LangSmith
LangChainReviews from AWS customer
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Great for agentic ai programming
What do you like best about the product?
The platform is easy to use, even if you only have a basic understanding of AI concepts. I found that navigating the features didn't require advanced technical knowledge, which made the experience straightforward and accessible.
What do you dislike about the product?
Sometimes, other frameworks appear to be simpler.
What problems is the product solving and how is that benefiting you?
I found that some integrations with cloud services were more straightforward and agnostic when using langchain.
Powerful Framework for Building AI Apps Quickly
What do you like best about the product?
I really like how LangChain brings all the moving parts of AI app development together in one place. The integration with different LLMs, vector databases, and APIs is super smooth, so I don’t waste time building connectors from scratch. The documentation is improving, and the community is very active, which makes finding examples and solutions easier. It’s also flexible enough to go from a quick prototype to a production grade application without completely rewriting the code it makes it a powerful tool to have.
What do you dislike about the product?
While LangChain is powerful it can feel overwhelming at first because of how many modules and options it offers. The documentation, though better now, still has gaps for more advanced use cases, and sometimes breaking changes in updates mean I need to adjust my code unexpectedly. It would be nice to have more structured learning paths for newcomers.
What problems is the product solving and how is that benefiting you?
LangChain helps me connect large language models with the right data sources, tools and workflows without having to build everything from scratch. Before using it, I had to manually handle API calls, parse responses, and manage context across different parts of the app, which slowed development. Now I can orchestrate prompts chain multiple steps together, and integrate with vector databases or APIs in a few lines of code. This saves a lot of development time, reduces errors, and lets me focus more on designing better AI experiences for users instead of building low-level infrastructure so its is kind to helpful to me.
Langchain Review -MLOps
What do you like best about the product?
Experiment Tracking via prompt templates,
Integration with Vector Database,
Pipeline Composition allowing mw to separate data ingestion, transformation and inference stages,
Reproducibility- it helps me LLM-powered workflows for CI/CD deployment.
Integration with Vector Database,
Pipeline Composition allowing mw to separate data ingestion, transformation and inference stages,
Reproducibility- it helps me LLM-powered workflows for CI/CD deployment.
What do you dislike about the product?
I have been facing complexity in debugging and challenges in scaling.
It has fast-evolving APIs which makes it difficult to track the backward copatibility.
It has fast-evolving APIs which makes it difficult to track the backward copatibility.
What problems is the product solving and how is that benefiting you?
Langchain is solving a set of practical problems around building and deploying applications powered by large language models (LLMs).
Prompt and Memory Management, LLM Orchestration, Data Connectivity
Prompt and Memory Management, LLM Orchestration, Data Connectivity
Powerful AI orchestration framework with a learning curve
What do you like best about the product?
Comprehensive abstractions for working with LLMs (chains, agents, tools)
Extensive integrations with various AI models and vector databases
Active community and rapid development pace
Flexibility in building complex AI workflows
Good documentation with practical examples
Memory management capabilities for conversational AI
Built-in prompt templates and output parsers
Extensive integrations with various AI models and vector databases
Active community and rapid development pace
Flexibility in building complex AI workflows
Good documentation with practical examples
Memory management capabilities for conversational AI
Built-in prompt templates and output parsers
What do you dislike about the product?
Steep learning curve for beginners
Frequent breaking changes between versions
Can be overly complex for simple use cases
Debugging can be challenging with nested chains
Performance overhead compared to direct API calls
Documentation sometimes lags behind new features
Abstractions can sometimes hide important details
Frequent breaking changes between versions
Can be overly complex for simple use cases
Debugging can be challenging with nested chains
Performance overhead compared to direct API calls
Documentation sometimes lags behind new features
Abstractions can sometimes hide important details
What problems is the product solving and how is that benefiting you?
LangChain significantly reduces the complexity of building production-ready AI applications by providing pre-built components for common patterns like RAG, conversational memory, and agent workflows. It allows our team to switch between different LLM providers without rewriting code, which helps optimize costs and avoid vendor lock-in. The framework handles the complex orchestration of multi-step AI workflows, enabling us to build sophisticated applications that can reason through problems, use external tools, and maintain context across conversations. This has accelerated our development timeline from months to weeks for AI features. The built-in prompt templates and output parsers ensure consistent and reliable responses in production, while the memory management capabilities have been crucial for building stateful AI assistants that remember user context. LangChain's abstractions for vector stores and document loaders have simplified the implementation of RAG systems that query our proprietary data. Overall, it's transformed how quickly we can prototype and deploy AI solutions, though the learning curve was initially steep.
Built advanced LLM apps with LangChain.
What do you like best about the product?
What I like best about LangChain is its flexibility to integrate models, data sources, and tools seamlessly, which made building and scaling complex LLM-powered workflows much faster in my projects.
What do you dislike about the product?
What I dislike about LangChain is that its rapid updates sometimes break existing code or change APIs, which can make maintaining long-term projects a bit challenging.
What problems is the product solving and how is that benefiting you?
LangChain solves the challenge of connecting LLMs with external data, tools, and workflows by providing a modular framework for retrieval, reasoning, and integration. This benefits me by allowing faster development of RAG pipelines, multi-agent systems, and AI applications without reinventing the orchestration logic, so I can focus more on solving domain-specific problems rather than low-level integration.
Langchain Research
What do you like best about the product?
open source Framework, modular architecture, and easy to integrate LLM models with external data. easy to use and create component like chains, agents etc.
What do you dislike about the product?
During the debugging the whole workflow, sometime Abstraction layers make it hard to trace issues or optimize performance, particularly with large-scale applications. Also, the rapid pace of updates can lead to deprecated features or breaking changes, which can frustrate developers trying to keep up.
What problems is the product solving and how is that benefiting you?
I used LangChain to build an agent-based tool for the Income Tax Department of India that enables intelligent document search and provides step-by-step ITR filing guidance, improving speed, accuracy, and user experience.
Really mind-blowing
What do you like best about the product?
I just say use it and ease your work and this will save your time and efficient your work
What do you dislike about the product?
Nothing I observed which could I can say useless It was really good tool
What problems is the product solving and how is that benefiting you?
It is beneficial in all aspects
Langchain is a key library for my Gen Ai projects
What do you like best about the product?
It's easy to use and does heavy lifting in the backend also it's open source community is good
What do you dislike about the product?
I don't dislike anything everything looks good only
What problems is the product solving and how is that benefiting you?
It's helping to build gen ai use cases with minimal code writing
Super useful in orchestrating AI workflows
What do you like best about the product?
Langchain is used to connect multi-agent system in your application. We used Langgraph which is based on Langchain that helps us orchestrate multiple workflows. It is easy to integrate and supports master-slave architecture.
What do you dislike about the product?
it tries to do everything in the LLM ecosystem, and that comes with trade-offs.
What problems is the product solving and how is that benefiting you?
I am implementing LLM as a judge with different guideline agents and using Langchain to orchectrate that.
Powerful framework for building LLM powered apps
What do you like best about the product?
LangChain makes connecting large language models with data sources and APIs very easily and simple. Its modular tools and ready integrations (like Pinecone, OpenAI and vector stores) save development time and make experimenting much easier.
What do you dislike about the product?
While LangChain is powerful, the documentation can feel overwhelming for beginners, especially when dealing with advanced features. Some integrations may break after version updates, requiring extra troubleshooting and more beginner friendly examples would be helpful.
What problems is the product solving and how is that benefiting you?
LangChain helps me connect LLMs to custom data sources and APIs without building everything from scratch. It has simplified the development of Retrieval Augmented Generation (RAG) pipelines for chatbots and automated workflows, saving both time and effort. This flexibility allows me to experiment quickly and deliver prototypes faster.
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