Skymel Agent Development Kit (ADK)

Build hands-off agents with multi-model reasoning

Skymel ADK is a complete agent development platform that replaces traditional LLM-only reasoning with a sophisticated multi-model brain. Unlike other frameworks that require complex workflow coding, Skymel agents are created from natural language descriptions and automatically generate specialized workflows for each task.

Key Features

• Multi-Model Reasoning Engine: Combines LLMs, traditional ML models, causal optimization, and external memory for reliable decision-making
• Dynamic Workflow Generation: Creates specialized execution plans for each unique task
• Automatic Error Recovery: Self-healing execution with intelligent failure handling
• Continuous Learning: Execution feedback automatically improves model performance
• Natural Language Interface: Deploy agents using simple descriptions, no complex coding required

Installation

Install via npm:

npm install skymel-adk-js-beta

Or import directly via CDN:

import {SkymelAgent} from "https://cdn.jsdelivr.net/npm/skymel-adk-js-beta@latest/dist/skymel-adk.esm.js";

Quick Start

Basic Usage

import {SkymelAgent} from "skymel-adk-js-beta";

const apiKey = "YOUR_API_KEY";
const agentCreationEndpointUrl = "https://skymel.com/websocket-dynamic-agent-generation-infer";
const agentCreationEndpointUrlIsWebSocketUrl = true;

// Create agent instance
const skymelAgent = new SkymelAgent(
    apiKey,
    agentCreationEndpointUrl,
    agentCreationEndpointUrlIsWebSocketUrl,
    "Binary Botwell", // Agent name
    "Process invoices, refunds, and update CRM with transaction status and amounts.", // Agent definition
    "Must confirm amounts greater than 10,000 dollars with a human admin.", // Restrictions
    "", // Extra developer configuration string (optional)
    false // Is MCP enabled?
);

// Generate specialized workflow for specific task
const taskDescription = "Process invoice INV-001 for Acme Corp from the attached file";
const invoiceFiles = await skymelAgent.getFileDataAndDetailsDictFromHtmlInputsForAgentGraphAttachment([htmlInputWithPdf]);
const workflowConfig = await skymelAgent.getAgenticWorkflowGraphJsonConfig(taskDescription, invoiceFiles);

// Execute the workflow
const result = await skymelAgent.runAgenticWorkflow(workflowConfig, invoiceFiles);
console.log("Task completed:", result);

Core Concepts

Agent Definition vs Task Specification

Agent Definition (agentDefinitionString): Describes what the agent is and its general capabilities

• Example: "Email marketing agent that creates and executes campaigns"

Task Specification (agentTaskString): Describes what you want the agent to do right now

• Example: "Create welcome email sequence for new SaaS trial users"

The agent uses its definition to understand its role, then generates a specialized workflow for the specific task.

Multi-Model Reasoning Engine

Unlike single-LLM agents, Skymel uses specialized components:

• Language Models (LLMs): Handle natural language understanding and workflow parameter generation
• Traditional ML Models: Identify domain-specific data and make specialized tool-use decisions
• Causal Models: Prevent goal drift, and implement agent restrictions compliance
• External Memory: Maintain context across different workflow steps and memorize outcome patterns across workflow sessions

Dynamic Workflow Generation

Each task generates a custom Directed Acyclic Graph (DAG) execution plan:

1. Agent analyzes the specific task requirements
2. Selects appropriate sub-task components and tools
3. Builds optimized execution sequence
4. Monitors execution and adapts to real-time outcomes as needed

API Reference

SkymelAgent Constructor

Creates a new Skymel Agent instance.

new SkymelAgent(
    apiKey,
    agentCreationEndpointUrl,
    agentCreationEndpointUrlIsWebSocketUrl,
    agentNameString,
    agentDefinitionString,
    agentRestrictionsString,
    developerConfigurationString,
    isMcpEnabled
)

Parameters

Methods

getAgenticWorkflowGraphJsonConfig()

Generates a specialized workflow configuration for the given task.

const workflowConfig = await skymelAgent.getAgenticWorkflowGraphJsonConfig(
    taskDescriptionString,
    processedTaskFileAttachments
);

Parameters:

• taskDescriptionString (string): Natural language description of the task to perform
• processedTaskFileAttachments ([object]): A list of processed input files that can be obtained by passing a list of HTML input elements to the getAgenticWorkflowGraphJsonConfig() method

Returns: Promise resolving to workflow configuration JSON object

runAgenticWorkflow()

Executes the generated workflow with monitoring and error recovery.

const result = await skymelAgent.runAgenticWorkflow(
    dynamicWorkflowGraphJsonConfig,
    workflowInputsDict
);

Parameters:

• dynamicWorkflowGraphJsonConfig (object): Workflow configuration from getAgenticWorkflowGraphJsonConfig()
• workflowInputsDict (object): A dictionary containing key and values where keys are the input names for the workflow config

Returns: Promise resolving to execution results

Examples

Customer Support Agent

const supportAgent = new SkymelAgent(
    process.env.SKYMEL_API_KEY,
    "https://skymel.com/websocket-dynamic-agent-generation-infer",
    true,
    "Customer Support Agent",
    "Handle customer inquiries, resolve issues, and escalate complex problems to human agents",
    "Always be polite and helpful. Escalate billing issues over $500 to human agents",
    "",
    true
);

// Handle specific customer inquiry
const inquiry = "Customer asking about refund for order #12345 worth $250";
const workflow = await supportAgent.getAgenticWorkflowGraphJsonConfig(inquiry);
const response = await supportAgent.runAgenticWorkflow(workflow);

Data Analysis Agent

const analystAgent = new SkymelAgent(
    process.env.SKYMEL_API_KEY,
    "https://skymel.com/websocket-dynamic-agent-generation-infer",
    true,
    "Sales Data Analyst",
    "Analyze sales data, generate reports, and provide business insights",
    "Only access sales database. Always include data sources in reports",
    "",
    false
);

// Generate monthly sales report
const task = "Create Q4 2024 sales summary with top performing products and regional breakdown";
const workflow = await analystAgent.getAgenticWorkflowGraphJsonConfig(task);
const report = await analystAgent.runAgenticWorkflow(workflow);

Content Marketing Agent

const contentAgent = new SkymelAgent(
    process.env.SKYMEL_API_KEY,
    "https://skymel.com/websocket-dynamic-agent-generation-infer",
    true,
    "", // Name in developerConfigurationString
    "", // Definition in developerConfigurationString
    "", // Restrictions in developerConfigurationString
    `
    Agent Name: Content Marketing Specialist
    Purpose: Create blog posts, social media content, and email campaigns that align with brand voice
    Restrictions: All content must be reviewed before publishing. Follow brand guidelines strictly.
    Brand Voice: Professional but approachable, focus on practical solutions
    `,
    true
);

// Create specific content piece
const contentTask = "Write LinkedIn post about AI automation trends for small businesses, include call-to-action for our webinar";
const workflow = await contentAgent.getAgenticWorkflowGraphJsonConfig(contentTask);
const content = await contentAgent.runAgenticWorkflow(workflow);

Architecture

Multi-Model Reasoning Flow

1. Task Input: Natural language task description
2. Context Analysis: Agent definition and restrictions inform approach
3. Workflow Planning: Generate specialized DAG for this specific task
4. Model Selection: Choose appropriate reasoning components
5. Execution: Run workflow with real-time monitoring
6. Recovery: Automatic error handling and alternative approaches
7. Feedback: Results improve future model performance

Execution Monitoring

Skymel continuously monitors:

• Decision Quality: Whether reasoning choices were effective
• Cost Efficiency: Token usage and API call optimization
• Success Rates: Task completion and error patterns
• Performance Trends: Speed and accuracy over time

This monitoring data automatically retrains the specialized models for better future performance.

Best Practices

Agent Definition Tips

Good agent definitions are:

• Specific about capabilities and scope
• Clear about expected inputs and outputs
• Explicit about limitations and boundaries

// Good
"Process customer orders, validate payment information, and send confirmation emails"

// Too vague
"Help with business tasks"

Task Specification Guidelines

Effective task descriptions:

• Include relevant context and data
• Specify desired output format
• Mention any constraints or requirements

// Good
"Analyze sales data from Q4 2024 CSV file, identify top 3 performing products, create executive summary"

// Too general
"Look at sales data"

Error Handling

Skymel agents automatically handle most errors, but you can add additional handling:

try {
    const result = await skymelAgent.runAgenticWorkflow(workflowConfig);
    console.log("Success:", result);
} catch (error) {
    console.error("Workflow failed after all recovery attempts:", error);
}

Troubleshooting

Common Issues

• Agent not responding: Check API key and endpoint URL validity
• Workflow generation fails: Ensure task description is clear and within agent's defined scope
• High costs: Review agent restrictions and add cost limits
• Poor results: Refine agent definition and provide more specific task descriptions

Performance Optimization

• Use specific agent definitions to improve workflow generation
• Include relevant context in task descriptions
• Set appropriate restrictions to prevent scope creep
• Monitor execution results to identify improvement opportunities

Support

• Documentation: docs.skymel.com
• API Reference: api.skymel.com
• Community: github.com/skymel/community
• Support: support@skymel.com