Persistent Context Transfer Between Multiple Agents

[EwoutH]

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🔖 Feature description

The proposed feature would involve enhancing the existing state serialization in L♾️pGPT to allow for the transfer of an agent's memory/context to one or more other agents. This functionality would be invaluable in scenarios where multiple agents need to interact with each other or return to previous discussions, maintaining awareness of the context of each conversation.

✔️ Solution

To accomplish this, we would need to extend the serialization and deserialization processes to not only handle saving and loading the state of a single agent, but also transferring this state between different agents.

This could be implemented as follows:

1. Agent State Export: An agent should be able to export its current state, including memory/context, into a standardized format that can be imported by other agents. This could be a serialized object, a JSON representation, or another appropriate format.

2. Agent State Import: An agent should be able to import a previously exported state. This would update its current memory/context to reflect the imported state.

This feature would add to the modularity of L♾️pGPT and improve its utility in multi-agent conversations and simulations. The ability to share context between agents would also allow for more seamless conversation continuity, as agents could "remember" previous discussions.

❓ Alternatives

An alternative solution could be to have a shared database or context store that all agents can access and update. While this might be simpler to implement, it could lead to potential synchronization issues and may not provide the same level of flexibility as the proposed solution.

📝 Additional Context

This feature would further extend the usefulness of L♾️pGPT's existing state serialization capabilities, allowing for more complex agent interactions and conversation simulations. It would be particularly useful in scenarios where continuity and context-awareness across multiple agents are important, such as in customer service chatbots, virtual assistants, or interactive storytelling applications.

Acknowledgements

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[swapneils]

I'm mainly concerned that this might interfere with the integration of other state-stores later on (e.g. Redis), unless serializing their state is more trivial than I expect. Perhaps it would be sufficient to run a "summarize" on the context (with a prompt that pushes towards detailed description) and provide that as textual input when creating other agents?