In Memoria: A Persistent Intelligence Infrastructure for AI Agents

In Memoria is a cutting-edge MCP server designed to enhance AI coding assistants with persistent memory and advanced pattern learning capabilities. Traditional AI coding tools often experience memory loss between sessions, requiring users to repeatedly explain their codebases and re-establish context for each interaction. This leads to inefficiencies and wasted resources in development workflows.

The Challenge of AI Session Amnesia

Currently, when working with AI tools such as Claude, Copilot, or Cursor, developers encounter the following hurdles:

• The need to re-analyze codebases with every new session, resulting in unnecessary time and budget consumption.
• Generic suggestions that fail to align with personal or team coding styles.
• Most AI tools do not retain a memory of architectural decisions or prior corrections, which leads to inconsistencies in proposed solutions.

Consider this scenario:

# What happens now
You: "Refactor this function using our established patterns"
AI: "What patterns? I don't know your codebase."
You: *explains architecture for the 50th time*

# What should happen
You: "Refactor this function using our established patterns"
AI: "Based on your preference for functional composition and your naming conventions..."

The In Memoria Solution

In Memoria addresses these issues by providing a server that facilitates access to persistent intelligence about your code. It allows AI tools to connect with specific insights tailored to your coding style and architectural decisions through the Model Context Protocol (MCP).

Technical Architecture

The system is structured with the following components:

┌─────────────────────┐    MCP    ┌──────────────────────┐    napi-rs    ┌─────────────────────┐
│  AI Tool (Claude)   │◄─────────►│  TypeScript Server   │◄─────────────►│     Rust Core       │
└─────────────────────┘           └──────────┬───────────┘               │  • AST Parser       │
                                             │                          │  • Pattern Learner  │
                                             │                          │  • Semantic Engine  │
                                             ▼                          └─────────────────────┘
                                   ┌──────────────────────┐
                                   │ SQLite + SurrealDB   │
                                   │  (Local Storage)     │
                                   └──────────────────────┘

Core Features

• AST Parser (Rust): Utilizes Tree-sitter for code parsing, complexity analysis, and symbol extraction.
• Pattern Learner (Rust): Learns coding styles by analyzing development patterns and making tailored suggestions.
• Semantic Engine (Rust): Understands code relationships and architectural concepts, offering insights into dependencies and structure.

Learning and Intelligence

In Memoria can effectively learn your coding patterns, naming conventions, and architecture decisions, providing suggestions that align with your style:

// Learns your preferred coding style
const processUser = pipe(validateUser, enrichUserData, saveUser);

// Future suggestions maintain this style

MCP Tools and Their Use Cases

In Memoria includes an array of tools for code analysis and intelligence gathering, such as:

• analyze_codebase: Generates an architectural overview with key metrics.
• search_codebase: Enables semantic search capabilities.
• get_pattern_recommendations: Provides style-consistent suggestions.
• get_developer_profile: Retrieves a summary of learned preferences.

Use Cases for Individuals and Teams

In Memoria is beneficial not only for individual developers seeking personalized assistance but also for teams looking to share intelligence and maintain consistency across collaborative projects:

# Export team knowledge
in-memoria export --format json > team-intelligence.json

# Import on another machine
in-memoria import team-intelligence.json

Performance Optimization

Designed for efficiency, In Memoria employs incremental analysis, processing only modified files. It supports substantial codebases with up to 100k files and runs smoothly across multiple platforms (Windows, macOS, Linux).

Conclusion

In Memoria significantly enhances the capabilities of AI coding assistants by implementing persistent memory and intelligent pattern learning, ultimately improving developer productivity and code quality. Test it out with: npx in-memoria server.