XCT-Agentic Platform

AI Model as a Service

Three-layer AI platform built on solar energy. From multi-engine orchestration to model routing to agent execution — powered by 100GW of clean energy.

Three Layers, One Platform

The XCT-Agentic Platform spans three architectural layers. Each layer is independently powerful, and together they form a complete stack from orchestration to execution.

L1 — Builder Layer

XCT-Builder:
Multi-Engine Orchestration

Don't choose one framework. Let the platform route tasks to the best engine for each job. XCT-Builder automatically selects between LangGraph, CrewAI, n8n, and Dify based on task complexity and structure.

  • Dynamic task routing — no manual engine selection
  • MCP (Model Context Protocol) for standardized context
  • Supports LangGraph, CrewAI, n8n, Dify out of the box
  • Extensible engine adapter interface
LangGraph CrewAI n8n Dify
xct_builder.route()
task = {"{"}
  type: "branching_workflow",
  complexity: "high",
  steps: 12
}"} 

engine = "langgraph"  // auto-selected

context = MCP.standardize(
  task, session, memory
)
XCT-Exchange Architecture
L2 — Exchange Layer

XCT-Exchange:
Four-Factor Trading Bus

A unified exchange for Model, Data, Compute, and Energy. Built on distributed bus architecture, XCT-Exchange connects AI workloads to the right resources at the right price — all powered by 100GW of solar capacity.

  • Model Hub — LiteLLM-powered multi-model routing (GPT, Claude, Gemini, Llama, Mistral)
  • Data Hub — Ocean Protocol C2D for privacy-preserving data exchange
  • Compute Hub — GPU cluster allocation for AI training and inference
  • Energy Hub — Solar compute tokens backed by 100GW capacity
🤖

Model Hub

50+ models via LiteLLM

🗄️

Data Hub

Ocean C2D privacy compute

Compute Hub

GPU cluster orchestration

☀️

Energy Hub

100GW solar capacity

L3 — Agent Layer

XCT-Agent:
Long-Term Memory & Sessions

Agents that remember. XCT-Agent combines persistent long-term memory with cross-model session management — enabling AI agents that build context over time, across sessions, and across models.

  • Long-term memory with vector search and structured recall
  • Cross-model session continuity (GPT ↔ Claude ↔ Gemini)
  • Persistent context across sessions and devices
  • Human-in-the-loop review for high-stakes decisions
Read the Docs →
XCT-Agent Memory Architecture
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Ready to Build on
XCT-Agentic Platform?

Access AI models, orchestration tools, and solar-powered compute — all in one platform built for the AI city-state.

Read the Docs Solar Compute