Building an AI Manager with Shared Memory Across Platforms
The article describes how the author built an AI Manager that can maintain a continuous conversation and shared memory across different communication channels like Telegram, WhatsApp, voice calls, and web interfaces.
Why it matters
This approach demonstrates how to build an AI assistant with true cross-platform continuity, which is crucial for providing a cohesive and efficient user experience.
Key Points
- 1Developed a ManagerConversation model to store persistent memory across all platforms
- 2Implemented ManagerMemory class to track actions, facts, and search past context
- 3Used a comprehensive system prompt to make the AI aware of workspace status and past actions
- 4Enabled seamless transitions between communication channels without losing context
Details
The key challenge the author faced was making the AI Manager feel like a single, continuous brain across Telegram, WhatsApp, voice calls, and web interfaces. To solve this, they created a ManagerConversation model to store persistent memory, and a ManagerMemory class to track actions, facts, and search past context. This allows the AI to maintain awareness of the user's previous interactions and the overall workspace status, even as the conversation shifts between different platforms. The comprehensive system prompt provided to the AI further enhances its contextual understanding. This enables features like the AI recalling and summarizing past actions, or seamlessly transitioning a conversation from one channel to another without losing context.
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