crewAI/docs/how-to/customize-prompts.mdx

---
title: "Customize Agent Prompts"
description: "Learn how to customize system and user prompts in CrewAI agents for precise control over agent behavior and output formatting."
---

# Customize Agent Prompts

CrewAI provides fine-grained control over how agents generate and format their responses through a sophisticated prompt generation system. This guide explains how system and user prompts are constructed and how you can customize them for your specific use cases.

## Understanding Prompt Generation

CrewAI uses a template-based system to generate prompts, combining different components based on agent configuration:

### Core Prompt Components

All prompt templates are stored in the internationalization system and include:

- **Role Playing**: `"You are {role}. {backstory}\nYour personal goal is: {goal}"`
- **Tools**: Instructions for agents with access to tools
- **No Tools**: Instructions for agents without tools  
- **Task**: The specific task execution prompt
- **Format Instructions**: Output formatting requirements

### Prompt Assembly Process

CrewAI assembles prompts differently based on agent type:

1. **Regular Agents**: Use the `Prompts` class to combine template slices
2. **LiteAgents**: Use dedicated system prompt methods with specific templates
3. **System/User Split**: When `use_system_prompt=True`, prompts are split into system and user components

## Basic Prompt Customization

### Custom System and Prompt Templates

You can override the default prompt structure using custom templates:

```python
from crewai import Agent, Task, Crew

# Define custom templates
system_template = """{{ .System }}

Additional context: You are working in a production environment.
Always prioritize accuracy and provide detailed explanations."""

prompt_template = """{{ .Prompt }}

Remember to validate your approach before proceeding."""

response_template = """Please format your response as follows:
{{ .Response }}
End of response."""

# Create agent with custom templates
agent = Agent(
    role="Data Analyst",
    goal="Analyze data with precision and accuracy",
    backstory="You are an experienced data analyst with expertise in statistical analysis.",
    system_template=system_template,
    prompt_template=prompt_template,
    response_template=response_template,
    use_system_prompt=True
)
```

### Template Placeholders

Custom templates support these placeholders:

- `{{ .System }}`: Replaced with the assembled system prompt components
- `{{ .Prompt }}`: Replaced with the task-specific prompt
- `{{ .Response }}`: Placeholder for the agent's response (used in response_template)

## System/User Prompt Split

Enable system/user prompt separation for better LLM compatibility:

```python
agent = Agent(
    role="Research Assistant",
    goal="Conduct thorough research on given topics",
    backstory="You are a meticulous researcher with access to various information sources.",
    use_system_prompt=True  # Enables system/user split
)
```

When `use_system_prompt=True`:
- **System Prompt**: Contains role, backstory, goal, and tool instructions
- **User Prompt**: Contains the specific task and expected output format

## Output Format Customization

### Structured Output with Pydantic Models

Control output formatting using Pydantic models:

```python
from pydantic import BaseModel
from typing import List

class ResearchOutput(BaseModel):
    summary: str
    key_findings: List[str]
    confidence_score: float

task = Task(
    description="Research the latest trends in AI development",
    expected_output="A structured research report",
    output_pydantic=ResearchOutput,
    agent=agent
)
```

### Custom Format Instructions

Add specific formatting requirements:

```python
task = Task(
    description="Analyze the quarterly sales data",
    expected_output="Analysis in JSON format with specific fields",
    output_format="""
    {
        "total_sales": "number",
        "growth_rate": "percentage", 
        "top_products": ["list of strings"],
        "recommendations": "detailed string"
    }
    """
)
```

## Stop Words Configuration

### Default Stop Words

CrewAI automatically configures stop words based on agent setup:

```python
# Default stop word is "\nObservation:" for tool-enabled agents
agent = Agent(
    role="Analyst",
    goal="Perform analysis tasks",
    backstory="You are a skilled analyst.",
    tools=[some_tool]  # Stop words include "\nObservation:"
)
```

### Custom Stop Words via Response Template

Modify stop words by customizing the response template:

```python
response_template = """Provide your analysis:
{{ .Response }}
---END---"""

agent = Agent(
    role="Analyst", 
    goal="Perform detailed analysis",
    backstory="You are an expert analyst.",
    response_template=response_template  # Stop words will include "---END---"
)
```

## LiteAgent Prompt Customization

LiteAgents use a simplified prompt system with direct customization:

```python
from crewai import LiteAgent

# LiteAgent with tools
lite_agent = LiteAgent(
    role="Code Reviewer",
    goal="Review code for quality and security",
    backstory="You are an experienced software engineer specializing in code review.",
    tools=[code_analysis_tool],
    response_format=CodeReviewOutput  # Pydantic model for structured output
)

# The system prompt will automatically include tool instructions and format requirements
```

## Advanced Customization Examples

### Example 1: Multi-Language Support

```python
# Custom templates for different languages
spanish_system_template = """{{ .System }}

Instrucciones adicionales: Responde siempre en español y proporciona explicaciones detalladas."""

agent = Agent(
    role="Asistente de Investigación",
    goal="Realizar investigación exhaustiva en español",
    backstory="Eres un investigador experimentado que trabaja en español.",
    system_template=spanish_system_template,
    use_system_prompt=True
)
```

### Example 2: Domain-Specific Formatting

```python
# Medical report formatting
medical_response_template = """MEDICAL ANALYSIS REPORT
{{ .Response }}

DISCLAIMER: This analysis is for informational purposes only."""

medical_agent = Agent(
    role="Medical Data Analyst",
    goal="Analyze medical data with clinical precision",
    backstory="You are a certified medical data analyst with 10 years of experience.",
    response_template=medical_response_template,
    use_system_prompt=True
)
```

### Example 3: Complex Workflow Integration

```python
from crewai import Flow

class CustomPromptFlow(Flow):
    
    @start()
    def research_phase(self):
        # Agent with research-specific prompts
        researcher = Agent(
            role="Senior Researcher",
            goal="Gather comprehensive information",
            backstory="You are a senior researcher with expertise in data collection.",
            system_template="""{{ .System }}
            
Research Guidelines:
- Verify all sources
- Provide confidence ratings
- Include methodology notes""",
            use_system_prompt=True
        )
        
        task = Task(
            description="Research the given topic thoroughly",
            expected_output="Detailed research report with sources",
            agent=researcher
        )
        
        return task.execute()
    
    @listen(research_phase)
    def analysis_phase(self, research_result):
        # Agent with analysis-specific prompts
        analyst = Agent(
            role="Data Analyst", 
            goal="Provide actionable insights",
            backstory="You are an expert data analyst specializing in trend analysis.",
            response_template="""ANALYSIS RESULTS:
{{ .Response }}

CONFIDENCE LEVEL: [Specify confidence level]
NEXT STEPS: [Recommend next actions]""",
            use_system_prompt=True
        )
        
        return f"Analysis based on: {research_result}"
```

## Best Practices

### Precision and Accuracy
- Use specific role definitions and detailed backstories for consistent behavior
- Include validation requirements in custom templates
- Test prompt variations to ensure predictable outputs

### Security Considerations  
- Validate all user inputs before including them in prompts
- Use structured output formats to prevent prompt injection
- Implement guardrails for sensitive operations

### Performance Optimization
- Keep system prompts concise while maintaining necessary context
- Use appropriate stop words to prevent unnecessary token generation
- Test prompt efficiency with your target LLM models

### Complexity Handling
- Break complex requirements into multiple template components
- Use conditional prompt assembly for different scenarios
- Implement fallback templates for error handling

## Troubleshooting

### Common Issues

**Prompt Not Applied**: Ensure you're using the correct template parameter names and that `use_system_prompt` is set appropriately.

**Format Not Working**: Verify that your `output_format` or `output_pydantic` model matches the expected structure.

**Stop Words Not Effective**: Check that your `response_template` includes the desired stop sequence after the `{{ .Response }}` placeholder.

### Debugging Prompts

Enable verbose mode to see the actual prompts being sent to the LLM:

```python
agent = Agent(
    role="Debug Agent",
    goal="Help debug prompt issues", 
    backstory="You are a debugging specialist.",
    verbose=True  # Shows detailed prompt information
)
```

This comprehensive prompt customization system gives you precise control over agent behavior while maintaining the reliability and consistency that CrewAI is known for in production environments.