Framework Startup and Operational Modes

The intelligent agent framework offers two primary execution modes: interactive web interface and headless task execution. Developers can launch services instantly with:

npx @ai-framework/dsh web

This command starts the web service on 127.0.0.1:3080 by default, featuring core components like tool panel, session management, and debugging console. For batch processing, the headless mode exits automatically after task completion:

npx @ai-framework/dsh headless --task=data-processing

Development requirements include Node.js 22.19+/24+ with full ESM module support. Dependency management uses pnpm workspaces with these workflow steps:

pnpm install          # Install dependencies
pnpm run build         # Generate type definitions
pnpm dsh web          # Launch development server

Modular Architecture Design

Repository Structure

The TypeScript monorepo contains 242 packages (219 under @ai-framework namespace) organized into:

• core/: 12 foundation modules for session management, prompt engineering, and tool registration
• llm/: Model adapter layer supporting multiple LLM providers
• client/: 40+ pluggable UI components for browser environments
• vendor/: Renamed versions of third-party dependencies

The test suite comprises 882 cases covering 92% of core logic using Vitest framework.

Plugin Architecture

The framework implements a “everything as plugin” philosophy through:

• Dynamic tool registry supporting hot-swapping
• Prompt template system with plugin-based configurations
• Workflow engine allowing execution strategy extensions

Plugin loading leverages Cordis’ dependency injection system. Each plugin must implement:

interface Plugin {
  initialize?(context: PluginContext): Promise<void>;
  execute?(payload: any): Promise<ExecutionResult>;
  teardown?(): Promise<void>;
}

Core Execution Workflow

Agent Processing Pipeline

Single agent execution follows five stages:

1. Input Parsing: Convert natural language/structured input to internal commands
2. Tool Routing: Match commands with optimal plugins using feature analysis
3. Context Injection: Load session history and environment variables
4. Execution Control: Manage side effects through effect system
5. Result Normalization: Standardize output format and trigger events

State machine implementation enables async operation interruption/recovery:

stateDiagram-v2
    [*] --> Idle
    Idle --> Processing: Request received
    Processing --> Waiting: Async operation
    Waiting --> Processing: Operation complete
    Processing --> Completed: Result generated
    Completed --> [*]
    Processing --> Failed: Exception handling

Tool Invocation Chain

Three security layers protect tool execution:

1. Permission Check: Validate caller access rights
2. Parameter Validation: Use Zod schema for input verification
3. Execution Sandbox: Isolate risky operations

Typical invocation pattern:

const result = await agent.useTool('data-query', {
  database: 'sales',
  query: 'SELECT * FROM orders WHERE date > $1',
  params: ['2024-01-01']
});

Engineering Constraints System

Development Standards

• Versioning: Semantic versioning with compatibility reviews for major releases
• Dependency: Third-party libraries restricted to vendor directory
• Code Style: Mandatory ESLint+Prettier with Git hook integration

Performance Optimization

• Lazy Loading: Initialize non-core plugins on first use
• Plan Caching: Optimize execution plans for repeated queries
• Resource Isolation: Worker Threads for CPU-intensive tasks

Debugging Toolkit

Three built-in debugging mechanisms:

1. Logging System: Six-level structured logging
2. Trace Panel: Visualize decision paths and tool calls
3. Time Travel: Execution state snapshots for retrospective analysis

Production Deployment Guide

Containerization

Standard Docker image with multi-stage build:

# Build stage
FROM node:24-alpine AS builder
WORKDIR /app
COPY . .
RUN pnpm install --frozen-lockfile && pnpm run build
# Runtime stage
FROM node:24-alpine
WORKDIR /app
COPY --from=builder /app/dist ./dist
CMD ["node", "dist/server.js"]

Monitoring Solution

Prometheus metrics endpoint exposes:

• Agent success rate
• Tool invocation latency (P99)
• Plugin load time
• Memory usage trends

Extensibility Options

Three expansion approaches supported:

1. Configuration: Define new tools via JSON Schema
2. Code: Develop custom modules implementing plugin interfaces
3. Composition: Orchestrate complex workflows through编排

This framework achieves optimal balance between flexibility and maintainability through modular design. Its plugin architecture reduces development costs while strict engineering constraints ensure system stability. Developers can selectively integrate modules or build enterprise solutions using the complete framework stack.