Persistent, self-curating memory for coding agents. It enables local, zero-cost context recall through MCP tools with hybrid retrieval and autonomous consolidation.
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.
Long-term memory for AI agents. Compiles conversations into a structured knowledge base with Claim/Evidence model, source provenance, append-only timeline, and contradiction detection. Multi-path retrieval (Exact + BM25 + Graph + weighted RRF + reranker) — 96.6% R@5 on LongMemEval-S, zero vector dependencies.
Provides a unified context layer for Cursor's agent with lossless token savings and verifiable memory, enabling efficient code exploration and cross-session continuity.
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.
Persistent memory for AI agents — organized by time and space. Important memories get promoted, noise decays naturally, and related knowledge clusters into a browsable topic tree. Fully automatic.