Is the In-Memoria MCP server actively maintained?

The In-Memoria MCP server was last committed 10mo ago and has 3 contributors. Issue health: 92% of issues resolved. AgentRank freshness score reflects real-time maintenance activity.

In-Memoria MCP Server

Name: pi22by7/In-Memoria
Rating: 3.2 (1 reviews)
Author: pi22by7

pi22by7/In-Memoria

Score: 54.7 Rank #10819 MCP Server

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Overview

pi22by7/In-Memoria is a Rust tool licensed under MIT. Persistent Intelligence Infrastructure for AI Agents Topics: ai-agents, ai-tools, codebase-intelligence, developer-tools, local-first, mcp-server, persistent-memory.

Ranked #10819 out of 24846 indexed tools.

Ecosystem

Rust MIT

ai-agentsai-toolscodebase-intelligencedeveloper-toolslocal-firstmcp-serverpersistent-memory

Score Breakdown

Stars 15% 174

174 stars → growing community

Freshness 25% 10mo ago

Last commit 10mo ago → stale

Issue Health 25% 92%

23/25 issues closed → responsive maintainer

Contributors 10% 3

3 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads N/A

Forks 33

Description Good

License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Description low impact

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Freshness high impact

Last commit was 323 days ago — a recent commit would boost your freshness score

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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Details

Stars: 174
Forks: 33
Open Issues: 2
Closed Issues: 23
Contributors: 3
Dependents: —
Language: Rust
License: MIT
Last Commit: 10mo ago

Matched Queries

topic:mcp-server

From the README

# In Memoria

**Giving AI coding assistants a memory that actually persists.**

## Quick Demo

_Watch In Memoria in action: learning a codebase, providing instant context, and routing features to files._

---

## The Problem: Session Amnesia

You know the drill. You fire up Claude, Copilot, or Cursor to help with your codebase. You explain your architecture. You describe your patterns. You outline your conventions. The AI gets it, helps you out, and everything's great.

Then you close the window.

Next session? Complete amnesia. You're explaining the same architectural decisions again. The same naming conventions. The same "no, we don't use classes here, we use functional composition" for the fifteenth time.

**Every AI coding session starts from scratch.**

This isn't just annoying, it's inefficient. These tools re-analyze your codebase on every interaction, burning tokens and time. They give generic suggestions that don't match your style. They have no memory of what worked last time

Read full README on GitHub →