Generative Music Co-Creation
This challenge tasks developers with building a multi-agent 'composer swarm' that collaboratively creates musical pieces based on high-level textual prompts. The system will leverage a hypothetical 'OpenAI o3' music generation model alongside a 2025-era LLM for creative direction and iterative refinement. Agents will communicate using an A2A (Agent-to-Agent) protocol to share ideas, propose sections, and provide feedback on generated musical segments. This advanced setup necessitates the implementation of extended thinking for creative problem-solving and a custom orchestration layer to manage the swarm's collaborative workflow, pushing the boundaries of generative AI beyond simple prompt-to-output paradigms.
What you are building
The core problem, expected build, and operating context for this challenge.
Orchestrate an A2A protocol multi-agent swarm using OpenAI o3 and GPT-5 to collaboratively generate musical compositions with extended thinking.
How work is evaluated
The evaluation will assess the system's ability to generate musically coherent and diverse compositions, the effectiveness of agent collaboration, and the sophistication of the A2A communication and extended thinking implementation.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Apply extended thinking techniques for iterative creative refinement.
Develop a custom orchestration layer for agent workflow management.
Integrate hypothetical OpenAI o3 music generation capabilities via tool use.
Design and implement an A2A protocol for secure agent communication.
Build a multi-agent swarm capable of collaborative music composition.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
The evaluation will assess the system's ability to generate musically coherent and diverse compositions, the effectiveness of agent collaboration, and the sophistication of the A2A communication and extended thinking...
Challenge input
JSON: { "genre": "Jazz Improv", "duration_seconds": 60, "mood": "upbeat and lively", "instrumentation": ["piano", "bass", "drums", "saxophone"], "k...
Agent execution
No agent tool is listed for this challenge.
Evaluated output
JSON: { "midi_data": "base64_encoded_midi_string", "composition_log": ["agent_name": "MelodyAgent", "action": "proposed_phrase", "time": "1.2s", "f...
- Verify that the 'composition_log' clearly demonstrates effective, multi-directional A2A communication betwe...
- Check if the generated MIDI data's duration is within +/- 10% of the requested duration.
- Musical Coherence Score target: 7
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Verify that the 'composition_log' clearly demonstrates effective, multi-directional A2A communication between distinct agents.
- Check if the generated MIDI data's duration is within +/- 10% of the requested duration.
Metrics
- Musical Coherence Score · Target 7
- Prompt Adherence Score · Target 8
- Agent Collaboration Depth · Target 15
Workflow tasks
- Task 1
Input: JSON: { "genre": "Jazz Improv", "duration_seconds": 60, "mood": "upbeat and lively", "instrumentation": ["piano", "bass", "drums", "saxophone"], "key": "C major", "tempo_bpm": 140 }
Output: JSON: { "midi_data": "base64_encoded_midi_string", "composition_log": ["agent_name": "MelodyAgent", "action": "proposed_phrase", "time": "1.2s", "feedback_from": "CriticAgent"...], "final_prompt_used": "Generated jazz improv in C major..." }
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.