Project Configuration

#Quick Start

When you run daita init, a basic daita-project.yaml file is created:

yaml

name: my_project
version: 1.0.0
description: A Daita AI agent project
created_at: '2025-01-15T10:00:00'
 
agents: []
workflows: []

As you create agents and workflows, they're automatically added to this configuration file.

#Agent Configuration

Define agents in your project with their settings and optional features:

yaml

agents:
  - name: data_processor
    description: 'Processes incoming data'
    file: agents/data_processor.py
    enabled: true
 
  - name: content_generator
    description: 'Generates content from templates'
    file: agents/content_generator.py
    enabled: true
 
    # Optional: Retry configuration
    enable_retry: true
    retry_policy:
      max_retries: 3
      base_delay: 1.0
      max_delay: 60.0
      strategy: exponential # fixed, exponential, or linear
 
    # Optional: Custom settings
    settings:
      temperature: 0.7
      max_tokens: 1000

#Retry Policies

Configure how agents handle failures:

yaml

agents:
  - name: resilient_agent
    enable_retry: true
    retry_policy:
      max_retries: 5 # Maximum retry attempts
      base_delay: 1.0 # Initial delay in seconds
      max_delay: 60.0 # Maximum delay cap
      strategy: exponential # exponential, fixed, or linear
      jitter: true # Add randomness to prevent thundering herd

Retry Strategies:

exponential: Delays increase exponentially (1s, 2s, 4s, 8s...)
fixed: Same delay between retries (1s, 1s, 1s...)
linear: Delays increase linearly (1s, 2s, 3s...)

#Workflow Configuration

Define workflows that orchestrate multiple agents:

yaml

workflows:
  - name: data_pipeline
    description: 'Complete data processing pipeline'
    file: workflows/data_pipeline.py
    type: standard
    enabled: true
 
  - name: content_workflow
    description: 'Content generation and review workflow'
    file: workflows/content_workflow.py
    type: standard
    enabled: true

#Webhook Configuration

Configure webhooks to trigger agents or workflows from external services (GitHub, Slack, custom APIs, etc.):

yaml

agents:
  - name: github_processor
    file: agents/github_processor.py
    webhooks:
      - slug: 'github-push'
        field_mapping:
          'repository.name': 'repo_name'
          'pusher.name': 'author'
          'commits[0].message': 'commit_message'
          'commits[0].url': 'commit_url'
          'ref': 'branch'
 
      - slug: 'slack-mention'
        field_mapping:
          'event.user': 'user_id'
          'event.text': 'message_text'
          'event.channel': 'channel_id'

#Webhook URL Format

After deployment, webhooks are available at:

python

https://api.daita-tech.io/api/v1/webhooks/trigger/{org_id}/{webhook_slug}

List your webhook URLs:

bash

daita webhook list

#Field Mapping

The field_mapping transforms incoming webhook payloads into agent inputs:

yaml

field_mapping:
  'source.path': 'target_name' # Simple field mapping
  'array[0].field': 'first_item' # Array indexing
  'nested.deep.value': 'value' # Nested object access

Example: GitHub push webhook payload:

json

{
  "repository": { "name": "my-repo" },
  "pusher": { "name": "john" },
  "commits": [
    {
      "message": "Fix bug",
      "url": "https://github.com/..."
    }
  ]
}

Maps to agent input:

json

{
  "repo_name": "my-repo",
  "author": "john",
  "commit_message": "Fix bug",
  "commit_url": "https://github.com/..."
}

#Schedule Configuration (Cron Jobs)

Schedule agents and workflows to run automatically using cron expressions:

yaml

schedules:
  agents:
    data_processor:
      cron: '0 */6 * * *' # Every 6 hours
      enabled: true
      timezone: 'UTC'
      description: 'Process accumulated data'
      data:
        batch_size: 1000
 
    report_generator:
      cron: '0 9 * * MON' # Every Monday at 9 AM
      enabled: true
      timezone: 'America/New_York'
      description: 'Generate weekly reports'
 
  workflows:
    backup_workflow:
      cron: '0 0 * * *' # Daily at midnight
      enabled: true
      timezone: 'UTC'
      description: 'Daily backup'

#Cron Expression Format

python

┌───────────── minute (0 - 59)
│ ┌───────────── hour (0 - 23)
│ │ ┌───────────── day of month (1 - 31)
│ │ │ ┌───────────── month (1 - 12)
│ │ │ │ ┌───────────── day of week (0 - 6) (Sunday to Saturday)
│ │ │ │ │
* * * * *

Common Patterns:

"*/15 * * * *" - Every 15 minutes
"0 */2 * * *" - Every 2 hours
"0 9 * * MON-FRI" - Weekdays at 9 AM
"0 0 1 * *" - First day of month at midnight
"0 0 * * SUN" - Every Sunday at midnight

#Enabling/Disabling Schedules

Control whether schedules are active:

yaml

schedules:
  agents:
    data_processor:
      cron: '0 */6 * * *'
      enabled: true # Active schedule
 
    backup_agent:
      cron: '0 0 * * *'
      enabled: false # Disabled (paused)

#Complete Example

Here's a comprehensive daita-project.yaml with all features:

yaml

name: my_production_app
version: 2.1.0
description: Production AI agent application
 
agents:
  # GitHub webhook processor
  - name: github_processor
    description: 'Processes GitHub webhook events'
    file: agents/github_processor.py
    enabled: true
    enable_retry: true
    retry_policy:
      max_retries: 3
      base_delay: 1.0
      strategy: exponential
    webhooks:
      - slug: 'github-push'
        field_mapping:
          'repository.name': 'repo_name'
          'commits[0].message': 'commit_message'
 
  # Scheduled data processor
  - name: data_processor
    description: 'Processes accumulated data'
    file: agents/data_processor.py
    enabled: true
    settings:
      batch_size: 1000
 
  # Report generator
  - name: report_generator
    description: 'Generates reports'
    file: agents/report_generator.py
    enabled: true
 
workflows:
  - name: data_pipeline
    description: 'Complete data processing pipeline'
    file: workflows/data_pipeline.py
    enabled: true
 
# Schedule automated tasks
schedules:
  agents:
    data_processor:
      cron: '0 */6 * * *' # Every 6 hours
      enabled: true
      timezone: 'UTC'
      description: 'Process accumulated data'
 
    report_generator:
      cron: '0 9 * * MON' # Monday mornings
      enabled: true
      timezone: 'America/New_York'
      description: 'Generate weekly reports'
 
  workflows:
    data_pipeline:
      cron: '0 2 * * *' # Daily at 2 AM
      enabled: true
      timezone: 'UTC'

#Working with the Configuration

#View Current Configuration

bash

# View your project configuration
cat daita-project.yaml
 
# Test deployment (dry run)
daita push --dry-run

#Update Configuration

Edit daita-project.yaml manually or let the CLI update it:

bash

# Create new agent (auto-adds to config)
daita create agent new_processor
 
# Create new workflow (auto-adds to config)
daita create workflow new_pipeline

#Deploy Configuration

bash

# Deploy with your configuration
daita push
 
# List webhook URLs
daita webhook list
 
# View deployment status
daita status

#Configuration Best Practices

#1. Version Your Configuration

Always update version when making significant changes:

yaml

name: my_app
version: 2.1.0 # Increment on changes

#2. Keep Webhooks Organized

Group related webhooks under the same agent:

yaml

agents:
  - name: integration_agent
    webhooks:
      - slug: 'github-push'
        field_mapping: ...
      - slug: 'github-pr'
        field_mapping: ...
      - slug: 'slack-mention'
        field_mapping: ...

#3. Document Your Schedules

Always include descriptions for scheduled tasks:

yaml

schedules:
  agents:
    processor:
      cron: '0 2 * * *'
      description: 'Daily data processing at 2 AM UTC' # Clear description

#4. Test Configuration Changes

Use dry-run before deploying:

bash

# See what will be deployed
daita push --dry-run
 
# Then deploy
daita push

#Next Steps

Create agents and configure them in daita-project.yaml
Set up authentication for LLM providers
Deploy to the cloud with your configuration using daita push