Quality Checking

Automatic quality assessment for task outputs with memory storage

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

1. 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()

2. 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:

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

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

Endpoint and Key Resolution

Memory.calculate_quality_metrics() picks its scoring endpoint and key from the memory config, so quality scoring reaches the same host you point memory at.

Config keys are read from the top level or a nested "config" dict, e.g. Memory(config={"base_url": ..., "api_key": ...}).

from praisonaiagents import Memory

memory = Memory(config={
    "base_url": "https://api.groq.com/openai/v1",
    "api_key": "gsk_...",
})

Configuration Options

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

Best Practices

Related