AI Agent Memory MCP server

AI Memory MCP Server

<!-- mcp-name: io.github.xiangzi1126/ai-agent-memory-mcp -->

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

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)

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: VOLCENGINEAPIKEY=...

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: SILICONFLOWAPIKEY=...

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: OPENAIAPIKEY=...

Any other OpenAI-compatible service: just fill in baseurl / model / apikey_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.

Shortened. The full README is on GitHub.