"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. "

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

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

"Context-aware reasoning for LLMs with LangChain."

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

"You can generate podcasts based on recent events because of the RAG capabilities of LangChain and Websearch. "

"The whole ecosystem has been a great help till now. Even though we often have to develop our own. Langsmith is one of our go to tool for tracing and evaluating. "

"We want to give a huge shoutout to Langchain’s Langsmith for being an invaluable tool in our arsenal. Langsmith has significantly enhanced our ability to monitor, debug, and fine-tune our agent logic. Its robust features have streamlined our workflow and improved our efficiency. We’re excited to announce that soon we’ll be enabling our users to connect their own Langsmith projects, offering enhanced traceability and insight into how their agents operate. Stay tuned for more updates!"

"We use Langchain to manage all our prompts as well as Langsmith to trace, analyze and debug our chain-of-thought and RAG processes. It really helps our team keep the complexity manageable in a way that works for all our stakeholders."

"I can find various very convenient tools in LangChain."

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

"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."

"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."

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

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

"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."

"At the start, we combine LangChain functionality, and then we improve our process so that we can now choose whether to use it or not"

"We build everyday using the Langchain ecosystem - LangSmith for observability and LangGraph for Agentic workflows"

"Nifty way to fine-tune LLMs to suit our AI Agent use-case."

"It is the most efficient and cost effective solution!"