oib/aitbc
1
0
Fork 0
Code Issues Pull Requests Actions 6 Packages Projects Releases Wiki Activity
Files
f2849ee4a941d2c944f3e5ee52d7236ed098a00a
aitbc/docs/12_issues/02_decentralized_memory.md
AITBC System b033923756 chore: normalize file permissions across repository 
- Remove executable permissions from configuration files (.editorconfig, .env.example, .gitignore)
- Remove executable permissions from documentation files (README.md, LICENSE, SECURITY.md)
- Remove executable permissions from web assets (HTML, CSS, JS files)
- Remove executable permissions from data files (JSON, SQL, YAML, requirements.txt)
- Remove executable permissions from source code files across all apps
- Add executable permissions to Python
2026-03-08 11:26:18 +01:00

2.3 KiB
Raw Blame History

Phase 2: Decentralized AI Memory & Storage

Overview

OpenClaw agents require persistent memory to provide long-term value, maintain context across sessions, and continuously learn. Storing large vector embeddings and knowledge graphs on-chain is prohibitively expensive. This phase integrates decentralized storage solutions (IPFS/Filecoin) tightly with the AITBC blockchain to provide verifiable, persistent, and scalable agent memory.

Objectives

  1. IPFS/Filecoin Integration: Implement a storage adapter service to offload vector databases (RAG data) to IPFS/Filecoin.
  2. On-Chain Data Anchoring: Link the IPFS CIDs (Content Identifiers) to the agent's smart contract profile ensuring verifiable data lineage.
  3. Shared Knowledge Graphs: Enable an economic model where agents can buy/sell access to high-value, curated knowledge graphs.

Implementation Steps

Step 2.1: Storage Adapter Service (Python)

  • Integrate ipfshttpclient or web3.storage into the existing Python services.
  • Update AdaptiveLearningService to periodically batch and upload recent agent experiences and learned policy weights to IPFS.
  • Store the returned CID.

Step 2.2: Smart Contract Updates for Data Anchoring

  • Update GovernanceProfile or create a new AgentMemory.sol contract.
  • Add functions to append new CIDs representing the latest memory state of the agent.
  • Implement ZK-Proofs (using the existing ZKReceiptVerifier) to prove that a given CID contains valid, non-tampered data without uploading the data itself to the chain.

Step 2.3: Knowledge Graph Marketplace

  • Create KnowledgeGraphMarket.sol to allow agents to list their CIDs for sale.
  • Implement access control where paying the fee via AITBCPaymentProcessor grants decryption keys to the buyer agent.
  • Integrate with MultiModalFusionEngine so agents can fuse newly purchased knowledge into their existing models.

Expected Outcomes

  • Infinite, scalable memory for OpenClaw agents without bloating the AITBC blockchain state.
  • A new revenue stream for "Data Miner" agents who specialize in crawling, indexing, and structuring high-quality datasets for others to consume.
  • Faster agent spin-up times, as new agents can initialize by purchasing and downloading a pre-trained knowledge graph instead of starting from scratch.