[Feature Request] Implement Shared Memory Mechanism for Workforce Handoff

[Aaron617]

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Motivation

Currently, the workforce system does not store the complete function-calling trajectory of each worker in memory. This creates challenges in task handoff, as subsequent agents do not have sufficient context to continue processing efficiently.

Proposed Solution:

Develop a shared memory mechanism to retain and share the memory context across all agents.

Modes of Memory Sharing:

All (High Priority): Share complete memory across all agents, ensuring the reasoning agent and other critical agents receive the full context.

Memory Pool: Create a configurable memory pool where relevant context can be shared between selected agents.

No Sharing (The current workforce): Disable memory sharing for specific tasks that do not require continuity.

Key Tasks:

Implement memory initialization configuration to define the desired memory-sharing mode.

Develop a memory pool manager to dynamically allocate and update shared context.

Enable seamless integration with the workforce’s function-calling trajectories.

Test and validate the performance of different memory-sharing modes.

Reference

1. https://github.com/openai/openai-agents-python

Solution

No response

Alternatives

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Additional context

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[lightaime]

Related memory features in CAMEL:

camel/camel/agents/chat_agent.py

Line 358 in 58a4f6e

def load_memory(self, memory: AgentMemory) -> None:

camel/camel/agents/chat_agent.py

Line 422 in 58a4f6e

def save_memory(self, path: str) -> None: