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Core Components

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Engine

The Engine is the central component that:
  • 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

The Scheduler manages task execution:
  • Tracks task states
  • Handles task dependencies
  • Manages execution order
  • Coordinates parallel tasks
Task priorities:
  1. Tasks with cached results
  2. Tasks with cached plans
  3. Subtasks of running tasks
  4. Tasks with paused executors
  5. Remaining tasks (FIFO)
Tasks can be in different states:
  • Scheduled: Task is queued for execution
  • Running: Task is currently executing
  • Completed: Task has finished execution

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Resource Manager

Handles system resources:
  • Browser instances
  • Memory usage
  • Cost tracking
  • Resource cleanup

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Cache Manager

Manages two types of caches both in memory and on disk:
  • Results cache (task outputs)
  • Plans cache (execution plans generated by LLM)

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Planner

Generates natural language execution plans given user-provided task description.

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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 and constraints control behavior: 
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
Environment variables for API keys: 
# 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

BLAST handles various error types:
  • Browser errors
  • Resource limits
  • Task failures
  • Cache issues
Error recovery:
  1. Log error details
  2. Clean up resources
  3. Retry if possible
  4. Report to user

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Extending BLAST

You can extend BLAST by:
  1. Adding tools for further optimization
  2. Creating custom executors
  3. Tune better scheduling policy

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Next Steps