AI Agent Memory

Name: AI Agent Memory
Author: xiangzi1126

by xiangzi1126version 0.1.1i★ 0iRuns on your computer

Registry nameio.github.xiangzi1126/ai-agent-memory-mcp

Install

Overview

Description from the maker (original English)

Agent-agnostic persistent memory MCP server. Local-first: memories travel with your project.

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 checksi2 of 2 passedChecked from public information; code not scanned yet
UsageiVery new↓ 104/wk · ★ 0

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: 2 of 2 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

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": {
    "ai-agent-memory": {
      "command": "uvx",
      "args": [
        "ai-agent-memory-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add ai-agent-memory -- uvx ai-agent-memory-mcp

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "ai-agent-memory": {
      "command": "uvx",
      "args": [
        "ai-agent-memory-mcp"
      ]
    }
  }
}

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": {
    "ai-agent-memory": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "ai-agent-memory-mcp"
      ]
    }
  }
}

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.

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 10, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account xiangzi1126, which owns the repo
✓ Pass
Still maintained
Code last changed 4 months ago
… 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 5862 servers in this category
Published to the registry more recently than 22% of the other 5862 servers in this category
Basic checks equal to or better than 100% of the other 5862 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 AI Agent Memory safe?

It passed 2 of 2 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 4 months ago, and the latest version (0.1.1) reached the registry 4 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: Ruflo, Context7, Metabase

Alternatives

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

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

Ruflo

Agent teams, swarms, memory and hooks for Claude Code and Codex, served over MCP.

by ruvnetWidely used↓ 79.7K/wk★ 81.4KUpdated today

Runs on your computer

Install

Context7

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

by upstashWidely used↓ 972.7K/wk★ 64.4KUpdated yesterday

Computer or cloud

Install

Metabase

Lets AI clients search, explore, query, and visualize data in a Metabase instance.

by metabaseWidely used★ 50.3KUpdated 6 months ago

Runs in the cloud

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.

AI Memory MCP Server

Agent-agnostic persistent memory as an MCP Server — local-first: memories travel with your project in .aamm/, shared across Claude Code / Qoder / Cursor.

中文 | 日本語

An agent-agnostic persistent memory layer exposed as an MCP Server. Any MCP client — Claude Code, Qoder, Cursor — can reuse it. Memories live in each project's .aamm/ directory and travel with the project; different agents working the same project share one memory store, with a source_agent stamp distinguishing writers.

Architecture

SQLite — structured source of truth (CRUD + FTS5 keyword search)
Chroma (embedded) — vector retrieval, persisted to .aamm/chroma/
Embedding — any OpenAI-compatible service (Volcengine / SiliconFlow / OpenAI / others); defaults to Volcengine doubao-embedding-vision
Markdown mirror — each memory is also written to .aamm/memories/ / .md, human-readable and editable

The three layers are joined by id.

Memory categories

category	use
`user`	user preferences (tech background / dev habits / answer style)
`project`	project knowledge (architecture / stack / layout / design decisions)
`process`	work process (solved issues / bugs / debugging / lessons)
`agent`	agent collaboration (what was done / handoff notes)

Install

From PyPI:

pip install ai-agent-memory-mcp

From source:

cd ai_agent_memory_mcp

pip install . # or: pip install -e . (editable, for development)

Requires Python 3.11+.

Configure embedding (any OpenAI-compatible service)

The embedding layer is a generic OpenAI-compatible client — Volcengine / SiliconFlow / OpenAI / any compatible service works. On first run a default config is generated at .aamm/config.yml; edit as needed.

Fields (`embedding` section of `.aamm/config.yml`)

field	meaning
`provider`	label (informational only)
`model`	embedding model name
`base_url`	OpenAI-compatible endpoint
`api_key_env`	which env var holds the key
`dim`	vector dim (must match the model)

Put the key in the project root .env, then edit the embedding section of config.yml.

Examples

Volcengine doubao-embedding-vision (default; Agent/Coding Plan keys must use the Plan endpoint /api/plan/v3 — standard /api/v3 returns 401)

embedding:

provider: volcengine model: doubao-embedding-vision base_url: https://ark.cn-beijing.volces.com/api/plan/v3 api_key_env: VOLCENGINE_API_KEY dim: 2048

`.env`: `VOLCENGINE_API_KEY=...`

SiliconFlow bge-large-zh (Chinese-text optimized)

embedding:

provider: siliconflow model: BAAI/bge-large-zh-v1.5 base_url: https://api.siliconflow.cn/v1 api_key_env: SILICONFLOW_API_KEY dim: 1024

`.env`: `SILICONFLOW_API_KEY=...`

OpenAI

embedding:

provider: openai model: text-embedding-3-small base_url: https://api.openai.com/v1 api_key_env: OPENAI_API_KEY dim: 1536

`.env`: `OPENAI_API_KEY=...`

Any other OpenAI-compatible service: just fill in base_url / model / api_key_env / dim.

After switching embedding model, old vectors may mismatch in dimension; clear .aamm/chroma/ and re-remember, or run python tests/rebuild_vectors.py.

Retrieval

recall uses three-way fused retrieval to maximize hit rate:

Vector (weight 0.6): Chroma cosine; embeddings are computed from title + tags + content, so title/tag signal enters the vector
Keyword (weight 0.25): SQLite FTS5 trigram
Title/tag match (weight 0.15): +0.15 if the query appears in the title, +0.075 if in a tag

Candidates are expanded to top_k*3, then fused down to top_k. If the query contains FTS5 special characters (., *, ", -, ...), the keyword branch falls back to LIKE substring matching instead of erroring.

Work journal

Besides searchable memories, aamm keeps a human-readable work journal. After completing a user request, the agent calls journal_entry() to log what was asked / what it did / any open question. Journals are for people reading a timeline; recall does not search them. Use search_journal() only as a fallback to recover "what happened in a past interaction".

Journals are written to .aamm/logs/:

journal.db — single SQLite store (the search source, spans all dates)
YYYY-MM-DD.md — one Markdown file per day, append-only timeline

.aamm/logs/

├── journal.db # search source (all dates) ├── 2026-07-14.md # per-day timeline └── 2026-07-15.md

MCP tools

Memory (8):

remember(title, content, category, tags?, scope?) — store (three-way sync, auto-embed)
recall(query, category?, top_k=5) — fused retrieval (vector + keyword + title match)
get_memory(id) — get one
search_memories(category?, tag?, agent?) — structured filter
update_memory(id, ...) — update (re-embed + refresh md)
forget(id) — delete (three-way sync)
list_memories(category?) — list
who_am_i() — current agent + project context

Journal (3):

journal_entry(question, answer_summary, key_points?, open_question?, session_id?) — log a timeline entry
search_journal(query, date_from?, date_to?, agent?, limit=10) — fallback search over journals
setup_profile(user_name) — set the user name (shown in journals)

Management CLI

python -m ai_agent_memory_mcp.cli init                  # initialize .aamm in the current project

python -m ai_agent_memory_mcp.cli status # store overview (categories / vectors / md / journal) python -m ai_agent_memory_mcp.cli export [--dir DIR] # export all memories to Markdown python -m ai_agent_memory_mcp.cli sync # rebuild SQLite + Chroma from Markdown python -m ai_agent_memory_mcp.cli check # consistency check (db / md / chroma) python -m ai_agent_memory_mcp.cli journal [--limit N] # show recent journal entries

Wire into Claude Code (user scope; shared code, per-project data)

From PyPI (no PYTHONPATH needed):

claude mcp add aamm -s user -- python -m ai_agent_memory_mcp --agent claude-code --project-from-cwd

From a source clone, add -e PYTHONPATH= \ai_agent_memory_mcp:

claude mcp add aamm -s user -e PYTHONPATH=<clone dir>\ai_agent_memory_mcp -- python -m ai_agent_memory_mcp --agent claude-code --project-from-cwd

Qoder / Cursor are the same — just change --agent.

Data layout

.aamm/

├── memory.db # SQLite: structured memories + FTS5 ├── chroma/ # Chroma vector store ├── memories/ / .md # Markdown mirror (editable) ├── logs/ │ ├── journal.db # work journal (search source) │ └── YYYY-MM-DD.md # per-day journal timeline ├── config.yml # embedding config └── profile.json # user name

License

MIT

AI Agent Memory

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

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

Ruflo

Context7

Metabase

README from the maker

AI Memory MCP Server

Architecture

Memory categories

Install

Configure embedding (any OpenAI-compatible service)

Fields (embedding section of .aamm/config.yml)

Examples

Retrieval

Work journal

MCP tools

Management CLI

Wire into Claude Code (user scope; shared code, per-project data)

Data layout

License

Fields (`embedding` section of `.aamm/config.yml`)