Agent Memory Os

Name: Agent Memory Os
Author: yamantaka520

by yamantaka520version 1.10.1i★ –iRuns on your computer

Registry nameio.github.yamantaka520/agent-memory-os

Install

Overview

Description from the maker (original English)

Local-first memory engine for AI-agent teams: team/project ACL, recall, and federated sync.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: AGENT_MEMORY_AGENT_ID, AGENT_MEMORY_HOME

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "agent-memory-os": {
      "command": "uvx",
      "args": [
        "agent-memory-os"
      ],
      "env": {
        "AGENT_MEMORY_AGENT_ID": "your-value",
        "AGENT_MEMORY_HOME": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add agent-memory-os --env AGENT_MEMORY_AGENT_ID=your-value --env AGENT_MEMORY_HOME=your-value -- uvx agent-memory-os

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "agent-memory-os": {
      "command": "uvx",
      "args": [
        "agent-memory-os"
      ],
      "env": {
        "AGENT_MEMORY_AGENT_ID": "your-value",
        "AGENT_MEMORY_HOME": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "agent-memory-os": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "agent-memory-os"
      ],
      "env": {
        "AGENT_MEMORY_AGENT_ID": "your-value",
        "AGENT_MEMORY_HOME": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

AGENT_MEMORY_AGENT_ID env
This agent's identity; memories default to this owner and recall carries its ACL.
AGENT_MEMORY_HOME env
Data home directory (default ~/.agent-memory).

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account yamantaka520, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7286 servers in this category
Published to the registry more recently than 46% of the other 7286 servers in this category
Basic checks equal to or better than 100% of the other 7286 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Agent Memory Os safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.10.1) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

English · 繁體中文

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.
Optional sidecars — semantic vector candidates (turbovec), MCP server, and a FastAPI Web UI, all behind extras; every candidate rejoins SQLite and passes hard gates before use.

How it compares

Most agent-memory systems optimize for LLM-driven extraction at hosted scale. AgentMemoryOS optimizes for a different point: local-first, team-scoped, and federated — memory you run yourself, shared across a fleet under a hard ACL. This is a *positioning* comparison (architecture, not a benchmark); verify each row against the projects' current docs.

	AgentMemoryOS	Mem0	Zep / Graphiti
Run it	One SQLite file, `pip install`	Self-host (configure LLM + vector DB) or hosted	Zep Cloud, or self-host Graphiti on Neo4j/FalkorDB
Core needs an LLM	No (FTS5 + optional local vectors)	Yes (LLM extraction, e.g. gpt-5-mini)	Yes (LLM builds the temporal graph)
External services	None required	LLM API + vector store	Graph DB + LLM + embeddings (3+ systems to self-host)
Scope / ACL model	Private / agent / team / project / global — hard gate before ranking	Per user / agent / session id	Per user / session graph
Cross-node federation	Yes — memories and org structure converge; revocation propagates	Centralized store	Centralized (Cloud or your graph DB)
Built-in MCP server	Yes	Via SDK	Via SDK
License / self-host	Apache-2.0, fully OSS	OSS core; graph & advanced tiers paid	Community Edition deprecated; self-host = raw Graphiti

Mem0 and Zep are strong at LLM-based extraction and managed-scale retrieval — things AgentMemoryOS deliberately doesn't do. Reach for AgentMemoryOS when you want a dependency-light memory you own, shared correctly across a *team* of agents, that keeps working offline and syncs on your terms.

Install

pip install 'agent-memory-os[full]'    # recommended: everything (Web UI, MCP, turbovec)

Or pick pieces: agent-memory-os (core, zero dependencies), [api] (Web UI), [mcp] (MCP server), [semantic] (turbovec vector recall).

Docker: the prebuilt multi-arch image is the complete AgentMemoryOS (web console + MCP server + CLI); the first argument picks the mode:

docker run -p 8000:8000 -v amos-data:/data yamantaka520/agent-memory-os        # web console (default)

docker run -i --rm yamantaka520/agent-memory-os mcp # stdio MCP server docker run --rm -v amos-data:/data yamantaka520/agent-memory-os check # any CLI command

Or docker compose up -d. Console at http://localhost:8000, memories persist in a volume. See the Docker guide (Docker Hub image + a two-node sync mesh).

Requires Python 3.11+ with SQLite FTS5 (included in standard CPython builds).

After installing, run two commands:

agent-memory doctor          # verifies FTS5, turbovec, and the other extras

(add --install to auto-install anything missing)

agent-memory token create # protects the Web UI API with a bearer token

The token is stored at /web_token (mode 600); agent-memory-web picks it up automatically and the console prompts for it on first use. Manage it later with agent-memory token show|rotate|disable. Two narrower tiers exist: --readonly (GET-only) and --sync (federation routes only — hand this to a peer instead of the admin token).

Quickstart

Prefer a runnable script? examples/team_memory.py shows three agents sharing one store under a hard ACL in ~40 lines — python examples/team_memory.py.

from agent_memory_os import MemoryClient, RecallProfile

client = MemoryClient(home="~/.agent-memory")

Write memories with ownership and visibility

client.add("User prefers dark mode.", owner="mizuki", type="preference", visibility=[]) # private to owner client.add("Deploy target is port 8000.", owner="neo", type="environment", visibility=["global"]) # visible to every agent

Requester-aware search: each agent sees only what it may see

hits = client.search("deploy port", requester_agent_id="neo")

Token-budgeted context pack for the prompt, with reinforcement loop closed

pack = client.context_pack("deploy port", requester_agent_id="neo", max_tokens=1200, auto_reinforce=True)

Associate memories; linked memories resonate into future recalls

a = client.add("Staging deploy failed with database lock.", visibility=["global"]) b = client.add("Always snapshot before schema changes.", visibility=["global"]) client.link(a.id, b.id, relation="caused_by", weight=0.8)

Persist an agent persona: soft ranking bias per memory type

client.save_profile(RecallProfile(agent_id="neo", type_weights={"procedure": 1.5, "note": 0.7}))

Periodic hygiene: merge duplicates, synthesize concept memories

client.consolidate()

Architecture

query

-> candidate providers (FTS5 | vector sidecar | resonance graph | fallback) -> merge/dedupe by stable memory_id -> rejoin authoritative rows from SQLite -> ACL hard gate -> expires_at hard gate -> scoring (relevance x importance x confidence x freshness x reinforcement) -> per-agent profile re-weighting (soft) -> truth arbitration + context budget allocation

Design invariants:

The SQLite memories table is the single source of truth; FTS/vector indexes are disposable and rebuildable (rebuild_indexes()).
Candidate providers return IDs and scores only — content is always re-read through SQLite behind the ACL and expiry hard gates.
Association edges (memory_links) are authoritative data, survive index rebuilds, decay when unused, and never let an invisible memory bridge two visible ones.

See SPEC.md for the full contract.

Storage engines: SQLite + turbovec

AgentMemoryOS uses two storage layers with strictly different authority:

SQLite (

Only the beginning is shown; read the rest in the repo

Agent Memory Os

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Why

Features

How it compares

Install

(add --install to auto-install anything missing)

Quickstart

Write memories with ownership and visibility

Requester-aware search: each agent sees only what it may see

Token-budgeted context pack for the prompt, with reinforcement loop closed

Associate memories; linked memories resonate into future recalls

Persist an agent persona: soft ranking bias per memory type

Periodic hygiene: merge duplicates, synthesize concept memories

Architecture

Storage engines: SQLite + turbovec