The product

The Network Operating System for verifiable AI.

Network Operating System (nOS) gives enterprise AI agents shared context graphs and turns every action into a signed, replayable decision trace. One API key. Any agent framework. Built on the OriginTrail DKG.

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The category replacement

Built for decisions, not objects.

Traditional systems of record were designed to store the state of things, accounts, tickets, transactions. nOS captures the intelligence that connects them: why a decision was made, what context informed it, and what precedent it sets.

Legacy systems of record
nOS context graphs
Record objects: accounts, cases, tickets
Record decisions: traces, precedent, reasoning
See data after the fact via ETL
Capture context at decision time, in the execution path
The "why" lives in Slack and email
Decision traces are structured, queryable, enshrined
Governance bolted on after deployment
Governance native to the orchestration layer
Vendor-controlled audit logs
Signed and independently verifiable
Each agent deployed = linear cost increase
Each agent deployed = compounding value from shared context
Scaling requires more infra per agent
Scaling is sublinear, graphs grow richer, not heavier
Architecture

From action to enshrinement, in four layers.

One API key. REST endpoints. Any agent framework. Every action becomes a decision trace. Every trace is enshrined in the OriginTrail DKG.

Layer01

nOS API

One API key. REST endpoints. Any agent framework, Claude, LangChain, CrewAI, AutoGen, custom.

Developer entry
Layer02

Context Agents

Specialized agents that read, write, and reason over shared context graphs. Each action emits a structured trace at execution time.

Execution
Layer03

Context Graphs

Structured, permissioned knowledge spaces where agents coordinate through shared context rather than passing documents.

Coordination
Layer04

OriginTrail DKG

The persistent, verifiable enshrinement layer. Decision traces are stored as Knowledge Assets, independently verifiable and owned by you.

Enshrinement
Agent action → Decision trace → Context graph → Enshrined in DKG
The wall

Agents hit the same ambiguity humans resolve with judgment.

The wall is not missing data. It is missing decision context.

01

Tribal exception logic

Exception logic lives in people's heads, not in any system. Agents have nothing to inherit.

02

Dead precedent

Past decisions are never linked or searchable. Every new decision starts from zero.

03

Synthesis in Slack

Cross-system synthesis happens in threads and on calls, never as durable artifacts.

04

Linear cost scaling

Every new agent costs as much as the last. Nothing compounds. Nothing learns from precedent.

The inversions

Five things nOS flips.

Lost reasoningDecision traces enshrined in the DKG
Dead precedentSearchable, compounding history
Black-box outputsReplayable decisions with full context
Vendor lock-inFramework-agnostic, portable traces
Linear cost scalingSublinear, compounding economics

Specialized agents. Shared traces.

Research Agent

Traces: Sources consulted, synthesis logic, confidence scores, conflicting findings.

Compliance Agent

Traces: Regulations checked, exceptions granted, approval chains, audit-ready reasoning.

Operations Agent

Traces: Workflow decisions, resource allocation logic, escalation rationale.

Analytics Agent

Traces: Data sources queried, statistical methods applied, confidence intervals, anomaly flags.

Code Agent

Traces: Architectural decisions, dependency choices, test coverage rationale, refactor logic.

Integration Agent

Traces: System mappings, data transformation rules, conflict resolution, sync decisions.

Benchmark

Measured on a 6.8M-token monorepo.

Context graph coordination compared with document-handoff baselines. Gains compound with scale.

60%
Time saved · agent loop
40%
Token cost reduction
3.2x
Cross-agent reuse multiplier
100%
Replayable decision traces
Why it scales

Each agent makes every other agent cheaper and smarter.

Legacy systems get more expensive with every agent you add. nOS gets cheaper. Coordination overhead drops as the shared memory grows.

01

Compounding precedent, not compounding cost.

Every decision trace becomes precedent for every future agent. Agent 50 inherits the compounded intelligence of agents 1–49.

02

Graphs get richer, not heavier.

Unlike databases that slow as they grow, shared context graphs become more useful with scale. More agents, more traces, richer precedent.

03

Cost per agent goes down, not up.

Token spend, compute, and coordination all decrease per agent as the graph grows. Economists call this sublinear scaling — no legacy stack can replicate it.

Cost per agent · 1 → 100LegacynOS
agent 1agent 25agent 100AGENTS DEPLOYED →COST →the gap
At agent 100, the per-agent cost on shared context graphs is a fraction of legacy infrastructure.
Pricing

Free to start. Pro when you scale.

The OriginTrail DKG is open-source and free forever. Pro adds custom agent frameworks, custom data pipelines, custom integrations, dedicated infrastructure, and enterprise support.

Free
$0
  • Open-source DKG access
  • Context Graphs, Knowledge Assets*, Decision Traces
  • Agent connectors (Hermes, OpenClaw, MCP)
  • Community support

Best for: personal agents, prototyping, developer evaluation

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Pro
$1,999/ mo
  • Everything in Free
  • Priority email support
  • 2.5M Knowledge Assets* / month
  • Managed agent memory infrastructure
  • Custom agent frameworks
  • Custom data pipelines & ETLs
  • Custom integrations (SAP, Salesforce, Oracle…)

Best for: production multi-agent deployments, enterprise product teams

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Enterprise
Custom
  • Unlimited Knowledge Assets*
  • Fully bespoke deployment
  • Co-engineered integrations
  • Dedicated DKG Core Node

Best for: large enterprise, government, regulated industries

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* Knowledge Assets can start from a file — a .md, .pdf, web page, dataset, invoice, research paper, meeting note, or shipment record — but in the DKG it becomes structured graph knowledge with provenance, so agents can ask “what does this contain?”, “who/what does it mention?”, “can I trust it?”, “where did it come from?”, and “how is it connected to other things?”

Dataownership

You don't have to trust us. That's the point.

Decision traces enshrined in the DKG are owned by you, not by Trace Labs. Private data never touches our servers. Proofs are independently verifiable by any third party. Replace us with a self-hosted instance anytime, zero data loss. This is not "you can export your data." This is "the data was never ours to begin with."

Start free

Power up your business with the OriginTrail DKG.

Create Knowledge Assets, build shared context graphs, enshrine decision traces. Open-source infrastructure. Community support. Zero cost.

Go Pro

The full Network Operating System.

Custom agent frameworks (Hermes, OpenClaw, LangChain, Claude). Custom data pipelines. Custom integrations with your enterprise systems. Dedicated infrastructure and support.