LangSmith
LangChainReviews from AWS customer
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Langchain: Best Framework for developing LLM powered application
What do you like best about the product?
Easy of access
Easy to start implementation
Fast and scalable
Easy to start implementation
Fast and scalable
What do you dislike about the product?
No support when we face any issues so no proper channels to raise support questions
What problems is the product solving and how is that benefiting you?
Creating chat bot like application for leading client.
Application is to provide excellent customer support and raise customer experience.
Application is to provide excellent customer support and raise customer experience.
LangChain
What do you like best about the product?
Easy to create the chatbot and user understand frame work.
What do you dislike about the product?
There no dislikes about Langchain framework.
What problems is the product solving and how is that benefiting you?
Easy integrating with the Langchain like memory prompt tools and llm's.
Generative ai
What do you like best about the product?
Langchain you can create any agent and app with integrate api key control flow which i feel best and langchain produce high quality agent and app
What do you dislike about the product?
Langchain work on control flow basically we need to integrate api and than that product will work based on your actions so may be in this case you cannot make best product so you should have knowledge deeply about drag and drop functions
What problems is the product solving and how is that benefiting you?
It can very useful making agent and app which you can use for your business or provide service to other as a saas product
A Swiss Army Knife for LLM Developers
What do you like best about the product?
LangChain brings order to the complexity of working with large language models. It streamlines the integration of models, memory, tools, and data sources, making development more intuitive. With built-in support for vector databases, APIs, and custom agents, it's well-suited for building scalable, production-ready AI applications—without the need for excessive glue code.
What do you dislike about the product?
LangChain’s greatest strength lies in its modular design. Whether you're building RAG systems, orchestrating multi-step workflows, or developing tool-using agents, it offers flexible building blocks to get started quickly. Integration with third-party services like OpenAI, Cohere, and Pinecone is seamless, enabling powerful end-to-end solutions. Plus, a vibrant community and well-maintained documentation support those ready to go beyond the basics.
What problems is the product solving and how is that benefiting you?
LangChain addresses the challenge of orchestration in applications powered by large language models. Rather than writing custom code to connect models with external data sources, APIs, or tools, developers can rely on its modular framework to manage that complexity. It offers high-level abstractions for prompt chaining, document retrieval from vector stores, conversation memory management, and agent-based decision-making.
Benefits of Langchain
What do you like best about the product?
Langchain is best for building and handling the RAG based application.
What do you dislike about the product?
Resource are very easily available and very user friendly interface
What problems is the product solving and how is that benefiting you?
Langchain is used to train the RAG based application and useful for LLM Model.
Best Framework for building AI Applications
What do you like best about the product?
Langchain has many set of modular tools which are very help full for building LLM as applications like RAG, chatbots, assistants etc.. It supports integrations with so many vector stores, LLM API providers, tools which makes it best and faster development. The documentation is so good and we get excellent support from community.
What do you dislike about the product?
I feel for freshers or new beginners in AI for them its quit difficult to understand and learn. In updates come like every 3 to 4 days very difficult to maintain stability.
What problems is the product solving and how is that benefiting you?
Langchain helps me a lot in feeding the different data sources like pdf, documents , csv files directly into RAG application as Knowledge base with only few lines of code which makes building enterprise or business chat bots easy. Its support for various LLMs providers like OpenAI, Gorq, Ollama helps to try with different LLMs for our business use cases and adopt that LLM saving alot of time.
Powerful Framework for Building LLM Applications Faster
What do you like best about the product?
Langchain abstracts away a lot of complexity when working with large language models. I especially like the modularity—how you can mix and match chains, tools, memory, and agents to build complex applications. The documentation is rich, and its growing community means there’s a lot of support and examples. Integrations with OpenAI, Pinecone, FAISS, and others are seamless and well-supported.
What do you dislike about the product?
Langchain can be overwhelming for newcomers due to its broad scope and somewhat steep learning curve. The API changes frequently, which can lead to outdated documentation or breaking changes in code. Some components are still experimental or lack thorough testing and type safety. Debugging agents and chains can sometimes be non-trivial, especially when errors are deep in nested components.
What problems is the product solving and how is that benefiting you?
Langchain solves the problem of orchestrating complex interactions with large language models (LLMs), such as chaining prompts, integrating memory, querying external tools/APIs, and retrieving context from databases or documents (RAG). Without Langchain, you’d have to build all this logic manually, which is time-consuming and error-prone. It abstracts away repetitive patterns and provides a unified interface for building intelligent applications. For me, this means faster prototyping, easier experimentation with new ideas, and a cleaner architecture for deploying production-grade AI assistants and chatbots. It allows me to focus on the core logic of the product rather than reinventing infrastructure.
Stable, Robust and Customizable Framework for building AI Apps
What do you like best about the product?
Its feature rich out of the box and also allows granular customizations to various components to achieve results.
What do you dislike about the product?
The learning curve can get a bit tricky at the beginning.
What problems is the product solving and how is that benefiting you?
It helps me build and interact with latest available models over the internet and also connect to local models to build workflows.
Our usecase is more towards Neo4J and Vector data knowledge graohs using langchain
What do you like best about the product?
Knowledge graphs with microsoft autogen campatibility and then quick visualizaton of vector data at scale with ease of usage in python
What do you dislike about the product?
At times while working with autogen lanchain fails to import certain library but this is mostly due to the beta version on the latest package build from pypi
What problems is the product solving and how is that benefiting you?
We create knowledge graphs on finops data with visualisation and autogen to process NL to ML and even more
Langchain review for AI and agentic usecase
What do you like best about the product?
The knowledge graph feature for visualisation
What do you dislike about the product?
Heavy datasets take longer on local development
What problems is the product solving and how is that benefiting you?
Agentic AI usecase with knowledge grapg
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