Documentation Index
Fetch the complete documentation index at: https://docs.swarms.world/llms.txt
Use this file to discover all available pages before exploring further.
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
id
str
default:"uuid.uuid4().hex"
Unique identifier for the prompt
description
str
default:"Simple Prompt"
Description of the prompt
The main content of the prompt (minimum 1 character)
created_at
str
default:"current_time"
Timestamp when the prompt was created
last_modified_at
str
default:"current_time"
Timestamp when the prompt was last modified
Number of times the prompt has been edited
History of all prompt versions
Enable automatic saving of the prompt
Folder path within WORKSPACE_DIR for autosaving
Enable auto-generation of prompts
parent_folder
str
default:"workspace_dir"
Parent folder for the autosave folder
Optional LLM instance for prompt generation
Methods
edit_prompt
Edit the prompt content and update version control.
prompt.edit_prompt("Updated prompt content here")
The updated content of the prompt
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)
The version index to roll back to (0 is the first version)
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)
The current prompt content
return_json
Return the prompt as a JSON string.
json_data = prompt.return_json()
print(json_data)
JSON representation of the prompt with all metadata
from swarms.tools import BaseTool
def my_tool(x: int) -> int:
"""Double the input."""
return x * 2
prompt.add_tools([my_tool])
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"
)
