Why we no longer use LangChain for building our AI agents

The article discusses the challenges faced by Octomind while using the LangChain framework for creating and fixing end-to-end tests in Playwright using AI agents with multiple LLMs. The author explains that while LangChain seemed promising initially, its rigid high-level abstractions and inflexibility became a source of friction as their requirements became more complex. The author criticizes LangChain's approach to abstractions, stating that it increased the complexity of the code without any perceivable benefits, and its inability to adapt to more complex architectures limited their development team.

The author then suggests that perhaps AI applications do not need a framework at all. They argue that starting without a framework might require more upfront learning and research, but it is a worthy investment in the long run. They recommend a building blocks approach that uses simple low-level code with carefully selected external packages, keeping the architecture lean and adaptable. This approach, they claim, has allowed their team to develop more quickly and with less friction.

Key takeaways:

Octomind moved away from using the LangChain framework due to its rigid high-level abstractions and inflexibility, which made their code more complex and difficult to maintain.
LangChain's approach to abstractions increased the complexity of the code with no perceivable benefits, often leading to debugging internal framework code instead of implementing new features.
LangChain limited Octomind's ability to innovate and iterate quickly, particularly when they wanted to move from a single sequential agent to a more complex architecture.
Octomind found that using modular building blocks with minimal abstractions, instead of a framework, allowed them to develop more quickly and with less friction.

Why we no longer use LangChain for building our AI agents

Key takeaways:

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