memory-persistencev1.0.1

A customer support AI agent stores user preferences, past issues, and resolutions as persistent memories across sessions. When a user returns, the agent recalls their history (e.g., 'User prefers dark theme', 'Previous bug #123 fixed') and provides personalized, context-aware help without repeating questions.

Multiple AI agents (e.g., coder, tester, reviewer) collaborate on a codebase using shared memories to record team decisions like 'Use React' or 'Bug #123 fixed'. Each agent can retrieve shared memories to stay synchronized, while keeping private notes about individual tasks and user preferences.

An executive assistant AI remembers user habits, meeting preferences, and work hours (e.g., 'Works from 9am to 6pm') to schedule meetings and prioritize tasks. Semantic search via embeddings allows the assistant to find relevant memories even when phrasing differs, such as 'what time does user work'.

An AI tutor stores a student's learning progress, preferred explanation styles, and recurring mistakes as memories. It uses conversation summarization to extract key takeaways from each session and adapts future lessons, while optionally sharing anonymized insights with other tutors via shared memory.

A healthcare AI assistant maintains private memories of a patient's symptoms, medication schedules, and preferences across visits. It uses embedding search to retrieve relevant history ('allergies to penicillin') and shared memories to coordinate with other care agents, ensuring continuity of care while respecting privacy.

The core memory-persistence skill is open-source and free to use with local/SQLite backends. Premium features include managed GitHub/Gitee sync, advanced embedding hosting, and priority support, sold as a subscription or one-time license.

Hosted memory backend service that handles storage, embedding generation, and multi-agent synchronization. Customers pay based on memory volume, API calls, and number of agents, with tiered plans for individuals, teams, and enterprises.

Offer custom integration services to embed the memory system into existing AI agent platforms (e.g., customer support, healthcare). Revenue comes from setup fees, training, and ongoing maintenance contracts.