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118SN118Blog · Jul 24, 2026

Agents need organised memory (Open Source Claude Cowork) Ditto SN118

This week on Novelty Search, the team behind Subnet 118 – Ditto joins to explain how long-term AI memory can become decentralized infrastructure on Bittensor. Ditto is building a persistent memory system for AI agents. Instead of relying only on a model’s context window, Ditto continuously stores, organizes, links, retrieves and refines knowledge over time, allowing agents to build lasting understanding across projects, conversations and workflows. The discussion explores why memory is becoming one of the most important components of agentic AI, how Ditto organizes information into evolving knowledge graphs, why benchmarks for long-term memory matter, how Ditto Bench incentivizes better memory systems through Bittensor, and how fast decentralized inference could enable swarms of AI agents working together in real time. Links Ditto Website: https://heyditto.ai/ Ditto GitHub: https://github.com/heyditto Subnet 118 GitHub: https://github.com/heyditto/sn118 Ditto X: https://x.com/heydittoai Bittensor: https://bittensor.com Novelty Search Playlist: https://www.youtube.com/@OpenTensorFoundation 00:00 Introduction 00:03 Security updates & Polkadot Vault signing 09:59 Introducing Ditto (Subnet 118) 12:36 Why AI needs long-term memory 19:43 MCP integration and developer workflows 24:55 Memory search, vaults & knowledge retrieval 29:53 Building evolving knowledge graphs 34:47 Ditto Bench and benchmarking AI memory 39:53 How modern AI memory systems are evolving 44:49 Incentivizing better memory on Bittensor 49:52 API design and authentication 54:56 Multi-agent orchestration and persistent memory 59:54 Fast decentralized inference on Bittensor 01:05:00 Future roadmap for Ditto 01:10:14 Outro

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