Decentralized Agentic Collaboration System
For secure, resilient communication channels in challenging environments, this challenge focuses on building a decentralized multi-agent system. This system will enable agents to collaborate on tasks, share information, and adapt to intermittent network connectivity or censorship, mimicking scenarios where traditional internet infrastructure is unreliable. The core challenge is to ensure robust A2A (Agent-to-Agent) communication and data persistence without reliance on a central server, using a combination of advanced AI and decentralized technologies.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct a decentralized multi-agent communication and collaboration system utilizing Langroid and IPFS for robust operation in challenging network conditions.
How work is evaluated
The evaluation will assess the resilience, reliability, and intelligence of the decentralized agent system. Key metrics will include message delivery success under various network conditions, data consistency across agents, and the agents' ability to collaboratively achieve complex tasks.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Master `Langroid` for building sophisticated multi-agent systems, focusing on asynchronous A2A communication patterns and persistent agent states.
Implement decentralized data storage and retrieval using `IPFS/Filecoin` to ensure data availability and censorship resistance for agent-shared information.
Integrate `libp2p` as the foundational networking layer for direct, peer-to-peer agent communication, bypassing central servers.
Deploy a lightweight, specialized generative model (e.g., a fine-tuned Llama 3 variant) using `Fireworks AI`'s serving capabilities for local, offline inference within agents.
Design agents with adaptive communication strategies that can detect network status and dynamically switch between direct P2P messaging and IPFS-based message queues.
Build a shared 'memory' for agents using IPFS CIDs to reference globally accessible, immutable data, enabling consistent knowledge across the decentralized network.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The evaluation will assess the resilience, reliability, and intelligence of the decentralized agent system. Key metrics will include message delivery success under various network conditions, data consistency across a...
Challenge input
{"sender_agent_id": "string", "receiver_agent_id": "string", "message_content": "string", "network_disruptions": "array<float>"}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{"delivery_status": "string", "delivery_latency_ms": "integer", "path_taken": "array<string>"}
- All messages in the 'ResilientMessageDelivery' task must eventually be delivered, even with simulated netwo...
- For collaborative tasks, the 'final_result_cid' must match the expected CID and be retrievable from IPFS.
- AverageDeliveryLatency target: 1000
- 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
- All messages in the 'ResilientMessageDelivery' task must eventually be delivered, even with simulated network disruptions.
- For collaborative tasks, the 'final_result_cid' must match the expected CID and be retrievable from IPFS.
Metrics
- AverageDeliveryLatency · Target 1000
- IPFSDataConsistency · Target 0.98
- TaskCompletionRate · Target 0.95
Workflow tasks
- Task 1
Input: {"sender_agent_id": "string", "receiver_agent_id": "string", "message_content": "string", "network_disruptions": "array<float>"}
Output: {"delivery_status": "string", "delivery_latency_ms": "integer", "path_taken": "array<string>"}
Execution harness: Python
- Task 2
Input: {"initiator_agent_id": "string", "task_description": "string", "expected_final_cid": "string"}
Output: {"task_completion_status": "string", "final_result_cid": "string", "agent_interaction_log": "json"}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.