Multi-Agent Protocol for Smart City Robotaxi Coordination
This challenge focuses on designing a multi-agent system for smart city traffic management. You will build an A2A protocol-enabled system using Langroid agents powered by DeepSeek to coordinate different robotaxi fleets. The system must optimize traffic flow, dynamically re-route vehicles, and resolve conflicts by leveraging real-time traffic data, extended thinking for predictive routing, and few-shot learning to adapt to evolving urban conditions. Emphasis will be placed on seamless inter-fleet cooperation and robust decision-making in a complex, dynamic environment. This project requires implementing sophisticated agent communication and autonomous reasoning. You will integrate MCP tools for real-time urban data streams and orchestrate large-scale agent deployments using OpenAI Swarm, pushing the envelope for urban mobility solutions.
What you are building
Build an A2A protocol multi-agent system with Langroid and DeepSeek V3 for smart city robotaxi coordination, using extended thinking and few-shot learning for adaptive traffic management and OpenAI Swarm for scaling.
How work is evaluated
The evaluation will focus on the multi-agent system's ability to optimize traffic flow, reduce congestion, and resolve routing conflicts in a simulated urban environment, emphasizing inter-fleet A2A communication, adaptive reasoning, and resilience.
Datasets
Public datasets, plus the private datasets you have access to.
Learning goals
Design and implement an A2A protocol for cross-fleet agent communication.
Develop Langroid agents for real-time traffic analysis and routing decisions using DeepSeek V3.
Orchestrate and scale agents using OpenAI Swarm for urban environments.
Integrate MCP tools for real-time urban data streams.
Apply extended thinking and few-shot learning for adaptive traffic management.
Resources
Sample data for 1 tasks
Challenge setup
The evaluation will focus on the multi-agent system's ability to optimize traffic flow, reduce congestion, and resolve routing conflicts in a simulated urban environment, emphasizing inter-fleet A2A communication, ada...
Challenge input
{"traffic_state": {"road_closures": ["string", ...], "event_locations": ["string", ...], "current_congestion": {"street_name": "level"}, "new_patte...
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{"fleet_A_new_routes": [{"id": "uuid", "optimized_path": ["node", ...]}], "fleet_B_new_routes": [{"id": "uuid", "optimized_path": ["node", ...]}],...
- Optimized routes must not create new, severe congestion points or cause gridlock in the simulated environment.
- Communication logs must show successful and structured A2A protocol interactions between fleet agents and a...
- The DeepSeek V3 agent's reasoning should demonstrate successful application of few-shot learning to adapt t...
- Overall Travel Time Reduction target: 0.25
- Python execution harness
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Optimized routes must not create new, severe congestion points or cause gridlock in the simulated environment.
- Communication logs must show successful and structured A2A protocol interactions between fleet agents and a central coordinator.
- The DeepSeek V3 agent's reasoning should demonstrate successful application of few-shot learning to adapt to specific, new traffic pattern examples provided in the input.
Metrics
- Overall Travel Time Reduction · Target 0.25
- Congestion Index Reduction · Target 0.2
- Decision-Making Latency (ms) · Target 1500
Workflow tasks
- Task 1
Input: {"traffic_state": {"road_closures": ["string", ...], "event_locations": ["string", ...], "current_congestion": {"street_name": "level"}, "new_patterns_examples": [{"input": "scenario_desc", "output": "expected_agent_action"}, ...]}, "fleet_A_trips": [{"start": "loc", "end": "loc", "id": "uuid"}], "fleet_B_trips": [{"start": "loc", "end": "loc", "id": "uuid"}]}
Output: {"fleet_A_new_routes": [{"id": "uuid", "optimized_path": ["node", ...]}], "fleet_B_new_routes": [{"id": "uuid", "optimized_path": ["node", ...]}], "a2a_communication_logs": [{"sender": "string", "receiver": "string", "message": "string", "timestamp": "datetime"}], "deepseek_reasoning_logs": "string"}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.