ADK-TSParallel Agents
Run multiple agents simultaneously for faster processing
Parallel agents execute multiple sub-agents simultaneously, allowing independent tasks to run concurrently for improved performance and efficiency.
How Parallel Agents Work
- Concurrent Execution: All agents start at the same time
- Independent Processing: Agents run without waiting for each other
- Result Aggregation: Collect and combine results when all agents complete
- Performance Optimization: Reduce total execution time for independent tasks
When to Use Parallel Agents
Parallel agents are ideal for:
- Independent tasks - Multiple research queries that don't depend on each other
- Data validation - Simultaneous fact-checking and grammar review
- Distributed processing - Processing different data partitions
- Comparative analysis - Getting multiple perspectives on the same topic
Independence Required
Use parallel agents only when tasks are truly independent and don't need to share results during execution.
Configuration
Basic Setup
- agents: List of agents to execute in parallel
- name: Unique identifier for the workflow
- description: Summary for other agents to understand the workflow
Advanced Options
- max_concurrent: Limit the number of agents running simultaneously
- timeout: Set maximum execution time for the parallel execution
- fail_fast: Stop all agents if one fails (default: false)
Common Patterns
Multi-Source Research
Query different information sources simultaneously for comprehensive research.
Content Review Workflow
Run fact-checking, grammar review, and style analysis in parallel.
Distributed Data Processing
Process different data sets or partitions simultaneously.
Comparative Analysis
Get multiple expert opinions or analysis perspectives on the same input.
Result Handling
Aggregation Strategies
- Combine all results: Merge outputs from all agents
- Best result selection: Choose the highest quality response
- Consensus building: Find common themes across results
- Structured compilation: Organize results by agent type or topic
Error Handling
- Graceful degradation: Continue with successful results if some agents fail
- Retry mechanisms: Restart failed agents while others continue
- Partial results: Handle scenarios where not all agents complete
Best Practices
Design Considerations
- Ensure tasks are truly independent before using parallel execution
- Consider resource usage when running many agents simultaneously
- Plan how to handle varying completion times
- Design clear result aggregation strategies
Performance Optimization
- Limit concurrent agents based on system resources
- Use timeouts to prevent hanging executions
- Consider the trade-off between parallelism and resource usage
Result Quality
- Design consistent output formats across parallel agents
- Plan for varying result quality and completeness
- Consider how to handle conflicting information
Resource Management
- Monitor system resource usage with parallel execution
- Consider rate limits for external API calls
- Plan for network and memory constraints