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Documentation Index

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Workflow Parallel Execution

Execute multiple steps concurrently and combine their results. This pattern is ideal for independent tasks that can run simultaneously.

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Quick Start

from praisonaiagents import AgentFlow, WorkflowContext, StepResult
from praisonaiagents import parallel
import time

# Parallel workers
def research_market(ctx: WorkflowContext) -> StepResult:
    time.sleep(0.1)  # Simulate work
    return StepResult(output="📊 Market: Growth 15% YoY")

def research_competitors(ctx: WorkflowContext) -> StepResult:
    time.sleep(0.1)  # Simulate work
    return StepResult(output="🏢 Competitors: 3 major players")

def research_customers(ctx: WorkflowContext) -> StepResult:
    time.sleep(0.1)  # Simulate work
    return StepResult(output="👥 Customers: 85% satisfaction")

# Aggregator
def summarize(ctx: WorkflowContext) -> StepResult:
    outputs = ctx.variables.get("parallel_outputs", [])
    summary = "📋 SUMMARY:\n" + "\n".join(f"  • {o}" for o in outputs)
    return StepResult(output=summary)

# Create workflow
workflow = AgentFlow(steps=[
    parallel([research_market, research_competitors, research_customers]),
    summarize
])

result = workflow.start("Analyze business", )
print(result["output"])
Output: 
⚡ Running 3 steps in parallel...
✅ Parallel complete: 3 results
✅ summarize: 📋 SUMMARY:...

📋 SUMMARY:
  • 📊 Market: Growth 15% YoY
  • 🏢 Competitors: 3 major players
  • 👥 Customers: 85% satisfaction

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API Reference

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parallel()

parallel(
    steps: List, 
    max_workers: Optional[int] = None,
    on_failure: str = "partial_ok"
) -> Parallel

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Parameters

ParameterTypeDefaultDescription
stepsList—List of steps to execute concurrently
max_workersOptional[int]NoneCap on ThreadPoolExecutor workers. Defaults to min(3, len(steps))
on_failure"partial_ok" | "fail_fast" | "fail_all""partial_ok"Failure-handling strategy

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Accessing Results

After parallel execution, results are available in ctx.variables:
VariableTypeDescription
parallel_outputsList[str]List of all outputs in order
def aggregator(ctx: WorkflowContext) -> StepResult:
    outputs = ctx.variables["parallel_outputs"]
    # outputs = ["Result A", "Result B", "Result C"]
    return StepResult(output=f"Combined: {len(outputs)} results")

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Examples

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With Agents

from praisonaiagents import Agent

researcher = Agent(name="Researcher", role="Research topics")
analyst = Agent(name="Analyst", role="Analyze data")
writer = Agent(name="Writer", role="Write content")

workflow = AgentFlow(steps=[
    parallel([researcher, analyst, writer]),
    final_aggregator
])

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Mixed Steps

workflow = AgentFlow(steps=[
    parallel([
        my_function,           # Function
        Agent(name="Bot"),     # Agent
        Task(...)      # Task
    ]),
    aggregator
])

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Nested Parallel

workflow = AgentFlow(steps=[
    parallel([
        parallel([task_a1, task_a2]),  # Group A
        parallel([task_b1, task_b2])   # Group B
    ]),
    final_aggregator
])

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Performance

Parallel execution uses Python’s ThreadPoolExecutor:
  • Concurrent I/O: Ideal for API calls, file operations
  • Thread-safe: Each step gets its own copy of variables
  • Automatic joining: All results collected before next step
# Performance comparison
# Sequential: 3 steps × 1s each = 3s total
# Parallel:   3 steps × 1s each = ~1s total (concurrent)

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Use Cases

Use CaseDescription
Multi-source ResearchQuery multiple APIs simultaneously
Data ProcessingProcess independent data chunks
Report GenerationGenerate sections in parallel
ValidationRun multiple validators concurrently
A/B ComparisonRun different approaches and compare

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Best Practices

  1. Independent tasks only - Parallel steps shouldn’t depend on each other
  2. Handle errors gracefully - One failure shouldn’t break all tasks
  3. Aggregate results - Always follow with a step that combines outputs
  4. Consider rate limits - Don’t overwhelm external APIs

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Failure Handling

Choose how a parallel block reacts when a branch fails using the on_failure parameter.

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Failure Strategies

StrategyBehaviorUse Case
partial_okContinue with partial results. Failed branches return "Error: <msg>"Data aggregation where some sources may be unavailable
fail_fastCancel remaining branches and raise WorkflowStepError on first failureCritical workflows where any failure invalidates results
fail_allWait for all branches, then raise WorkflowStepError if any failedComprehensive error reporting and debugging

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Examples

from praisonaiagents import Agent, Workflow, parallel, WorkflowStepError

# partial_ok (default) — continue even if one branch fails
workflow = Workflow(steps=[
    parallel([agent_a, agent_b, agent_c]),
    aggregator,
])

# fail_fast — abort on first failure
workflow = Workflow(steps=[
    parallel([agent_a, agent_b, agent_c], on_failure="fail_fast"),
    aggregator,
])

# fail_all — gather all errors then fail
workflow = Workflow(steps=[
    parallel([agent_a, agent_b, agent_c], on_failure="fail_all"),
    aggregator,
])

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Error Handling with partial_ok

def aggregator(ctx: WorkflowContext) -> StepResult:
    outputs = ctx.variables["parallel_outputs"]
    
    # Filter successful results
    successful = [o for o in outputs if not o.startswith("Error:")]
    errors = [o for o in outputs if o.startswith("Error:")]
    
    if len(successful) >= 2:  # Minimum threshold
        return StepResult(output=f"Processed {len(successful)} sources")
    else:
        return StepResult(output=f"Insufficient data: {len(errors)} failures")

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Exception Handling with fail_fast/fail_all

try:
    result = workflow.start("Process all branches")
except WorkflowStepError as e:
    print(f"Workflow failed: {e}")
    print(f"Root cause: {e.cause}")
    for error in e.errors:
        print(f"  Branch {error['step']}: {error['error']}")

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See Also