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Single-Node Graph

import asyncio
from upsonic import Agent, Task, Graph
from upsonic.eval import PerformanceEvaluator

agent = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="GraphAgent",
)

graph = Graph(
    default_agent=agent,
    show_progress=False,
)

graph_task = Task(description="What is 7 + 3?")
graph.add(graph_task)

evaluator = PerformanceEvaluator(
    agent_under_test=graph,
    task=Task(description="placeholder"),
    num_iterations=3,
    warmup_runs=1,
)

result = asyncio.run(evaluator.run(print_results=True))

print(f"Avg latency: {result.latency_stats['average'] * 1000:.0f} ms")
print(f"Peak memory avg: {result.memory_peak_stats['average'] / 1024:.1f} KB")
When profiling a Graph, the task parameter is not used directly — the graph executes its own internal nodes. Pass any placeholder Task.

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Multi-Node Chain

import asyncio
from upsonic import Agent, Task, Graph
from upsonic.eval import PerformanceEvaluator

agent = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="ChainAgent",
)

graph = Graph(
    default_agent=agent,
    show_progress=False,
)

t1 = Task(description="What is the capital of Spain?")
t2 = Task(description="What is a famous landmark in that city?")
graph.add(t1 >> t2)

evaluator = PerformanceEvaluator(
    agent_under_test=graph,
    task=Task(description="placeholder"),
    num_iterations=3,
    warmup_runs=1,
)

result = asyncio.run(evaluator.run(print_results=True))

print(f"Avg latency: {result.latency_stats['average'] * 1000:.0f} ms")
for i, run in enumerate(result.all_runs):
    print(f"  Run {i+1}: {run.latency_seconds:.2f}s")