Internals

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Core Components

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Engine

• Manages task execution
• Coordinates resources
• Handles caching
• Controls parallelism

from blastai import Engine

engine = await Engine.create(
    settings=settings,
    constraints=constraints
)

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Scheduler

• Tracks task states
• Handles task dependencies
• Manages execution order
• Coordinates parallel tasks

1. Tasks with cached results
2. Tasks with cached plans
3. Subtasks of running tasks
4. Tasks with paused executors
5. Remaining tasks (FIFO)

• Scheduled: Task is queued for execution
• Running: Task is currently executing
• Completed: Task has finished execution

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Resource Manager

• Browser instances
• Memory usage
• Cost tracking
• Resource cleanup

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Cache Manager

• Results cache (task outputs)
• Plans cache (execution plans generated by LLM)

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Planner

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Data Flow

1. Task Creation
   task_id = scheduler.schedule_task(
       description="Search Python docs",
       cache_control=""
   )
   
2. Cache Check
   • Check results cache
   • Check plans cache
   • Return cached result if available
3. Resource Allocation
   • Wait for prerequisites
   • Allocate browser if needed
   • Assign executor
4. Execution
   • Run task via executor
   • Stream progress updates
   • Cache results
5. Cleanup
   • Release resources
   • Update cache
   • Handle errors

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Code Structure

blastai/
├── __init__.py      # Package initialization
├── engine.py        # Main engine implementation
├── scheduler.py     # Task scheduling
├── cache.py         # Caching system
├── config.py        # Configuration
├── planner.py       # Task planning
├── executor.py      # Task execution
├── tools.py         # Tools for parallelism
└── utils.py         # Utilities

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Configuration

settings:
  persist_cache: true
  logs_dir: "blast-logs/"      # Log to files (null for terminal-only)
  blastai_log_level: "info"    # BLAST engine log level
  browser_use_log_level: "info" # Browser operations log level

constraints:
  # Resource limits
  max_memory: "4GB"
  max_concurrent_browsers: 4
  
  # Model configuration
  llm_model: "openai:gpt-4.1"           # Main model for complex tasks
  llm_model_mini: "openai:gpt-4.1-mini" # Model for simpler tasks
  
  # Parallelism settings
  allow_parallelism:
    task: true
    data: true

# OpenAI configuration
OPENAI_API_KEY=sk-...
OPENAI_BASE_URL=https://your-endpoint.com  # Optional

# Google Gemini configuration
GOOGLE_API_KEY=AIza...  # From aistudio.google.com

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Error Handling

• Browser errors
• Resource limits
• Task failures
• Cache issues

1. Log error details
2. Clean up resources
3. Retry if possible
4. Report to user

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Extending BLAST

1. Adding tools for further optimization
2. Creating custom executors
3. Tune better scheduling policy

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Next Steps

• Read the Setup Guide
• Learn how to Contribute
• Check the Roadmap