How do I speed up my AI agent?

To optimize the performance of AI agents, developers often focus on reducing latency and costs by identifying and addressing specific bottlenecks, which may involve diagnosing latency sources and employing tools like LangSmith for better visibility. Strategies include modifying the user experience to manage perceived latency through streaming responses or running agents in the background, as well as minimizing the number of large language model (LLM) calls by integrating code with LLMs. Developers can also speed up LLM calls by choosing faster models, though this may affect accuracy, and controlling input length for quicker responses. Utilizing parallel processing where applicable, supported by frameworks like LangGraph, is another effective approach. Ultimately, enhancing AI agent speed involves balancing performance, cost, and capability, sometimes by rethinking user interaction rather than purely technical adjustments.

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