LangChain vs Haystack

The duct tape of LLM development—holds everything together until you realize you're building a Rube Goldberg machine meets the duct tape for rag pipelines. Here's our take.

LangChain

Nice Pick

The duct tape of LLM development—holds everything together until you realize you're building a Rube Goldberg machine.

Pros

• Modular components make it easy to swap LLMs and tools without rewriting everything
• Excellent for rapid prototyping of complex AI agents and retrieval-augmented generation (RAG) systems
• Strong community support with extensive documentation and pre-built integrations

Cons

• Abstraction layers can obscure what's actually happening, leading to debugging nightmares
• Steep learning curve for beginners who just want to call an API

Haystack

The duct tape for RAG pipelines. Because sometimes you just need to glue an LLM to your docs without reinventing the wheel.

Pros

• Pre-built components for document indexing, retrieval, and LLM integration
• Supports multiple vector databases and LLM providers out of the box
• Pipeline-based architecture makes complex workflows manageable

Cons

• Steep learning curve for customizing beyond basic use cases
• Documentation can be overwhelming for beginners

The Verdict

These tools serve different purposes. LangChain is a frameworks while Haystack is a ai assistants. We picked LangChain based on overall popularity, but your choice depends on what you're building.

The Bottom Line

LangChain wins

Based on overall popularity. LangChain is more widely used, but Haystack excels in its own space.