dvcrn Muninn - The Universal Memory Layer
Muninn is an advanced per-project memory system designed for AI agents. It uses the Context Exchange Protocol (CXP) to provide high-performance, token-efficient context management.
🛡️ Security & Privacy (Local-First)
Muninn is designed with a Privacy-First philosophy.
- Local Storage: All project indexes and memories are stored exclusively on the host machine within the
.muninn/folder (per project) or~/.muninn/(for global settings). - No Data Exfiltration: Muninn does not transmit any code, context, or memories to third-party servers or remote APIs. All processing (semantic search, indexing, and context injection) happens locally using the high-performance Rust-based CXP engine.
- Transparent Control: Memories are stored as standard Markdown files. You can inspect, edit, or delete them at any time.
⚙️ Integration
Muninn operates as an MCP (Model Context Protocol) server.
🚀 The Muninn Protocol (ENFORCED)
As an agent, you MUST follow the "Brain-First" workflow to remain efficient and accurate. The MCP server ensures project safety by validating context before allowing sensitive tool operations.
- Orient Yourself (
brain_check): Call this at the start of every session or when switching projects. It loads the local project index and identifies key files and patterns. - Search Before You Code (
search_context): Never assume you know the codebase. Use local semantic search to find existing patterns or prior decisions. - Learn as You Go (
add_memory): Every significant fix, architectural decision, or user preference should be saved locally as a memory.
🛠 Tool Reference (MCP)
init_project: Initialize a local Muninn Brain in a project directory.brain_check: MANDATORY orientation step. Loads local context and patterns.add_memory: Persist facts and decisions to the local.muninn/memoriesfolder.search_context: Query the local project index semantically.reindex_context: Rebuild the local semantic index.health_check: Verify the status of the local Muninn system.
🦞 Pro-Tip for Agents
Muninn surgically retrieves only what's needed. Instead of reading large files, use search_context to find the exact lines that matter. This significantly reduces context window overhead and token costs while keeping your work strictly local and secure.