Ariel Memory MCP server

a-memory

Your AI agents forget. a-memory makes them remember.

4-tier agent memory with hybrid search and a real knowledge graph — all in plain SQLite files. Zero cloud. Zero external APIs.

Also available on PyPI: pip install a-memory —

optional extras: a-memory[embeddings] for real multilingual embeddings.

Why SQLite?

Every other memory server sends your agent's data through a cloud API or requires a separate vector database.

a-memory stores everything in SQLite files on your machine.

• Zero infrastructure. No Docker, no database server, no embedding API keys.
• Zero data leaving your network. Works air-gapped.
• Layer-isolated by design. User facts and agent identity never share a namespace.
• One directory = entire memory. Back up with cp, sync with rsync.

Why this exists

Three problems a-memory solves:

① Agent self-evolution — your AI stops repeating mistakes between sessions. It remembers decisions, errors, and corrections in a dedicated agent layer, and an hourly consolidation sweep promotes what matters into long-term facts.

② User persona persistence — your agent knows who it's talking to even after weeks of silence. Preferences, history, emotional context live in the user layer, isolated from agent identity.

③ Project continuity — project tracks per-project context: decisions with rationale and outcomes, artifact maps, a graphify-powered code index — so a fresh session picks up where the last one left off.

Get started

pip install a-memory
a-memory          # MCP server on stdio — connect from any MCP client

Point your MCP client at it:

{
  "mcpServers": {
    "a-memory": {
      "command": "a-memory"
    }
  }
}

HTTP transport with dashboard:

a-memory --transport http --port 8000 --dashboard

Or run from source:

git clone https://github.com/Cipher208/a-memory.git
cd a-memory
uv sync
uv run ariel-memory

The five primitives

Agents see exactly six tools — one verb per intent (5 verbs + memory_hook), no tool-choice paralysis:

Quick demo — Python MCP client:

# think — routed to the right store automatically
await session.call_tool("think", {"text": "User prefers dark mode", "layer": "user"})

Shortened. The full README is on GitHub.