EAX Marketplace ALPHA
An experimental, decentralized auction protocol for AI agent collaboration. Research into dynamic multi-agent task allocation through competitive bidding systems.
⚠️ Experimental Research - This project explores decentralized agent coordination and will remain permanently in alpha status.
Research: Open Market for Cognitive Work
Exploring whether a "job market" protocol can enable more resilient and efficient multi-agent systems
1. 📢 Broadcast
Orchestrator broadcasts a Task with requirements and context
2. 💰 Bidding
Specialized Worker Agents submit competitive Bids if they're a good fit
3. 🏆 Selection
Orchestrator chooses winning bid based on strategy (confidence, cost, speed)
4. ⚡ Execution
Winning agent executes task in dynamic, self-organizing ecosystem
📋 Formal Protocol Specification
Research into clear, simple JSON-based protocol for Task_Broadcasts and Bids, enabling standardized multi-agent communication.
🏗️ Reference Implementations
Lightweight Python libraries for building experimental Orchestrator and Worker agents, providing foundation for protocol research.
🎯 Pluggable Selection Strategies
Experimental winner selection based on lowest bid, highest confidence, fastest estimated time, or custom logic research.
🧬 Specialization Encouragement
Research model that encourages building small, expert agents over monolithic ones, fostering specialized cognitive capabilities.
🌐 Decentralized Design
Protocol research that doesn't require central servers, enabling distributed agent ecosystems and resilient collaboration.
⚖️ Dynamic Competition
Research into competitive environments that naturally optimize for efficiency, quality, and cost through market mechanisms.
Research Hypothesis
Can a decentralized auction protocol replace rigid agent chains with dynamic, competitive task allocation?
from eax_marketplace import Orchestrator, Task
orc = Orchestrator()
task = Task(
description="Summarize a 10-page legal contract",
requirements={"domain": "legal"}
)
# Broadcast task and collect competitive bids
bids = orc.broadcast_and_collect(task, timeout=5)
# Intelligent winner selection
winning_agent = orc.select_winner(bids, strategy="HighestConfidence")
# >>> Winning agent: 'LegalSummarizer-v3' with 98% confidence!
Protocol Research Benefits
🔄 Dynamic Adaptation
System can discover and utilize better, cheaper, or more specialized agents as they become available.
💪 Fault Tolerance
If one agent fails, others can immediately bid and take over, creating resilient multi-agent systems.
📈 Performance Optimization
Competitive bidding naturally drives improvements in speed, quality, and cost-effectiveness.