Build An AI Companion App With Voice And Persistent Memory

Mem0 can provide a voice-based wellness companion with persistent cross-session context by storing and retrieving each person’s memories under a stable `user_id`, allowing the system to reference prior conversations rather than restart each visit. The example application combines Mem0 for per-user memory, GPT-4o mini for responses, faster-whisper for speech transcription, edge-tts for spoken replies, and Streamlit for the interface. Its core memory operations are adding, searching, and retrieving all memories, with semantic search supplemented by a `get_all` fallback because newly stored memories may not immediately be indexed. For short check-ins, disabling Mem0’s default fact extraction with `infer=False` preserves raw exchanges that might otherwise not be stored, while longer conversations may benefit from structured extraction. The implementation also notes current Mem0 filter requirements, response pagination handling, and the importance of binding `user_id` to a durable authentication identifier to avoid accidentally creating separate memory histories. Because wellness conversations can involve crisis, medication, or self-harm content, the system should screen transcripts before sending them to either the language model or memory service and direct affected users to appropriate real-world support resources.

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