Persistent long-term memory for AI agents via MCP, saving 80-90% memory-related token costs by enabling on-demand recall instead of always-injecting context.
Local-first persistent memory for coding agents and MCP clients. It keeps important project context across sessions and reduces wasted tokens by retrieving only relevant memories instead of replaying unnecessary history.
Persistent, self-curating memory for coding agents. It enables local, zero-cost context recall through MCP tools with hybrid retrieval and autonomous consolidation.
Model-agnostic code memory MCP server using budget-aware graph retrieval for AI agents, providing sub-millisecond queries, token budgeting, and deterministic results without embeddings or vector databases.
MCP-native persistent-memory agent that remembers user preferences across sessions, forgets superseded facts, and recalls relevant memories within a tight token budget.