NetLogo LLM Extension

🤖 Intelligent Agent-Based Modeling with LLMs

The NetLogo LLM Extension bridges the gap between traditional agent-based modeling and modern AI, enabling NetLogo agents to leverage Large Language Model capabilities for more sophisticated behaviors and decision-making.

Project Overview

Core Concept

This extension allows NetLogo agents to:

• Make decisions using natural language reasoning
• Communicate with each other using LLM-generated dialogue
• Adapt behaviors based on complex contextual understanding
• Learn from interactions and evolve strategies

Key Innovation

Integrating LLM capabilities directly into NetLogo’s agent-based modeling environment opens new possibilities for simulating complex social systems, emergent behaviors, and intelligent multi-agent interactions.

Technical Architecture

Extension Components

LLM Integration Layer

; Example NetLogo code with LLM extension
to-report llm:generate-action [context goal]
  ; Agent uses LLM to determine next action
  let prompt (word "Context: " context " Goal: " goal)
  report llm:query prompt
end

to communicate-with-llm
  ; Agents communicate using natural language
  let message llm:generate-message [state] [neighbor-states]
  ask neighbors [ process-message message ]
end

Core Features

• Natural Language Processing: Agents process and generate text
• Context-Aware Decision Making: LLM-informed agent behaviors
• Dynamic Strategy Evolution: Learning from simulation outcomes
• Multi-Agent Communication: Rich inter-agent dialogue

Implementation Details

Application Scenarios

Social System Modeling

• Simulate complex social interactions with natural language
• Model opinion dynamics with nuanced communication
• Study emergence of social norms through dialogue
• Analyze information spread with semantic understanding

Economic Simulations

• Agents negotiate using natural language
• Market dynamics with intelligent trading strategies
• Supply chain optimization with adaptive agents
• Consumer behavior modeling with preferences

Educational Applications

• Interactive learning environments
• Student-agent dialogue systems
• Exploratory simulations with explanations
• Adaptive tutoring systems

Extension API

Basic Commands

; Initialize LLM connection
llm:setup [api-key] [model-name]

; Query LLM for decision
llm:query [prompt]

; Generate agent dialogue
llm:generate-dialogue [agent-state] [context]

; Analyze simulation state
llm:analyze-pattern [world-state]

; Store conversation history
llm:add-to-memory [agent-id] [interaction]

Advanced Features

• Batch processing for efficiency
• Caching for repeated queries
• Rate limiting and error handling
• Asynchronous operations
• Context window management

Performance Considerations

Optimization Strategies

• Query batching to reduce API calls
• Response caching for common scenarios
• Selective LLM usage for critical decisions
• Local model fallbacks for simple tasks

Scalability

• Supports hundreds of agents with smart queuing
• Configurable API rate limits
• Parallel processing capabilities
• Efficient memory management

Research Applications

This extension enables novel research in:

• Emergent intelligence in multi-agent systems
• Language-based coordination mechanisms
• Social learning and cultural evolution
• Human-AI hybrid simulations

Installation & Setup

# Add to NetLogo extensions folder
extensions/llm/

# In NetLogo model
extensions [llm]

# Initialize in setup
to setup
  llm:initialize "your-api-key"
  ; ... rest of setup
end

Repository