为了服务于大众
永不遗忘的全量记忆系统 — Event-sourced long-term memory for AI agents: process memory, mistake books, experience crystallization, confidence-based decision blocking, counterfactual branches, cross-domain analogy, trajectory replay, proactive partner, semantic vector retrieval, reranker, memory graph, time decay and metacognitive knowledge-gaps. Ships as a local-first MCP server with 23 tools. Your data never leaves your machine.
Most memory systems store your data in the cloud, need a heavy dependency stack, or only do keyword search. wanyimem is local-first, single-file SQLite, and installs with one dependency (numpy).
wanyimem typical memory server --------- Data never leaves your machine (zero telemetry) cloud / SaaS Infra SQLite single file, no separate vector DB Qdrant / Neo4j / Postgres Defaults shadows the agent, blocks high-risk actions, opens counterfactual branches stores & retrieves Resources runs on 2-core / 2GB heavier Evolves zero-participation (learns from your mistakes automatically) manual "remember this"
23 MCP tools, open source (MIT), Python 3.10+, pip install wanyimem.
LLM agents forget. Every chat window is amnesia: preferences, lessons, and hard-won experience evaporate when the session ends.
WanYi Memory Core is a local-first, full-quantity, self-evolving memory system:
- Event sourcing — an append-only WAL is the single source of truth. Nothing is ever deleted; decay only affects retrieval ranking. - Semantic recall — hybrid retrieval: BM25 keywords + local Chinese embedding (BAAI/bge-small-zh-v1.5) + reranker (BAAI/bge-reranker-base) + knowledge-graph e
From the project README.
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