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Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse, with 35.24% token savings
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Latest capture 2026-07-15 03:05
5 captures since 2026-05-25
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Scanned 2026-07-15 03:05
pyproject.toml
python ecosystem,
48 dependencies
poetry.lock
python ecosystem,
0 dependencies
uv.lock
python ecosystem,
0 dependencies
docker/requirements.txt
python ecosystem,
127 dependencies
apps/MemOS-Cloud-OpenClaw-Plugin/package.json
javascript ecosystem,
0 dependencies
apps/memos-local-openclaw/package.json
javascript ecosystem,
14 dependencies
apps/memos-local-plugin/package.json
javascript ecosystem,
15 dependencies
apps/openwork-memos-integration/package.json
javascript ecosystem,
3 dependencies
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Universal memory layer for AI Agents
Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori works with the data infrastructure you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises.
Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc.
TencentDB Agent Memory delivers fully local long-term memory for AI Agents via a 4-tier progressive pipeline, with zero external API dependencies.
The AI agent memory layer you can audit — local-first memory governance for AI agents: citations, trust policies, trace receipts, rollback. SQLite, sidecar-first, OpenClaw plugin.
Personal memory across agents
