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We want to be able to persist the state of a multi-agent workflow and restart from a given state. This is required for large-scale deployment of AutoGen workflows. The key steps are:
How to inject state (e.g., conversation history) into a multi-agent workflow so it starts from a given state. This functionality can also be used to implement in-context learning for individual agents, so they learn from a curated set of instructions and examples before participating in a workflow.
How to export the state of a multi-agent workflow.
We start building (1) and (2) for individual class:
single-agent
two-agent chat
sequential chats
group chat,
and then move on to think about a composed workflow of multiple conversation patterns.
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Hi! I have following use case:
In multi-agen conversation with human, there is an Agent named "collector" which collects info about the user to json from conversation. That meta-information I want to use sometimes, and "coordinator" will decide when to call it to context.
Is this feature about state management? I want to implement it for my project, and may be it will be nice to PR this to main repo too?
You are thinking about a usage case that is a natural language text to JSON conversion. You can certainly create a two-agent chats to collect human input and then in the initiate_chat use a summary_method=reflection_with_llm and summary_args={"summary_prompt": "extract the following information ... and returns a JSON"}. This is not directly really related to the state management.
We want to be able to persist the state of a multi-agent workflow and restart from a given state. This is required for large-scale deployment of AutoGen workflows. The key steps are:
We start building (1) and (2) for individual class:
and then move on to think about a composed workflow of multiple conversation patterns.
Tasks
initiate_chat
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