What I like most is how strong it is for RAG and data integration. It comes with ready-made connectors, indexing and retrieval, query engines, and agent workflows, so you can build data-aware LLM applications without having to write all the plumbing yourself.

LangGraph solves the challenge of building reliable, stateful, multi-step AI agent workflows. Instead of managing complex agent logic with scattered code and ad-hoc state handling, it provides a structured graph-based approach for defining how an agent should reason, use tools, make decisions, and move between tasks. Manages state across multiple steps and interactions. Supports branching and loops, making complex agent workflows easier to control. Enables human-in-the-loop workflows when an agent needs approval or intervention. Makes retries and error recovery easier to structure. Helps build more predictable and maintainable AI agents. In my workflow, LangGraph helps me turn complicated AI processes into clearly defined workflows. I can control exactly how an agent moves between tasks instead of relying entirely on autonomous behavior. The biggest benefit is better reliability and control over AI agents. It makes complex workflows easier to build, test, debug, and maintain while reducing the amount of custom state-management logic I need to write.

It boasts over 160 pre-built data connectors (LlamaHub) allowing you to easily ingest data from APIs, SQL databases, Notion, Google Workspace, and more. Its premium managed service, LlamaParse, is widely considered a standout tool for accurately extracting data from messy, complex documents like PDFs with nested tables or scanned images.