NetLogo LLM Extension
Table of Contents
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 #
Technical Stack
- NetLogo 6.x core
- Java extension API
- LLM API integration
- Async processing
Supported LLMs
- OpenAI GPT models
- Anthropic Claude
- Local models via API
- Custom endpoints
Use Cases
- Social simulations
- Economic modeling
- Ecological systems
- Educational tools
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