Centralized multi-project state & handoff MCP server that lets AI agents save checkpoints in one session and resume in another with minimal token overhead, including git snapshots and code-graph integration.
Local-first MCP server that persists AI coding agent memory and session context, enabling seamless resume across sessions with searchable memories and checkpoints.
Local MCP server that lets your AI coding agent query its own cross-tool project history - file/command freshness, past test failures, cost & token spend, cache status, and session handoff - over stdio, 100% local, no telemetry.
A local-first MCP server that snapshots project working state into a structured context object, enabling agents to share and resume sessions seamlessly without leaving your machine.
Provides operational continuity for AI coding agents, preserving task state, decisions, checkpoints, and project context across sessions and model switches via MCP.
