ystem for AI-agent teams** — not just giving one agent a memory, but a shared memory fabric for a *fleet* of agents working together: private, team, and project-scoped memories behind a hard ACL, associative recall, and federated sync that keeps a mesh of nodes (and their org structure) in agreement.
claude mcp add agent-memory-os -- npx -y agent-memory-os
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7 ★ · yamantaka520/Agent-Memory-OS · read from the repository page · (checked 2026-10-16 20:00:20)
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collected from the punkpeye/awesome-mcp-servers list · type: MCP server
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first seen 2026-10-09 · last seen 2026-10-16 · seen 32× · listed by 7 sources
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score 703 · 7 ★ · 7 sources · mcp
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A **local-first memory system for AI-agent teams** — not just giving one agent a memory, but a shared memory fabric for a *fleet* of agents working together: private, team, and project-scoped memories behind a hard ACL, associative recall, and federated sync that keeps a mesh of nodes (and their org structure) in agreement. One SQLite file, zero required dependencies, Apache-2.0. Why · Compare · Install · Quickstart · Features · Federation · Web UI · User Guide The built-in web console: token-usage by agent/team/project, memory browse, and the ACL-safe association graph. Why Real work happens in **teams of agents** — a project might mix Claude Code, Codex, OpenClaw, and several Hermes profiles, across multiple teams and projects, on one machine or many. They need to share the *right* knowledge with the *right* teammates and keep private what should stay private: - **Per-agent memory** is the floor, not the ceiling: durable facts, preferences, procedures, and lessons that survive across sessions. - **Team & project memory** is the point: a team sees team: memory; a project (a subset of the team) sees project: memory; nothing leaks across the boundary. Membership is first-class and manageable, and drives the ACL. - **Federation** keeps a mesh honest: memories *and* the org structure (teams/projects/memberships) converge across nodes, so project: means the same thing everywhere. - **Local-first** avoids the latency, cost, and privacy tradeoffs of cloud memory platforms — memories live in a local SQLite file, and each prompt receives only the relevant, budgeted slice. Features - **Local-first, zero-dependency core** — one SQLite file (FTS5), no server required. pip install and go. - **Teams & projects, first-class** — teams are sets of node members; a project's members are a subset of its team. team: memory reaches the whole team, project: memory only that project — a hard ACL, managed in the console/CLI/API. Removing a member re-scopes recall instantly; deleting a scope revokes its memory. - **Federated across nodes, with a real trust model** — portable bundles + peer sync converge memories, links, profiles, **and the org structure** (teams/projects/memberships) with last-writer-wins + tombstones. Per-peer policy (shared/full/team:/project:) is an **enforced authorization scope**: a peer can only assert membership within its own scope and can only *shrink* a memory's visibility, never widen it — no cross-scope escalation from a bundle. - **Revocation that propagates** — an independent ACL clock carries a post-hoc share/revoke across the mesh, so revoking access actually retracts it on peers that already synced the memory — without disturbing the decay clock. - **Requester-aware ACL** — every agent has private, agent, team, project, and global memories; visibility is a hard gate enforced before ranking, never a soft score. Candidate indexes return IDs only; content is re-read through the gate. - **Dynamic context packs** — token-budgeted, auditable memory selection per prompt (context_pack_report() explains every include/exclude decision). - **Truth arbitration** — duplicate suppression, contradiction detection (CONFLICT markers), and reserved budget for core memories. - **Associative recall (resonance)** — an authoritative memory_links graph lets related memories surface even when they share no query terms; traversal is ACL-safe (invisible nodes are untraversable). - **Hebbian reinforcement** — memories recalled together grow stronger links (record_recall, or auto_reinforce=True on context packs); unhelpful recalls weaken links and confidence (helpful=False). - **Per-agent recall profiles** — different agent personas weight memory types differently (an engineer leans on procedure, a companion on preference); profiles persist in the database and re-weight ranking only, never bypassing ACL. - **Memory lifecycle** — exponential/linear decay, pinning, hard expiry, and a write-side consolidate() pass that merges duplicates and synthesizes strongly co-recalled clusters into concept memories.