Templates

Customize agent behavior with system, prompt, and response templates

Templates let you control exactly what an agent says, how it says it, and how it formats its response — all in a few lines.

from praisonaiagents import Agent, TemplateConfig

agent = Agent(
    name="Support Bot",
    templates=TemplateConfig(
        system="You are a friendly support agent for Acme Corp.",
        use_system_prompt=True,
    ),
)
agent.start("How do I reset my password?")

The user sends a message; system, prompt, and response templates shape what the agent sees and how it replies.

Quick Start

1. Custom System Prompt

   Override the default system message so the agent stays in character.

   from praisonaiagents import Agent
   
   agent = Agent(
       name="Support Bot",
       system_prompt="You are a friendly customer support agent for Acme Corp. Always be concise and helpful.",
   )
   
   agent.start("How do I reset my password?")

2. With TemplateConfig

   Use TemplateConfig for full control over system, prompt, and response templates.

   from praisonaiagents import Agent, TemplateConfig
   
   agent = Agent(
       name="Analyst",
       instructions="Analyze the data provided.",
       templates=TemplateConfig(
           system="You are a data analyst. Return only structured insights.",
           prompt="Analyze this: {input}",
           response="Summary:\n{response}\n\nKey Points:\n- {points}",
           use_system_prompt=True,
       )
   )
   
   agent.start("Sales grew 20% in Q3")

3. YAML Template Variables

   Define reusable workflows with {placeholder} variables in YAML.

   # workflow.yaml
   variables:
     topic: climate change
   
   agents:
     researcher:
       name: Researcher
       instructions: "Research {topic} thoroughly"
   
   steps:
     - agent: researcher
       action: "Find the latest data on {topic}"

   from praisonaiagents import Agent, YAMLWorkflowParser
   
   parser = YAMLWorkflowParser()
   workflow = parser.parse_file("workflow.yaml", extra_vars={"topic": "renewable energy"})
   workflow.run()

How It Works

Templates are applied in a specific order before the LLM sees your request.

Configuration Options

TemplateConfig Fields

Common Patterns

Structured Output Format

Force the agent to return a consistent format every time.

from praisonaiagents import Agent, TemplateConfig

agent = Agent(
    name="Reporter",
    instructions="Summarize news articles.",
    templates=TemplateConfig(
        system="You are a news summarizer. Always output valid JSON.",
        response='{"headline": "...", "summary": "...", "sentiment": "positive|negative|neutral"}',
        use_system_prompt=True,
    )
)

result = agent.start("OpenAI released a new model today with improved reasoning.")
print(result)

Persona Agent

Lock the agent into a role with a strong system template.

from praisonaiagents import Agent, TemplateConfig

agent = Agent(
    name="Pirate Guide",
    instructions="Help users navigate the Caribbean.",
    templates=TemplateConfig(
        system="You are a 17th century Caribbean pirate. Speak in pirate dialect. Never break character.",
        use_system_prompt=True,
    )
)

agent.start("How do I get to Tortuga?")

YAML Variable Substitution

Run the same workflow against different inputs without editing the YAML.

from praisonaiagents import YAMLWorkflowParser

parser = YAMLWorkflowParser()

# Same workflow, different topics
for topic in ["AI safety", "quantum computing", "space exploration"]:
    workflow = parser.parse_file("research_workflow.yaml", extra_vars={"topic": topic})
    workflow.run()

Best Practices

Related