MemAuthority MCP server

MemAuthority — Git-Backed Long-Term Memory for AI Agents

中文

MemAuthority is a Git-backed long-term memory system for AI agents, exposed through MCP (Model Context Protocol). It lets a new session, another machine, or another agent recover the project's accumulated context and continue working instead of rediscovering the same decisions and pitfalls.

Rather than attempting to record every single detail, it focuses on distilling:

Lessons and conclusions that future agents should never have to rediscover from scratch.

A clear division of responsibility:

• You act as the gatekeeper: deciding what is truly worth keeping for the long haul;
• The Agent handles execution: understanding, retrieving, summarizing, updating, and pruning memories;
• MemAuthority provides the safety net: keeping long-term memory reliable and available on demand while protecting it from stale writes, duplicate retries, and interrupted updates.

The agent still decides what the content means and whether it should change. MemAuthority makes sure the memory that remains is reliable over time.

Who Is This For?

MemAuthority is designed for users who prioritize the quality of long-term memory.

You may have experienced the lack of control with various memory tools:

• Unclear why something was remembered;
• Uncertain when it will be recalled;
• Unable to verify whether information has become obsolete;
• Left with no clean way to make precise edits or deletions.

Or perhaps you have tried maintaining a memory store, only to watch it grow bloated and messy over time — eventually confusing the agent rather than helping it.

MemAuthority is a good fit when:

Want to invest minimal effort curating memory quality, while delegating all routine organization to the agent.

"Investing effort" does not mean manually editing files all day. It means making high-leverage decisions at key moments:

• Is this information worth keeping long-term?
• When should the memory be reviewed or cleaned up?
• Does any of this involve sensitive data or secrets?
• Does the agent's proposed curation align with your actual intent?

All mechanical heavy lifting — formatting, categorizing, archiving, and targeted retrieval — is handled entirely by the agent.

Why Not Just Use MEMORY.md?

If your project's memory is small, rarely changes, or you simply do not want to spend any attention on memory maintenance, sticking with a plain MEMORY.md is the easiest choice.

Once that file stops being just a note and becomes long-term project state that future sessions need to trust, MemAuthority starts to become useful.

It addresses a higher-order need:

Turning long-term memory maintenance into a reliable, controllable, and engineered workflow.

It provides far more than just "letting an agent edit Markdown." It delivers a robust operational architecture:

• Precise On-Demand Loading: Loads memory only when a task actually needs it, preventing irrelevant noise from bloating or polluting the context window;
• Clear Role Separation: Distinctly separates handoff state, standing rules, milestone progress, and pitfall avoidance;
• Concurrency & Version Safety: Enforces strict version checks (CAS — Compare-And-Swap) before writes, preventing stale revisions from silently overwriting fresh content;
• Idempotency & Deduplication: Safe against retries caused by network glitches or aborted runs without creating duplicate memories;
• Convergence & Full History: Keeps the active working memory lean and compact, while relying on Git for complete, auditable revision history;
• Transactions & Crash Recovery: Features explicit journaling and recovery mechanisms in case of interrupted or failed writes.

A useful way to think about it is:

Authority stores the state a future agent should inherit now; Git stores the full history.

In short:

Shortened. The full README is on GitHub.