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"I MUST use these formats, my job depends on it!"

"IMPORTANT: Use the following format in your response:

Thought: you should always think about what to do
Action: the action to take, only one name of [tool_names]
Action Input: the input to the action, just a simple JSON object...

"Ensure your final answer contains only the content in the following format: {output_format}
Ensure the final output does not include any code block markers like ```json or ```python."

from crewai import Agent, Crew, Task
from crewai.utilities.prompts import Prompts

# Create your agent
agent = Agent(
    role="Data Analyst",
    goal="Analyze data and provide insights",
    backstory="You are an expert data analyst with 10 years of experience.",
    verbose=True
)

# Create a sample task
task = Task(
    description="Analyze the sales data and identify trends",
    expected_output="A detailed analysis with key insights and trends",
    agent=agent
)

# Create the prompt generator
prompt_generator = Prompts(
    agent=agent,
    has_tools=len(agent.tools) > 0,
    use_system_prompt=agent.use_system_prompt
)

# Generate and inspect the actual prompt
generated_prompt = prompt_generator.task_execution()

# Print the complete system prompt that will be sent to the LLM
if "system" in generated_prompt:
    print("=== SYSTEM PROMPT ===")
    print(generated_prompt["system"])
    print("\n=== USER PROMPT ===")
    print(generated_prompt["user"])
else:
    print("=== COMPLETE PROMPT ===")
    print(generated_prompt["prompt"])

# You can also see how the task description gets formatted
print("\n=== TASK CONTEXT ===")
print(f"Task Description: {task.description}")
print(f"Expected Output: {task.expected_output}")

from crewai import Agent

# Define your own system template without default instructions
custom_system_template = """You are {role}. {backstory}
Your goal is: {goal}

Respond naturally and conversationally. Focus on providing helpful, accurate information."""

custom_prompt_template = """Task: {input}

Please complete this task thoughtfully."""

agent = Agent(
    role="Research Assistant",
    goal="Help users find accurate information",
    backstory="You are a helpful research assistant.",
    system_template=custom_system_template,
    prompt_template=custom_prompt_template,
    use_system_prompt=True  # Use separate system/user messages
)

{
  "slices": {
    "no_tools": "\nProvide your best answer in a natural, conversational way.",
    "tools": "\nYou have access to these tools: {tools}\n\nUse them when helpful, but respond naturally.",
    "formatted_task_instructions": "Format your response as: {output_format}"
  }
}

crew = Crew(
    agents=[agent],
    tasks=[task],
    prompt_file="custom_prompts.json",
    verbose=True
)

from crewai.utilities.i18n import get_i18n

i18n = get_i18n("custom_prompts.json")
format_slice = i18n.slice("format")
tool_prompt = i18n.tools("ask_question")

agent = Agent(
    role="Analyst",
    goal="Analyze data",
    backstory="Expert analyst",
    use_system_prompt=False  # Disables system prompt separation
)

{
  "slices": {
    "format": "When responding, follow this structure:\n\nTHOUGHTS: Your step-by-step thinking\nACTION: Any tool you're using\nRESULT: Your final answer or conclusion"
  }
}

from crewai import Agent, Crew, Task, Process

# Create agents and tasks as normal
researcher = Agent(
    role="Research Specialist",
    goal="Find information on quantum computing",
    backstory="You are a quantum physics expert",
    verbose=True
)

research_task = Task(
    description="Research quantum computing applications",
    expected_output="A summary of practical applications",
    agent=researcher
)

# Create a crew with your custom prompt file
crew = Crew(
    agents=[researcher],
    tasks=[research_task],
    prompt_file="path/to/custom_prompts.json",
    verbose=True
)

# Run the crew
result = crew.kickoff()

from crewai import Agent, Crew, Task, Process
from crewai_tools import DirectoryReadTool, FileReadTool

# Define templates for system, user (prompt), and assistant (response) messages
system_template = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>{{ .System }}<|eot_id|>"""
prompt_template = """<|start_header_id|>user<|end_header_id|>{{ .Prompt }}<|eot_id|>"""
response_template = """<|start_header_id|>assistant<|end_header_id|>{{ .Response }}<|eot_id|>"""

# Create an Agent using Llama-specific layouts
principal_engineer = Agent(
    role="Principal Engineer",
    goal="Oversee AI architecture and make high-level decisions",
    backstory="You are the lead engineer responsible for critical AI systems",
    verbose=True,
    llm="groq/llama-3.3-70b-versatile",  # Using the Llama 3 model
    system_template=system_template,
    prompt_template=prompt_template,
    response_template=response_template,
    tools=[DirectoryReadTool(), FileReadTool()]
)

# Define a sample task
engineering_task = Task(
    description="Review AI implementation files for potential improvements",
    expected_output="A summary of key findings and recommendations",
    agent=principal_engineer
)

# Create a Crew for the task
llama_crew = Crew(
    agents=[principal_engineer],
    tasks=[engineering_task],
    process=Process.sequential,
    verbose=True
)

# Execute the crew
result = llama_crew.kickoff()
print(result.raw)