Note: Built independently in January 2026. Similar architecture now being deployed by Nethermind's ChaosChain for autonomous agent accountability using Ethereum + IPFS + DKG patterns.
- ✅ Core DAMN system: Implemented and deployed
- ✅ Multi-agent demo: Completed
- 🚀 Exploring production deployment & partnerships
- 📝 Available for collaboration/research opportunities
DAMN enables autonomous AI agents and robots to store, share, and reuse learned experiences without catastrophic forgetting.
Built on Ethereum + IPFS for decentralized, persistent memory across agents.
Catastrophic Forgetting:
AI systems lose previously learned behaviors when trained on new tasks. Traditional approaches require retraining entire models. DAMN creates a persistent, shared memory layer so knowledge is never lost and agents learn from each other's experiences.
flowchart TB
subgraph "Agent Layer"
A1[Agent 1<br/>UAV-001]
A2[Agent 2<br/>UAV-002]
end
subgraph "Client Layer"
Client[Python Client<br/>web3.py + IPFS API]
end
subgraph "Storage Layer"
IPFS[(IPFS<br/>Pinata)]
Contract[Smart Contract<br/>0xacA...B8c4]
end
A1 -->|"1. Learn Experience<br/>(obstacle, maneuver)"| Client
Client -->|"2. Upload JSON"| IPFS
IPFS -->|"3. Return CID<br/>(QmXyz...)"| Client
Client -->|"4. Store CID + Metadata"| Contract
Contract -->|"5. Event: MemoryStored"| A2
A2 -->|"6. Query by Location"| Contract
Contract -->|"7. Return CID"| A2
A2 -->|"8. Fetch Memory"| IPFS
IPFS -->|"9. Return Experience Data"| A2
style A1 fill:#e1f5e1
style A2 fill:#e1f5e1
style IPFS fill:#fff4e1
style Contract fill:#e1e5ff
| Component | Role | Technology |
|---|---|---|
| Agent Layer | Autonomous systems (UAVs, robots) | Any language with Web3 support |
| Python Client | Handles IPFS upload + blockchain interaction | web3.py, requests |
| IPFS Storage | Stores actual memory data (JSON) | Pinata cloud pinning |
| Smart Contract | Immutable ledger of memory CIDs | Solidity, Ethereum Sepolia |
Agent 1 stores memory:
{
"agent_id": "UAV-001",
"location": {"lat": 28.61, "lon": 77.21},
"obstacle_type": "building",
"learned_action": "climb_to_200m_then_proceed",
"success_rate": 0.98
}↓ Uploaded to IPFS
↓ Returns CID: QmXyz...
↓ Stored on-chain
↓ Agent 2 queries by location
↓ Retrieves CID, fetches from IPFS
↓ Reuses knowledge without retraining
- Immutability: On-chain CIDs cannot be altered after storage
- Verifiability: Each memory cryptographically linked to originating agent
- Decentralization: No single point of failure (IPFS + blockchain)
- Persistence: Memories survive agent crashes, server downtime
- Network: Ethereum Sepolia Testnet
- Contract:
0xacAABF9A47d1Df7f2f698ad9033da10CD374B8c4 - Verified:
✅ Sourcify | Blockscout - Status: Operational (2+ memories stored)
- UAV-001 encounters a building obstacle at (28.61°N, 77.21°E)
- Learns safe maneuver:
climb_to_200m_then_proceed - Stores experience on IPFS + Blockchain
- UAV-002 approaches same area
- Retrieves UAV-001's memory
- Successfully navigates using learned behavior
- Success rate: 98% ✅
Result: Zero retraining required. Knowledge persists across agent swarm.
- Smart Contract: Solidity 0.8.0
- Blockchain: Ethereum (Sepolia Testnet)
- Storage: IPFS via Pinata
- Integration: Python + Web3.py
- Infrastructure: Lightning AI (T4 GPU)
- Sepolia ETH: https://sepoliafaucet.com
- Pinata Account: https://pinata.cloud
# Clone repo
git clone https://github.com/rahulkhunte/DAMN-prototype.git
cd DAMN-prototype
# Install dependencies
pip install -r requirements.txt
# Setup environment
cp .env.example .env
# Edit .env with your credentials
# Run demo
jupyter notebook demo.ipynbDAMN-prototype/
├── README.md
├── DAMN.sol
├── demo.ipynb
├── requirements.txt
├── .env.example
├── .gitignore
└── demos/
├── blockchain_transaction.png
├── contract_verification.png
├── ipfs_storage.png
├── multi_agent_demo.png
└── network_stats.png
- Autonomous Drones: Swarm coordination without central server
- Robotics: Manufacturing robots sharing assembly techniques
- Healthcare: Surgical robots learning from collective experiences
- Space Exploration: Mars rovers sharing terrain navigation data
- Smart Cities: IoT devices learning optimal traffic patterns
DAMN's architecture is well-suited for academic research in autonomous systems, particularly UAV swarms and multi-robot coordination.
Potential Collaboration Areas:
- Autonomous vehicle testbeds (UAV, ground robots)
- Edge AI + blockchain integration research
- Multi-agent coordination without centralized control
- Real-world deployment on hardware platforms
Technical Goals for Production:
- Optimize retrieval latency to <100ms
- Implement memory quality scoring and reputation system
- Scale to 100+ agent networks with optimized indexing
- Hardware integration with autonomous platforms
- Gas optimization and cost-effective contract design
- ✅ Smart contract deployment (Jan 8, 2026)
- ✅ Multi-agent demo (Jan 9, 2026)
- ✅ Contract verification (Sourcify, Blockscout)
- ⏳ Memory quality scoring system
- ⏳ Real-time retrieval optimization (<100ms)
- ⏳ Production security audit
- ⏳ Mainnet deployment
DAMN is designed to be quantum-ready.
In future research phases, we will explore hybrid quantum–classical methods to enhance DAMN through:
- Post-quantum cryptography for memory authentication
- Quantum-inspired optimization for memory retrieval
- Hybrid simulation using Qiskit and quantum simulators
Status:
- DAMN: Implemented and deployed
- Q-DAMN: Research-phase extension (exploratory)
MIT License
Rahul Khunte
Protocol Engineer | AI/ML & Blockchain | B.Tech Civil Engineering (2022) | BIT Raipur
- 📧 Email: rahulk.rk903@gmail.com
- 🔗 GitHub: https://github.com/rahulkhunte
- 🌐 Portfolio: https://rahulkhunte.github.io/portfolio/
- Lightning AI (for GPU compute)
- Ethereum Foundation (Sepolia testnet)
- Pinata (IPFS infrastructure)
- Open-source Web3 community