Prompts

from swarms import Prompt

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

prompt.edit_prompt("Updated prompt content here")

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

content = prompt.get_prompt()
print(content)

json_data = prompt.return_json()
print(json_data)

from swarms import FINANCE_AGENT_PROMPT, Agent

agent = Agent(
    agent_name="Financial-Analyst",
    system_prompt=FINANCE_AGENT_PROMPT,
    model_name="gpt-5.4",
)

from swarms import LEGAL_AGENT_PROMPT, Agent

agent = Agent(
    agent_name="Legal-Advisor",
    system_prompt=LEGAL_AGENT_PROMPT,
    model_name="gpt-5.4",
)

from swarms import PRODUCT_AGENT_PROMPT

from swarms import OPERATIONS_AGENT_PROMPT

from swarms import GROWTH_AGENT_PROMPT

from swarms import CODE_INTERPRETER, Agent

agent = Agent(
    agent_name="Code-Interpreter",
    system_prompt=CODE_INTERPRETER,
    model_name="gpt-5.4",
)

from swarms import DOCUMENTATION_WRITER_SOP

from swarms import AUTO_AGENT_BUILDER_SYSTEM_PROMPT

from swarms import autonomous_agent_system_prompt

prompt = autonomous_agent_system_prompt()

from swarms import get_autonomous_agent_prompt

prompt = get_autonomous_agent_prompt(include_think_tool=True)

from swarms import get_autonomous_agent_prompt_with_context

prompt = get_autonomous_agent_prompt_with_context(
    agent_name="MarketAnalyst",
    agent_description="Analyzes financial markets with a focus on tech stocks",
    available_tools=["search_web", "get_stock_price"],
)

from swarms.prompts.agent_system_prompts import build_agent_system_prompt

prompt = build_agent_system_prompt()

from swarms.prompts.llm_council_prompts import get_evaluation_prompt

prompt = get_evaluation_prompt(
    query="Which database should we use?",
    responses={"A": "Postgres, because...", "B": "SQLite, because..."},
    evaluator_name="Claude-Councilor",
)

import json
import os
import uuid

# Set before importing swarms, which resolves WORKSPACE_DIR on import
os.environ["WORKSPACE_DIR"] = "./workspace"

from swarms import BaseTool, Prompt

# Create a prompt with autosave
prompt = Prompt(
    id=uuid.uuid4().hex,
    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

# Append a tool schema to the prompt
def calculate_sharpe_ratio(returns: float, risk_free_rate: float, std_dev: float) -> float:
    """Calculate Sharpe ratio for investment analysis.

    Args:
        returns: Portfolio return.
        risk_free_rate: Risk-free rate.
        std_dev: Standard deviation of returns.

    Returns:
        The Sharpe ratio.
    """
    return (returns - risk_free_rate) / std_dev

schema = BaseTool(tools=[calculate_sharpe_ratio]).convert_tool_into_openai_schema()
prompt.edit_prompt(prompt.get_prompt() + "\n\n" + json.dumps(schema, indent=2))

# 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)

from swarms import FINANCE_AGENT_PROMPT, Agent, Prompt

# Using pre-built prompts
agent1 = Agent(
    agent_name="Financial-Analyst",
    system_prompt=FINANCE_AGENT_PROMPT,
    model_name="gpt-5.4"
)

# 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-5.4"
)