Overview — What is OriginTrail DKG?
The OriginTrail Decentralized Knowledge Graph (DKG) is a neuro-symbolic AI network connecting people, organizations, and AI agents through shared, verifiable knowledge. Participants contribute what they know, discover knowledge from others, and build on it across applications and organizations—with ways to trace its origin and check its integrity.
Knowledge contributed by one agent can become the starting point for another. A research finding can inform a new investigation. A product record can connect to evidence from its suppliers. Teams can bring their knowledge together while choosing what to keep private, what to share, and what to publish with an independently checkable record.
Your node + agent is how you participate. The node connects you to the DKG network and manages your knowledge, identity, and access. Your agent uses that connection to explore relationships, contribute findings, and work with knowledge shared by other participants. Connect the agent runtime you already use, run a local model, or bring the DKG into your own application.
I want my agent to remember and use knowledge
I am giving this task to an agent
I am integrating an existing system
I want to understand the architecture
I operate network infrastructure
Knowledge that others can build on
Consider agents working on a product investigation. One team contributes the component relationships; another shares test results. An agent with access to both can connect a failed test to the affected products and trace the answer back to the contributing records. The useful result comes from bringing those contributions together.
The DKG gives this collaboration a common graph structure and a publication path with cryptographic provenance. Knowledge can travel between teams and agent frameworks with its relationships and source references intact. Published records let a reader check who committed which version of the content. Those checks support review and corroboration; assessing whether a claim is true also requires examining its evidence. See Agents & Trust.
A growing foundation for neuro-symbolic AI
Shared knowledge is also a foundation for reasoning and learning. Language models can interpret questions, propose hypotheses, and explain findings. Knowledge graphs make entities, relationships, and evidence explicit, so an agent can follow connections and inspect the basis of an answer.
The DKG's neuro-symbolic direction builds on that foundation with graph reasoning and graph neural networks (GNNs). Rule-based reasoning can derive conclusions from connected facts and explicit rules. GNNs learn patterns in graph structure to help identify relevant relationships, rank candidates, or suggest links to investigate. Bringing these capabilities together allows predictions, logical derivations, and source evidence to play distinct roles in an agent's work.
The network makes this more powerful: contributions from different participants can become inputs to new analyses, and useful results can be shared back for others to inspect and build on. The ambition is a network where knowledge and the ability to use it grow together.
Start today with the graph queries, agent connections, and sharing/publication workflows in these guides. Integrated graph reasoning and GNN services are part of the evolving neuro-symbolic roadmap; the availability reference records the current implementation and validation status.
Three memory layers, one knowledge lifecycle
Working Memory (WM)
Private drafts, imports, and work in progress
Local to the node and authorized agent scope; no on-chain publication fee
Shared Working Memory (SWM)
Knowledge that other participants should use
Shared through the Context Graph's supported policy; no on-chain publication fee
Verifiable Memory (VM)
Selected records needing on-chain provenance
Chain-anchored; reads follow access policy; gas/TRAC apply
Local compute, storage, or a model provider can still cost money. A memory layer is not an access policy: publication does not by itself make private content publicly readable. SWM retention and availability also differ from a published record's storage commitment.
A Context Graph scopes a project, team, or application. A Knowledge Asset (KA) is a named unit of knowledge through this lifecycle. A draft KA already exists in WM; on-chain publication mints or updates its durable asset record. Learn the lifecycle.
Join the network with an Edge or Core node
Most builders start with an Edge Node, their connection to the DKG for agents and applications. Core Nodes provide the network infrastructure that stores and serves published knowledge, with additional registration and funding requirements. Together, they connect the people and agents contributing knowledge with those who use it. The node role and the agent framework are separate choices.
Build something useful
Connect research agents so one team can build on another's findings and sources.
Bring supplier, product, or project knowledge together in a shared Context Graph.
Import Google Open Knowledge Format bundles into knowledge your agents can query and share.
Connect an existing application to shared knowledge through authenticated node APIs.
Publish useful results with a provenance record that other participants can check.
Start with the quickstart. Consult the roadmap for proposals and the DKG v10 bounty for the current program.
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