An ultra-minimal implementation of emergent intelligence through 3 simple boids rules applied to tool creation and usage. This system demonstrates how local interactions between agents can create emergent specialization, collaboration, and tool ecosystems.

The key breakthrough: Agents follow just 3 simple local rules yet emergent complex tool ecosystems arise naturally.

1. SEPARATION:  Avoid building exact same tools as neighbors (creates niches)
2. ALIGNMENT:   Copy successful neighbors' strategies (spreads good patterns)  
3. COHESION:    Use neighbors' tools when possible (creates collaboration)

Example Emergent Behavior:

• Agent A builds data_tool_v1 → Agent B sees this → applies SEPARATION → builds logic_tool_v1
• Agent C sees A is successful → applies ALIGNMENT → also builds data tools
• Agent B uses A's data tool → applies COHESION → creates collaboration
• Result: Natural specialization and tool ecosystem without central coordination!

• Python 3.8+
• No external dependencies needed!

1. Clone and run:

git clone <your-repo-url>
cd boids-evolution
python3 main_simple.py

2. Try different configurations:

# Default: 3 agents, triangle topology, 20 steps
python3 main_simple.py

# More agents and steps
python3 main_simple.py --agents 5 --steps 50

# Different network topologies
python3 main_simple.py --topology line --agents 4
python3 main_simple.py --topology star --agents 6

# Default simulation
python3 main_simple.py

# More agents for richer dynamics
python3 main_simple.py --agents 6 --steps 30

# Different topologies create different behaviors
python3 main_simple.py --topology line --agents 5
python3 main_simple.py --topology star --agents 4

# Export results for analysis
python3 main_simple.py --export results.json --quiet

--agents N      Number of agents (default: 3)
--steps N       Simulation steps (default: 20) 
--topology T    Network topology: triangle/line/star (default: triangle)
--quiet         Minimal output mode
--export FILE   Export results to JSON file

Each agent observes only its immediate neighbors:

Agent_01 observes: Agent_02 has [data_tool_v1], Agent_03 has [logic_tool_v1]

Agent calculates preferences based on the rules:

SEPARATION: Agent_02 has data tool → reduce preference for building data tools
ALIGNMENT: Agent_02 seems successful → increase preference for their recent actions  
COHESION: Neighbors have tools → increase preference for using their tools

Rules are combined with genetic weights:

final_preference = (
    separation_preference * 0.4 +
    alignment_preference * 0.3 +
    cohesion_preference * 0.3
)

• Specialization: Agents naturally focus on different tool types
• Collaboration: Agents use each other's tools
• Diversity: All 4 tool types emerge without coordination

💬 Agent_01 says:
┌──────────────────────────────────────────────────────────┐
│ Propose tool: Matrix_Calculator - Handles 2D matrix ops │
│ like multiplication, inversion for ML applications       │
└──────────────────────────────────────────────────────────┘

🛠️  Tool Marketplace Summary:
  📋 Active proposals: 3
  🔨 In development: 1  
  ✅ Completed tools: 2

🔥 Hot discussions:
  • Universal_Data_Parser: 2.5 support
  • Matrix_Calculator: 1.0 support
  • API_Connector: 3.0 support

🔨 Tool building: +10 (built Matrix_Calculator)
💬 Communication: +3 (supported 2 proposals)  
💰 Tool utility: +15 (your Data_Parser used in 3 new tools)

⭐ Agent_02 Reputation: 23 points
   📋 Proposals: 2 (×3 points each)
   🔨 Builds: 3 (×5 points each)  
   👍 Supports: 8 (×1 point each)

This implementation validates breakthrough concepts in collaborative AI:

• Collaborative creation: Tools emerge from community needs, not individual planning
• Specialization: Some agents become "architects" (proposers), others "builders" (implementers)
• Natural selection: Only tools with community support get built

• Utility rewards: Tool creators prosper when their tools enable others to build MORE tools
• Sustainable incentives: Creating foundational tools provides ongoing energy income
• Ecosystem growth: Each new tool expands possibilities for future tools

• Social coordination: Agents convince each other what tools are worth building
• Distributed planning: No central authority, yet coherent tool ecosystem emerges
• Peer review: Community support acts as quality filter

• Modular architecture: New tools seamlessly integrate into existing ecosystem
• Incremental complexity: Simple tools become building blocks for sophisticated systems
• Network effects: More agents = more diverse tools = more building possibilities

The marketplace amplifies the basic loop:

1. Talk: Agents propose tool ideas and convince others of their value
2. Act: Agents support promising proposals and build accepted tools
3. Reward: Energy flows to proposers, supporters, and builders
4. Utility: Long-term energy when tools become building blocks for others

• Proposal discussions: Agents "sell" their tool ideas to gain support
• Collaborative building: Multiple agents can work on complex tools
• Knowledge sharing: Tool descriptions and dependencies guide ecosystem growth
• Peer validation: Community support ensures tools meet real needs

From this v0.0 foundation, natural progressions include:

• LLM-generated code: Agents write sophisticated tool implementations using AI
• Tool testing: Automatic validation and unit testing of agent-created tools
• Version control: Tools evolve through community improvements
• Dependency management: Automatic resolution of tool requirements

• Energy markets: Agents trade energy for exclusive tool access
• Tool licensing: Creators set usage prices and licensing terms
• Quality metrics: Community ratings and performance benchmarks
• Tool fusion: Combining multiple tools into new hybrid capabilities

• Tool mutation: Variations and improvements on existing tools
• Competitive building: Multiple implementations of the same concept
• Ecosystem niches: Specialized tools for specific domains emerge
• Survival pressure: Unused tools eventually disappear

• Tool guilds: Groups of agents specializing in specific domains
• Collaborative projects: Large tools built by agent teams
• Knowledge transfer: Agents teaching each other building techniques
• Cultural evolution: Building patterns and practices spread through society

🐦 Simple Boids Simulation Starting
   Agents: 3 | Topology: triangle
   Steps: 10
   Rules: Separation + Alignment + Cohesion

📊 Step 1:
   ⚡ Agent_01: tried to use tool (none available) (total tools: 0)
   📊 Agent_02: built data_tool_v1_Agent_02 (total tools: 1)
   🔧 Agent_03: built utility_tool_v1_Agent_03 (total tools: 1)

📊 Step 2:
   🔧 Agent_01: built utility_tool_v1_Agent_01 (total tools: 1)
   🧠 Agent_02: built logic_tool_v1_Agent_02 (total tools: 2)
   ⚡ Agent_03: used logic_tool_v1_Agent_02 (total tools: 1)

📊 Step 3:
   🔗 Agent_01: built connector_tool_v1_Agent_01 (total tools: 2)
   ⚡ Agent_02: used utility_tool_v1_Agent_01 (total tools: 2)
   🔗 Agent_03: built connector_tool_v1_Agent_03 (total tools: 2)

🧬 BOIDS EMERGENCE ANALYSIS:
   📊 Total Tools Created: 21
   🎯 Tool Type Distribution: {'utility': 6, 'connector': 8, 'logic': 3, 'data': 4}
   🤖 Agent Specializations: {'Agent_01': 'connector', 'Agent_02': 'data', 'Agent_03': 'connector'}
   ✨ SPECIALIZATION EMERGED! 2 different niches
   🎨 DIVERSITY EMERGED! 4 tool types created
   🤝 COLLABORATION EMERGED! 7 tool usage events

• SimpleBoid: Ultra-minimal agent with 3 boids rules
• SimpleBoidsNetwork: Network topology management and simulation orchestration
• Local Observation: Agents only see immediate neighbors (no global state)
• Weighted Action Selection: Genetic weights control rule influence

1. Local Observation: Agent observes neighbor tools and recent actions
2. Rule Application: Apply separation, alignment, cohesion rules independently
3. Weighted Combination: Combine rule preferences using genetic weights
4. Action Execution: Build specific tool type or use neighbor tool
5. Pattern Emergence: Specialization and collaboration emerge naturally

boids-evolution/
├── src/
│   ├── simple_boids_agent.py     # Ultra-minimal boids agent
│   ├── simple_boids_network.py   # Network topology and simulation
│   ├── enhanced_agent.py         # Legacy marketplace agent
│   └── [other legacy files]      # Original marketplace implementation
├── main_simple.py                # Simple boids entry point
├── main.py                       # Legacy marketplace entry point
└── personal_tools/               # Legacy tool storage

Agents discussing tool needs leads to better-targeted tool development than individual planning.

Complex tools emerge naturally when simple tools can be combined and built upon.

Tool creators have ongoing incentives to build foundational tools others need.

Without explicit programming, agents develop roles as architects, builders, and integrators.

Community support filtering ensures only valuable tools get built.

# Start the simple boids simulation
python3 main_simple.py

# Try different configurations
python3 main_simple.py --agents 5 --steps 30 --topology star

# Watch 3 simple rules create emergent specialization!

Experience how 3 simple local rules can create complex collaborative patterns without any central coordination. 🚀

The original marketplace implementation is still available:

python main.py --demo --agents 3 --rounds 5  # Requires Azure OpenAI setup