memorykit-mcp-server

MemoryKit MCP Server

Cognitive memory for AI coding assistants — gives Claude Desktop, Claude Code, GitHub Copilot, and Cursor persistent memory across conversations. No database, no Docker, no API keys required.

The Memory That Pays for Itself

Most memory tools store everything and make you pay full re-discovery cost on every retrieval. MemoryKit doesn't:

• Costs scale with the question, not your history — a quick check-in costs ~200 tokens, a deep recall costs up to 1,500. Never the whole store.
• Junk never gets stored — low-value and duplicate entries are rejected before they're written, so you're not paying to filter noise later.
• It shrinks itself — stale entries auto-prune, important ones auto-promote. No manual cleanup, no slow bloat after months of use.
• You see the receipt — every retrieval reports real numbers: tokens spent learning a fact vs. tokens spent recalling it.

You ask	Context budget
"what was I doing?"	~200 tokens
"how do I deploy this?"	~300 tokens
"what's our DB choice?"	~500 tokens
"what happened last week?"	~1,500 tokens

Example: spend 1,200 tokens figuring something out once, recall it for ~70 tokens later — MemoryKit reports that 94% efficiency gain back to you, every time.

Accurate, Not Just Fast

• Understands meaning, not just keywords — semantic search catches paraphrased questions a plain keyword match would miss.
• Recent + important wins over "kinda similar" — stale notes don't outrank what actually matters right now.
• Catches contradictions — conflicting info gets flagged, not silently duplicated.

How It Works

MemoryKit stores memories as Markdown files on your local filesystem using a brain-inspired 4-layer architecture:

Layer	What it stores	Lifetime
Working	Active session context, in-progress tasks	Short-lived (decays after 7 days)
Facts	Architecture decisions, tech stack choices	Permanent
Episodes	Bugs found, incidents, debugging sessions	Medium-term (compacted after 30 days)
Procedures	Coding rules, conventions, how-to guides	Permanent

Memories are stored under ~/.memorykit/<project-name>/ — isolated per project via automatic git root detection. No configuration required for basic use.

Quick Start

1. Install

npm install -g memorykit-mcp-server

2. Initialize in your project

cd /your/project
memorykit init

This creates:

• ~/.memorykit/<project-name>/ — Memory storage directory
• .vscode/mcp.json — GitHub Copilot MCP server config
• .mcp.json — Claude Code MCP server config
• CLAUDE.md — Claude Code instructions to use memory proactively
• .github/copilot-instructions.md — GitHub Copilot instructions to use memory proactively

The instruction files tell AI models to automatically check memory before starting tasks and save learnings when completing work. This ensures memory is used consistently without manual prompting.

3. Configure your AI assistant

GitHub Copilot in VS Code — Already configured! memorykit init creates .vscode/mcp.json and .github/copilot-instructions.md automatically. The instructions tell Copilot to check memory before tasks and save learnings after.

Claude Code in VS Code — Already configured! memorykit init creates .mcp.json and CLAUDE.md automatically. The instructions tell Claude to check memory before tasks and save learnings after.

Claude Desktop — Edit the config file:

OS	Path
Windows	%APPDATA%\Claude\claude_desktop_config.json
macOS	~/Library/Application Support/Claude/claude_desktop_config.json
Linux	~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "memorykit": {
      "command": "memorykit",
      "env": {
        "MEMORYKIT_PROJECT": "/absolute/path/to/your/project"
      }
    }
  }
}

Cursor — Add to Cursor MCP settings using the same format as Claude Desktop.

4. Restart your AI assistant

The 6 MemoryKit tools will appear in the tool list.

Available Tools

store_memory

Save a new memory entry. Importance is scored automatically (0.0–1.0) and the correct layer is selected based on content type.

Parameter	Type	Required	Description
content	string	✅	The memory content
tags	string[]	❌	Categorization tags (auto-detected if omitted)
layer	enum	❌	working, facts, episodes, procedures (auto-detected if omitted)
scope	enum	❌	project (default) or global
file_hint	string	❌	Target filename within layer (e.g. "technology")
acquisition_context	object	❌	ROI tracking: { tokens_consumed: number, tool_calls: number }

Example:

{
  "content": "We decided to use PostgreSQL as the primary database because of ACID guarantees and existing team expertise.",
  "tags": ["database", "architecture"],
  "acquisition_context": { "tokens_consumed": 1200, "tool_calls": 3 }
}

retrieve_context

Get relevant memory context for a query. The Prefrontal Controller classifies your query and routes to the appropriate memory layers automatically.

Parameter	Type	Required	Description
query	string	✅	Natural language question or topic
max_tokens	number	❌	Token budget override (default: 4000)
layers	string[]	❌	Restrict to specific layers
scope	enum	❌	all (default), project, or global

Example:

{
  "query": "what database are we using and why?",
  "scope": "all"
}

update_memory

Modify an existing memory entry by ID.

Parameter	Type	Required	Description
entry_id	string	✅	Entry ID to update
content	string	❌	New content
tags	string[]	❌	Updated tags
importance	number	❌	Manual importance override (0.0–1.0)

forget_memory

Delete a memory entry by ID.

Parameter	Type	Required	Description
entry_id	string	✅	Entry ID to delete

consolidate

Run memory maintenance: prune stale working memory, promote high-importance entries to long-term layers, and compact old episode files.

Parameter	Type	Required	Description
scope	enum	❌	project (default), global, or all
dry_run	boolean	❌	Preview changes without modifying files

list_memories

Browse the memory structure and see entry counts per layer.

Parameter	Type	Required	Description
scope	enum	❌	all (default), project, or global
layer	enum	❌	Filter to a specific layer

Memory Quality Gates

MemoryKit filters low-quality entries automatically before storing:

• Importance Floor — Content scoring below 0.15 is rejected as routine or trivial.
• Duplicate Detection — Near-duplicate entries are blocked; the existing entry ID is returned so you can update it instead.
• Contradiction Warning — If new content potentially conflicts with existing knowledge, storage proceeds but a warning is returned.

CLI Commands

# Initialize memory for the current project
memorykit init

# Initialize global memory shared across all projects
memorykit init --global

# Show memory statistics (entry counts, file sizes, last consolidation)
memorykit status

# Run memory consolidation (prune stale, promote important, compact old episodes)
memorykit consolidate

# Preview consolidation without making changes
memorykit consolidate --dry-run

Configuration

After memorykit init, a memorykit.yaml is created at ~/.memorykit/<project-name>/memorykit.yaml:

version: "0.1"

working:
  max_entries: 50
  decay_threshold_days: 7
  promotion_threshold: 0.70

facts:
  max_entries_per_file: 100

episodes:
  compaction_after_days: 30

consolidation:
  auto: true
  interval_minutes: 0

global:
  enabled: true
  priority: "project"

context:
  max_tokens_estimate: 4000

quality_gates:
  importance_floor: 0.15
  duplicate_jaccard_threshold: 0.6
  duplicate_word_overlap: 3

Storage Layout

~/.memorykit/
├── <project-name>/
│   ├── memorykit.yaml
│   ├── working/
│   │   └── session.md
│   ├── facts/
│   │   ├── architecture.md
│   │   ├── technology.md
│   │   └── general.md
│   ├── episodes/
│   │   └── 2026-03-04.md
│   └── procedures/
│       └── general.md
└── facts/                       # Global memory (shared across all projects)
    └── ...

Architecture

AI Assistant (Claude / Copilot / Cursor)
     │  MCP Protocol (stdio)
     ↓
MemoryKit MCP Server (Node.js)
     ├── Prefrontal Controller   Query classification & file routing
     ├── Amygdala Engine         Importance scoring (9-signal, 0.0–1.0)
     ├── Quality Gates           Importance floor, duplicate detection, contradiction warning
     ├── Normalizer              Prose-to-MML normalization pipeline
     └── File Storage            Local Markdown files (~/.memorykit/)

Query Classification (Prefrontal): Routes retrieve_context queries to the right files:

• Continuation → working/session.md
• Fact retrieval → facts/*.md
• Deep recall → episodes/*.md
• Procedural → procedures/*.md
• Complex → all layers

Importance Scoring (Amygdala): 9 signals scored 0.0–1.0 — decision language, explicit importance markers, code blocks, technical depth, novelty, sentiment, conversation context, question patterns, and MML structure.

Environment Variables

Variable	Description	Default
MEMORYKIT_PROJECT	Absolute path to project root	Auto-detected from git root

Requirements

• Node.js 18+
• No database required
• No Docker required
• No API keys required

Contributing

See CONTRIBUTING.md.

Changelog

See CHANGELOG.md.

License

MIT — see LICENSE for details.

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions