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name: inmemoria description: Use when building persistent codebase intelligence for AI agents or integrating knowledge systems via MCP

In Memoria: Persistent Codebase Intelligence

In Memoria is an MCP server that learns your codebase patterns once, then exposes that intelligence to AI agents persistently. Instead of re-analyzing code on every interaction, it maintains a semantic understanding of your architecture, conventions, and decisions.

Core Concept

Setup → Learn → Verify → Serve. After that, AI agents query persistent intelligence without repeated parsing.

Quick Start (5 minutes)

# 1. Configure for your project
npx in-memoria setup --interactive

# 2. Build intelligence database
npx in-memoria learn ./src

# 3. Verify it worked
npx in-memoria check ./src --verbose

# 4. Keep it fresh (optional but recommended)
npx in-memoria watch ./src

# 5. Expose to agents via MCP
npx in-memoria server

When to Use

✅ Use In Memoria:

• Building long-lived AI agent partnerships (Claude, Copilot, etc.)
• Projects where consistency across sessions matters
• Teams wanting shared codebase intelligence

❌ Skip it:

• One-off analysis (use npx in-memoria analyze [path] directly)
• Simple projects agents can read directly

The 5 Core Commands

Command	Purpose	When
setup --interactive	Configure exclusions, paths, preferences	First time only
learn [path]	Build/rebuild intelligence database	After setup, major refactors
check [path]	Validate intelligence layer	After learn, before server
watch [path]	Auto-update intelligence on code changes	During development (optional)
server	Start MCP server for agent queries	After check passes

Key difference: learn builds persistent knowledge. analyze is one-time reporting only.

What Agents See

When connected, agents can query:

• Project structure - Tech stack, entry points, architecture
• Code patterns - Your naming conventions, error handling, patterns used
• Smart routing - "Add password reset" → suggests src/auth/password-reset.ts
• Semantic search - Find code by meaning, not keywords
• Work context - Track decisions, tasks, approach consistency

Troubleshooting

Issue	Fix
Learn fails	Verify path is correct; check file permissions
Check reports missing intelligence	Run learn [path] again
Agent doesn't see new code	Is watch running? Start it: npx in-memoria watch ./src
Server won't start	Run check --verbose first; if issues, rebuild: rm .in-memoria/*.db && npx in-memoria learn ./src
Multiple projects conflict	Use server --port 3001 (or different port per project)

Performance Notes

• Small projects (<1K files): 5-15s to learn
• Medium (1K-10K files): 30-60s
• Large (10K+ files): 2-5min

If learning stalls (>10min), verify you're not indexing node_modules/, dist/, or build artifacts—use setup's exclusion patterns.

Key Principles

1. Local-first - Everything stays on your machine; no telemetry
2. Persistent - One learning pass; intelligence updates incrementally with watch
3. Agent-native - Designed for MCP; works with Claude, Copilot, and any MCP-compatible tool
4. Pattern-based - Learns from your actual code, not rules you define

Deployment Pattern (3 terminals)

# Terminal 1: One-time setup
npx in-memoria setup --interactive
npx in-memoria learn ./src
npx in-memoria check ./src --verbose

# Terminal 2: Keep intelligence fresh
npx in-memoria watch ./src

# Terminal 3: Expose to agents
npx in-memoria server

# Now agents (Claude, Copilot, etc.) have persistent codebase context

See GitHub for full API docs and agent integration examples.