Graph-Based Workflows
Build scalable multi-agent systems with directed acyclic graphs
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0 / 5 completedParallel Execution
Parallel execution is the primary advantage of graph-based workflows. By analyzing dependencies, the system automatically identifies tasks that can run concurrently, dramatically reducing total execution time.
Interactive: Worker Pool Simulator
Adjust the number of workers and see how parallel execution affects throughput.
1 worker6 workers
Parse Data
3s duration
Validate Schema
2s duration
Transform Records
4s duration
Enrich Metadata
3s duration
Calculate Stats
2s duration
Generate Report
3s duration
Optimization Strategies
Worker Pools
Limit concurrent execution to available resources
Task Prioritization
Schedule critical path tasks first
Load Balancing
Distribute work evenly across workers
Real-World Applications
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Data Pipelines
Process multiple records simultaneously
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ML Training
Parallel hyperparameter tuning
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API Orchestration
Concurrent external service calls
💡 Key Insight
Parallelism is automatic in graph workflows. You define dependencies, and the execution engine handles scheduling. With 3 workers, our 17-second sequential workflow completed in ~6 seconds—a 3× speedup. This scales to hundreds of tasks in production systems, turning hours into minutes.