# pi22by7/In-Memoria [Health: Active]

**Category:** 🧠 Knowledge & Memory  
**Repository:** https://github.com/pi22by7/In-Memoria  
**GitHub Stars:** 175  
**npm Downloads (last month):** 343  
**Views:** 1  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/pi22by7-in-memoria

## Description
Persistent intelligence infrastructure for agentic development that gives AI coding assistants cumulative memory and pattern learning. Hybrid TypeScript/Rust implementation with local-first storage using SQLite + SurrealDB for semantic analysis and incremental codebase understanding.

## Tools
Capabilities this server exposes over MCP:

- **analyze_codebase** — Analyze files/directories with concepts, patterns, complexity (Phase 4: now handles both files and directories)
- **search_codebase** — Multi-mode search (semantic/text/pattern)
- **learn_codebase_intelligence** — Deep learning to extract patterns and architecture
- **get_project_blueprint** — Instant project context with tech stack and entry points ⭐ (Phase 4: includes learning status)
- **get_semantic_insights** — Query learned concepts and relationships
- **get_pattern_recommendations** — Get patterns with related files for consistency
- **predict_coding_approach** — Implementation guidance with file routing ⭐
- **get_developer_profile** — Access coding style and work context
- **contribute_insights** — Record architectural decisions
- **auto_learn_if_needed** — Smart auto-learning with staleness detection ⭐ (Phase 4: includes quick setup functionality)
- **get_system_status** — Health check
- **get_intelligence_metrics** — Analytics on learned patterns
- **get_performance_status** — Performance diagnostics

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `npx` (confidence: high):

```json
"mcpServers": {
  "in-memoria": {
    "command": "npx",
    "args": ["-y","in-memoria"]
  }
}
```

## Documentation

## What pi22by7/In-Memoria does

The pi22by7/In-Memoria MCP server maintains project intelligence across coding sessions. It analyzes a codebase to identify concepts, structural patterns, complexity, technology choices, entry points, and architectural relationships. The resulting information can be queried by an MCP-compatible AI assistant instead of being rebuilt from scratch during every session.

Its intended use is development work where an assistant needs to follow an existing project’s conventions. For example, an agent can search for related code, inspect a project blueprint, receive file-routing guidance for a feature, review learned pattern recommendations, or retrieve recorded architectural decisions. The server also exposes developer-profile information describing coding style and work context.

## How it works

The implementation combines a TypeScript MCP server with a Rust processing core connected through napi-rs. Rust components handle Tree-sitter-based parsing, blueprint analysis, pattern learning, and semantic processing.

Storage is local-first. SQLite holds structured patterns and metadata, while SurrealDB with a SurrealKV backend stores persistent vector embeddings. Local transformers.js models generate embeddings. File watching supports incremental updates, and the server includes staleness detection so automatic learning can run when the stored intelligence needs refreshing.

Search supports semantic, text, and pattern modes. Semantic search finds related code by meaning, while text and pattern modes address keyword or structural queries.

## Setup and configuration

Install the package globally with npm or invoke it through npx:

- `npm install -g in-memoria`
- `npx in-memoria --help`

Learn a project explicitly with `npx in-memoria learn ./my-project`, then start the MCP process with `npx in-memoria server`. The `auto_learn_if_needed` tool can also trigger learning and includes quick setup functionality.

Claude Desktop can launch the server with the `npx in-memoria server` command and no listed environment variables. Claude Code can register the same command with `claude mcp add in-memoria -- npx in-memoria server`.

## Tools and capabilities

The server exposes 13 tools in four groups:

- Analysis: `analyze_codebase` handles files and directories; `search_codebase` provides semantic, text, and pattern search.
- Intelligence: learning, blueprints, semantic insights, pattern recommendations, coding-approach prediction, developer profiles, and contributed insights.
- Automation: `auto_learn_if_needed` checks whether learning is stale and can initiate setup or learning.
- Monitoring: system health, intelligence metrics, and performance diagnostics.

The pi22by7/In-Memoria MCP server is a fit when an agent needs both code understanding and retained project-specific guidance, including implementation routing and architectural context.

## Limitations and notes

The README identifies version 0.6.0 and an MIT license. It also describes the system as focused on codebase intelligence, so the material does not establish general-purpose memory for unrelated personal or non-code information.

_Full upstream README: https://allmcps.com/mcp/pi22by7-in-memoria/readme_