Kye Gomez
4 weeks ago
parent
7312393a71
commit
b3e3f68ee8
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from swarms import Agent, ConcurrentWorkflow
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from swarms_tools import coin_gecko_coin_api
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# Create specialized agents for Solana, Bitcoin, Ethereum, Cardano, and Polkadot analysis using CoinGecko API
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market_analyst_solana = Agent(
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agent_name="Market-Trend-Analyst-Solana",
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system_prompt="""You are a market trend analyst specializing in Solana (SOL).
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Analyze SOL price movements, volume patterns, and market sentiment using real-time data from the CoinGecko API.
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Focus on:
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- Technical indicators and chart patterns for Solana
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- Volume analysis and market depth for SOL
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- Short-term and medium-term trend identification
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- Support and resistance levels
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Always use the CoinGecko API tool to fetch up-to-date Solana market data for your analysis.
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Provide actionable insights based on this data.""",
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model_name="claude-sonnet-4-20250514",
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max_loops=1,
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temperature=0.2,
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tools=[coin_gecko_coin_api],
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)
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market_analyst_bitcoin = Agent(
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agent_name="Market-Trend-Analyst-Bitcoin",
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system_prompt="""You are a market trend analyst specializing in Bitcoin (BTC).
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Analyze BTC price movements, volume patterns, and market sentiment using real-time data from the CoinGecko API.
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Focus on:
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- Technical indicators and chart patterns for Bitcoin
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- Volume analysis and market depth for BTC
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- Short-term and medium-term trend identification
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- Support and resistance levels
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Always use the CoinGecko API tool to fetch up-to-date Bitcoin market data for your analysis.
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Provide actionable insights based on this data.""",
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model_name="claude-sonnet-4-20250514",
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max_loops=1,
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temperature=0.2,
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tools=[coin_gecko_coin_api],
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)
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market_analyst_ethereum = Agent(
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agent_name="Market-Trend-Analyst-Ethereum",
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system_prompt="""You are a market trend analyst specializing in Ethereum (ETH).
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Analyze ETH price movements, volume patterns, and market sentiment using real-time data from the CoinGecko API.
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Focus on:
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- Technical indicators and chart patterns for Ethereum
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- Volume analysis and market depth for ETH
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- Short-term and medium-term trend identification
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- Support and resistance levels
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Always use the CoinGecko API tool to fetch up-to-date Ethereum market data for your analysis.
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Provide actionable insights based on this data.""",
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model_name="claude-sonnet-4-20250514",
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max_loops=1,
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temperature=0.2,
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tools=[coin_gecko_coin_api],
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)
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market_analyst_cardano = Agent(
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agent_name="Market-Trend-Analyst-Cardano",
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system_prompt="""You are a market trend analyst specializing in Cardano (ADA).
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Analyze ADA price movements, volume patterns, and market sentiment using real-time data from the CoinGecko API.
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Focus on:
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- Technical indicators and chart patterns for Cardano
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- Volume analysis and market depth for ADA
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- Short-term and medium-term trend identification
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- Support and resistance levels
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Always use the CoinGecko API tool to fetch up-to-date Cardano market data for your analysis.
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Provide actionable insights based on this data.""",
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model_name="claude-sonnet-4-20250514",
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max_loops=1,
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temperature=0.2,
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tools=[coin_gecko_coin_api],
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)
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market_analyst_polkadot = Agent(
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agent_name="Market-Trend-Analyst-Polkadot",
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system_prompt="""You are a market trend analyst specializing in Polkadot (DOT).
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Analyze DOT price movements, volume patterns, and market sentiment using real-time data from the CoinGecko API.
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Focus on:
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- Technical indicators and chart patterns for Polkadot
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- Volume analysis and market depth for DOT
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- Short-term and medium-term trend identification
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- Support and resistance levels
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Always use the CoinGecko API tool to fetch up-to-date Polkadot market data for your analysis.
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Provide actionable insights based on this data.""",
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model_name="claude-sonnet-4-20250514",
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max_loops=1,
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temperature=0.2,
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tools=[coin_gecko_coin_api],
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)
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# Create concurrent workflow
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crypto_analysis_swarm = ConcurrentWorkflow(
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agents=[
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market_analyst_solana,
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market_analyst_bitcoin,
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market_analyst_ethereum,
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market_analyst_cardano,
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market_analyst_polkadot,
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],
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max_loops=1,
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)
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crypto_analysis_swarm.run(
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"Analyze your own specified coin and create a comprehensive analysis of the coin"
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)
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@ -0,0 +1,267 @@
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import time
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from typing import Dict, List
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from swarms import Agent
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from swarms.utils.litellm_tokenizer import count_tokens
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class LongFormGenerator:
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"""
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A class for generating long-form content using the swarms Agent framework.
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This class provides methods for creating comprehensive, detailed content
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with support for continuation and sectioned generation.
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"""
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def __init__(self, model: str = "claude-sonnet-4-20250514"):
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"""
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Initialize the LongFormGenerator with specified model.
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Args:
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model (str): The model to use for content generation
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"""
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self.model = model
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def estimate_tokens(self, text: str) -> int:
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"""
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Estimate token count for text.
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Args:
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text (str): The text to estimate tokens for
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Returns:
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int: Estimated token count
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"""
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return count_tokens(text=text, model=self.model)
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def create_expansion_prompt(
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self, topic: str, requirements: Dict
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) -> str:
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"""
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Create optimized prompt for long-form content.
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Args:
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topic (str): The main topic to generate content about
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requirements (Dict): Requirements for content generation
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Returns:
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str: Formatted prompt for content generation
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"""
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structure_requirements = []
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if "sections" in requirements:
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for i, section in enumerate(requirements["sections"]):
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structure_requirements.append(
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f"{i+1}. {section['title']} - {section.get('description', 'Provide comprehensive analysis')}"
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)
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length_guidance = (
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f"Target length: {requirements.get('min_words', 2000)}-{requirements.get('max_words', 4000)} words"
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if "min_words" in requirements
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else ""
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)
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prompt = f"""Create a comprehensive, detailed analysis of: {topic}
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REQUIREMENTS:
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- This is a professional-level document requiring thorough treatment
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- Each section must be substantive with detailed explanations
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- Include specific examples, case studies, and technical details where relevant
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- Provide multiple perspectives and comprehensive coverage
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- {length_guidance}
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STRUCTURE:
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{chr(10).join(structure_requirements)}
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QUALITY STANDARDS:
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- Demonstrate deep expertise and understanding
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- Include relevant technical specifications and details
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- Provide actionable insights and practical applications
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- Use professional language appropriate for expert audience
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- Ensure logical flow and comprehensive coverage of all aspects
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Begin your comprehensive analysis:"""
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return prompt
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def generate_with_continuation(
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self, topic: str, requirements: Dict, max_attempts: int = 3
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) -> str:
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"""
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Generate long-form content with continuation if needed.
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Args:
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topic (str): The main topic to generate content about
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requirements (Dict): Requirements for content generation
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max_attempts (int): Maximum number of continuation attempts
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Returns:
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str: Generated long-form content
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"""
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initial_prompt = self.create_expansion_prompt(
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topic, requirements
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)
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# Create agent for initial generation
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agent = Agent(
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name="LongForm Content Generator",
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system_prompt=initial_prompt,
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model=self.model,
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max_loops=1,
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temperature=0.7,
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max_tokens=4000,
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)
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# Generate initial response
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content = agent.run(topic)
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target_words = requirements.get("min_words", 2000)
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# Check if continuation is needed
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word_count = len(content.split())
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continuation_count = 0
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while (
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word_count < target_words
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and continuation_count < max_attempts
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):
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continuation_prompt = f"""Continue and expand the previous analysis. The current response is {word_count} words, but we need approximately {target_words} words total for comprehensive coverage.
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Please continue with additional detailed analysis, examples, and insights. Focus on areas that could benefit from deeper exploration or additional perspectives. Maintain the same professional tone and analytical depth.
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Continue the analysis:"""
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# Create continuation agent
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continuation_agent = Agent(
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name="Content Continuation Agent",
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system_prompt=continuation_prompt,
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model=self.model,
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max_loops=1,
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temperature=0.7,
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max_tokens=4000,
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)
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# Generate continuation
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continuation_content = continuation_agent.run(
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f"Continue the analysis on: {topic}"
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)
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content += "\n\n" + continuation_content
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word_count = len(content.split())
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continuation_count += 1
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# Rate limiting
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time.sleep(1)
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return content
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def generate_sectioned_content(
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self,
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topic: str,
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sections: List[Dict],
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combine_sections: bool = True,
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) -> Dict:
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"""
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Generate content section by section for maximum length.
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Args:
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topic (str): The main topic to generate content about
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sections (List[Dict]): List of section definitions
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combine_sections (bool): Whether to combine all sections into one document
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Returns:
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Dict: Dictionary containing individual sections and optionally combined content
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"""
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results = {}
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combined_content = ""
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for section in sections:
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section_prompt = f"""Write a comprehensive, detailed section on: {section['title']}
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Context: This is part of a larger analysis on {topic}
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Requirements for this section:
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- Provide {section.get('target_words', 500)}-{section.get('max_words', 800)} words of detailed content
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- {section.get('description', 'Provide thorough analysis with examples and insights')}
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- Include specific examples, technical details, and practical applications
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- Use professional language suitable for expert audience
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- Ensure comprehensive coverage of all relevant aspects
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Write the complete section:"""
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# Create agent for this section
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section_agent = Agent(
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name=f"Section Generator - {section['title']}",
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system_prompt=section_prompt,
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model=self.model,
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max_loops=1,
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temperature=0.7,
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max_tokens=3000,
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)
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# Generate section content
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section_content = section_agent.run(
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f"Generate section: {section['title']} for topic: {topic}"
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)
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results[section["title"]] = section_content
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if combine_sections:
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combined_content += (
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f"\n\n## {section['title']}\n\n{section_content}"
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)
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# Rate limiting between sections
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time.sleep(1)
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if combine_sections:
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results["combined"] = combined_content.strip()
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return results
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# Example usage
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if __name__ == "__main__":
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# Initialize the generator
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generator = LongFormGenerator()
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# Example topic and requirements
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topic = "Artificial Intelligence in Healthcare"
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requirements = {
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"min_words": 2500,
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"max_words": 4000,
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"sections": [
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{
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"title": "Current Applications",
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"description": "Analyze current AI applications in healthcare",
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"target_words": 600,
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"max_words": 800,
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},
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{
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"title": "Future Prospects",
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"description": "Discuss future developments and potential",
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"target_words": 500,
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"max_words": 700,
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},
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],
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}
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# Generate comprehensive content
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content = generator.generate_with_continuation(
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topic, requirements
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)
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print("Generated Content:")
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print(content)
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print(f"\nWord count: {len(content.split())}")
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# Generate sectioned content
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sections = [
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{
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"title": "AI in Medical Imaging",
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"description": "Comprehensive analysis of AI applications in medical imaging",
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"target_words": 500,
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"max_words": 700,
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},
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{
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"title": "AI in Drug Discovery",
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"description": "Detailed examination of AI in pharmaceutical research",
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"target_words": 600,
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"max_words": 800,
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},
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]
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sectioned_results = generator.generate_sectioned_content(
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topic, sections
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)
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print("\nSectioned Content:")
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for section_title, section_content in sectioned_results.items():
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if section_title != "combined":
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print(f"\n--- {section_title} ---")
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print(section_content[:200] + "...")
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@ -0,0 +1,29 @@
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from swarms import Agent
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def generate_comprehensive_content(topic, sections):
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prompt = f"""You are tasked with creating a comprehensive, detailed analysis of {topic}.
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This should be a thorough, professional-level document suitable for expert review.
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Structure your response with the following sections, ensuring each is substantive and detailed:
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{chr(10).join([f"{i+1}. {section} - Provide extensive detail with examples and analysis" for i, section in enumerate(sections)])}
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For each section:
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- Include multiple subsections where appropriate
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- Provide specific examples and case studies
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- Offer detailed explanations of complex concepts
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- Include relevant technical details and specifications
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- Discuss implications and considerations thoroughly
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Aim for comprehensive coverage that demonstrates deep expertise. This is a professional document that should be thorough and substantive throughout."""
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agent = Agent(
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name="Comprehensive Content Generator",
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system_prompt=prompt,
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model="claude-sonnet-4-20250514",
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max_loops=1,
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temperature=0.5,
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max_tokens=4000,
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)
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return agent.run(topic)
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