G2

Akash K.

Software Engineer

Computer Software

Small-Business (50 or fewer emp.)

10/3/2026

"LangChain Makes Building AI Agents and RAG Apps Fast and Flexible"

4.5/5

What do you like best about Langchain?

What I like most about LangChain is how much easier it makes building AI applications and agents. I use LangChain and LangGraph in my workspace for creating AI agents and RAG applications, and the framework gives me useful building blocks for connecting LLMs with tools, data sources, memory, and different workflows. I especially like the flexibility to create more structured agent workflows instead of managing everything from scratch. It saves development time and makes it easier to experiment with and improve AI applications as the project grows. Review collected by and hosted on G2.com.

What do you dislike about Langchain?

The main thing I find challenging with LangChain is that it can feel a bit overwhelming when you’re getting started. There are quite a few abstractions and components to understand, and the framework changes fairly often, so older examples or tutorials may not always match the latest version. Debugging can also take some time when multiple chains, tools, agents, and integrations are involved. For simple use cases, it can sometimes feel like more setup than necessary. Review collected by and hosted on G2.com.

What problems is Langchain solving and how is that benefiting you?

LangChain helps me avoid building the entire AI application infrastructure from scratch. I use it mainly for developing RAG applications and AI agents, where I need to connect LLMs with documents, retrievers, tools, and external services. LangGraph is especially useful for managing more complex agent workflows and making the steps easier to organize. This saves development time, makes my projects easier to maintain, and lets me focus more on the actual application logic rather than writing all the supporting components myself. Review collected by and hosted on G2.com.