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LangSmith Agent Engineering Platform (Self-Hosted)
LangSmith provides tools for developing, debugging, and deploying LLM applications. It helps you trace requests, evaluate outputs, test prompts, and manage deployments in one place.
LangSmith is framework agnostic, so you can use it with or without LangChain open-source libraries langchain and langgraph.
Prototype locally, then move to production with integrated monitoring and evaluation to build more reliable AI systems.
LangSmith provides:
Observability to see exactly how your agent thinks and acts with detailed tracing and aggregate trend metrics.
Evaluation to test and score agent behavior on production data and offline datasets for continuous improvement.
Deployment to ship your agent in one click, using scalable infrastructure built for long-running tasks.
Reviews (138)
Rohit K.
Saves time while creating multi agent system
Reviewed on Oct 04, 2026
Review provided by G2
What do you like best about the product?
It's the best among the other applications for the role of making advanced multi agent systems and to easily create a loops. And if you want to switch from one model to another model then it only needs 1 to 2 lines of code depending on the model
What do you dislike about the product?
It's good for the advance but not for the basic things like it requires the unnecessary code for it and also important the framework takes a massive third party integration which you even not use and which leads to the increase in the size of the project and as we are using the third party so it has the risk also
What problems is the product solving and how is that benefiting you?
It's helping me in like I am working the side by side on the AI and machine learning so it is useful for me when I am learning about the LLM, API creation etc and while creating the advance code and working on a project it's a lot of time and also they gave us a very high security like they don't have any access to our private data like files if you are doing something on our private project so they don't interfere there
Chaitanya T.
Simplifies Building LLM Apps with Reusable Prompt, Model, and Tool Components
Reviewed on Sep 30, 2026
Review provided by G2
What do you like best about the product?
I like it because it simplifies building LLM-powered applications by providing reusable components for prompts, models and tools.
What do you dislike about the product?
It can sometimes feel complex because it has many abstractions and components and its API changes frequently.
What problems is the product solving and how is that benefiting you?
It simplifies the development of LLM-powered applications by providing reusable components for prompts, model integration and retrieval. It helps me connect different AI components into a complex workflow more easily.
Jai Y.
Eases AI Pipeline Building, Needs Better Debugging
Reviewed on Sep 29, 2026
Review provided by G2
What do you like best about the product?
I use LangChain to create agent and workflow chains. I find its modular architecture and standard interfaces really useful as they make it incredibly easy to string together LLMs, vector data, and memory into scalable, production-ready AI pipelines. With its modular design, I can swap models or databases with a single line of code without having to rewrite my app. It's very efficient in providing me with reusable session handling, prompt, output, input tool calling, so I don't have to create these things manually when creating an agent. Also, the initial setup was fairly easy for someone with knowledge about agent frameworks.
What do you dislike about the product?
LangChain's complex abstractions hide the underlying logic, making it difficult to debug, customize, and maintain as your project grows. It could simplify its abstractions, improve transparency into execution flow, and provide better debugging tools so developers can easily customize and troubleshoot complex workflows.
What problems is the product solving and how is that benefiting you?
Langchain provides reusable session handling and standard interfaces, so I don't have to manually create them, making it easy to build scalable AI pipelines.
Anonymous
Efficient Data Management with Slight Command Complexity
Reviewed on Sep 29, 2026
Review provided by G2
What do you like best about the product?
Langchain is great because it saves all my conversations and data, including large documents. I often forget to save my documents, but with Langchain, I just need to give a command, and it retrieves the document for me. The initial setup was easy, which I appreciated.
What do you dislike about the product?
Sometimes it's complicated to give commands because it adds extra layers just for one command and response feature.
What problems is the product solving and how is that benefiting you?
Langchain creates an efficient chatbot for me, saving all conversations, data, and PDFs. I can quickly access documents with a command, which is really helpful when I forget to store them.
Pharmaceuticals
Saves Development Time with Flexible LLM Support and Fast RAG Pipelines
Reviewed on Sep 28, 2026
Review provided by G2
What do you like best about the product?
makes it easy to connect different components together. The prompt templates, memory management, retrievers, vector databases integrations, & agent framework save a lot of development time. I especially like the support for multiple LLM providers, which makes it easy to switch between OpenAI, Anthropic, Azure OpenAI, & others without having to rewrite large portions of code.
Another feature I found really useful is the document processing pipeline. Loading PDFs, chunking documents, generating embeddings, & building a RAG application can be done fairly quickly compared to building everything from scratch.
Another feature I found really useful is the document processing pipeline. Loading PDFs, chunking documents, generating embeddings, & building a RAG application can be done fairly quickly compared to building everything from scratch.
What do you dislike about the product?
Sometimes examples from a few months ago no longer work because APIs have changed or modules have been deprecated. Documentation has improved but there are still occasions where I needed to go through GitHub issues or community posts to figure things out. There is no natural or automated way to automatically update the API if the former has been updated.
What problems is the product solving and how is that benefiting you?
For me, the biggest benefit has been in building RAG applications & AI assistants. It makes it much easier to connect LLMs with company documents, vector databases, APIs, & any external tools. Instead of spending weeks building the infrastructure, I can focus more on the actual business problem I'm trying to solve.
Izzy H.
Flexible AI Experimentation, but a Steep Learning Curve and Tricky Troubleshooting
Reviewed on Sep 14, 2026
Review provided by G2
What do you like best about the product?
What I like best about LangChain is the flexibility it gives me to experiment with AI without feeling locked into one specific setup. I can connect different models, data sources, and tools and see how they work together for a particular use case.
What do you dislike about the product?
The biggest challenge for me with LangChain is the learning curve. There are a lot of concepts, components, and different ways to approach the same task, so it can feel overwhelming when you're still getting familiar with the platform.
I've also found that troubleshooting isn't always straightforward. Sometimes a workflow that seems fairly simple can require more configuration than expected, and when something doesn't work, it can take time to figure out exactly where the issue is coming from.
I've also found that troubleshooting isn't always straightforward. Sometimes a workflow that seems fairly simple can require more configuration than expected, and when something doesn't work, it can take time to figure out exactly where the issue is coming from.
What problems is the product solving and how is that benefiting you?
LangChain helps me bridge the gap between having an idea for an AI solution and actually testing how it could work in practice. Instead of working with an AI model in isolation, I can connect it to different tools, information sources, and processes to create something more useful.
Deepak A.
Easy Model Switching Across Providers
Reviewed on Sep 04, 2026
Review provided by G2
What do you like best about the product?
I can use it with any model provider. Switching between models is easy.
What do you dislike about the product?
I find it very unstable, with every new release my project breaks.
What problems is the product solving and how is that benefiting you?
I’m using it to build an autonomous API testing framework that can detect backend APIs and generate automation code within the automation framework.
Saanvi P.
Flexible toolkit for developing and composing AI-driven applications.
Reviewed on Sep 04, 2026
Review provided by G2
What do you like best about the product?
LangChain allows you to connect LLMs to your apps, data sources, and external tools with ease. Provides helpful building blocks if you want to build applications that need workflows such as searching documents, RAG-based applications, or AI assistants without starting from scratch. Great if you already know how to code APIs/backend but want some guidance on how to chain them together with LLMs.
What do you dislike about the product?
Documentation is getting there but sometimes you have to experiment to see what works best for your use case.
What problems is the product solving and how is that benefiting you?
LangChain allows us to abstract some of the heavy lifting of stitching AI into our applications. Rather than having to manually connect models to APIs/data sources/etc. we can build repeatable AI workflows much quicker. Has increased dev velocity on projects involving smart search, automation, and AI assistants.
Saurabh Z.
LangChain Makes Working with LLMs Easier and More Flexible
Reviewed on Sep 02, 2026
Review provided by G2
What do you like best about the product?
I like best about langchain is that it makes working with LLM much easier I like the flexibility it provides for connecting models with tools,data and API
What do you dislike about the product?
It can feel a little complex first especially with number of concepts and available components.
What problems is the product solving and how is that benefiting you?
Langchain solves the hassle of managing different part of an LLM application in one place.It makes it easier to connect models with data,tools and API.
Sindhu S.
Flexible Framework for AI Application Development
Reviewed on Sep 02, 2026
Review provided by G2
What do you like best about the product?
It makes building LLM apps easier by handling chains, tools, prompts, and integrations. For me, Lang chain provides good value considering how much development time it can save. I don't have to build the whole LLM workflow, prompt handling, retrieval, and tool integration from scratch. There is still some overhead when projects get more complex, and debugging can take time, but I think the flexibility and integrations make it worth the cost, especially when working on multiple AI features or prototypes.
What do you dislike about the product?
Some abstractions feel heavy, and debugging chains can get tricky when workflows become complex.
What problems is the product solving and how is that benefiting you?
LangChain helps me speed up the development of LLM-based features without building everything from scratch. I mainly use it for managing prompts, connecting models with tools, handling retrieval, and building multi-step workflows. It also makes it easier to experiment with different models and integrations. From a development point of view, it saves time during prototyping and lets me focus more on the actual application logic instead of writing a lot of boilerplate code.