Output

Control how agents display responses — from silent to verbose with streaming

Output controls what agents print, how much detail they show, and whether they stream responses in real time.

from praisonaiagents import Agent

# All spellings resolve to the 'verbose' preset (verbose=True, markdown=True):
for value in ("verbose", " verbose ", "VERBOSE"):
    agent = Agent(instructions="t", output=value)
    assert agent.verbose is True and agent.markdown is True

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
    output="verbose",
)

agent.start("Explain how neural networks learn.")

The user runs the agent; output mode controls how much detail prints or streams during the run.

Quick Start

1. Level 1 — String (preset)

   Pick a named output mode — the shortest way to control what the agent prints.

   from praisonaiagents import Agent
   
   agent = Agent(
       name="Assistant",
       instructions="You are a helpful assistant.",
       output="verbose",
   )
   agent.start("What is the fastest sorting algorithm?")

2. Level 2 — Config class (full control)

   Use OutputConfig to combine formatting flags like streaming and markdown.

   from praisonaiagents import Agent, OutputConfig
   
   agent = Agent(
       name="Assistant",
       instructions="You are a helpful assistant.",
       output=OutputConfig(
           verbose=True,
           markdown=True,
           stream=True,
       ),
   )
   agent.start("Write a comparison of React vs Vue.js.")

Output Presets

How It Works

Concurrent execution (PR #5219)

output="status" and output="trace" route through a shared per-process singleton. As of PR #5219 (fixes #5162), that singleton’s _output_lock is actually acquired around every timing and token/cost update, so an asyncio.gather over several agents no longer corrupts the aggregate metrics.

Configuration Options

The most common options at a glance:

Common Patterns

Pattern 1 — Streaming for real-time UX

from praisonaiagents import Agent, OutputConfig

agent = Agent(
    instructions="You are a writing assistant.",
    output=OutputConfig(stream=True, markdown=True),
)
response = agent.start("Write a short story about a robot learning to paint.")
print(response)

Pattern 2 — Save output to file

from praisonaiagents import Agent, OutputConfig

agent = Agent(
    instructions="You are a report writer.",
    output=OutputConfig(output_file="report.md", markdown=True),
)
agent.start("Generate a quarterly sales analysis report.")

Pattern 3 — JSON output for pipelines

from praisonaiagents import Agent, OutputConfig

agent = Agent(
    instructions="You are a data extraction agent.",
    output=OutputConfig(json_output=True),
)
agent.start("Extract all company names from this text: Apple launched in 1976, Google in 1998.")

Best Practices

Related