Technical Features
Deep dive into TeGo's technical implementation details, including LAMP collaboration framework, DAG scheduling engine, experience pool learning, A2UI dynamic interface, and Agent Skills system
LAMP Three-Stage Reasoning Framework
Language-enhanced Multi-Agent Planning · Advanced collaborative decision system for AI agents
Think - Continuous Reasoning
Hybrid reasoning mechanism with parallel short-term and long-term analysis, detecting environmental shocks and assessing severity
- Parallel short-term and long-term reasoning
- Environmental shock detection and severity assessment
- Trend tracking and strategic analysis
- Anomaly identification and early warning
Speak - Intelligent Communication
Natural language-based semantic communication protocol between agents, enabling information sharing and knowledge transfer
- 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
Adaptive intelligent decision-making through multi-source information fusion, combining numerical data, reasoning results, and belief states
- Three-source data fusion (numerical, reasoning, belief)
- Weighted scoring and LLM-based decision making
- Fallback mechanism for system stability
- Explainable decision output
DAG Concurrent Scheduling Engine
Intelligent task scheduling based on directed acyclic graphs, automatically analyzing dependencies to maximize parallel execution efficiency
Topological Sorting
Automatically analyze task dependencies, build execution topology, determine optimal execution order
Parallel Execution
Launch independent tasks simultaneously, transform serial to concurrent, dramatically reduce completion time
Dynamic Adjustment
Runtime dynamic concurrency adjustment, intelligent optimization based on resource status and task characteristics
Exception Isolation
Single task exceptions don't affect other tasks, ensuring overall process stability
Experience Pool Continuous Learning
Vector storage of historical successful experiences, intelligent retrieval and reuse of best strategies, getting smarter over time
Vector Storage
Convert successful execution trajectories into vector representations, efficiently stored in experience pool
Intelligent Retrieval
New tasks automatically retrieve similar historical experiences, quickly match best strategies
Strategy Reuse
Reuse historical successful strategies, reducing planning time by 40-60%
Continuous Evolution
Failed cases serve as counter-examples to avoid mistakes, system continuously optimizes
A2UI Dynamic Interface Engine
Dynamic interface rendering engine based on Google A2UI protocol. AI no longer just outputs text, but directly generates interactive interface components - charts, forms, buttons, what you see is what you get. We led Google's official release by implementing the first complete React renderer.
- 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
Self-developed MCP Framework
Based on proprietary MCP framework, using @Tool decorators to quickly define agent tools, significantly improving development efficiency.
Standardized Tool Registration & Auto Validation
Support automatic metadata declaration and structured registration of tool capabilities, automatic parameter validation and type checking.
Define Intelligent Services in Few Lines
Minimal development process, define an invocable intelligent service with just a few lines of code, rapid deployment and iteration.
Multi-Agent Collaboration (LAMP Framework)
Patented Technology: Language-enhanced multi-agent collaborative decision system featuring Think-Speak-Decide three-stage reasoning pipeline. Enables semantic communication between agents, continuous reasoning analysis, and multi-source information fusion for intelligent decisions. Supports DAG concurrent scheduling and dynamic replanning, achieving 15-25% improvement in task success rate and 2-5x performance boost.
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
Decorator-driven development model allows developers to focus on business logic without worrying about tedious details like tool registration and validation. Declarative development approach greatly improves development efficiency and code maintainability, reducing technical barriers and delivery costs.