Skip to main content
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 API Reference

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

​
TemplateConfig Fields

FieldTypeDefaultDescription
systemstr | NoneNoneOverride the system prompt
promptstr | NoneNoneTemplate for the user message; use {input} as a placeholder
responsestr | NoneNoneFormat template applied to the LLM response
use_system_promptboolTrueWhether to send a system message at all

​
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

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.
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.
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.
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.

YAML Template Variables

Safely use {placeholder} in YAML alongside JSON literals

Industry Templates

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