📇 🦀 🏠 🍎 🐧 🪟 - Cumulative memory and pattern learning for coding assistants, with local SQLite/SurrealDB…
In Memoria
以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →
**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._
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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, what you rejected, or why.
The Solution: Persistent Intelligence
In Memoria is an MCP server that learns from your actual codebase and remembers across sessions. It builds persistent intelligence about your code (patterns, architecture, conventions, decisions) that AI assistants can query through the Model Context Protocol.
Think of it as giving your AI pair programmer a notepad that doesn't get wiped clean every time you restart the session.
**Current version: 0.6.0** - See what's changed
What It Does
- **Learns your patterns** - Analyzes your code to understand naming conventions, architectural choices, and structural preferences
- **Instant project context** - Provides tech stack, entry points, and architecture in
error handling convention..."
Next session (days later):
You: "Where did we put the password reset code?"
AI: *queries In Memoria*
"In src/auth/password-reset.ts, following the pattern we
established in our last session..."
bash
Install globally
npm install -g in-memoria
Or use directly with npx
npx in-memoria --help
json
{
"mcpServers": {
"in-memoria": {
"command": "npx",
"args": ["in-memoria", "server"]
}
}
}
bash
claude mcp add in-memoria -- npx in-memoria server
bash
Analyze and learn from your project
npx in-memoria learn ./my-project
Or let AI agents trigger learning automatically
(Just start the server and let auto_learn_if_needed handle it)
npx in-memoria server
┌─────────────────────┐ MCP ┌──────────────────────┐ napi-rs ┌─────────────────────┐
│ AI Tool (Claude) │◄──────────►│ TypeScript Server │◄─────────────►│ Rust Core │
└─────────────────────┘ └──────────┬───────────┘ │ • AST Parser │
│ │ • Pattern Learner │
│ │ • Semantic Engine │
▼ │ • Blueprint System │
┌──────────────────────┐ └─────────────────────┘
│ SQLite (persistent) │
│ SurrealDB (in-mem) │
└──────────────────────┘
json
{
"servers": {
"in-memoria": {
"command": "npx",
"args": ["in-memoria", "server"]
}
}
}
.github/agents/
.github/chatmodes/
.github/agents/
*.chatmode.md → *.agent.md
inmemoria-feature.chatmode.md → inmemoria-feature.agent.md
text
Where is the authentication logic?
text
Add password reset functionality
text
Review this code for consistency
bash
git clone https://github.com/pi22by7/in-memoria
cd in-memoria
npm install
npm run build
bash
npm run dev # Start in development mode
npm test # Run test suite (98.3% pass rate)
npm run build:rust # Build Rust components