Self Reflection - PraisonAI

Enable reflection=True on an agent and it critiques its own answer before returning — improving quality within the same task.

from praisonaiagents import Agent

agent = Agent(
    name="Analyst",
    instructions="Provide thorough, accurate analysis.",
    reflection=True,
)
agent.start("Summarise recent AI safety developments")

The user asks a question; the agent drafts an answer, reflects on quality, then returns the improved response.

Quick Start

Simple Usage

from praisonaiagents import Agent

agent = Agent(
    name="Analyst",
    instructions="Provide thorough, accurate analysis.",
    reflection=True,
)
agent.start("Summarise recent AI safety developments")

With Configuration

from praisonaiagents import Agent, ReflectionConfig

agent = Agent(
    name="Analyst",
    instructions="Provide thorough, accurate analysis.",
    reflection=ReflectionConfig(
        min_iterations=1,
        max_iterations=3,
        llm="gpt-4o",
        prompt="Check accuracy, completeness, and clarity.",
    ),
)
agent.start("Summarise recent AI safety developments")

How It Works

After each response, the agent runs one or more reflection passes (between min_iterations and max_iterations). Each pass asks whether the answer meets quality criteria; if not, the agent revises before returning.

Configuration Options

ReflectionConfig SDK Reference

Full parameter reference for ReflectionConfig

Precedence ladder — choose the level you need:

# Level 1: Bool (simplest — enable with defaults)
agent = Agent(reflection=True)

# Level 2: ReflectionConfig (full control)
agent = Agent(reflection=ReflectionConfig(
    min_iterations=1,
    max_iterations=3,
    llm="gpt-4o",
    prompt="Evaluate for accuracy, completeness, and clarity.",
))

Option	Type	Default	Description
`min_iterations`	`int`	`1`	Minimum reflection passes
`max_iterations`	`int`	`3`	Maximum reflection passes
`llm`	`str \| None`	`None`	Model for reflection (defaults to agent model)
`prompt`	`str \| None`	`None`	Custom evaluation prompt

Choosing Reflection Strength

Best Practices

Disable for tool-heavy agents

Reflection adds an extra LLM pass per turn. Set reflection=False on agents that call tools frequently to keep latency down.

Pair with a dedicated reflection model

Use ReflectionConfig(llm="gpt-4o") when the main model is fast but you want a stronger critic.

Do not confuse with self_improve

reflection improves this answer within the task. self_improve captures reusable skills for next time — they compose independently.

Set max_iterations for cost control

Cap max_iterations in production to avoid runaway loops on open-ended prompts.

Self Improve

Capture reusable skills after each task

Guardrails

Validate agent outputs with policies

Planning Mode

Let agents plan before acting

Execution Systems

Configure agent execution limits

Generate Synthetic Reasoning Data Agents

Model Router System

​Quick Start

​How It Works

​Configuration Options