why we no longer use LangChain for building our AI agents

At Octomind, we transitioned away from LangChain for AI agent development due to its rigid high-level abstractions which made maintenance challenging as our requirements evolved. By switching to modular building blocks, we achieved a simpler, more flexible codebase that improved productivity and clarity. LangChain initially helped us start quickly, but its complexity and nested abstractions hindered our progress for more complex tasks and agile iterations. We now advocate for a minimalistic, building block approach for developing LLM-powered applications, allowing for easier understanding and faster innovation.