LangGraph is an open-source framework designed to help developers build more efficient and cost-effective AI agents. The framework's core principle is combining code with Large Language Models (LLM) calls, allowing for a hybrid approach that balances performance, cost, and capability. Developers can apply various techniques to speed up their AI agents, including reducing LLM calls, speeding up individual LLM calls, making fewer LLM calls, changing the user experience to reduce perceived latency, and making LLM calls in parallel. By understanding specific performance bottlenecks and applying these techniques selectively based on use cases, developers can optimize their AI agents for better performance, cost, and reliability. Effective methods often involve rethinking how users interact with the agent, rather than solely relying on technical solutions.