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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

Langchain provides a modular and extensible way to work with large language models. Its ability to chain together LLMs with tools, memory, and external data sources makes it incredibly powerful for real-world applications. The support for various model providers (OpenAI, Anthropic, etc.) and integrations with tools like Pinecone, Chroma, and Vector DBs is also a big plus. Review collected by and hosted on G2.com.

The learning curve can be steep for newcomers, especially those without experience in working with LLMs or Python. The documentation, while extensive, can sometimes be overwhelming or slightly out of sync with the latest releases. Breaking changes in updates can also make it hard to maintain older projects unless you pin versions carefully. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

i really had fun building RAG with Langchain, the option it provides are really amazing it supports mullitple vendors model for llm for example openai, oolama, mistral ai if you want to go with open source models ofcourse huggingface is there for us well langchain support that as well, the implementation is really easy and about the documentation it is really good straight at point even a basic python language understanding coder can start with langchain in no time, i had integrated langchain with langflow that is also an amazing open source product Review collected by and hosted on G2.com.

Well i have no particular dislikes for langchain but as a beginer in langchain i had issue with respective dependency conflict between langchain and langchain community library and other dependecy conflicts other than that i think i have not faced that many issues such that i can say as my disliked towards langchain overall really amazing work from langhcain community Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.