scripts/config/README.md

LM Studio Multi-Server Configuration

This directory contains configuration files for running multiple LM Studio servers simultaneously with the Felix Framework.

Configuration Files

multi_model_config.json ⭐ RECOMMENDED

Multi-model configuration for single LM Studio server:

• Research Model: qwen/qwen3-4b-2507 - Fast exploration
• Analysis Model: qwen/qwen3-4b-thinking-2507 - Reasoning focused
• Synthesis Model: google/gemma-3-12b - High-quality output
• All models use same server: http://127.0.0.1:1234/v1

server_config.json

Multi-server configuration supporting up to 4 different LM Studio servers:

• Creative Server (port 1234): Fast model for research agents
• Analytical Server (port 1235): Balanced model for analysis/critic agents
• Synthesis Server (port 1236): High-quality model for synthesis agents
• Fallback Server (port 1237): Fast backup model (disabled by default)

single_server_config.json

Single-server configuration for comparison testing.

Setting Up Multiple LM Studio Servers

1. Start LM Studio instances on different ports:

   # Terminal 1: Creative server
   lms server start --port 1234 --model mistral-7b-instruct
   
   # Terminal 2: Analytical server  
   lms server start --port 1235 --model llama-3.1-8b-instruct
   
   # Terminal 3: Synthesis server
   lms server start --port 1236 --model mixtral-8x7b-instruct
   

2. Or use LM Studio GUI:

   • Launch multiple LM Studio instances
   • Set different ports in Settings → Developer → Port
   • Load different models in each instance
   • Start servers

Agent Type Mappings

• Research agents → Creative server (Mistral) for broad exploration
• Analysis agents → Analytical server (Llama) for focused analysis
• Synthesis agents → Synthesis server (Mixtral) for high-quality output
• Critic agents → Analytical server (Llama) for reasoning/validation

Usage

With Multi-Model Config (Recommended):

python examples/blog_writer.py "AI safety" --server-config config/multi_model_config.json --debug

Test Multi-Model Setup:

python examples/test_multi_model.py

With Multi-Server Config:

python examples/blog_writer.py "AI safety" --server-config config/server_config.json --debug

With Single-Server Config:

python examples/blog_writer.py "AI safety" --server-config config/single_server_config.json --debug

Performance Comparison:

python examples/test_multi_server_performance.py

Configuration Options

Server Settings:

• name: Unique server identifier
• url: LM Studio server URL
• model: Model name to use
• timeout: Request timeout in seconds
• max_concurrent: Maximum concurrent requests
• weight: Load balancing weight
• enabled: Whether server is active

Load Balancing Strategies:

• agent_type_mapping: Use agent type mappings (recommended)
• round_robin: Rotate between servers
• least_busy: Use server with lowest load
• fastest_response: Use server with best response time

Health Monitoring

The system automatically:

• Checks server health every 30 seconds
• Fails over to available servers
• Monitors response times and load
• Displays server status in debug mode

Performance Benefits

Multi-server setup provides:

• True parallelism: Agents process simultaneously
• Model specialization: Each agent type uses optimal model
• Load distribution: Spread across multiple GPUs/servers
• Fault tolerance: Continue if one server fails
• 3-4x performance: With proper server setup