Easy to integrate, community support and easy to learn, great updates.

AI framework for handling multiple LLMs

LangChain’s suite of products supports AI development We orchestrate all our AI agents using Langchain - it has been integral to our development.

Whatever you build with AI, there is something useful in Langchain's tool set.

LangGraph by LangChain made it possible to design the agentic workflow as state-graphs providing greater control and reliability. Also, LangSmith for observabilty of the Agentic actions.

LangGraph powers our assistant with robust capabilities. With support for over 15 tools, including an extensive GitHub toolkit, our assistant offers seamless out-of-the-box integration to handle almost every operation with confidence.

Easily build any tools and assign it to AI.

Powering our backend.

It makes connecting complex data sources so much easier and faster.

We orchestrate all our AI agents using Langchain - it has been integral to our development.

We used langchain framework both for it's library agnostic methods for various AI platforms, as well as various helpers such as document parsers, chunking utilities, e.t.c. Moreover it has great agentic development support with langgraph. As an alternative we could have used OpenAI APIs directly however due to its fast changing nature, and also because we don't plan to depend on it long-term, it was better to go with langchain.

Langgraph Studio helps us easily build and ideate agents

We use it to establish connections with LLMs and create agents.

Huge thanks to LangChain for helping us build Indigo’s seamless AI integration and powerful command features! 🚀

Context-aware reasoning for LLMs with LangChain.

Has an amazing collection of various integrations that make working with different vector databases and models a breeze.

We chose to integrate with LangChain as it is the leading LLM framework. We are happy that it was so straightforward to integrate with LangChain and build some use cases already.

Archie builds all interactions with LLM using Langchain. We evaluated multiple frameworks and even worked directly with vendors' API, but quickly realized that investing in Langchain would allow us to leverage a robust ecosystem. These days, it is pretty simple for our AI labs to switch prompts, sequences, LLM, and agents, given the framework's flexibility.

The whole Agent Architecture is based on Langchain's ReAct Agent, which allows model changes without changing the rest of the code.

Langgraph studio for agents, Langsmith for observability, Langgraph Cloud for hosting