Output Continuation

Output continuation lets an agent transparently finish a long answer the model cut off at its output-token limit.

Quick Start

On by default

from praisonaiagents import Agent

# Nothing to configure — the agent auto-continues up to 3 times
# when the output-token limit truncates its answer.
agent = Agent(
    name="Writer",
    instructions="You write long, detailed essays."
)

agent.start("Write a 3000-word essay about the history of the internet")

Raise the cap

from praisonaiagents import Agent, ExecutionConfig

agent = Agent(
    name="Writer",
    instructions="You write long, detailed essays.",
    execution=ExecutionConfig(output_continuation=5)
)

agent.start("Write a 3000-word essay about the history of the internet")

Turn it off

from praisonaiagents import Agent, ExecutionConfig

# A truncated answer surfaces `length_truncated` immediately.
agent = Agent(
    name="Writer",
    instructions="You write long, detailed essays.",
    execution=ExecutionConfig(output_continuation=False)
)

agent.start("Write a 3000-word essay about the history of the internet")
print(agent.last_stop_reason)

Pair with a low max_tokens

from praisonaiagents import Agent

agent = Agent(
    name="Writer",
    instructions="You write long, detailed essays.",
    llm={"model": "gpt-4o-mini", "max_tokens": 200}
)

print(agent.start("Write a detailed essay about the Roman Empire"))
print(agent.last_stop_reason)  # "completed" once the pieces are stitched

What the User Sees

The user gets one complete answer and never has to type “continue”.

How It Works

The agent keeps a per-turn counter and stitches each piece onto the accumulated text.

Phase	What happens
1. First response	The model stops with `finish_reason == "length"`.
2. Classify	Only a length stop on a final answer triggers continuation. Content filters, refusals and normal stops never do.
3. Re-prompt	The agent adds the partial assistant turn plus a fixed “continue exactly where you left off” user turn to a local copy of the conversation.
4. Stitch	Each new piece is appended to the earlier text; the stitched text becomes the final answer.
5. Cap	Up to 3 continuations by default. Past the cap, the agent returns the stitched text and `last_stop_reason == "length_truncated"`.

When Continuation Is Skipped

Case	Why
`output_json=` / `output_pydantic=`	Stitching text onto a JSON schema would break parsing.
`response_format` other than `{"type": "text"}`	Same reason. `{"type": "text"}` still continues.
Tool-call turns	Only final answers continue.
Empty partial text	Nothing to continue from.
`output_continuation=False` or `0`	Explicitly disabled.

Value Shapes

output_continuation is a field on the existing ExecutionConfig, not a separate Agent(...) parameter.

Value	Meaning
`True` (default)	Up to 3 continuations.
`None`	Same as `True`.
`False` or `0`	Disabled — `length_truncated` surfaces on the first truncation.
`int >= 1`	Custom cap, e.g. `output_continuation=10`.

Configuration Options

Option	Type	Default	Description
`output_continuation`	`Union[bool, int]`	`True`	Auto-continue a final answer cut off by `finish_reason == "length"`. `True`/`None` = cap of 3, `False`/`0` = disabled, `int` = custom cap. Ignored for structured outputs.

ExecutionConfig API Reference

Full ExecutionConfig options

Common Patterns

Long-form writer
Pin the old behaviour
Structured output

from praisonaiagents import Agent

agent = Agent(
    name="Essayist",
    instructions="You write complete essays.",
    llm={"model": "gpt-4o-mini", "max_tokens": 300}
)

essay = agent.start("Write an essay on renewable energy")

from praisonaiagents import Agent, ExecutionConfig

agent = Agent(
    name="Reporter",
    instructions="Answer in detail.",
    execution=ExecutionConfig(output_continuation=False)
)

agent.start("Explain how vaccines work in depth")
if agent.last_stop_reason == "length_truncated":
    print("Partial answer — caller decides what to do")

from pydantic import BaseModel
from praisonaiagents import Agent, Task, PraisonAIAgents

class Report(BaseModel):
    title: str
    summary: str

agent = Agent(name="Analyst", instructions="Write reports.")
task = Task(
    description="Write a report on solar power",
    agent=agent,
    output_pydantic=Report
)

# Continuation is skipped automatically — raise max_tokens instead.
PraisonAIAgents(agents=[agent], tasks=[task]).start()

Best Practices

Leave it on by default

The common failure is a half-finished answer the user has to re-prompt. The default cap of 3 covers it.

Lower the cap for cost-sensitive pipelines

output_continuation=1 keeps one safety net without large extra token spend.

Pair with max_steps for predictable budgets

max_steps caps the tool-use loop; output continuation caps single-answer overflow. They are independent.

Raise max_tokens for structured output

Continuation is skipped for JSON/schema outputs, so give those calls enough max_tokens up front.

Pin the terminal outcome in tests

Set output_continuation=False to assert on last_stop_reason == "length_truncated" without extra model calls.

Execution Config

All execution options

Run Outcome

The length_truncated outcome

Agent Run Outcomes

last_stop_reason values

Turn Completion Notes

Note shown when the cap is exhausted

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Getting Started (No Code)

Output Continuation

Quick Start

What the User Sees

How It Works

When Continuation Is Skipped

Value Shapes

Configuration Options

ExecutionConfig API Reference

Common Patterns

Best Practices

Execution Config

Run Outcome

Agent Run Outcomes

Turn Completion Notes

​Quick Start

​What the User Sees

​How It Works

​When Continuation Is Skipped

​Value Shapes

​Configuration Options

ExecutionConfig API Reference

​Common Patterns

​Best Practices

​Related

Execution Config

Run Outcome

Agent Run Outcomes

Turn Completion Notes

Quick Start

What the User Sees

How It Works

When Continuation Is Skipped

Value Shapes

Configuration Options

Common Patterns

Best Practices

Related