Agent Output

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

Output configuration controls how agents display their responses - silent for programmatic use, verbose for interactive sessions, or streaming for real-time feedback.

Quick Start

1. Default (Silent)

   from praisonaiagents import Agent
   
   # Default is silent - no output overhead
   agent = Agent(
       name="API Agent",
       instructions="You process data"
   )
   
   result = agent.chat("Analyze this data")  # Returns result, no display
   
2. Verbose Mode

   agent = Agent(
       name="Interactive Agent",
       instructions="You help users",
       output="verbose"  # Rich panels and formatting
   )
   
   agent.start("Help me with Python")  # Shows formatted output
   

---

Output Presets

Preset	Display	Use Case
silent	None	Programmatic use, fastest
actions	Tool calls + final output	Debugging
verbose	Rich panels	Interactive sessions
json	JSONL events	Piping to other tools

# Preset examples
agent = Agent(instructions="...", output="silent")   # Default
agent = Agent(instructions="...", output="actions")  # Debug
agent = Agent(instructions="...", output="verbose")  # Interactive
agent = Agent(instructions="...", output="json")     # Piping

---

Configuration Options

from praisonaiagents import OutputConfig

config = OutputConfig(
    verbose=True,           # Enable verbose output
    markdown=True,          # Format as markdown
    stream=True,            # Enable streaming
    metrics=True,           # Show token metrics
    reasoning_steps=True,   # Show reasoning
    actions_trace=False,    # Tool call trace
    json_output=False,      # JSONL output
    simple_output=False,    # Plain text only
    show_parameters=False,  # Show LLM parameters (debug)
    status_trace=False,     # Clean inline status updates
)

Option	Type	Default	Description
verbose	bool	False	Enable verbose output
markdown	bool	False	Format as markdown
stream	bool	False	Enable streaming
metrics	bool	False	Show token metrics
reasoning_steps	bool	False	Show reasoning process
actions_trace	bool	False	Show tool calls
json_output	bool	False	Output JSONL events
simple_output	bool	False	Plain text without panels
show_parameters	bool	False	Show LLM parameters (debug)
status_trace	bool	False	Clean inline status updates

---

Streaming

Real-time output as the agent generates:

agent = Agent(
    instructions="You write stories",
    output=OutputConfig(stream=True)
)

# Streams response in real-time
agent.start("Write a short story about AI")

---

Verbose vs Silent

Silent (Default)

# Fastest - no display overhead
agent = Agent(instructions="...")
result = agent.chat("Query")  # Just returns result

Verbose

# Rich formatted output
agent = Agent(instructions="...", output="verbose")
agent.start("Query")  # Shows panels, formatting

---

JSON Output

For piping to other tools:

agent = Agent(
    instructions="You analyze data",
    output=OutputConfig(json_output=True)
)

# Outputs JSONL events to stderr
# {"event": "llm_start", "model": "gpt-4o", ...}
# {"event": "llm_end", "tokens": 150, ...}
# {"event": "response", "content": "...", ...}

---

Metrics Display

Show token usage and timing:

agent = Agent(
    instructions="You help with tasks",
    output=OutputConfig(
        verbose=True,
        metrics=True,  # Show token counts
    )
)

# Output includes:
# Tokens: 150 input, 200 output
# Time: 1.2s

---

Method-Specific Output

Different methods have different default behaviors:

Method	Default Output	Override
agent.chat()	Silent	Use output= param
agent.start()	Verbose + Stream	Use output= param
agent.run()	Silent	Use output= param

# chat() is silent by default
result = agent.chat("Query")

# start() is verbose by default
agent.start("Query")

# Override defaults
agent.chat("Query", stream=True)  # Force streaming

---

Best Practices

Use silent for APIs

Default silent mode has zero overhead - ideal for programmatic use.

Use verbose for debugging

See exactly what the agent is doing with verbose mode.

Enable streaming for UX

Users prefer seeing progress - enable streaming for interactive apps.

Use JSON for pipelines

JSONL output integrates well with log aggregators and pipelines.

---

Related

Streaming

Real-time output

Execution

Execution limits