Best LLM Frameworks (2025)
Ranked picks for llm frameworks. No "it depends."
🧊Nice Pick
LangChain
The duct tape of LLM development—holds everything together until you realize you're building a Rube Goldberg machine.
Full Rankings
#1
Details →LangChain
Nice PickThe 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
Microsoft's multi-agent playground. Because one AI isn't enough to mess things up.
Pros
- +Simplifies building complex AI workflows with multiple agents
- +Seamless integration with LLMs like GPT-4
- +Customizable agent roles and conversation management
Cons
- -Steep learning curve for orchestrating agent interactions
- -Documentation can be sparse for advanced use cases
Compare:vs LangChain
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
Head-to-head comparisons
Missing a tool?
Email nice@nicepick.dev and I'll add it to the rankings.