Parallel Execution

This example demonstrates running multiple agents concurrently.

examples/parallel_execution_example.py

import sys
import os
import time
from kibo_core import AgentConfig, create_agent

def main():
    print("--- Kibo Parallel Execution Example (Blueprint) ---")

    tasks = [
        ("Task A", 2),
        ("Task B", 2),
        ("Task C", 2),
        ("Task D", 2),
        ("Task E", 2),
    ]

    print(f"\nSubmitting {len(tasks)} tasks to Kibo...")
    start_time = time.time()

    futures = []

    for name, duration in tasks:
        agent_def = AgentConfig(
            name=f"Worker-{name}",
            description="Simulates work.",
            instructions="Sleep for a bit.",
            agent="mock",
            model="none",
            config={"duration": duration}
        )

        agent = create_agent(agent_def)
        future = agent.run_async(name)

        futures.append(future)
        print(f"Submitted {name}")

    print("\nAll tasks submitted. Waiting for results...")

    results = [f.result() for f in futures]

    end_time = time.time()
    total_time = end_time - start_time

    print("\n--- Results ---")
    for res in results:
        node = res.metadata.get("node", "unknown")
        pid = res.metadata.get("pid", "unknown")
        print(f" - {res.output_data} | Executed on: {node} (PID: {pid})")

    print(f"\nTotal execution time: {total_time:.2f} seconds")

    seq_time = sum(t[1] for t in tasks)
    print(f"Theoretical sequential time: {seq_time:.2f} seconds")
    print(f"Speedup: {seq_time / total_time:.2f}x")

if __name__ == "__main__":
    main()

How it works

1. Creation: We create a list of agents (Poet, Joker, Historian).
2. Dispatch: We loop through them calling run_async(), collecting the futures.
3. Collection: We verify the results.