the initial setup could be simpler.Clearer onboarding and easier navigation would make the overall experience better for new users. **What problems is LangSmith solving and how is that benefiting you?** LangSmith helps me understand what’s happening inside my LLM applications by making it easier to trace requests, find errors, and evaluate AI responses. The integrations make it simple to connect with my existing workflow, and the monitoring tools help me spot performance issues faster. Overall, it saves me time on debugging and testing, which improves my productivity and gives me better value for the time and effort I put into building AI features. ### 33. LangSmith Makes LLM Monitoring **Rating:** 4.0/5.0 stars **Reviewed by:** Nithink u. | Graphic Designer, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Source: Organic:** Organic review. This review was written entirely without invitation or incentive from G2, a seller, or an affiliate. **Reviewed Date:** August 10, 2026 **What do you like best about LangSmith?** I like that LangSmith makes it much easier to monitor, debug, and improve LLM applications. The tracing is especially useful because I can see what happened at each step, identify where a prompt or model response went wrong, and quickly make adjustments. It saves a lot of time compared with trying to troubleshoot everything manually. **What do you dislike about LangSmith?** The main thing I dislike is that it can feel a bit overwhelming at first, especially when there are a lot of traces and debugging options. Some parts of the interface could be more intuitive, and it can take time to understand which features are most useful for a specific workflow. **What problems is LangSmith solving and how is that benefiting you?** LangSmith helps address the challenge of understanding what’s really happening inside LLM applications. It gives me clear visibility into prompts, model responses, traces, and errors, which makes it much easier to debug issues and improve my workflows. The biggest benefit for me is the time it saves, since I can spot problems faster instead of having to troubleshoot everything manually. ### 34. Makes Tracing and Debugging LLM Workflows Effortless **Rating:** 4.0/5.0 stars **Reviewed by:** RISHABH Y. | Freelance Graphic Designer, Design, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 21, 2026 **What do you like best about LangSmith?** What I like most is how easy it makes it to trace and debug LLM workflows. Being able to see exactly what happened in a run helps a lot when something doesn't work as expected. **What do you dislike about LangSmith?** The learning curve can be a little steep at first, especially with all the features and settings. Some of the more advanced monitoring and tracing options also take time to get used to. **What problems is LangSmith solving and how is that benefiting you?** It helps me in understanding what's actually happening inside my LLM workflows, especially when something goes wrong. The tracing and debugging make it much easier to find issues, test changes and improve the overall reliability of our AI applications. ### 35. Making LLM Debugging and Testing Easier That Saves Time **Rating:** 4.0/5.0 stars **Reviewed by:** Jeeshan K. | Director, Market Research, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 25, 2026 **What do you like best about LangSmith?** LangSmith is best about is how easy it make it to track and debug LLM applications. I can use see what's happening in each step, find issues quickly and understand the model gave a certain response. It save a lot of time when testing and improving prompts. **What do you dislike about LangSmith?** One thing I dislike about LangSmith is that it can feel a little confusing at first especially when working with traces and evaluations. It take some time to understand all the features, but once you get used to it , it become s easier. **What problems is LangSmith solving and how is that benefiting you?** LangSmith helps me find issues in LLM application by showing how each request is processed step by step. It makes debugging, testing prompts and checking model responses much easier. ### 36. Mind-Blowing API Debugger and a Community-Driven Prompt Hub **Rating:** 5.0/5.0 stars **Reviewed by:** Rajveer M. | AI/ML Engineer, Computer Software, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 11, 2026 **What do you like best about LangSmith?** Prompt hub feels like a real community-building space. They’re pioneers in prompt repositories, and the plug-and-play approach makes it easy to build as you go. The API debugging and step-by-step logging—enabled with just a link in the code—is genuinely mind-blowing. The Dashboard UI/UX is remarkably useful and easy to adapt. **What do you dislike about LangSmith?** The recently introduced pricing is a concern for me. The limit of 200 traces feels too low, especially during the development stage, which is when we need it the most. Without being able to use it as needed, the integration time for the LLM and other models will likely increase. **What problems is LangSmith solving and how is that benefiting you?** API tracing, observability features, and strong logging capabilities have become essential for faster debugging of API solutions today. When we use this tool during development, its performance and latency insights are a valuable by-product, and they help a lot in spotting small nuisances in our AI agents. ### 37. Easy AI App Tracking and Debugging, but a Steep Learning Curve **Rating:** 3.5/5.0 stars **Reviewed by:** Mahika S. | Social Media Strategist, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 26, 2026 **What do you like best about LangSmith?** 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 LangSmith?** 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 LangSmith solving and how is that benefiting you?** Langsmith helps me understand what's 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. ### 38. LangSmith Tracing Makes LLM Workflows Easy to Understand **Rating:** 4.5/5.0 stars **Reviewed by:** kartik k. | data analyst, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 12, 2026 **What do you like best about LangSmith?** What I like best about LangSmith is how easy it makes it to understand what’s happening inside an LLM workflow. The tracing feature is really helpful for finding issues quickly, checking outputs, and improving prompts without having to guess what went wrong. **What do you dislike about LangSmith?** What I dislike about LangSmith is that the interface can feel a little overwhelming when working with a lot of traces and runs. It sometimes takes extra time to find the exact information I need. Better filtering, clearer navigation, and a simpler layout would make debugging much faster and easier. **What problems is LangSmith solving and how is that benefiting you?** LangSmith helps me understand and debug LLM workflows more clearly. Instead of guessing why an output was wrong, I can review traces, prompts, and responses step by step. This saves me time during testing and makes it easier to improve prompt quality and catch issues before they affect users. ### 39. Excellent Workflow Monitoring and Tracing with LangSmith **Rating:** 5.0/5.0 stars **Reviewed by:** KharanKumar R. | Software Engineer II, Computer Software, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google One Tap using a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** August 10, 2026 **What do you like best about LangSmith?** We like to use LangSmith mainly for its workflow monitoring and tracing in our multi agent nodes workkflow process to check the log and were error occurs is comes and that point out that orchestration to correct it and write rules. **What do you dislike about LangSmith?** Need to have more traces under the each LangSmith workflow, just giving our code issue comment but we need the actual code area from the node it came from. **What problems is LangSmith solving and how is that benefiting you?** LangSmith mainly solving our multi agent workflow failures and finds the issues exactly which helps in our project and benefitting by time reduce and it's monitoring and tracing are very benefiting to resolve fastly. ### 40. LangSmith Makes LLM Tracing, Debugging, and Collaboration Effortless **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google using a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** July 21, 2026 **What do you like best about LangSmith?** Lengsmith is a great product that allows you to understand what's going into your LLM calls, inspect the input context in production or staging for that matter, and see which tools are getting called, whether or not everything, all the inputs, metadata, and the tool calls are working as intended. would expect them to and then see the output format and everything. So it's pretty good at understanding the cost of your LLM calls. You can take coverage. You can also use it for debugging issues in production. It is also the best way to share progress by sharing the traces of Smith on products that are in development and on how it's working and just makes the collaboration between the developers and prompt writers. It's very easy to use. **What do you dislike about LangSmith?** What I dislike about LangSmith is the setup. You have to pass the metadata. For example, for traces, you need to pass the trace ID all the way down to where your actual event is, and passing the metadata is the hardest part, I would say. There was some sort of SD card where you can have a context at the start of the request, and then you can just use that anywhere in the cycle of that application. The setup is the hardest part. **What problems is LangSmith solving and how is that benefiting you?** It helps me debug issues. It helps me collaborate with prompt writers. It helps me understand how my LLM calls are being made. It helps me understand whether or not I'm using the API correctly, making sure that the tool calls are correct, making sure that the input prompt is as expected, all the variables and everything are working as I would expect them to, and then analyzing the final input prompt and also the output. ### 41. LangSmith makes Debugging and Monitoring Much Easier **Rating:** 4.5/5.0 stars **Reviewed by:** Hrithik Y. | manager, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 23, 2026 **What do you like best about LangSmith?** 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 LangSmith?** 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 LangSmith 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. ### 42. Clean Trace Visualization and Powerful Prompt Debugging in LangSmith **Rating:** 4.0/5.0 stars **Reviewed by:** Ravi P. | Sales Professional, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through Google One Tap using a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 20, 2026 **What do you like best about LangSmith?** What I like most about LangSmith is its trace visualization and prompt-debugging workflow. The UI is clean and makes it easy to follow a user request end to end—through retrieval, tool calls, prompts, and model responses—all in one place. Integration with LangChain and OpenAI was quick, which made onboarding straightforward. The AI evaluation features also helped me compare prompt changes more objectively, and performance has been reliable even when I’m analyzing a large volume of traces. **What do you dislike about LangSmith?** The biggest downside for me is that some of the more advanced evaluation and experimentation features come with a learning curve, especially for new users. The platform is clearly powerful, but it can take time to fully understand how datasets, evaluators, and regression workflows fit together and how to use them effectively. I’d also appreciate clearer pricing guidance for higher production usage, along with more step-by-step onboarding examples for teams that are new to LLM observability. **What problems is LangSmith solving and how is that benefiting you?** Before LangSmith, we struggled to pinpoint why our AI assistant was producing incorrect or inconsistent answers. We had access to application logs, but we couldn’t see the complete prompt, the retrieval context, or the model’s reasoning path end to end. With LangSmith, we can trace each request, inspect the retrieved documents, and run automated quality evaluations. As a result, we’ve significantly reduced debugging time, improved response accuracy, and gained much more confidence when deploying prompt and model changes, while also making token usage and costs easier to monitor. ### 43. Clear Observability and Evaluation Tools That Improve LLM App Quality **Rating:** 4.5/5.0 stars **Reviewed by:** Shubhamm D. | Data Science Intern, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 27, 2026 **What do you like best about LangSmith?** 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 LangSmith?** 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 LangSmith 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 ### 44. LangSmith Makes LLM Debugging and Traceability Effortless **Rating:** 5.0/5.0 stars **Reviewed by:** Sumit T. | Quality Assurance Specialist, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through Google using a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 15, 2026 **What do you like best about LangSmith?** From a QA perspective, LangSmith has been a valuable tool for improving the reliability of LLM applications. The ability to trace each execution, inspect inputs and outputs, and review intermediate steps makes it much easier to reproduce issues, understand what happened, and pinpoint the root cause when something fails. I also appreciate the visibility LangSmith provides into overall application behavior. The detailed traces make it easier to validate tool calls, prompt execution, and model responses, and the historical comparisons help confirm whether fixes truly resolved the original issues or ended up introducing new ones. **What do you dislike about LangSmith?** Nothing to dislike but one area that could be added is the reporting and QA-focused dashboards for tracking pass/fail trends and recurring issues. It will enhance the testing workflow. **What problems is LangSmith solving and how is that benefiting you?** Before using LangSmith, validating our LLM applications depended largely on manual testing and digging through logs. That made it hard to reproduce issues reliably or to understand why the model produced a specific response. Debugging complex workflows with multiple prompts and tool calls also took a lot of time, and regression testing required substantial hands-on effort. With LangSmith, we now have much clearer visibility into each run through detailed traces. This makes it easier to diagnose failures, confirm expected behavior, and spot where things go wrong. Overall, it has streamlined our QA process, increased confidence in releases, and cut down the time we spend investigating defects. ### 45. Robust LLM-as-a-Judge Evaluations with A/B Prompt Testing **Rating:** 5.0/5.0 stars **Reviewed by:** Nirmal K. | Manager, E-Learning, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** August 08, 2026 **What do you like best about LangSmith?** It features robust tools for "LLM-as-a-judge" evaluations, allowing you to create datasets, run A/B testing on prompts, and automatically generate evaluators from production traces to catch regressions before they hit users. **What do you dislike about LangSmith?** While it offers SDKs for non-LangChain code, the platform is fundamentally designed around the LangChain ecosystem. Teams using competing frameworks (like LlamaIndex or CrewAI) often find tools like Langfuse or Future AGI to be a more framework-agnostic fit. **What problems is LangSmith solving and how is that benefiting you?** It has evolved past simple monitoring to include full agent-engineering tools like "Fleet" (for deploying agents), "Sandboxes" for safe code execution, and centralized prompt management (the LangSmith Hub). ### 46. LangSmith Makes LLM Debugging Easy with Clear Tracing and Evaluation **Rating:** 4.5/5.0 stars **Reviewed by:** Rehan A. | Artificial Intelligence Engineer, Information Technology and Services, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account added to their profile **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** August 16, 2026 **What do you like best about LangSmith?** I use LangSmith to trace and evaluate LLM workflows. I like being able to see individual steps, prompts, outputs, and errors in one place. It makes debugging AI applications much easier than checking everything manually. **What do you dislike about LangSmith?** There is a learning curve when setting up tracing and evaluations. The amount of information in the dashboard can also feel overwhelming initially. **What problems is LangSmith solving and how is that benefiting you?** LangSmith helps me understand how an AI workflow is behaving and quickly identify where something went wrong. It saves debugging time and makes it easier to improve prompts and overall application performance. ### 47. Great Run Visibility and Tracing, Though There’s Room to Improve **Rating:** 3.5/5.0 stars **Reviewed by:** Nisha M. | Teacher, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Organic:** Organic review. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** September 01, 2026 **What do you like best about LangSmith?** What I like most about LangSmith is the visibility it provides into AI application runs. I can trace individual steps, review and outputs, and quickly understand where a workflow needs improvement. **What do you dislike about LangSmith?** The main drawback for me is the learning curve around some of the more advanced tracing and evaluation features. The platform is powerful, but it can take some time to understand which features are most useful for a particular workflow. **What problems is LangSmith solving and how is that benefiting you?** LangSmith helps solve the challenge of debugging and improve AI workflows when the cause of an issue isn't immediately clear. Tracing runs and reviewing inputs and outputs gives me better visibility, helping me troubleshoot faster and improve the overall workflows. ### 48. End-to-End Visibility That Makes AI Debugging Concrete **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 26, 2026 **What do you like best about LangSmith?** 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 LangSmith?** 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 LangSmith 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. ### 49. Best-in-Class LLM Tracing & Observability for Faster Debugging and Optimization **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 24, 2026 **What do you like best about LangSmith?** 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 LangSmith?** 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 LangSmith 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. ### 50. LangSmith Makes Tracing and Debugging LLM Workflows Effortless **Rating:** 4.5/5.0 stars **Reviewed by:** Rakshith K. | Chief Executive Officer, E-Learning, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 06, 2026 **What do you like best about LangSmith?** LangSmith makes it easier to trace and debug LLM workflows. I especially like being able to see each step of an agent run, compare outputs and quickly identify where prompts or tools are failing instead of debugging it blindly.. **What do you dislike about LangSmith?** LangSmith can feel bit overwhelming at first, especially when there are many traces and runs to inspect. The interface is powerful, but filtering and navigating large volumes of debugging data could be simpler and more intuitive. **What problems is LangSmith solving and how is that benefiting you?** LangSmith helps us understand why an LLM workflow is failing instead of guessing. By tracing each step, comparing runs and evaluating outputs, we can identify weak prompts or tool errors faster, improve response quality, and reduce the time spent debugging AI workflows. - [View LangSmith pricing details and edition comparison](https://www.g2.com/products/langsmith/reviews?section=pricing&secure%5Bexpires_at%5D=2026-09-16+10%3A33%3A54+-0500&secure%5Bsession_id%5D=b42350cc-38fc-473d-82a8-7f9d789e41fa&secure%5Btoken%5D=a112a28874a21102c2b4b1eec6486d67ac46988e98dcd027f24eebdda3542baf&format=llm_user) ## LangSmith Features **Tracing & Debugging** - Agent Debugging - Trace Visualization - End-to-End Agent Tracing **Evaluation & Quality** - Regression Testing - Hallucination Detection - Automated Output Evaluation **Production Monitoring** - Alerts & Notifications - Latency Monitoring - Token Usage & Cost Tracking **Agent Discovery & Governance** - Audit Logging - Agent Discovery - Policy Compliance Monitoring ## Top LangSmith Alternatives - [Monte Carlo](https://www.g2.com/products/monte-carlo/reviews) - 4.3/5.0 (544 reviews) - [Arize AX](https://www.g2.com/products/arize-ax/reviews) - 4.3/5.0 (76 reviews) - [Acceldata](https://www.g2.com/products/acceldata/reviews) - 4.4/5.0 (54 reviews)