LangSmith
LangChainReviews from AWS customer
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The go-to framework for Generative AI solutions
What do you like best about the product?
The best thing is its comprehensive documentation which gives a clear direction of how to build any generative ai solutions from scratch. Also,there are lot of integration available making it easier to be plugged with existing infrastructure.
What do you dislike about the product?
It can improve in providing a strategy towards more scalability and productionizable solutions.
What problems is the product solving and how is that benefiting you?
It is helping in building all the generative ai solutions.
framework for building llm
What do you like best about the product?
LangChain’s agent framework allows models to make decisions and call tools dynamically
What do you dislike about the product?
Too much abstraction: For simple tasks, LangChain introduces multiple layers of abstraction (e.g., chains, agents, tools), which can make it feel bloated
What problems is the product solving and how is that benefiting you?
accesing extenal documents to provide context to llm
Langchain usage
What do you like best about the product?
What I like most about LangChain is how seamlessly it helps connect large language models (like OpenAI or Cohere) with real-world tools, data, and APIs. It’s not just about prompting a model—it’s about chaining steps together, adding memory, working with documents, and integrating logic to make the AI actually useful in a workflow. The modularity is great; you can use just what you need without being forced into a monolith. Plus, the active community and fast development pace really help when you're building and need support or new features.
What do you dislike about the product?
While LangChain is powerful, the learning curve can be a bit steep, especially when you're just getting started. The documentation is improving, but at times it still feels scattered or too focused on advanced use cases, which can be overwhelming for beginners. Also, with frequent updates and breaking changes, it can be tough to keep up if you're working on a production-grade project—some things that worked a week ago might need refactoring today. Better version stability and clearer upgrade paths would definitely help.
What problems is the product solving and how is that benefiting you?
LangChain solves one of the biggest challenges with using LLMs: turning them from a simple prompt-and-response system into something that can handle complex, multi-step workflows with memory, context, and real-time data. In our case, we needed to build a retrieval-augmented generation (RAG) pipeline that could query internal documents and give context-aware answers. LangChain made it much easier to connect vector databases, integrate tools like OpenAI functions, and manage conversation history—all within a consistent framework. It saves a ton of development time and helps us move faster from prototype to production.
Powerful framework for building LLM-powered applications
What do you like best about the product?
Langchain is effective at enabling users to interface with large language models. Its modular design is captivating; integrating prompt templates, memory, and component interaction is straightforward unlike anything I have seen before. The integration with OpenAI, Hugging Face, and vector stores such as Pinecone or FAISS is done exceptionally well. Langchain has helped with prototype creation and experimentation with various LLM workflows. The active community and abundance of open-source materials helps developers troubleshoot and learn new features with ease.
What do you dislike about the product?
The documentation is a little inconsistent. Even though the fundamental ideas are presented quite clearly, I frequently have to sift through GitHub issues or Discord threads to understand how specific parts are supposed to function in real-world scenarios.
What problems is the product solving and how is that benefiting you?
At my organization, we are putting together an internal personal code assistant tool, and have found Langchain to be really fast-tracking this process. One of the most complex tasks was managing the interaction between our LLM and the various tools (e.g. code repositories, vector databases, and various APIs). Langchain has simplified the coordination across the various components in a consistent and maintainable way.
Langchain has also taken away a lot of boilerplate and manual work by simplifying context management, memory, and prompt-chaining out of the box. All this has roughly sped up our development work by providing us more time to focus on the features that matter instead of the infrastructure.
Langchain has also taken away a lot of boilerplate and manual work by simplifying context management, memory, and prompt-chaining out of the box. All this has roughly sped up our development work by providing us more time to focus on the features that matter instead of the infrastructure.
Best Framework for Prototyping with LLMs
What do you like best about the product?
Honestly, what I love most about LangChain is how it takes the fear out of working with large language models. Before I found it, trying to piece everything together — APIs, memory, logic, vector stores — felt like wrestling with a bunch of puzzle pieces that didn’t quite fit. But LangChain gives you a solid toolkit that actually makes sense. It’s super modular and flexible, and once you get the hang of it, things just click. I’ve been able to build full LLM workflows way faster than before, and the best part is, I’m not stuck starting from scratch every time I want to try something new.
What do you dislike about the product?
While I like to work with LangChain, sometimes it does feel a bit daunting when diving into the documentation or trying to wrap your head around how all the various modules fit together. There is a learning curve, and particularly when you're just starting out. Also, because the ecosystem is moving so quickly, things will break or change unexpectedly, and it's hard to keep up if you're actually deploying it in production. A bit more stability and more examples would be wonderful.
What problems is the product solving and how is that benefiting you?
LangChain addresses the challenge problem of building applications from large language models. Instead of needing to wire APIs, memory, databases, and logic together manually, LangChain gives me a systematic way of dealing with all of that. It makes the entire development process straightforward, which saves me hours of time and frustration. I can invest more time developing and experimenting with ideas, rather than hours of trying to get things to stick together. It's been a game-changer for building smarter, more interactive AI tools without needing to restart from scratch every time.
Langchain a Smart Framework to het successfull in AI world
What do you like best about the product?
Langchain could be used to access different LLMs easily, Connect to Real World data and build RAG systems. It can help us in creating smart Agents. we cn also integrate different tools with the help of this framework.
What do you dislike about the product?
It can take lot of time for beginners to learn as framework implements and improves very frequently. It might get too costly as there is a dependency on lot of extra packages.
What problems is the product solving and how is that benefiting you?
I have implemented a seamless flow of integrating Open AIs GPT easily for my use case to generate synthetic data for domain.
Langchain Review
What do you like best about the product?
The framework is really good, and building an RAG pipeline is very easy and robust; part from that, making complex and advanced RAG pipelines is simple enough while being scalable at the same time.
What do you dislike about the product?
Due to changes in the functions, some functions are deprecated that chatGPT is yet to identify, so it sometimes adds time to go through the documentation.
What problems is the product solving and how is that benefiting you?
helps building RAG pipleines easy havinh open sources models to use also gives a very easy intefgraito of paid mdels using API keys. Framwork is well made and the community is really good, growing and helpful.
Langchain for GEN AI Project
What do you like best about the product?
We use Langchain LLM for our text to text chatbot development in our organization which is working really good. We like about its performance.
What do you dislike about the product?
Still we need more optimization for LLM to perform good and reduce storage part.
What problems is the product solving and how is that benefiting you?
We are using to train the document to our chatbot model and it is really working good and many people in our organization and our clients using chatbots. So it is more beneficial for us.
Use full capabilities of GenAI without a hassel
What do you like best about the product?
I use langchain.js , and I like its composability and availablilty of different readers or database drivers with it
What do you dislike about the product?
I have nothing to dislike about it, Langchain is really a great product
What problems is the product solving and how is that benefiting you?
most of the time we use langchain for our RAG applications but apart from this we have Integrated many AI based workflows as well which acyually calls multiple chains and workflows based on conditions
The best framework for building RAG , enjoyed and loved it
What do you like best about the product?
Langchain is the best framework for building RAG applications its supports all find of Large Language Models that is both open-source like llama, mistral and closed-source models like OpenAI, and Anthropic using their Access token. It also supports using local LLMs using the Ollama. I build RAG application for our enterprise data using it , its very simple to build and it has many features like a chain of thoughts and memory. They have clean documentation which we can refer to and add more features. We can easily integrate with their other products like Langsmith and Langgraph
What do you dislike about the product?
I didn't feel any downside while using the Langchain but one thing is they have many version depecdences which will through error if you don't install the correct version.
What problems is the product solving and how is that benefiting you?
I was asked to build an RAG Module on our enterprise data so that our company employees can make use of it to search and get information from our database like documents,PPTs,pdfs etc.. Using Langchain I was able to build it using the opensource LLM model like Llama 3.1 8B, their documentation made it very easy to refer and build. And using the Langsmith which is their other product which helped me for productionization of our enterpirse RAG.I felt Langchain is very easy to implement compared to others like LlamaIndex.
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