Quality Checking

Tasks with quality_check=True score outputs and store high-quality results in memory.

from praisonaiagents import Agent, Task, AgentTeam, Memory

agent = Agent(
    name="Writer",
    instructions="Write clear, complete articles.",
    memory=Memory(),
)

task = Task(
    description="Write a short article on AI ethics",
    expected_output="Introduction, three sections, and conclusion",
    agent=agent,
    quality_check=True,
)

AgentTeam(agents=[agent], tasks=[task]).start()

The user runs a team task; quality scoring decides whether outputs land in long-term memory.

How It Works

Quick Start

Simple Usage

Enable quality checking on a task (default is True):

from praisonaiagents import Agent, Task, AgentTeam, Memory

agent = Agent(name="Writer", instructions="Be thorough.", memory=Memory())

task = Task(
    description="Explain quantum computing in plain English",
    expected_output="500-word explanation with examples",
    agent=agent,
    quality_check=True,
)

AgentTeam(agents=[agent], tasks=[task]).start()

With Configuration

Disable for fast runs or use execution presets:

from praisonaiagents import Agent, Task, AgentTeam

agent = Agent(name="Draft", instructions="Quick drafts only.")

task = Task(
    description="Draft a tweet",
    agent=agent,
    quality_check=False,
)

AgentTeam(agents=[agent], tasks=[task]).start()

How It Works

When quality_check=True and memory is configured:

Agent completes the task
Memory.calculate_quality_metrics() scores completeness, relevance, clarity, accuracy via LLM
finalize_task_output() stores in long-term memory only when score exceeds 0.7
Quality metadata attaches to the task result

Memory is required — without it, quality checking logs a warning and skips storage.

Configuration Options

Option	Type	Default	Description
`quality_check`	`bool`	`True`	Enable LLM quality assessment
`expected_output`	`str`	`None`	Benchmark for scoring (strongly recommended)
`memory`	`Memory`	`None`	Required for quality storage

Execution presets: "fast" disables quality check; "balanced" and "thorough" enable it.

Best Practices

Always set expected_output

Clear expectations produce meaningful scores — vague tasks score inconsistently.

Configure memory first

Quality checking stores to memory — attach memory=Memory() to the agent or task.

Disable for drafts

Set quality_check=False on brainstorming or speed-critical tasks.

Retrieve high-quality history

Search with min_quality=0.7 to reuse past strong outputs as context.

Quality-Based RAG

Quality scoring for retrieval

Memory

Memory configuration and search

Policy Engine

Safe Math Eval in Custom Tools

Getting Started

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Getting Started (No Code)

How It Works

Quick Start

How It Works

Configuration Options

Best Practices

Quality-Based RAG

Memory

​How It Works

​Quick Start

​How It Works

​Configuration Options

​Best Practices

​Related

Quality-Based RAG

Memory

How It Works

Quick Start

How It Works

Configuration Options

Best Practices

Related