How does conflict arbitration work?
Conflicts are resolved using a dual-timeline priority: user-confirmed entries take precedence, then event time, source trust level, and finally ingest time.
@billy12151
Share one memory store across all your AI coding tools. Memory Arbiter is a lightweight, fully local MCP Server with built-in dual-timeline conflict arbitration. No cloud, no LLM calls — just SQLite.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"memory-arbiter": {
"command": "memory-arbiter-mcp",
"env": {
"MEMORY_ARBITER_CLIENT": "zcode",
"MEMORY_ARBITER_AGENT_ID": "zcode-default",
"MEMORY_ARBITER_DB_PATH": "~/.local/share/memory-arbiter/memory.sqlite3"
}
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
Memory Arbiter MCP is a Model Context Protocol server that provides a shared local memory store, backed by SQLite, for multiple AI coding tools such as ZCode, Codex, Cursor, and Claude Code. It is designed for developers who need a consistent memory layer across different assistants without relying on cloud services.
Install the package with pip install memory-arbiter-mcp, then add it as an MCP server to each AI coding tool you use. The shared memory database is stored locally in an SQLite file; no additional infrastructure is required.
Conflicts are resolved using a dual-timeline priority: user-confirmed entries take precedence, then event time, source trust level, and finally ingest time.
No. User-confirmed memories are locked and cannot be overwritten by agents, ensuring critical context remains stable.
No. Everything runs locally on SQLite with no cloud dependencies and zero LLM calls for memory operations.
The server degrades gracefully through fallbacks: first FTS5, then LIKE queries, and finally reading from a JSONL backup.
Each tool connects to the same MCP server, which reads and writes a single local SQLite database file.
Memlord is a remote MCP server available at https://app.memlord.com.

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