Technical Platform
Explore the technical foundation behind Tego OS: LAMP orchestration, DAG scheduling, experience memory, A2UI interfaces, and Agent Skills.
LAMP Three-Stage Reasoning Framework
Language-enhanced multi-agent planning for coordinated, explainable decisions
Think · Continuous Reasoning
Parallel short-term and long-term reasoning for task state, risk signals, and trend analysis.
- Parallel short-term and long-term reasoning
- Environmental shock detection and severity assessment
- Trend tracking and strategic analysis
- Anomaly identification and early warning
Speak · Agent Communication
Semantic communication between agents, with information sharing, knowledge transfer, and trust updates.
- Multi-candidate message generation with quality scoring
- Belief state management and trust updating
- Shared knowledge base construction
- Strategy preference learning and adaptation
Decide · Decision Fusion
Combine observations, reasoning outputs, and belief states into adaptive, explainable decisions.
- Three-source data fusion (numerical, reasoning, belief)
- Weighted scoring and LLM-based decision making
- Fallback mechanism for system stability
- Explainable decision output
DAG Scheduling Engine
Dependency-aware scheduling that maximizes parallel execution.
Topological Sorting
Analyze dependencies, build an execution topology, and determine the right execution order.
Parallel Execution
Run independent tasks at the same time to shorten total completion time.
Dynamic Adjustment
Adjust concurrency at runtime based on resources and task characteristics.
Exception Isolation
Isolate task-level exceptions so one failure does not interrupt the full workflow.
Experience Memory
Store successful workflows as vectors, retrieve similar cases, and reuse proven strategies over time.
Vector Storage
Convert successful execution trajectories into vector representations and store them in the experience memory.
Smart retrieval
Retrieve similar historical workflows for new tasks and match them to proven strategies.
Strategy Reuse
Reuse historical successful strategies, reducing planning time by 40-60%
Continuous Evolution
Use failed cases as counterexamples so the system can avoid repeating mistakes.
A2UI Dynamic Interface Engine
The A2UI engine lets AI generate interactive interface components such as charts, forms, and buttons instead of returning text only. Tego implemented a complete React renderer ahead of Google’s official release.
- Full implementation of A2UI protocol specification
- 15+ standard components (Text, Card, Chart, Form, etc.)
- Independent style system, zero Tailwind dependency
- Declarative data format, no code injection risk
- Support for streaming rendering and real-time updates
# SKILL.md
---
name: contract-reviewer
description: Professional legal contract review and risk analysis
version: 1.0.0
compatibility:
- tego-os
- claude
- cursor
---Agent Skills Ecosystem
Extend AI capabilities like installing apps. Standardized skill definition specification, compatible with Claude Agent Skills open standard, cross-platform usable. Supports Cowork collaboration mode for dynamic multi-skill combination without predefined workflows.
- Standardized SKILL.md format definition
- Cross-platform compatible (Tego, Claude, Cursor)
- Cowork collaboration mode, intelligent multi-skill orchestration
- Secure sandbox isolated execution
- Progressive loading, optimized Token consumption
MCP Development Framework
Decorator-driven development model that greatly simplifies agent tool development
Purpose-built MCP framework
Define agent tools quickly with @Tool decorators and structured metadata, reducing boilerplate and improving delivery speed.
Standardized Tool Registration and Validation
Tool capabilities are registered with structured metadata, automatic parameter validation, and type checking.
Define agent services in a few lines
A minimal development model helps teams expose callable AI tools quickly and iterate safely.
Multi-Agent Collaboration with LAMP
The patented LAMP framework uses Think-Speak-Decide reasoning, semantic agent communication, decision fusion, DAG scheduling, and dynamic replanning to improve task success rates by 15-25% and execution speed by 2-5x.
Decorator-Driven Development
The project uses @Tool decorators to define tools, providing clear metadata declarations:
@Tool({
name: 'saveMedicalRecord',
description: 'Save medical record to database/保存电子病历',
parameters: [
{
name: 'tenantId',
type: 'string',
description: 'The tenant id/租户id',
required: true
}
// ... 更多参数
]
})
export class MedicalRecordTool extends BaseTool {
// 工具实现
}Complete TypeScript type support
Automatic parameter validation and type checking
Core Advantages Summary
The decorator-driven model lets developers focus on business logic while Tego OS handles registration, validation, and operational integration. This improves delivery speed, maintainability, and enterprise readiness.