ZipAgent implements professional conversation state management through the Context object, solving the problem of history memory and state maintenance in multi-round conversations. This feature eliminates the need for developers to manually handle complex dialog logic and greatly improves development efficiency.
The context management system has the following features:
- Automatic recording of complete dialog history, including user input and Agent responses
- Supports message retention and referencing across rounds
- Provides collection of dialog statistics such as Token usage and number of dialog rounds
- Serializable storage for easy persistence and recovery
Typical usage flow is as follows:
# 创建上下文实例
context = Context()
# 第一轮对话
Runner.run(agent, "我的名字是小明", context=context)
# 第二轮对话
response = Runner.run(agent, "我叫什么？", context=context)
print(response.content) # 输出："你叫小明"This mechanism is particularly suitable for application scenarios that require long-term memory, such as personalized assistants and customer service robots.
This answer comes from the articleZipAgent: a lightweight Python framework for building exclusive AI assistants in 5 minutesThe
May not be reproduced without permission:AI productivity tools " ZipAgent's Context Management Mechanism Simplifies Maintenance of Complex Dialog State
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