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Templates let you customize how agents construct their prompts - system instructions, user prompts, and response formats.
Quick Start
Basic Templates
from praisonaiagents import Agent , TemplateConfig
agent = Agent (
name = " Custom Agent " ,
instructions = " You are a helpful assistant " , # System Prompt: defines agent identity
templates = TemplateConfig (
system = " You are {role} . {instructions} " , # System Prompt template
prompt = " User request: {input} " , # User Prompt template
)
)
Response Format
agent = Agent (
name = " Structured Agent " ,
instructions = " You provide structured answers " ,
templates = TemplateConfig (
response = """ Format your response as:
## Summary
[Brief summary]
## Details
[Detailed explanation]
## Next Steps
[Recommendations] """
)
)
Template Types
Template Purpose When Applied systemAgent identity, rules, context Start of conversation promptFormat user input Each request responseStructure output format Each response
Configuration Options
from praisonaiagents import TemplateConfig
config = TemplateConfig (
system = " You are {role} ... " , # System Prompt: agent identity (WHO you are)
prompt = " Query: {input} " , # User Prompt: task input (WHAT to do)
response = " Format: ... " , # Response format (HOW to answer)
use_system_prompt = True , # Include system prompt
)
Option Type Default Description systemstrNoneSystem prompt template promptstrNoneUser prompt template responsestrNoneResponse format template use_system_promptboolTrueWhether to use system prompt
Template Variables
Templates support variable substitution:
agent = Agent (
name = " Support Agent " ,
role = " Customer Support Specialist " ,
instructions = " Help customers with their issues " ,
templates = TemplateConfig (
system = """ You are {role} .
Your goal: {goal}
Background: {backstory}
{instructions} """
)
)
Available Variables
Variable Source {name}Agent name {role}Agent role {goal}Agent goal {backstory}Agent backstory {instructions}Agent instructions {input}User input
Common Patterns
Structured Output
agent = Agent (
instructions = " You analyze data " ,
templates = TemplateConfig (
response = """ Provide your analysis in this format:
**Finding**: [Main finding]
**Evidence**: [Supporting data]
**Confidence**: [High/Medium/Low]
**Recommendation**: [Action to take] """
)
)
Role-Based System
agent = Agent (
name = " Expert " ,
role = " Senior Software Engineer " ,
backstory = " 10 years of experience in Python " ,
templates = TemplateConfig (
system = """ # Role
You are {role} with the following background:
{backstory}
# Guidelines
{instructions}
# Constraints
- Always explain your reasoning
- Provide code examples when relevant
- Cite best practices """
)
)
Chain-of-Thought
agent = Agent (
instructions = " You solve problems step by step " ,
templates = TemplateConfig (
response = """ Think through this step by step:
1. **Understanding**: What is being asked?
2. **Analysis**: What are the key factors?
3. **Solution**: What is the answer?
4. **Verification**: Is this correct?
Final Answer: [Your answer] """
)
)
Disabling System Prompt
For some use cases, you may want to skip the system prompt:
agent = Agent (
instructions = " ... " ,
templates = TemplateConfig (
use_system_prompt = False # No system prompt
)
)
Template Inheritance
Templates combine with agent properties:
# Agent properties
agent = Agent (
name = " Helper " ,
role = " Assistant " ,
instructions = " Be helpful and concise " ,
templates = TemplateConfig (
system = " {role} : {instructions} "
)
)
# Results in: "Assistant: Be helpful and concise"
Best Practices
Each template should have a clear purpose. Don’t overload the system template.
Use response templates for consistency
Define response formats to get predictable, structured outputs.
Test template combinations
Variables are substituted at runtime - test with different agent configurations.
Document custom templates
If using custom templates, document what variables are expected.
Output Display configuration
Structured Output Pydantic output models