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()
   

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How It Works

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

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Configuration Options

TemplateConfig API Reference

Full API reference for TemplateConfig — system, prompt, response, use_system_prompt

TemplateConfig Fields

Field	Type	Default	Description
system	str | None	None	Override the system prompt
prompt	str | None	None	Template for the user message; use {input} as a placeholder
response	str | None	None	Format template applied to the LLM response
use_system_prompt	bool	True	Whether to send a system message at all

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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()

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Best Practices

Keep system templates focused

A system template that tries to do everything usually does nothing well. One clear role per template — “You are a customer support agent for Acme Corp” — produces better results than a long list of unrelated instructions.

Use {input} placeholder in prompt templates

When your prompt template includes {input}, PraisonAI substitutes the user’s actual request at runtime. This keeps your template generic and reusable across many different queries.

Test response templates with diverse outputs

Response templates are applied as a format instruction to the LLM. Test with edge-case inputs (short answers, long answers, refusals) to make sure the template doesn’t corrupt valid responses.

Prefer YAML templates for team workflows

When multiple people run the same workflow, store templates in a YAML file rather than hardcoding them in Python. YAML files are easier to version, review, and swap without touching code.

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Related

YAML Template Variables

Safely use {placeholder} in YAML alongside JSON literals

Industry Templates

Pre-built agent workforces for Manufacturing, Energy, Healthcare, and more