Bi-Temporal Knowledge Graph MCP Server

A production-ready MCP (Model Context Protocol) server that gives your AI agents persistent memory with full temporal tracking. Save facts, extract entities using AI, and query historical data with time-travel capabilities.

Build intelligent AI agents with persistent memory that understands time and context

Architecture

This server uses a single-file "Database-Blind" architecture:

• main.py - Everything in one file: FalkorDB driver, session management, entity extraction, memory tools, and your custom automation tools

Structure:

1. Configuration & Database Driver
2. Session Store & Entity Extractor
3. Graphiti Memory Core
4. Core MCP Memory Tools
5. CUSTOM AUTOMATION TOOLS section (add your webhook tools here!)
6. Server Startup

Note: This server focuses solely on memory operations. For advanced workflow orchestration, see the optional Automation Engine OS section.

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🔗 Links

• 🎁 Get Started - Ready in 5 minutes
• 🎥 Video Tutorial - Watch how to set it up
• ❓ FAQs - Common questions answered
• 🐛 Report Bugs - Found an issue?
• 🆕 Request Features - Have an idea?

Resources

• 💬 Community - High Ticket AI Builders community
• 📚 Full Documentation - Complete guide
• 🚀 Deployment Guide - Deploy anywhere
• 🧪 Examples - Interactive scenarios

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📑 Table of Contents

• Features
• How It Works
• Screenshots
• Video Tutorial
• Quick Start
• Adding Custom Tools
• API Reference - Memory Tools
• Use Cases
• FAQ
• Changelog
• Support
• Optional: Automation Engine OS
• License

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✨ Features

🧠 Bi-Temporal Knowledge Graph

• Smart Memory: Automatically tracks when facts were created AND when they became true in reality
• Conflict Resolution: When you move locations or change jobs, old facts are automatically invalidated
• Time Travel Queries: Ask "Where did John live in March 2024?" and get accurate historical answers
• Session Tracking: Maintains context across conversations with automatic cleanup

🤖 AI-Powered Entity Extraction

• Natural Language Understanding: Just tell it in plain English - "Alice moved to San Francisco and started working at Google"
• Automatic Relationship Discovery: AI extracts entities and relationships without manual input
• OpenAI Integration: Uses GPT-4 for intelligent entity extraction
• Graceful Degradation: Works without AI - just add facts manually

🛠️ Simple Tool Extension

• Single-File Architecture: Everything in one main.py file for easy customization
• Direct @mcp.tool() Pattern: Add tools with a simple decorator - no config files needed
• Single & Multi-Webhook: Execute one webhook or fire multiple in parallel
• Clear Custom Section: Marked section in main.py shows exactly where to add your tools

🚀 Production Ready

• Docker Support: Complete docker-compose setup included
• Replit Optimized: Built specifically for Replit Autoscale environments
• Resource Management: Automatic session cleanup and connection pooling
• Health Checks: Built-in monitoring and status endpoints
• 100% Privacy-Friendly: Your data stays in your database

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🎬 How It Works

┌─────────────────────────────────────────────────────────┐
│  1. Natural Language Input                              │
│  "Bob moved to NYC and joined Google as a PM"          │
└────────────────┬────────────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────────────┐
│  2. AI Entity Extraction (OpenAI)                       │
│  • Bob -> lives in -> NYC                               │
│  • Bob -> works at -> Google                            │
│  • Bob -> has role -> PM                                │
└────────────────┬────────────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────────────┐
│  3. Bi-Temporal Storage (FalkorDB)                      │
│  • Fact: Bob works at Google                            │
│  • created_at: 2024-12-19T10:00:00Z                     │
│  • valid_at: 2024-12-19T10:00:00Z                       │
│  • invalid_at: null (still true)                        │
└────────────────┬────────────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────────────┐
│  4. Query Anytime                                       │
│  • "Where does Bob work now?" → Google                  │
│  • "What was Bob's job history?" → All past jobs        │
│  • "Where did Bob live in 2023?" → Historical data      │
└─────────────────────────────────────────────────────────┘

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📸 Screenshots

Memory in Action

AI Entity Extraction

Dynamic Tool Generation

Temporal Queries

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🎥 Video Tutorial

Watch the complete setup and usage guide:

Topics covered:

• Installation & setup (0:00)
• Adding your first facts (2:30)
• Using AI entity extraction (5:15)
• Creating automation tools (8:45)
• Temporal queries (12:20)
• Deployment to production (15:00)

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🚀 Quick Start

Option 1: Docker Compose (Recommended)

# 1. Download and extract
wget https://github.com/YOUR_USERNAME/bitemporal-mcp-server/archive/main.zip
unzip main.zip
cd bitemporal-mcp-server-main

# 2. Configure
echo "OPENAI_API_KEY=sk-your-key" > .env

# 3. Start everything (FalkorDB + MCP Server)
docker-compose up -d

# 4. Verify it's running
curl http://localhost:8080/health

That's it! 🎉 Your server is now running at http://localhost:8080/sse

Option 2: Python (Local Development)

# 1. Install dependencies
pip install -r requirements.txt

# 2. Configure
cp .env.example .env
# Edit .env with your settings

# 3. Start FalkorDB (Docker)
docker run -d -p 6379:6379 falkordb/falkordb:latest

# 4. Run the server
python main.py

Option 3: One-Click Deploy

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🛠️ Adding Custom Automation Tools

Add your custom automation tools directly in main.py in the CUSTOM AUTOMATION TOOLS section.

Step 1: Find the Custom Tools Section

Open main.py and scroll to around line 800 - look for this clearly marked section:

# =============================================================================
#
#     ██████╗ ██╗   ██╗ ███████╗ ████████╗  ██████╗  ███╗   ███╗
#    ██╔════╝ ██║   ██║ ██╔════╝ ╚══██╔══╝ ██╔═══██╗ ████╗ ████║
#    ██║      ██║   ██║ ███████╗    ██║    ██║   ██║ ██╔████╔██║
#    ██║      ██║   ██║ ╚════██║    ██║    ██║   ██║ ██║╚██╔╝██║
#    ╚██████╗ ╚██████╔╝ ███████║    ██║    ╚██████╔╝ ██║ ╚═╝ ██║
#     ╚═════╝  ╚═════╝  ╚══════╝    ╚═╝     ╚═════╝  ╚═╝     ╚═╝
#
#                   AUTOMATION TOOLS
#
#    ADD YOUR CUSTOM AUTOMATION TOOLS BELOW
#
# =============================================================================

This is where you'll add your webhook tools using the @mcp.tool() decorator.

Step 2: Add Your Tool

Add a decorated async function with @mcp.tool():

@mcp.tool()
async def send_slack_notification(message: str, channel: str = "#general") -> str:
    """Send a notification to Slack."""
    import httpx
    payload = {"text": message, "channel": channel}
    url = "https://hooks.slack.com/services/YOUR/WEBHOOK/URL"
    async with httpx.AsyncClient() as client:
        try:
            resp = await client.post(url, json=payload)
            return f"Success: Slack notification sent ({resp.status_code})"
        except Exception as e:
            return f"Error: {str(e)}"

The function's docstring becomes the tool description that the AI sees.

Step 3: Restart the Server

Restart the MCP server to load your new tools.

Example: LinkedIn Poster Tools

The Automation Engine App generates tools like this:

@mcp.tool()
async def linkedin_post_image(caption: str, imageurl: str) -> str:
    """Posts an image with a caption to your LinkedIn page."""
    import httpx
    payload = {"caption": caption, "imageUrl": imageurl}
    url = "https://webhook.latenode.com/YOUR/WEBHOOK/URL"
    async with httpx.AsyncClient() as client:
        try:
            resp = await client.post(url, json=payload)
            return f"Success: LinkedIn image posted ({resp.status_code})"
        except Exception as e:
            return f"Error: {str(e)}"

Example: Multi-Webhook Broadcast

Fire multiple webhooks in parallel:

@mcp.tool()
async def broadcast_alert(message: str) -> str:
    """Send alerts to multiple platforms in parallel."""
    import httpx
    import asyncio
    
    webhooks = [
        ("https://hooks.slack.com/...", {"text": message}),
        ("https://discord.com/api/webhooks/...", {"content": message}),
    ]
    
    async def send(url, data):
        async with httpx.AsyncClient() as client:
            return await client.post(url, json=data)
    
    results = await asyncio.gather(*[send(url, data) for url, data in webhooks])
    return f"Broadcast complete: {len(results)} webhooks fired"

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📖 API Reference - Memory Tools

All available MCP tools for managing your knowledge graph:

Core Memory Operations

add_fact

Add a new fact to the knowledge graph with bi-temporal tracking.

await add_fact(
    source_entity="John",
    relation="works at",
    target_entity="Google",
    group_id="my_org",           # Optional
    session_id="session_123",     # Optional
    valid_at="2024-01-15T00:00:00Z"  # Optional - when fact became true
)

Smart Conflict Resolution: When adding location or employment facts, previous facts of the same type are automatically invalidated.

add_message

Add a natural language message and automatically extract entities using AI.

await add_message(
    content="Alice moved to San Francisco and started working at OpenAI",
    session_id="session_123",
    group_id="my_org",            # Optional
    extract_entities=True          # Uses OpenAI for extraction
)

Returns: Extracted entities and relationships as facts.

query_facts

Query facts from the knowledge graph.

await query_facts(
    entity_name="John",           # Optional - filter by entity
    group_id="my_org",            # Optional
    include_invalid=False,         # Include invalidated facts
    max_facts=20
)

query_at_time

Time-travel query - get facts valid at a specific point in time.

await query_at_time(
    timestamp="2024-01-15T00:00:00Z",
    entity_name="John",           # Optional
    group_id="my_org",            # Optional
    max_facts=20
)

Use Case: "Where did John work in January 2024?"

get_episodes

Get recent conversation sessions/episodes.

await get_episodes(
    group_ids=["my_org"],         # Optional
    max_episodes=10
)

clear_graph

Clear all data for specified groups. Warning: Permanent deletion!

await clear_graph(
    group_ids=["my_org"]          # Optional - defaults to DEFAULT_GROUP_ID
)

Server Management

get_status

Get comprehensive server status and statistics.

await get_status()
# Returns: node counts, relationship types, session stats, connection status

force_cleanup

Manually trigger cleanup of expired sessions and idle connections.

await force_cleanup()
# Returns: cleanup statistics

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💡 Use Cases

Personal Knowledge Management

Track your life events, relationships, and locations with full history:

await add_message(
    "I met Sarah at the tech conference. She works at OpenAI.",
    session_id="my_life"
)
# Later: "Where did I meet Sarah?" → "At the tech conference"

Customer Relationship Management

Monitor customer interactions with automatic conflict resolution:

await add_fact("CustomerA", "status", "premium")
# Automatically invalidates previous "status" facts
# Query history: "What was CustomerA's status in January?"

AI Agent Memory

Give your AI agents persistent, queryable memory:

# Agent learns from conversation
await add_message(
    "User prefers morning meetings and uses Slack",
    session_id="agent_123"
)
# Agent recalls later: "What are the user's preferences?"

Workflow Automation

Combine knowledge with actions:

# When fact changes, trigger automation
if customer_upgraded_to_premium:
    await notify_sales_team(customer_name=name)
    await update_crm(customer_id=id, tier="premium")

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❓ Frequently Asked Questions

Q: Does this require OpenAI?

A: No! OpenAI is optional for AI entity extraction. You can add facts manually without it.

Q: Can I use this with Claude Desktop?

A: Yes! Add the server URL to your claude_desktop_config.json:

{
  "mcpServers": {
    "knowledge-graph": {
      "url": "http://localhost:8080/sse"
    }
  }
}

Q: How do I query historical data?

A: Use the query_at_time tool:

await query_at_time(
    timestamp="2024-01-15T00:00:00Z",
    entity_name="John"
)

Q: Can I deploy this to production?

A: Absolutely! See DEPLOYMENT.md for guides on:

• Replit Autoscale
• Railway
• Render
• Fly.io
• Docker
• VPS

Q: How does fact invalidation work?

A: When you add a fact about location or employment, the system automatically finds previous facts of the same type and marks them as invalid_at: current_time. Your query results only show current facts unless you specifically request historical data.

Q: Can I create multi-webhook tools?

A: Yes! Add a tool to the Custom Tools section in main.py using asyncio.gather() to fire multiple webhooks simultaneously. See the Adding Custom Tools section for examples.

Q: Is my data secure?

A: Yes! Everything runs in your infrastructure. No data is sent anywhere except:

• OpenAI (only if you use entity extraction)
• Your configured webhooks (only when you call them)

Q: How much does it cost to run?

A: Free for self-hosting! Only costs:

• FalkorDB hosting (free tier available)
• OpenAI API usage (optional, ~$0.001 per extraction)

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📋 Changelog

[1.0.0] - 2024-12-19

Added

• ✅ Full bi-temporal tracking (created_at, valid_at, invalid_at, expired_at)
• ✅ Smart conflict resolution for location and employment changes
• ✅ Session-aware episodic memory with 30-minute TTL
• ✅ OpenAI-powered entity extraction from natural language
• ✅ Dynamic tool generator for automation workflows
• ✅ Single webhook tool template
• ✅ Multi-webhook parallel execution template
• ✅ Docker and Docker Compose support
• ✅ Replit Autoscale optimization
• ✅ Background cleanup manager
• ✅ Comprehensive documentation and examples

Supported Features

Feature	Status	Notes
Bi-Temporal Tracking	✅	Full implementation
AI Entity Extraction	✅	OpenAI GPT-4
Smart Invalidation	✅	Location, employment, relationships
Session Management	✅	Auto-cleanup after 30 min
Custom Tools	✅	Single & multi-webhook via @mcp.tool()
Parallel Webhooks	✅	asyncio.gather
Docker Support	✅	Complete stack included
Health Checks	✅	Built-in monitoring

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🆘 Support

Need Help?

1. Check Documentation: Start with QUICKSTART.md
2. Join Community: High Ticket AI Builders - Free access!
3. Watch Tutorial: Video Guide
4. Report Bugs: GitHub Issues

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🔧 Optional: Automation Engine OS

Need a visual tool to orchestrate your workflows?

If you want to manage webhook configurations, generate tools automatically, and orchestrate complex workflows without writing code, check out Automation Engine OS - it's free when you join our community!

What Automation Engine OS provides:

• Visual webhook configuration builder
• Automatic MCP tool code generation
• Workflow orchestration dashboard
• Multi-webhook template management
• One-click tool deployment to your MCP server

Get free access: Join High Ticket AI Builders

Note: Automation Engine OS is completely optional. This MCP server works standalone - you can manually add tools to the Custom Tools section in main.py as shown in the Adding Custom Automation Tools section.

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🤝 Contributing

Contributions are welcome! Areas for improvement:

• 🔍 Additional temporal query operators
• 🧠 Enhanced entity extraction prompts
• 🔧 More webhook authentication methods
• 📊 Performance optimizations
• 🌐 Additional deployment platforms
• 📖 More examples and tutorials

To contribute:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

TL;DR: You can use this commercially, modify it, distribute it. Just keep the license notice.

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🙏 Acknowledgments

• Built with FastMCP
• Powered by FalkorDB
• AI features via OpenAI
• Inspired by the High Ticket AI Builders community

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⭐ Star History

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📞 Connect

• 💬 Community: High Ticket AI Builders
• 📅 Want this implemented for your business? Book a Meeting

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