This project provides a system for managing agent context and session memory, featuring an agent context compactor, an AI session memory manager, and a tool output sandbox. It functions as a middleware layer and server extension for the Model Context Protocol to optimize context windows and reduce token usage.
The system optimizes agent performance by sandboxing tool outputs and externalizing large data sets, replacing raw I/O with pointers and concise summaries. It employs a persistent knowledge base that indexes session history and tool outputs for retrieval via full-text search, ensuring session continuity across compaction events.
The capability surface includes full-text indexing for web and local content, parallel I/O orchestration for concurrent network and shell commands, and an isolated environment for polyglot code execution. It also incorporates security primitives such as credential redaction, command permission enforcement, and network fetch hardening to block dangerous URL schemes.
The toolkit includes system health verification and diagnostic tools to track context savings and maintain the internal knowledge base.
