Documentation | Augment AI Platform

Installation

Deploy AI WorkflowBuilder to start creating intelligent automation workflows from natural language descriptions.

System Requirements

Bun runtime for high-performance JavaScript execution
PostgreSQL 12+ with pgvector extension for AI embeddings
Docker 20.0+ for containerized deployment
Minimum 8GB RAM (16GB recommended for AI agent orchestration)
Network access for real-time collaboration and API integrations

Install via Package Manager

Installationbash

# Clone the repository
git clone https://github.com/aiaugments-collab/ai-workflow-builder
cd ai-workflow-builder

# Install dependencies with Bun
bun install

# Set up PostgreSQL with pgvector
docker run --name workflow-db \
  -e POSTGRES_PASSWORD=your_password \
  -e POSTGRES_DB=workflow_builder \
  -p 5432:5432 -d \
  pgvector/pgvector:pg17

# Configure environment
cp apps/sim/.env.example apps/sim/.env

# Run database migrations
cd packages/db
bunx drizzle-kit migrate --config=./drizzle.config.ts

# Start development servers
bun run dev:full

Authentication Setup

Configure your API keys and service connections:

Authenticationbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Initialize workflow builder
workflow-builder auth login

# Verify authentication
workflow-builder auth status

# Connect external services (optional)
workflow-builder connect slack
workflow-builder connect github
workflow-builder connect aws

Quick Start

Create your first AI-powered workflow in minutes using natural language descriptions.

1. Create Your First Workflow

Natural Language Workflowbash

# Create workflow from natural language
workflow-builder create "When a new issue is created in GitHub, send a message to Slack and create a task in Jira"

# Or use interactive mode
workflow-builder create --interactive

# Initialize project with templates
workflow-builder init --template "ci-cd-pipeline"

2. Test Your Workflow

Workflow Testingbash

# Test workflow with sample data
workflow-builder test my-workflow --sample-data

# Run workflow manually
workflow-builder run my-workflow --input '{"issue_title": "Bug fix needed"}'

# Debug workflow execution
workflow-builder debug my-workflow --verbose

3. Deploy and Monitor

Deploymentbash

# Deploy workflow to production
workflow-builder deploy my-workflow --environment production

# Monitor workflow execution
workflow-builder monitor my-workflow --live

# View execution logs
workflow-builder logs my-workflow --tail 100

Configuration

Configure AI WorkflowBuilder to integrate with your tools and match your automation requirements.

Basic Configuration

.workflow-builder.yamlyaml

version: "1.0"
organization: "your-company"
environment: "production"

ai_settings:
  model: "gpt-4"
  confidence_threshold: 0.85
  language: "en"
  custom_instructions: |
    Generate workflows for enterprise software development team.
    Focus on security and compliance requirements.

integrations:
  slack:
    webhook_url: "https://hooks.slack.com/..."
    default_channel: "#automation"
  github:
    api_token: "GITHUB_TOKEN"
    default_repo: "my-org/my-repo"
  jira:
    server_url: "https://company.atlassian.net"
    api_token: "JIRA_TOKEN"
    default_project: "DEV"

workflow_settings:
  default_timeout: 300
  retry_attempts: 3
  error_notifications: true
  execution_history: 30
  
security:
  secret_encryption: true
  audit_logging: true
  rbac_enabled: true

Natural Language Processing

AI WorkflowBuilder understands natural language and converts it into executable workflows automatically.

Supported Language Patterns

Event-Driven Patterns

• "When [event] happens, do [action]"
• "If [condition] is true, then [action]"
• "Every [time period], [action]"
• "On [schedule], [action]"

Action Sequences

• "First [action], then [action]"
• "[Action] and then [action]"
• "After [action], [action]"
• "Simultaneously [action] and [action]"

Conditional Logic

• "If [condition], otherwise [action]"
• "Unless [condition], [action]"
• "When [condition] and [condition]"
• "Either [action] or [action]"

Data Processing

• "Extract [data] from [source]"
• "Transform [data] to [format]"
• "Filter [data] where [condition]"
• "Aggregate [data] by [field]"

Example Natural Language Workflows

Example Descriptionstext

# Customer Support Automation
"When a new support ticket is created with high priority, 
immediately notify the on-call engineer via SMS and create 
a calendar meeting for the next hour"

# CI/CD Pipeline
"When code is pushed to main branch, run tests, build Docker image, 
deploy to staging, run smoke tests, and if all pass, deploy to production"

# Data Processing
"Every morning at 8 AM, extract sales data from the database, 
generate a summary report, and email it to the sales team"

# Incident Response
"If server CPU usage exceeds 90% for more than 5 minutes, 
scale up the infrastructure, notify DevOps team, and create 
an incident ticket in Jira"

Environment Variables

Configure AI WorkflowBuilder behavior using environment variables for different deployment scenarios.

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
WORKFLOW_BUILDER_CONFIG	Path to configuration file	.workflow-builder.yaml
WORKFLOW_EXECUTION_TIMEOUT	Default workflow timeout in seconds	300
WORKFLOW_BUILDER_DEBUG	Enable debug logging	false

Basic Usage

Learn the fundamental workflow creation and management patterns.

Workflow Commands

Workflow Managementbash

# Create workflow from natural language
workflow-builder create "Send email when form is submitted"

# List all workflows
workflow-builder list

# Edit workflow visually
workflow-builder edit my-workflow --visual

# Validate workflow syntax
workflow-builder validate my-workflow

# Export workflow definition
workflow-builder export my-workflow --format yaml

CLI Commands Reference

Complete reference for all workflow creation and management commands.

create

Create a new workflow from natural language description

workflow-builder create [description] [options]

Options:
  --name <name>             Workflow name
  --description <desc>      Detailed description
  --template <template>     Use predefined template
  --interactive            Interactive creation mode
  --output <file>          Save workflow definition to file
  --format <format>        Output format (yaml|json)
  --dry-run                Preview workflow without creating
  --validate               Validate workflow before creation

run

Execute a workflow with optional input data

workflow-builder run <workflow-name> [options]

Options:
  --input <data>           Input data as JSON string
  --input-file <file>      Input data from file
  --environment <env>      Target environment
  --async                  Run asynchronously
  --timeout <seconds>      Execution timeout
  --no-retry               Disable retry on failure
  --output <file>          Save execution results

Best Practices

Workflow automation best practices to create reliable and maintainable automation pipelines.

Workflow Design Principles

Start with simple workflows and gradually add complexity
Use clear, descriptive names for workflows and steps
Implement proper error handling and retry logic
Test workflows in staging before production deployment
Monitor workflow performance and optimize bottlenecks
Document workflow purpose and business logic

Integrations

AI WorkflowBuilder integrates with hundreds of popular services and tools.

Popular Integrations

Communication

Slack, Teams, Discord

Development

GitHub, GitLab, Jira

Cloud

AWS, Azure, GCP

Productivity

Notion, Airtable, Zapier

Custom Integrations

Custom Integrationbash

# Create custom integration
workflow-builder integration create --name "my-api" \
  --base-url "https://api.myservice.com" \
  --auth-type "bearer" \
  --auth-token "MY_API_TOKEN"

# Test custom integration
workflow-builder integration test my-api

# Use custom integration in workflow
workflow-builder create "When webhook received, call my-api with the data"

Workflow Templates

Pre-built workflow templates for common automation scenarios.

Available Templates

CI/CD Pipeline

Automated testing, building, and deployment workflow

workflow-builder create --template ci-cd-pipeline \
  --repo "my-org/my-app" \
  --deploy-target "production"

Incident Response

Automated incident detection and escalation

workflow-builder create --template incident-response \
  --alerting-tool "pagerduty" \
  --chat-channel "#incidents"

API Integration

Integrate AI WorkflowBuilder into your applications and automation platforms.

REST API

API Usagebash

# Create workflow via API
curl -X POST https://api.augment.cfd/v1/workflows \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "email-automation",
    "description": "Send welcome email when user signs up",
    "trigger": {
      "type": "webhook",
      "path": "/user-signup"
    }
  }'

JavaScript SDK

JavaScript Integrationjavascript

const { WorkflowBuilder } = require('@augment/workflow-builder-sdk');

// Initialize workflow builder
const builder = new WorkflowBuilder({
  apiKey: process.env.AUGMENT_API_KEY
});

// Create workflow from natural language
const workflow = await builder.createFromDescription(
  "When a new customer signs up, send a welcome email and create a CRM record"
);

// Execute workflow
const result = await builder.executeWorkflow(workflow.id, {
  customer_email: "user@example.com",
  customer_name: "John Doe"
});

console.log(`Workflow executed: ${result.status}`);

API Reference

Complete API documentation for integrating workflow automation into your applications.

Workflow Creation Endpoint

POST /v1/workflows

Create a new workflow from natural language or structured definition.

Request Body:

{
  "name": "customer-onboarding",
  "description": "When a new customer signs up, send welcome email and create CRM record",
  "natural_language": true,
  "trigger": {
    "type": "webhook|schedule|event",
    "config": {
      "path": "/webhook/customer-signup",
      "method": "POST"
    }
  },
  "actions": [
    {
      "type": "email",
      "config": {
        "to": "{{customer.email}}",
        "template": "welcome-email"
      }
    }
  ],
  "settings": {
    "timeout": 300,
    "retry_attempts": 3,
    "error_handling": "notify"
  }
}

Response:

{
  "workflow_id": "wf-123456",
  "name": "customer-onboarding",
  "status": "active",
  "created_at": "2025-09-18T10:30:00Z",
  "trigger_url": "https://api.augment.cfd/webhooks/wf-123456",
  "actions": [
    {
      "id": "action-001",
      "type": "email",
      "status": "configured"
    },
    {
      "id": "action-002", 
      "type": "crm_create",
      "status": "configured"
    }
  ],
  "execution_stats": {
    "total_runs": 0,
    "success_rate": null,
    "avg_duration": null
  }
}

Troubleshooting

Common issues and solutions when creating and executing workflows.

Common Issues

Workflow Creation Failed

Error: Unable to parse natural language description

Solutions:

Use more specific and clear language
Break complex workflows into smaller steps
Check for supported integrations and actions
Review example patterns in documentation

Workflow Execution Timeout

Workflows timing out during execution

Solutions:

Increase timeout values in workflow settings
Optimize API calls and data processing
Use async execution for long-running workflows
Split complex workflows into smaller parts

Integration Authentication Failed

Unable to connect to external services

Solutions:

Verify API keys and credentials are current
Check if service requires OAuth re-authorization
Ensure proper permissions for service accounts
Test connection with integration test command

Workflow Documentation Complete!

You now have comprehensive knowledge to implement AI WorkflowBuilder in your automation strategy. From natural language workflow creation to advanced API integrations, you're equipped to transform business processes with intelligent automation.

Ready to automate your workflows? Start your free trial today and discover how natural language can transform your business processes into intelligent automation.

Installation

Deploy AI WorkflowBuilder to start creating intelligent automation workflows from natural language descriptions.

System Requirements

Bun runtime for high-performance JavaScript execution
PostgreSQL 12+ with pgvector extension for AI embeddings
Docker 20.0+ for containerized deployment
Minimum 8GB RAM (16GB recommended for AI agent orchestration)
Network access for real-time collaboration and API integrations

Install via Package Manager

Installationbash

# Clone the repository
git clone https://github.com/aiaugments-collab/ai-workflow-builder
cd ai-workflow-builder

# Install dependencies with Bun
bun install

# Set up PostgreSQL with pgvector
docker run --name workflow-db \
  -e POSTGRES_PASSWORD=your_password \
  -e POSTGRES_DB=workflow_builder \
  -p 5432:5432 -d \
  pgvector/pgvector:pg17

# Configure environment
cp apps/sim/.env.example apps/sim/.env

# Run database migrations
cd packages/db
bunx drizzle-kit migrate --config=./drizzle.config.ts

# Start development servers
bun run dev:full

Authentication Setup

Configure your API keys and service connections:

Authenticationbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Initialize workflow builder
workflow-builder auth login

# Verify authentication
workflow-builder auth status

# Connect external services (optional)
workflow-builder connect slack
workflow-builder connect github
workflow-builder connect aws

Quick Start

Create your first AI-powered workflow in minutes using natural language descriptions.

1. Create Your First Workflow

Natural Language Workflowbash

# Create workflow from natural language
workflow-builder create "When a new issue is created in GitHub, send a message to Slack and create a task in Jira"

# Or use interactive mode
workflow-builder create --interactive

# Initialize project with templates
workflow-builder init --template "ci-cd-pipeline"

2. Test Your Workflow

Workflow Testingbash

# Test workflow with sample data
workflow-builder test my-workflow --sample-data

# Run workflow manually
workflow-builder run my-workflow --input '{"issue_title": "Bug fix needed"}'

# Debug workflow execution
workflow-builder debug my-workflow --verbose

3. Deploy and Monitor

Deploymentbash

# Deploy workflow to production
workflow-builder deploy my-workflow --environment production

# Monitor workflow execution
workflow-builder monitor my-workflow --live

# View execution logs
workflow-builder logs my-workflow --tail 100

Configuration

Configure AI WorkflowBuilder to integrate with your tools and match your automation requirements.

Basic Configuration

.workflow-builder.yamlyaml

version: "1.0"
organization: "your-company"
environment: "production"

ai_settings:
  model: "gpt-4"
  confidence_threshold: 0.85
  language: "en"
  custom_instructions: |
    Generate workflows for enterprise software development team.
    Focus on security and compliance requirements.

integrations:
  slack:
    webhook_url: "https://hooks.slack.com/..."
    default_channel: "#automation"
  github:
    api_token: "GITHUB_TOKEN"
    default_repo: "my-org/my-repo"
  jira:
    server_url: "https://company.atlassian.net"
    api_token: "JIRA_TOKEN"
    default_project: "DEV"

workflow_settings:
  default_timeout: 300
  retry_attempts: 3
  error_notifications: true
  execution_history: 30
  
security:
  secret_encryption: true
  audit_logging: true
  rbac_enabled: true

Natural Language Processing

AI WorkflowBuilder understands natural language and converts it into executable workflows automatically.

Supported Language Patterns

Event-Driven Patterns

• "When [event] happens, do [action]"
• "If [condition] is true, then [action]"
• "Every [time period], [action]"
• "On [schedule], [action]"

Action Sequences

• "First [action], then [action]"
• "[Action] and then [action]"
• "After [action], [action]"
• "Simultaneously [action] and [action]"

Conditional Logic

• "If [condition], otherwise [action]"
• "Unless [condition], [action]"
• "When [condition] and [condition]"
• "Either [action] or [action]"

Data Processing

• "Extract [data] from [source]"
• "Transform [data] to [format]"
• "Filter [data] where [condition]"
• "Aggregate [data] by [field]"

Example Natural Language Workflows

Example Descriptionstext

# Customer Support Automation
"When a new support ticket is created with high priority, 
immediately notify the on-call engineer via SMS and create 
a calendar meeting for the next hour"

# CI/CD Pipeline
"When code is pushed to main branch, run tests, build Docker image, 
deploy to staging, run smoke tests, and if all pass, deploy to production"

# Data Processing
"Every morning at 8 AM, extract sales data from the database, 
generate a summary report, and email it to the sales team"

# Incident Response
"If server CPU usage exceeds 90% for more than 5 minutes, 
scale up the infrastructure, notify DevOps team, and create 
an incident ticket in Jira"

Environment Variables

Configure AI WorkflowBuilder behavior using environment variables for different deployment scenarios.

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
WORKFLOW_BUILDER_CONFIG	Path to configuration file	.workflow-builder.yaml
WORKFLOW_EXECUTION_TIMEOUT	Default workflow timeout in seconds	300
WORKFLOW_BUILDER_DEBUG	Enable debug logging	false

Basic Usage

Learn the fundamental workflow creation and management patterns.

Workflow Commands

Workflow Managementbash

# Create workflow from natural language
workflow-builder create "Send email when form is submitted"

# List all workflows
workflow-builder list

# Edit workflow visually
workflow-builder edit my-workflow --visual

# Validate workflow syntax
workflow-builder validate my-workflow

# Export workflow definition
workflow-builder export my-workflow --format yaml

CLI Commands Reference

Complete reference for all workflow creation and management commands.

create

Create a new workflow from natural language description

workflow-builder create [description] [options]

Options:
  --name <name>             Workflow name
  --description <desc>      Detailed description
  --template <template>     Use predefined template
  --interactive            Interactive creation mode
  --output <file>          Save workflow definition to file
  --format <format>        Output format (yaml|json)
  --dry-run                Preview workflow without creating
  --validate               Validate workflow before creation

run

Execute a workflow with optional input data

workflow-builder run <workflow-name> [options]

Options:
  --input <data>           Input data as JSON string
  --input-file <file>      Input data from file
  --environment <env>      Target environment
  --async                  Run asynchronously
  --timeout <seconds>      Execution timeout
  --no-retry               Disable retry on failure
  --output <file>          Save execution results

Best Practices

Workflow automation best practices to create reliable and maintainable automation pipelines.

Workflow Design Principles

Start with simple workflows and gradually add complexity
Use clear, descriptive names for workflows and steps
Implement proper error handling and retry logic
Test workflows in staging before production deployment
Monitor workflow performance and optimize bottlenecks
Document workflow purpose and business logic

Integrations

AI WorkflowBuilder integrates with hundreds of popular services and tools.

Popular Integrations

Communication

Slack, Teams, Discord

Development

GitHub, GitLab, Jira

Cloud

AWS, Azure, GCP

Productivity

Notion, Airtable, Zapier

Custom Integrations

Custom Integrationbash

# Create custom integration
workflow-builder integration create --name "my-api" \
  --base-url "https://api.myservice.com" \
  --auth-type "bearer" \
  --auth-token "MY_API_TOKEN"

# Test custom integration
workflow-builder integration test my-api

# Use custom integration in workflow
workflow-builder create "When webhook received, call my-api with the data"

Workflow Templates

Pre-built workflow templates for common automation scenarios.

Available Templates

CI/CD Pipeline

Automated testing, building, and deployment workflow

workflow-builder create --template ci-cd-pipeline \
  --repo "my-org/my-app" \
  --deploy-target "production"

Incident Response

Automated incident detection and escalation

workflow-builder create --template incident-response \
  --alerting-tool "pagerduty" \
  --chat-channel "#incidents"

API Integration

Integrate AI WorkflowBuilder into your applications and automation platforms.

REST API

API Usagebash

# Create workflow via API
curl -X POST https://api.augment.cfd/v1/workflows \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "email-automation",
    "description": "Send welcome email when user signs up",
    "trigger": {
      "type": "webhook",
      "path": "/user-signup"
    }
  }'

JavaScript SDK

JavaScript Integrationjavascript

const { WorkflowBuilder } = require('@augment/workflow-builder-sdk');

// Initialize workflow builder
const builder = new WorkflowBuilder({
  apiKey: process.env.AUGMENT_API_KEY
});

// Create workflow from natural language
const workflow = await builder.createFromDescription(
  "When a new customer signs up, send a welcome email and create a CRM record"
);

// Execute workflow
const result = await builder.executeWorkflow(workflow.id, {
  customer_email: "user@example.com",
  customer_name: "John Doe"
});

console.log(`Workflow executed: ${result.status}`);

API Reference

Complete API documentation for integrating workflow automation into your applications.

Workflow Creation Endpoint

POST /v1/workflows

Create a new workflow from natural language or structured definition.

Request Body:

{
  "name": "customer-onboarding",
  "description": "When a new customer signs up, send welcome email and create CRM record",
  "natural_language": true,
  "trigger": {
    "type": "webhook|schedule|event",
    "config": {
      "path": "/webhook/customer-signup",
      "method": "POST"
    }
  },
  "actions": [
    {
      "type": "email",
      "config": {
        "to": "{{customer.email}}",
        "template": "welcome-email"
      }
    }
  ],
  "settings": {
    "timeout": 300,
    "retry_attempts": 3,
    "error_handling": "notify"
  }
}

Response:

{
  "workflow_id": "wf-123456",
  "name": "customer-onboarding",
  "status": "active",
  "created_at": "2025-09-18T10:30:00Z",
  "trigger_url": "https://api.augment.cfd/webhooks/wf-123456",
  "actions": [
    {
      "id": "action-001",
      "type": "email",
      "status": "configured"
    },
    {
      "id": "action-002", 
      "type": "crm_create",
      "status": "configured"
    }
  ],
  "execution_stats": {
    "total_runs": 0,
    "success_rate": null,
    "avg_duration": null
  }
}

Troubleshooting

Common issues and solutions when creating and executing workflows.

Common Issues

Workflow Creation Failed

Error: Unable to parse natural language description

Solutions:

Use more specific and clear language
Break complex workflows into smaller steps
Check for supported integrations and actions
Review example patterns in documentation

Workflow Execution Timeout

Workflows timing out during execution

Solutions:

Increase timeout values in workflow settings
Optimize API calls and data processing
Use async execution for long-running workflows
Split complex workflows into smaller parts

Integration Authentication Failed

Unable to connect to external services

Solutions:

Verify API keys and credentials are current
Check if service requires OAuth re-authorization
Ensure proper permissions for service accounts
Test connection with integration test command

Workflow Documentation Complete!

Ready to automate your workflows? Start your free trial today and discover how natural language can transform your business processes into intelligent automation.

AI WorkflowBuilder

Natural Language Workflow Creation

Visual Workflow Editor

AI Agent Orchestration

Real-Time Execution Engine

Installation

System Requirements

Install via Package Manager

Authentication Setup

Quick Start

1. Create Your First Workflow

2. Test Your Workflow

3. Deploy and Monitor

Configuration

Basic Configuration

Natural Language Processing

Supported Language Patterns

Event-Driven Patterns

Action Sequences

Conditional Logic

Data Processing

Example Natural Language Workflows

Environment Variables

Basic Usage

Workflow Commands

CLI Commands Reference

create

run

Best Practices

Workflow Design Principles

Integrations

Popular Integrations

Custom Integrations

Workflow Templates

Available Templates

CI/CD Pipeline

Incident Response

API Integration

REST API

JavaScript SDK

API Reference

Workflow Creation Endpoint

POST /v1/workflows

Request Body:

Response:

Troubleshooting

Common Issues

Workflow Creation Failed

Workflow Execution Timeout

Integration Authentication Failed

Workflow Documentation Complete!

AI WorkflowBuilder

Natural Language Workflow Creation

Visual Workflow Editor

AI Agent Orchestration

Real-Time Execution Engine

Installation

System Requirements

Install via Package Manager

Authentication Setup

Quick Start

1. Create Your First Workflow

2. Test Your Workflow

3. Deploy and Monitor

Configuration

Basic Configuration

Natural Language Processing

Supported Language Patterns

Event-Driven Patterns

Action Sequences

Conditional Logic

Data Processing

Example Natural Language Workflows

Environment Variables

Basic Usage

Workflow Commands

CLI Commands Reference

create

run

Best Practices