Local LangGraph AI Cluster 🚀

A complete guide to setting up a distributed LangGraph infrastructure using your local hardware - zero external API costs!

🏗️ Architecture Overview

Your optimal setup uses your available hardware efficiently:

• jetson-node (Orin Nano 8GB): Primary LLM server (Ollama + small/fast models)
• cpu-node (32GB Intel): Coordinator + heavy LLM tasks (Ollama + large models) + HAProxy + Redis
• rp-node (8GB ARM): Embeddings server (efficient ARM processing)
• worker-node3 (6GB VM): Tools execution server (web search, scraping, commands)
• worker-node4 (6GB VM): Monitoring and health checks (optional)

🚀 Quick Start

1. Set Up Your Machines (Modular Approach)

Follow the setup guides in order - each guide is self-contained and updated from the comprehensive SOT:

# 1. Setup Jetson Orin Nano (Primary LLM Server)
# Follow: setup_guides/01_jetson_setup.md
# Sets up Ollama + TensorRT optimizations on jetson-node (192.168.1.177)

# 2. Setup CPU Coordinator (Heavy LLM + Load Balancer + Cache)  
# Follow: setup_guides/02_cpu_setup.md
# Sets up Ollama + HAProxy + Redis on cpu-node (192.168.1.81)

# 3. Setup LangGraph Integration (Workflows + Routing)
# Follow: setup_guides/03_langgraph_integration.md  
# Creates intelligent routing and tool integration

# 4. Setup Worker Nodes (Embeddings + Tools + Monitoring)
# Follow: setup_guides/04_distributed_coordination.md
# Sets up rp-node, worker-node3, worker-node4 + orchestration

2. Start Your Cluster

# All IPs are pre-configured for your actual nodes!
cd ~/ai-infrastructure/langgraph-config
source ~/langgraph-env/bin/activate

# Start entire cluster
python3 cluster_orchestrator.py start

# Check cluster status  
python3 cluster_orchestrator.py status

# Test all services
python3 cluster_orchestrator.py test

3. Test Your Setup

# Test LangGraph workflows
cd ~/ai-infrastructure/langgraph-config
python3 main_app.py

# Run example workflows
cd /home/sanzad/git/langgraph/examples/
python3 example_workflows.py

🎯 Single Source of Truth

• Complete Guide: 00_complete_deployment_guide.md - Full walkthrough with all commands
• Modular Guides: 01-04 are extracted and synchronized from the complete guide

📊 Performance Expectations

Machine	Model	RAM Usage	Speed	Use Case
jetson-node	Llama 3.2 3B	~3GB	15-25 tok/s	General chat
jetson-node	Llama 3.2 1B	~1.5GB	30-50 tok/s	Quick responses
cpu-node	Mistral 7B	~4.4GB	8-15 tok/s	Complex analysis
rp-node	all-MiniLM-L6-v2	~200MB	1000+ emb/s	Semantic intelligence (ARM)

📚 Documentation

• LangGraph Concepts & Workflows - How LangGraph orchestrates your distributed setup ⭐
• Embedding Server Deep Dive - Complete guide to the semantic intelligence layer 🧠
• Architecture Diagrams - System diagrams and visual flow charts
• Example Workflows - Complete LangGraph workflow implementations

🎯 Example Workflows

Research Assistant

# Automatically searches web, synthesizes findings
result = await research_workflow.invoke("What are the latest AI trends?")

Coding Assistant

# Routes simple/complex coding questions appropriately
result = await coding_workflow.invoke("Build a FastAPI app with auth")

Data Analysis

# Scrapes data and provides analysis
result = await data_workflow.invoke("Analyze this dataset: https://...")

🔧 Key Features

• 🆓 Zero Cost: No LLM API costs ever (local inference only)
• ⚡ Smart Routing: Auto-routes tasks to optimal hardware
• 🔄 Load Balancing: HAProxy distributes load automatically
• 📊 Monitoring: Real-time health checks + optional Langfuse/Helicone
• 🛡️ Fault Tolerance: Automatic failover and restart
• 📈 Auto-scaling: Dynamic model loading based on load

🌟 Why This Setup?

For your hardware specifically:

• Jetson advantages: ARM efficiency, unified memory, low power
• Skip RTX cards: They're overkill for learning and consume 175W+ each
• Distributed approach: Maximizes utilization of all machines
• Local-first: Complete privacy and control

🔍 Monitoring

# Check cluster health
curl http://192.168.1.191:8083/cluster_health

# View load balancer stats
open http://192.168.1.81:9000/haproxy_stats

# Monitor real-time performance
htop  # On each machine

🛠️ Customization

Add New Models

# On Jetson
ollama pull codellama:7b

# On CPU
wget https://huggingface.co/.../model.bin

Scale Up/Down

# Add more workers by modifying config.py
# Adjust model sizes based on available RAM

Custom Workflows

# Create new workflows in examples/
# Follow the existing patterns for routing and tools

Add Advanced Monitoring (Optional)

# Option 1: Langfuse (LangSmith alternative) - Advanced tracing & analytics
# Follow: setup_guides/05_langfuse_setup.md

# Option 2: Helicone (Alternative monitoring) - Real-time monitoring & debugging  
# Follow: setup_guides/06_helicone_setup.md

# Both are completely free and self-hosted!

📁 Setup Guide Structure

Guide	Purpose	Machine(s)	Status
00_complete_deployment_guide.md	🎯 Master SOT - Complete walkthrough	All machines	✅ Production ready
01_jetson_setup.md	Jetson Orin Nano setup	jetson-node	✅ Synced from SOT
02_cpu_setup.md	CPU coordinator setup	cpu-node	✅ Synced from SOT
03_langgraph_integration.md	LangGraph workflows	cpu-node	✅ Synced from SOT
04_distributed_coordination.md	Worker nodes + orchestration	All workers	✅ Synced from SOT
05_langfuse_setup.md	Optional: Advanced monitoring	cpu-node	✅ Optional feature
06_helicone_setup.md	Optional: Alternative monitoring	cpu-node	✅ Optional feature

Benefits of this structure:

• ✅ Modular: Focus on one machine/service at a time
• ✅ Updated: All guides synced from the comprehensive SOT
• ✅ Flexible: Use individual guides or complete guide
• ✅ Maintained: Single source of truth prevents sync issues

🚨 Troubleshooting

Common Issues

Out of Memory

# Switch to smaller models
ollama pull tinyllama:1.1b

Service Not Starting

# Check logs
sudo journalctl -u ollama -f
sudo systemctl restart ollama

Network Issues

# Test connectivity
curl http://MACHINE_IP:PORT/health

📚 Learning Resources

This setup is perfect for learning:

• LangGraph workflow patterns
• Distributed AI systems
• Local model deployment
• Resource optimization
• MLOps practices

🎉 What You've Built

• Production-ready local AI infrastructure
• Cost-effective learning environment
• Scalable architecture that grows with you
• Privacy-focused - your data never leaves your network

🔗 Next Steps

1. Experiment with the example workflows
2. Create your own domain-specific flows
3. Scale up by adding more models or machines
4. Optimize based on your specific use cases
5. Share your workflows with the community!

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Happy Learning! 🎓 You now have a professional-grade local AI infrastructure that rivals cloud solutions - for free!