Prompts

Overview

The swarms.prompts module provides tools for creating, managing, and versioning prompts with comprehensive history tracking, autosaving capabilities, and integration with agent systems.

Prompt Class

A powerful class for managing prompts with version control, edit history, and autosaving features.

from swarms.prompts import Prompt

prompt = Prompt(
    name="analysis_prompt",
    description="Financial analysis prompt",
    content="Analyze the following financial data...",
    autosave=True,
    autosave_folder="prompts"
)

Constructor

str

default:"uuid.uuid4().hex"

Unique identifier for the prompt

str

default:"prompt"

Name of your prompt

str

default:"Simple Prompt"

Description of the prompt

str

required

The main content of the prompt (minimum 1 character)

str

default:"current_time"

Timestamp when the prompt was created

str

default:"current_time"

Timestamp when the prompt was last modified

int

default:"0"

Number of times the prompt has been edited

List[str]

default:"[]"

History of all prompt versions

bool

default:"False"

Enable automatic saving of the prompt

str

default:"prompts"

Folder path within WORKSPACE_DIR for autosaving

bool

default:"False"

Enable auto-generation of prompts

str

default:"workspace_dir"

Parent folder for the autosave folder

Any

default:"None"

Optional LLM instance for prompt generation

Methods

edit_prompt

Edit the prompt content and update version control.

prompt.edit_prompt("Updated prompt content here")

str

required

The updated content of the prompt

Raises:

ValueError: If the new content is identical to the current content

Thread-safe: This method is safe for concurrent access.

rollback

Roll back the prompt to a previous version.

# Rollback to the first version
prompt.rollback(version=0)

int

required

The version index to roll back to (0 is the first version)

Raises:

IndexError: If the version number is out of range

get_prompt

Return the current prompt content as a string.

content = prompt.get_prompt()
print(content)

str

The current prompt content

return_json

Return the prompt as a JSON string.

json_data = prompt.return_json()
print(json_data)

str

JSON representation of the prompt with all metadata

add_tools

Add tool schemas to the prompt content.

from swarms.tools import BaseTool

def my_tool(x: int) -> int:
    """Double the input."""
    return x * 2

prompt.add_tools([my_tool])

List[Callable]

required

List of callable functions to add as tools

Pre-built Prompt Templates

The module includes numerous pre-built prompt templates for common agent roles:

Finance Prompts

FINANCE_AGENT_PROMPT

Comprehensive prompt for financial analysis agents.

from swarms.prompts import FINANCE_AGENT_PROMPT

agent = Agent(
    agent_name="Financial-Analyst",
    system_prompt=FINANCE_AGENT_PROMPT,
    # ... other config
)

Legal Prompts

LEGAL_AGENT_PROMPT

Prompt template for legal analysis and research agents.

from swarms.prompts import LEGAL_AGENT_PROMPT

agent = Agent(
    agent_name="Legal-Advisor",
    system_prompt=LEGAL_AGENT_PROMPT,
    # ... other config
)

Product & Operations

PRODUCT_AGENT_PROMPT

Prompt for product management agents.

from swarms.prompts import PRODUCT_AGENT_PROMPT

OPERATIONS_AGENT_PROMPT

Prompt for operations management agents.

from swarms.prompts import OPERATIONS_AGENT_PROMPT

GROWTH_AGENT_PROMPT

Prompt for growth and marketing agents.

from swarms.prompts import GROWTH_AGENT_PROMPT

Development Prompts

CODE_INTERPRETER

Prompt for code interpretation and execution agents.

from swarms.prompts import CODE_INTERPRETER

agent = Agent(
    agent_name="Code-Interpreter",
    system_prompt=CODE_INTERPRETER,
    # ... other config
)

DOCUMENTATION_WRITER_SOP

Standard operating procedure for documentation writing agents.

from swarms.prompts import DOCUMENTATION_WRITER_SOP

Autonomous Agent Prompts

AUTONOMOUS_AGENT_SYSTEM_PROMPT

Base system prompt for autonomous agents.

from swarms.prompts import AUTONOMOUS_AGENT_SYSTEM_PROMPT

get_autonomous_agent_prompt

Generate an autonomous agent prompt.

from swarms.prompts import get_autonomous_agent_prompt

prompt = get_autonomous_agent_prompt()

get_autonomous_agent_prompt_with_context

Generate an autonomous agent prompt with additional context.

from swarms.prompts import get_autonomous_agent_prompt_with_context

prompt = get_autonomous_agent_prompt_with_context(
    context="Analyze financial markets with focus on tech stocks"
)

Example: Complete Prompt Management

from swarms.prompts import Prompt
from swarms.tools import BaseTool
import os

# Set workspace directory
os.environ["WORKSPACE_DIR"] = "./workspace"

# Create a prompt with autosave
prompt = Prompt(
    name="market_analysis",
    description="Financial market analysis prompt",
    content="""You are a financial analyst. Analyze the following:
    1. Market trends
    2. Risk factors
    3. Investment opportunities
    """,
    autosave=True,
    autosave_folder="financial_prompts"
)

# Edit the prompt
prompt.edit_prompt("""
You are an expert financial analyst specializing in equity markets.

Analyze the following aspects:
1. Current market trends and momentum
2. Key risk factors and hedging strategies
3. High-conviction investment opportunities
4. Portfolio allocation recommendations

Provide detailed analysis with supporting data.
""")

print(f"Edit count: {prompt.edit_count}")  # 1
print(f"Total versions: {len(prompt.edit_history)}")  # 2

# Add tools to the prompt
def calculate_sharpe_ratio(returns: float, risk_free_rate: float, std_dev: float) -> float:
    """Calculate Sharpe ratio for investment analysis."""
    return (returns - risk_free_rate) / std_dev

prompt.add_tools([calculate_sharpe_ratio])

# Rollback if needed
prompt.rollback(version=0)  # Back to original version

# Get the current prompt
current = prompt.get_prompt()
print(current)

# Export as JSON
json_export = prompt.return_json()
print(json_export)

Best Practices

Enable autosave: Always enable autosave for important prompts to prevent data loss
Use descriptive names: Give prompts clear, descriptive names for easy identification
Version control: Leverage the built-in version control to track prompt evolution
Add context: Include detailed descriptions to document the prompt’s purpose
Organize by folder: Use the autosave_folder parameter to organize prompts by category
Regular rollbacks: Test different prompt versions using rollback functionality
Tool integration: Use add_tools() to seamlessly integrate function calling

Thread Safety

The Prompt class implements thread-safe operations for:

Edit operations
Rollback operations
Autosaving

This makes it safe to use in concurrent environments and multi-agent systems.

Autosave Behavior

When autosave is enabled:

Prompts are saved to {WORKSPACE_DIR}/{autosave_folder}/prompt-id-{id}.json
Each edit automatically triggers a save
Rollback operations also trigger saves
The entire prompt state (including history) is preserved

Integration with Agents

from swarms import Agent
from swarms.prompts import Prompt, FINANCE_AGENT_PROMPT

# Using pre-built prompts
agent1 = Agent(
    agent_name="Financial-Analyst",
    system_prompt=FINANCE_AGENT_PROMPT,
    model_name="gpt-4"
)

# Using custom Prompt class
custom_prompt = Prompt(
    name="custom_analyst",
    content="You are a specialized quantitative analyst...",
    autosave=True
)

agent2 = Agent(
    agent_name="Quant-Analyst",
    system_prompt=custom_prompt.get_prompt(),
    model_name="gpt-4"
)

Core Classes

Multi-Agent Harnesses

Tools & Utilities

Deployment

Overview

Prompt Class

Constructor

Methods

edit_prompt

rollback

get_prompt

return_json

add_tools

Pre-built Prompt Templates

Finance Prompts

FINANCE_AGENT_PROMPT

Legal Prompts

LEGAL_AGENT_PROMPT

Product & Operations

PRODUCT_AGENT_PROMPT

OPERATIONS_AGENT_PROMPT

GROWTH_AGENT_PROMPT

Development Prompts

CODE_INTERPRETER

DOCUMENTATION_WRITER_SOP

Autonomous Agent Prompts

AUTONOMOUS_AGENT_SYSTEM_PROMPT

get_autonomous_agent_prompt

get_autonomous_agent_prompt_with_context

Example: Complete Prompt Management

Best Practices

Thread Safety

Autosave Behavior

Integration with Agents

​Overview

​Prompt Class

​Constructor

​Methods

​edit_prompt

​rollback

​get_prompt

​return_json

​add_tools

​Pre-built Prompt Templates

​Finance Prompts

​FINANCE_AGENT_PROMPT

​Legal Prompts

​LEGAL_AGENT_PROMPT

​Product & Operations

​PRODUCT_AGENT_PROMPT

​OPERATIONS_AGENT_PROMPT

​GROWTH_AGENT_PROMPT

​Development Prompts

​CODE_INTERPRETER

​DOCUMENTATION_WRITER_SOP

​Autonomous Agent Prompts

​AUTONOMOUS_AGENT_SYSTEM_PROMPT

​get_autonomous_agent_prompt

​get_autonomous_agent_prompt_with_context

​Example: Complete Prompt Management

​Best Practices

​Thread Safety

​Autosave Behavior

​Integration with Agents

Overview

Prompt Class

Constructor

Methods

edit_prompt

rollback

get_prompt

return_json

add_tools

Pre-built Prompt Templates

Finance Prompts

FINANCE_AGENT_PROMPT

Legal Prompts

LEGAL_AGENT_PROMPT

Product & Operations

PRODUCT_AGENT_PROMPT

OPERATIONS_AGENT_PROMPT

GROWTH_AGENT_PROMPT

Development Prompts

CODE_INTERPRETER

DOCUMENTATION_WRITER_SOP

Autonomous Agent Prompts

AUTONOMOUS_AGENT_SYSTEM_PROMPT

get_autonomous_agent_prompt

get_autonomous_agent_prompt_with_context

Example: Complete Prompt Management

Best Practices

Thread Safety

Autosave Behavior

Integration with Agents