Memory Arbiter Mcp

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Memory Arbiter is a trustworthy local fact layer for AI agents — not just shared memory, but shared facts that are current, trusted, traceable, and safe to use. It is a local SQLite service exposed over MCP: four product tools, evidence-based recall, advisory conflict notices, and user-authorized governance. Every fact is stored once in local SQLite and every model it can call runs locally.

Current release: 0.16.9 (0.16.6-line repair release: write-time content dedup gate, internal eval harness, first-run demo protocol, similarity-hint redesign, conflict-detection repair wave).

Why trust it

• One complete source of truth. Every memory keeps its full original text. Evidence vectors, full-text search, and rankings are all derived indexes — rebuildable, never the only copy.
• Provenance on every write. Each memory carries source_type, source_ref, event_time, and ingest_time. The user_confirmed label is reserved by convention for facts the user explicitly verified; technically enforced protection is what happens after labeling — a user_confirmed memory is locked against silent edits.
• Trust levels. normal/protected/locked protection levels prevent an agent from silently overwriting what is locked; memory_govern(confirm) promotes a memory to user_confirmed only with per-action user authorization.
• Full version history. Every edit appends to memory_history with a version bump, and supersede chains keep old facts traceable instead of silently replaced.
• One conflict record per event. A single conflicts table holds the immutable detection snapshot, value groups, decision, and application results for each one-to-many conflict event. Qwen proposes no winner and never edits memory.
• Authorized governance. Every state-changing memory_govern action requires per-action authorized=true after the user confirms that specific action.
• Local-only. Embeddings run on a local GGUF model; the optional Qwen filter is a local GGUF too. The single outbound call is an optional PyPI update check, disabled with update_check.enabled=false.

Install & quickstart

Install with your AI Agent

Paste this into Codex, Claude Code, Cursor, or another coding agent with terminal access:

Read the latest README at https://github.com/billy12151/memory-arbiter-mcp.
Install and configure the latest mema release for my operating system and current AI client.
Preserve any existing config and database; do not overwrite or delete existing data.
Ask me before choosing between materially different install modes, changing existing config,
or performing any destructive or privileged action. When finished, run mema doctor and report
the install method, config path, database path, client integration, and verification result.

The agent should treat this README as the source of truth, inspect the local environment before choosing uvx, core, vec, or semantic-local, and stop for user input when a safe choice cannot be inferred. A successful install is not complete until mema doctor has run and any warning has been reported.

Install manually

# One command: package + deps + both models + config.json (resumable downloads, ModelScope fallback)
curl -fsSL https://memarbiter.cn/install.sh | bash

Or step by step:

pip install "memory-arbiter-mcp[vec,semantic-local]"  # core + sqlite-vec + local GGUF runtime
mema setup --install   # downloads both models (~800MB, resumable) and writes config.json
mema doctor            # verify