OriginTrail
  • Get started with OriginTrail
  • OriginTrail Ecosystem — Call for Papers (Coming Soon)
  • Key Concepts
    • Decentralized Knowledge Graph (DKG)
    • DKG key concepts
  • TRAC: DKG utility token
  • Build with DKG
    • Quickstart (test drive the DKG in 5 mins)
      • Quickstart with Node.js
      • Quickstart with Python
    • Querying the DKG
    • DKG Edge Node
      • DKG Edge Node architecture
      • Get started with the Edge Node boilerplate
        • Automated setup with the installer
        • Manual setup
        • Usage example
      • Customize & build with the Edge Node
      • Knowledge Mining and dRAG examples
      • Deploy your Edge Node based project
        • Automated deployment with installer
      • DKG Edge Node inception program
      • DKG Edge Node API documentation
    • DKG Core Node
      • Run a V8 Core Node on testnet
        • Preparation for V8 DKG Core Node deployment
        • V8 DKG Core Node installation
      • Run a V8 Core Node on mainnet
        • Preparation for V8 DKG Core Node deployment
        • V8 DKG Core Node installation
      • How to open up your node for publishing
    • DKG toolkit
      • DKG SDK
        • Development environment setup
        • DKG Javascript SDK (dkg.js)
          • Interact with DKG paranets
          • Knowledge submission & curation
          • Paranet's incentives pool implementation
        • DKG Python SDK (dkg.py)
      • DKG paranets
        • Deploying a DKG paranet
        • Building with DKG paranets
        • Syncing a DKG Paranet
        • Initial Paranet Offerings (IPOs)
          • IPO specification
          • Launching your IPO
          • Paranet's incentives pool
          • IPO voting
      • AI agents
        • ElizaOS DKG agent
        • Custom DKG Python agent
        • Custom DKG JavaScript agent
      • Using the DKG with MCP
    • Ecosystem call for papers
  • DKG under the hood
    • Introduction
    • Delegated staking
      • Step-by-step staking
      • Redelegating stake
  • Random Sampling DKG Proof System
    • Random sampling rollout
    • Random Sampling FAQ
  • DKG Sync
  • Integrated Blockchains
    • Base blockchain
      • Connect to Base
    • Gnosis chain
      • Connect to Gnosis
    • NeuroWeb
    • Teleport instructions - NeuroWeb
    • Bridging to Moonbeam
    • Deployed smart contracts
  • Bounties & rewards
    • General bug bounty
    • Code contributions & V8 bug bounty
  • Whitepapers & RFCs
    • OriginTrail whitepaper
    • OriginTrail RFCs
  • Useful Resources
    • What's new with OriginTrail V8
    • DKG V8 guidebook
      • Protocol updates
      • Feature roadmap
      • How to upgrade to V8?
    • Public nodes
    • Tutorials
    • Test token faucet
    • Development principles
    • Community created resources
    • Linked data & knowledge graphs
    • Available networks, network details and RPCs
    • OT Node Engine implementation details
      • Modules
      • Command Executor
    • Contribution guidelines
      • Guidelines for automated test contributions
    • Explore the OriginTrail ecosystem
Powered by GitBook
On this page

Was this helpful?

Edit on GitHub
  1. Build with DKG
  2. DKG toolkit

AI agents

Create, expand, and share your AI agents’ memories in a transparent and verifiable way on the DKG

PreviousIPO votingNextElizaOS DKG agent

Last updated 4 months ago

Was this helpful?

AI agents can leverage the OriginTrail Decentralized Knowledge Graph (DKG) to create knowledge-graph-based, collective, persistent memory for individual agents or agentic swarms. This functionality enables advanced use cases like long-term interaction tracking, knowledge storage, and retrieval.

Get started

There are several ways to create your DKG-enabled AI agent:

  • Easiest: Create an using the popular ElizaOS framework.

    • Full guide

  • Advanced: Use one of the DKG SDKs to build your own custom agent.

    • agent-building guide

    • agent-building guide (coming soon)

ElizaOS DKG Agent
here
Python SDK
Javascript SDK