What is the Prompt Module?

The prompt module in CAMEL guides AI models to produce accurate, relevant, and personalized outputs. It provides a library of templates and dictionaries for diverse tasks — like role description, code generation, evaluation, embeddings, and even object recognition.

You can also craft your own prompts to precisely shape your agent’s behavior.

​
Using Prompt Templates

CAMEL provides many ready-to-use prompt templates for quickly spinning up task-specific agents. 
from camel.agents import TaskSpecifyAgent
from camel.configs import ChatGPTConfig
from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType, TaskType

model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O_MINI,
)
task_specify_agent = TaskSpecifyAgent(
    model=model, task_type=TaskType.AI_SOCIETY
)
specified_task_prompt = task_specify_agent.run(
    task_prompt="Improving stage presence and performance skills",
    meta_dict=dict(
        assistant_role="Musician", user_role="Student", word_limit=100
    ),
)

print(f"Specified task prompt:\n{specified_task_prompt}\n")
Set task_type=TaskType.AI_SOCIETY to use the default society prompt template, or define your own.

​
Using Your Own Prompt

Create and pass your own prompt template with full flexibility: 
from camel.agents import TaskSpecifyAgent
from camel.configs import ChatGPTConfig
from camel.models import ModelFactory
from camel.prompts import TextPrompt
from camel.types import ModelPlatformType, ModelType

model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O_MINI,
)

my_prompt_template = TextPrompt(
    'Here is a task: I\'m a {occupation} and I want to {task}. Help me to make this task more specific.'
)
task_specify_agent = TaskSpecifyAgent(
    model=model, task_specify_prompt=my_prompt_template
)
response = task_specify_agent.run(
    task_prompt="get promotion",
    meta_dict=dict(occupation="Software Engineer"),
)
print(response)

​
Introduction to the CodePrompt Class

The CodePrompt class represents a code prompt and extends TextPrompt. It’s perfect for code generation and execution tasks. 
from camel.prompts import CodePrompt

​
Creating a CodePrompt

code_prompt = CodePrompt("a = 1 + 1", code_type="python")

​
Accessing and Modifying the Code and Type

print(code_prompt)          # >>> "a = 1 + 1"
print(code_prompt.code_type)  # >>> "python"

code_prompt.set_code_type("python")
print(code_prompt.code_type)  # >>> "python"

​
Executing the Code

code_prompt = CodePrompt("a = 1 + 1\nb = a + 1\nprint(a,b)", code_type="python")
output = code_prompt.execute()
# Running code? [Y/n]: y
print(output)
# >>> 2 3

​
Write Your Prompts with the TextPrompt Class

The TextPrompt class is a subclass of Python’s str, with extra features for managing key words and advanced formatting. 
from camel.prompts import TextPrompt

prompt = TextPrompt('Please enter your name and age: {name}, {age}')
print(prompt)
# >>> 'Please enter your name and age: {name}, {age}'

​
The key_words Property

from camel.prompts import TextPrompt

prompt = TextPrompt('Please enter your name and age: {name}, {age}')
print(prompt.key_words)
# >>> {'name', 'age'}

​
The format Method (Partial Formatting Supported)

from camel.prompts import TextPrompt

prompt = TextPrompt('Your name and age are: {name}, {age}')
name, age = 'John', 30
formatted_prompt = prompt.format(name=name, age=age)
print(formatted_prompt)
# >>> "Your name and age are: John, 30"

# Partial formatting
partial_formatted_prompt = prompt.format(name=name)
print(partial_formatted_prompt)
# >>> "Your name and age are: John, {age}"

​
Manipulating TextPrompt Instances

You can concatenate, join, and use string methods with TextPrompt just like Python strings: 
from camel.prompts import TextPrompt

prompt1 = TextPrompt('Hello, {name}!')
prompt2 = TextPrompt('Welcome, {name}!')

# Concatenation
prompt3 = prompt1 + ' ' + prompt2
print(prompt3)  
# >>> "Hello, {name}! Welcome, {name}!"
print(isinstance(prompt3, TextPrompt))  # >>> True
print(prompt3.key_words)                # >>> {'name'}

# Joining
prompt4 = TextPrompt(' ').join([prompt1, prompt2])
print(prompt4)
# >>> "Hello, {name}! Welcome, {name}!"
print(isinstance(prompt4, TextPrompt))  # >>> True
print(prompt4.key_words)                # >>> {'name'}

# String methods
prompt5 = prompt4.upper()
print(prompt5)
# >>> "HELLO, {NAME}! WELCOME, {NAME}!"
print(isinstance(prompt5, TextPrompt))  # >>> True
print(prompt5.key_words)                # >>> {'NAME'}

​
Supported Prompt Templates

This class defines prompt templates for the AI Society role-playing and task handling workflow.Templates include:
  • GENERATE_ASSISTANTS: List roles the AI assistant can play.
  • GENERATE_USERS: List common user groups or occupations.
  • GENERATE_TASKS: List diverse tasks for assistants.
  • TASK_SPECIFY_PROMPT: Detail a task given assistant and user roles.
  • ASSISTANT_PROMPT: Rules for assistants to complete tasks.
  • USER_PROMPT: Rules for giving instructions to assistants.
  • CRITIC_PROMPT: Criteria for critics choosing among proposals.
This class provides prompts for code-related tasks (language, domain, code task generation, and instructions for coders).Templates include:
  • GENERATE_LANGUAGES: List computer programming languages.
  • GENERATE_DOMAINS: List common programming domains.
  • GENERATE_TASKS: List tasks for programmers.
  • TASK_SPECIFY_PROMPT: Specify a programming-related task.
  • ASSISTANT_PROMPT: Rules for completing code tasks.
  • USER_PROMPT: User instructions for coding agents.
Prompts for generating questions to evaluate knowledge.Templates include:
  • GENERATE_QUESTIONS: Create question sets for evaluating knowledge emergence, with optional field-specific examples.
Prompts for generating text embedding tasks and synthetic data for embedding model improvement.Templates include:
  • GENERATE_TASKS: Generate synthetic text embedding tasks.
  • ASSISTANT_PROMPT: Generate synthetic queries (JSON), positive docs, and hard negatives.
Prompts to test model alignment by introducing misleading or jailbreak tasks.Templates include:
  • DAN_PROMPT: Do-Anything-Now jailbreak prompt.
  • GENERATE_TASKS: List unique malicious tasks.
  • TASK_SPECIFY_PROMPT: Specify a malicious task in detail.
  • ASSISTANT_PROMPT: Rules for misaligned assistant tasks.
  • USER_PROMPT: User instructions in misalignment contexts.
Prompts for object recognition tasks.Templates include:
  • ASSISTANT_PROMPT: Detect all objects in images, minimizing redundancy.
Inherits from AISocietyPromptTemplateDict and adds prompts for describing roles and responsibilities.Templates include:
  • ROLE_DESCRIPTION_PROMPT: Explain roles and responsibilities for agents.
Prompts to focus AI on finding solutions using particular knowledge.Templates include:
  • ASSISTANT_PROMPT: Rules for extracting and presenting solutions.
Prompts for translation assistants (English → target language).Templates include:
  • ASSISTANT_PROMPT: Rules for completing translation tasks.
Prompts for describing video content.Templates include:
  • ASSISTANT_PROMPT: Rules for generating video descriptions.