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今天的主题是：TapeAgents: a Holistic Framework for Agent Development and Optimization

Summary

The sources present TapeAgents, a novel framework for developing and optimizing large language model (LLM) agents. It leverages a structured log, called a tape, that records the agent's reasoning and actions, facilitating various aspects of the LLM agent lifecycle. TapeAgents allows for session persistence, debugging, evaluation, and data-driven optimization techniques like prompt-tuning and fine-tuning. The framework is designed to be modular and extensible, supporting both monolithic agents and multi-agent teams. The authors highlight the advantages of TapeAgents over existing frameworks by showcasing its unique combination of features, including resumable state machines, granular logs, and the ability to transform logs into training data. The sources also include examples and a case study demonstrating the use of TapeAgents for building a cost-effective enterprise form-filling assistant.

原文链接：https://www.servicenow.com/research/TapeAgentsFramework.pdf

代码：https://github.com/ServiceNow/TapeAgents