local-context-memgpt

You can persist agent context across sessions by using MemGPT with a local SQLite backend. This local-first memory system stores mod history and user preferences, enabling semantic search over past interactions without relying on external servers.

When the model context window is exceeded, MemGPT offloads data into archival_memory while retaining critical data in core_memory. This layering enables semantic search over extensive mod history locally, preventing context overflow without losing important details.

Yes, this local-first memory approach ensures safe offline operation and data sovereignty. By using a local SQLite backend, all mod history and personalized rules reside entirely on the user's machine, preserving data residency without external server calls.

Yes, enabling long-term memory for Sky-Claw agents requires MemGPT with a local SQLite backend and integration via MemGPTBridge. This configuration manages memory layering and cross-session persistence for mod history and user preferences.

The best way to manage memory layering is using MemGPT's archival_memory and core_memory tiers. This flexible retention strategy allows agents to keep essential preferences active while archiving extensive mod histories for semantic search retrieval.