Enables AI agents to automatically capture durable knowledge and retrieve only relevant, token-bounded context from a secure local-first long-term memory, with support for progressive disclosure, snapshots, health diagnostics, and background tasks.
TinyContext provides a local, token-efficient memory layer for AI agents. It enables saving and recalling memories via MCP tools, using hybrid BM25 and dense retrieval to return only context that fits the requested token budget.
Provides persistent, local-first memory with knowledge graph and hybrid search for AI coding agents, reducing token usage by storing decisions, patterns, and codebase 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.
Provides a unified context layer for Cursor's agent with lossless token savings and verifiable memory, enabling efficient code exploration and cross-session continuity.
