memory-journal-mcp 2.1.0

Project context management for AI-assisted development - Persistent knowledge graphs and intelligent context recall across fragmented AI threads

Memory Journal MCP Server

Last Updated November 26, 2025 - Production/Stable v2.1.0

Project context management for AI-assisted development - Bridge the gap between fragmented AI threads with persistent knowledge graphs and intelligent context recall

🎯 Solve the AI Context Problem: When working with AI across multiple threads and sessions, context is lost. Memory Journal maintains a persistent, searchable record of your project work, decisions, and progress - making every AI conversation informed by your complete project history.

🚀 Quick Deploy:

• PyPI Package - pip install memory-journal-mcp
• Docker Hub - Alpine-based (225MB) with full semantic search
• MCP Registry - Discoverable by MCP clients

📚 Full Documentation: GitHub Wiki

📰 Read the v2.0.0 Release Article - Learn about knowledge graphs, performance optimizations, and relationship mapping

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🎯 Why Memory Journal?

The Fragmented AI Context Problem

When managing large projects with AI assistance, you face a critical challenge:

• Thread Amnesia - Each new AI conversation starts from zero, unaware of previous work
• Lost Context - Decisions, implementations, and learnings scattered across disconnected threads
• Repeated Work - AI suggests solutions you've already tried or abandoned
• Context Overload - Manually copying project history into every new conversation is tedious and incomplete

The Solution: Persistent Project Memory

Memory Journal acts as your project's long-term memory, bridging the gap between fragmented AI threads:

For Developers:

• 📝 Automatic Context Capture - Git commits, branches, GitHub issues, PRs, and project state captured with every entry
• 🔗 Knowledge Graph - Link related work (specs → implementations → tests → PRs) to build a connected history
• 🔍 Intelligent Search - Find past decisions, solutions, and context across your entire project timeline
• 📊 Project Analytics - Track progress from issues through PRs, generate reports for standups/retrospectives/code reviews

For Project Managers:

• 👥 Team Context Continuity - Maintain shared project memory across team members and time
• 📈 Progress Tracking - Monitor milestones, velocity, and cross-project insights from issues to PRs
• 🎯 Status Reporting - Generate comprehensive project summaries with PR metrics and timelines
• 🔄 GitHub Integration - Connect entries with Projects, Issues, and Pull Requests for unified tracking

For AI-Assisted Work:

• 💡 AI can query your complete project history in any conversation
• 🧠 Semantic search finds conceptually related work, even without exact keywords
• 📖 Context bundles provide AI with comprehensive project state instantly
• 🔗 Relationship visualization shows how different pieces of work connect

Real-World Example

Without Memory Journal:
Thread 1: "Help me design the authentication system"
Thread 2 (next day): "How should I implement user sessions?"  
         AI: *suggests approach you already decided against*
Thread 3 (next week): "What was our decision about JWT tokens?"
         AI: *no memory of previous threads*

With Memory Journal:
Thread 1: Work captured → "Decided on JWT with refresh tokens"
Thread 2: AI queries history → "I see you decided on JWT. Let's implement refresh token rotation..."
Thread 3: AI finds related entries → "Based on your design from Oct 15, here's the implementation..."

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✨ What's New in v2.1.0 (November 26, 2025)

🎉 Complete GitHub Actions Integration

• GitHub Actions Resources - 5 new MCP resources for CI/CD visibility
• Actions Visual Graph - memory://graph/actions - CI/CD narrative with Mermaid diagrams
• Failure Digest Prompt - actions-failure-digest - Comprehensive failure analysis with root cause identification
• Workflow Run Linking - Link journal entries to specific workflow runs

🔍 GitHub Issues & Pull Requests Integration

• GitHub Issues - Auto-fetch, link entries, detect from branch names
• GitHub Pull Requests - Auto-detect current PR, track lifecycle, link entries
• 3 PR Workflow Prompts - pr-summary, code-review-prep, pr-retrospective
• 3 New Resources - Issue/PR entries, PR timelines

✅ True Pyright Strict Type Compliance

• 700+ type issues fixed - Complete strict mode compliance
• All exclusions removed - Zero compromises on type safety
• Better IDE support - Enhanced autocomplete and error detection

Current Capabilities

• 16 MCP tools - Complete development workflow
• 14 workflow prompts - Standups, retrospectives, PR workflows, CI/CD failure analysis
• 13 MCP resources - Recent entries, graphs, project/issue/PR timelines, GitHub Actions
• GitHub Integration - Projects, Issues, Pull Requests, Actions with auto-linking
• Smart caching - 80%+ API reduction (15min issues, 5min PRs/workflows, 1hr projects)
• Knowledge graphs - 5 relationship types, Mermaid visualization
• 10x faster startup - Lazy ML loading (14s → 2-3s)

Complete CHANGELOG → | Architecture Details →

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🎯 Workflow Prompts (14 Total)

• find-related - Discover connected entries via semantic similarity
• prepare-standup - Daily standup summaries
• prepare-retro - Sprint retrospectives
• weekly-digest - Day-by-day weekly summaries
• analyze-period - Deep period analysis with insights
• goal-tracker - Milestone and achievement tracking
• get-context-bundle - Project context with Git/GitHub
• get-recent-entries - Formatted recent entries
• project-status-summary - GitHub Project status reports
• project-milestone-tracker - Milestone progress tracking
• pr-summary - Pull request journal activity summary
• code-review-prep - Comprehensive PR review preparation
• pr-retrospective - Completed PR analysis with learnings
• actions-failure-digest - NEW CI/CD failure analysis with root cause identification

📡 Resources (13 Total)

MCP Server Identifier: user-memory-journal-mcp (when using recommended config name; Cursor prefixes your config key with user-)

• memory://recent - 10 most recent entries
• memory://significant - Significant milestones and breakthroughs
• memory://graph/recent - Live Mermaid diagram of recent relationships
• memory://team/recent - Recent team-shared entries
• memory://projects/{number}/timeline - Project activity timeline
• memory://issues/{issue_number}/entries - All entries linked to a specific issue
• memory://prs/{pr_number}/entries - All entries linked to a specific pull request
• memory://prs/{pr_number}/timeline - Combined PR + journal timeline

GitHub Actions Resources:

• memory://graph/actions - CI/CD narrative graph: commits → runs → failures → entries → fixes → deployments
• memory://actions/recent - Recent workflow runs with CI status (supports ?branch=X&workflow=Y&commit=SHA&pr=N&limit=10)
• memory://actions/workflows/{name}/timeline - Workflow-specific timeline blending runs, journals, PRs
• memory://actions/branches/{branch}/timeline - Branch CI timeline with PR lifecycle
• memory://actions/commits/{sha}/timeline - Commit-specific CI timeline

🗄️ Database Improvements

• Automatic schema migrations (seamless v1.0 → v1.1 upgrades)
• Soft delete support with deleted_at column
• New relationships table with cascading deletes
• Enhanced indexes for optimal query performance

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🚀 Quick Start

Option 1: PyPI (Fastest - 30 seconds)

Step 1: Install the package

pip install memory-journal-mcp

Step 2: Add to ~/.cursor/mcp.json

{
  "mcpServers": {
    "memory-journal-mcp": {
      "command": "memory-journal-mcp"
    }
  }
}

Step 3: Restart Cursor

Restart Cursor or your MCP client, then start journaling!

Option 2: Docker (Full Features - 2 minutes)

Step 1: Pull the Docker image

docker pull writenotenow/memory-journal-mcp:latest

Step 2: Create data directory

mkdir data

Step 3: Add to ~/.cursor/mcp.json

{
  "mcpServers": {
    "memory-journal-mcp": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i", 
        "-v", "./data:/app/data",
        "writenotenow/memory-journal-mcp:latest",
        "python", "src/server.py"
      ]
    }
  }
}

Step 4: Restart Cursor

Restart Cursor or your MCP client, then start journaling!

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⚡ Install to Cursor IDE

One-Click Installation

Click the button below to install directly into Cursor:

Or copy this deep link:

cursor://anysphere.cursor-deeplink/mcp/install?name=Memory%20Journal%20MCP&config=eyJtZW1vcnktam91cm5hbCI6eyJhcmdzIjpbInJ1biIsIi0tcm0iLCItaSIsIi12IiwiLi9kYXRhOi9hcHAvZGF0YSIsIndyaXRlbm90ZW5vdy9tZW1vcnktam91cm5hbC1tY3A6bGF0ZXN0IiwicHl0aG9uIiwic3JjL3NlcnZlci5weSJdLCJjb21tYW5kIjoiZG9ja2VyIn19

Prerequisites

• ✅ Docker installed and running
• ✅ ~500MB disk space for data directory

Configuration

After installation, Cursor will use this Docker-based configuration. If you prefer manual setup, add this to your ~/.cursor/mcp.json:

{
  "memory-journal": {
    "command": "docker",
    "args": [
      "run", "--rm", "-i",
      "-v", "./data:/app/data",
      "writenotenow/memory-journal-mcp:latest",
      "python", "src/server.py"
    ]
  }
}

📖 See Full Installation Guide →

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📋 Core Capabilities

🛠️ 16 MCP Tools

Entry CRUD • Triple search (FTS5/semantic/date) • Knowledge graphs • Analytics • Export
Complete tools reference →

🎯 14 Workflow Prompts

Standups • Retrospectives • Weekly digests • PR workflows • Goal tracking
Complete prompts guide →

🔄 Git & GitHub Auto-Context

Every entry captures: repo, branch, commit, issues, PRs, projects (user & org)
Integration details →

🔧 Configuration

Optional GitHub Integration:

export GITHUB_TOKEN="your_token"              # For Projects/Issues/PRs
export GITHUB_ORG_TOKEN="your_org_token"      # Optional: org projects
export DEFAULT_ORG="your-org-name"            # Optional: default org

Scopes: repo, project, read:org (org only) • Fallback: Uses gh CLI if tokens not set
Full configuration guide →

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📖 Usage Examples

Create an Entry with GitHub Projects

// Create an entry linked to a GitHub Project
create_entry({
  content: "Completed Phase 1 of GitHub Projects integration - all core features implemented!",
  entry_type: "technical_achievement",
  tags: ["github-projects", "integration", "milestone"],
  project_number: 1,  // Links to GitHub Project #1
  significance_type: "technical_breakthrough"
})
// Context automatically includes GitHub Projects info

// Search entries by project
search_entries({
  project_number: 1,
  limit: 10
})

// Filter by project and date range
search_by_date_range({
  start_date: "2025-10-01",
  end_date: "2025-10-31",
  project_number: 1
})

Create an Entry with Relationships

// Create a technical achievement
create_entry({
  content: "Implemented lazy loading for ML dependencies - 10x faster startup!",
  entry_type: "technical_achievement",
  tags: ["performance", "optimization", "ml"],
  significance_type: "technical_breakthrough"
})
// Returns: Entry #55

// Link related work
link_entries({
  from_entry_id: 56,  // Testing entry
  to_entry_id: 55,    // Implementation
  relationship_type: "implements"
})

// Visualize the connections
visualize_relationships({
  entry_id: 55,
  depth: 2
})

Search and Analyze

// Full-text search with highlighting
search_entries({ query: "performance optimization", limit: 5 })

// Semantic search for concepts
semantic_search({ query: "startup time improvements", limit: 3 })

// Date range with tags
search_by_date_range({
  start_date: "2025-10-01",
  end_date: "2025-10-31",
  tags: ["performance"]
})

// Get analytics
get_statistics({ group_by: "week" })

Generate Visual Maps

// Visualize entry relationships
visualize_relationships({
  entry_id: 55,  // Root entry
  depth: 2       // 2 hops out
})

// Filter by tags
visualize_relationships({
  tags: ["visualization", "relationships"],
  limit: 20
})

// Listing resources - IMPORTANT: Call with NO parameters first
list_mcp_resources()  // ✅ Returns actual server identifier (e.g., user-memory-journal-mcp)

// Then fetch using exact identifier from list output
fetch_mcp_resource({
  server: "user-memory-journal-mcp",  // Use exact name from list_mcp_resources()
  uri: "memory://graph/recent"
})

// Available resource URIs:
memory://graph/recent  // Most recent 20 entries with relationships
memory://team/recent   // Recent team-shared entries (v2.0.0)

Note: Always call list_mcp_resources() without parameters first. MCP clients like Cursor may prefix your config name (e.g., memory-journal-mcp becomes user-memory-journal-mcp).

Using Workflow Prompts

Ask Cursor's AI naturally:

"Show me my recent journal entries"
"Prepare my standup for today"
"Generate a weekly digest"
"Find entries related to refactoring"

See all 14 prompts → | Complete examples →

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🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│ MCP Server Layer (Async/Await)                              │
│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────┐  │
│  │ Entry Creation  │  │ Triple Search   │  │ Relationship│  │
│  │ with Context    │  │ FTS5/Date/ML    │  │ Mapping     │  │
│  └─────────────────┘  └─────────────────┘  └─────────────┘  │
├─────────────────────────────────────────────────────────────┤
│ Thread Pool Execution Layer                                 │
│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────┐  │
│  │ Git Operations  │  │ Database Ops    │  │ Lazy ML     │  │
│  │ (2s timeout)    │  │ Single Conn     │  │ Loading     │  │
│  └─────────────────┘  └─────────────────┘  └─────────────┘  │
├─────────────────────────────────────────────────────────────┤
│ SQLite Database with FTS5 + Relationships                   │
│  ┌─────────────────────────────────────────────────────────┐│
│  │ entries + tags + relationships + embeddings + FTS       ││
│  └─────────────────────────────────────────────────────────┘│
└─────────────────────────────────────────────────────────────┘

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🔧 Technical Highlights

Performance & Security

• Python 3.14 - Latest Python with free-threaded support (PEP 779), deferred annotations (PEP 649), and performance optimizations
• 10x faster startup - Lazy loading of ML dependencies (2-3s vs 14s)
• Thread-safe operations - Zero race conditions in tag creation
• WAL mode - Better concurrency and crash recovery
• Database lock prevention - Single-connection transactions
• Aggressive timeouts - Git operations fail-fast (2s per command)
• Input validation - Length limits, parameterized queries, SQL injection prevention

Semantic Search (Optional)

• Model: all-MiniLM-L6-v2 (384-dimensional embeddings)
• Storage: FAISS index for fast similarity search
• Graceful degradation: Works perfectly without ML dependencies

Data & Privacy

• Local-first: Single SQLite file, you own your data
• Portable: Move your .db file anywhere
• Secure: No external API calls, non-root Docker containers

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📚 Documentation & Resources

• GitHub Wiki - Complete documentation
• Practical Examples Gists - 7 curated use cases
• Docker Hub - Container images
• PyPI Package - Python distribution
• Issues - Bug reports & feature requests

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📄 License

MIT License - See LICENSE file for details.

🤝 Contributing

Built by developers, for developers. PRs welcome! See CONTRIBUTING.md for guidelines.