Kye Gomez
4 months ago
parent
506d11601c
commit
311d5451b1
@ -1,36 +0,0 @@
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from swarms.structs.agent import Agent
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from swarms.structs.groupchat import GroupChat
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if __name__ == "__main__":
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# Example agents
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agent1 = Agent(
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agent_name="Financial-Analysis-Agent",
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system_prompt="You are a financial analyst specializing in investment strategies.",
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model_name="gpt-4o",
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max_loops=1,
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dynamic_temperature_enabled=True,
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)
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agent2 = Agent(
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agent_name="Tax-Adviser-Agent",
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system_prompt="You are a tax adviser who provides clear and concise guidance on tax-related queries.",
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model_name="gpt-4o",
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max_loops=1,
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dynamic_temperature_enabled=True,
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)
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agents = [agent1, agent2]
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chat = GroupChat(
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name="Investment Advisory",
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description="Financial and tax analysis group",
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agents=agents,
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max_loops=1,
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)
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history = chat.run(
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"How to optimize tax strategy for investments?"
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)
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print(history)
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import asyncio
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from swarms import Agent
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from dotenv import load_dotenv
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from swarms_tools import coin_gecko_coin_api
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load_dotenv()
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CRYPTO_ANALYST_SYSTEM_PROMPT = """
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You are an expert cryptocurrency financial analyst with deep expertise in:
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1. Technical Analysis
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- Chart patterns and indicators (RSI, MACD, Bollinger Bands)
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- Volume analysis and market momentum
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- Support and resistance levels
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- Trend analysis and price action
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2. Fundamental Analysis
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- Tokenomics evaluation
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- Network metrics (TVL, daily active users, transaction volume)
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- Protocol revenue and growth metrics
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- Market capitalization analysis
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- Token utility and use cases
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3. Market Analysis
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- Market sentiment analysis
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- Correlation with broader crypto market
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- Impact of macro events
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- Institutional adoption metrics
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- DeFi and NFT market analysis
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4. Risk Assessment
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- Volatility metrics
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- Liquidity analysis
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- Smart contract risks
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- Regulatory considerations
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- Exchange exposure risks
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5. Data Analysis Methods
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- On-chain metrics analysis
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- Whale wallet tracking
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- Exchange inflow/outflow
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- Mining/Staking statistics
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- Network health indicators
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When analyzing crypto assets, always:
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1. Start with a comprehensive market overview
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2. Examine both on-chain and off-chain metrics
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3. Consider multiple timeframes (short, medium, long-term)
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4. Evaluate risk-reward ratios
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5. Assess market sentiment and momentum
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6. Consider regulatory and security factors
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7. Analyze correlations with BTC, ETH, and traditional markets
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8. Examine liquidity and volume profiles
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9. Review recent protocol developments and updates
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10. Consider macro economic factors
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Format your analysis with:
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- Clear section headings
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- Relevant metrics and data points
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- Risk warnings and disclaimers
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- Price action analysis
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- Market sentiment summary
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- Technical indicators
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- Fundamental factors
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- Clear recommendations with rationale
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Remember to:
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- Always provide data-driven insights
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- Include both bullish and bearish scenarios
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- Highlight key risk factors
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- Consider market cycles and seasonality
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- Maintain objectivity in analysis
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- Cite sources for data and claims
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- Update analysis based on new market conditions
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"""
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# Initialize multiple crypto analysis agents with different specialties
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technical_analyst = Agent(
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agent_name="Technical-Analyst",
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agent_description="Expert in technical analysis and chart patterns",
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system_prompt=CRYPTO_ANALYST_SYSTEM_PROMPT,
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max_loops=1,
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model_name="gpt-4o",
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dynamic_temperature_enabled=True,
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user_name="tech_analyst",
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output_type="str",
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)
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# List of coins to analyze
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coins = ["solana", "raydium", "aixbt", "jupiter"]
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# Dictionary to store analyses
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coin_analyses = {}
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async def analyze_coin(coin, technical_analyst):
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print(f"\n=== Technical Analysis for {coin.upper()} ===\n")
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# Fetch market data
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gecko_data = coin_gecko_coin_api(coin)
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# Get technical analysis
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analysis = await technical_analyst.arun(
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f"""Analyze {coin}'s technical indicators and price action using this data:
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CoinGecko Data: {gecko_data}
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Focus on:
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- Chart patterns and trends
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- Support/resistance levels
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- Momentum indicators
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- Price targets and risk levels
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- Overall technical strength rating (1-10)
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End with a clear technical strength score out of 10.
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"""
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)
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return coin, analysis
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async def main():
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# Create tasks for concurrent execution
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tasks = [analyze_coin(coin, technical_analyst) for coin in coins]
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# Execute all analyses concurrently
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results = await asyncio.gather(*tasks)
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# Store results in coin_analyses
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for coin, analysis in results:
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coin_analyses[coin] = analysis
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# Have technical analyst compare and recommend best investment
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consensus = await technical_analyst.arun(
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f"""Based on your technical analysis of these coins:
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Solana Analysis:
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{coin_analyses['solana']}
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Raydium Analysis:
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{coin_analyses['raydium']}
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Jupiter Analysis:
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{coin_analyses['jupiter']}
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AIXBT Analysis:
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{coin_analyses['aixbt']}
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Please:
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1. Rank the coins from strongest to weakest technical setup
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2. Identify which coin has the best risk/reward ratio
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3. Make a clear recommendation on which coin is the best investment opportunity and why
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4. Note any key risks or concerns with the recommended coin
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"""
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)
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return consensus
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# Run the async main function
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consensus = asyncio.run(main())
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@ -0,0 +1,38 @@
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from typing import Literal
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agent_roles = Literal[
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"supervisor",
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"director",
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"boss",
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"generator",
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"evaluator",
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"revisor",
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"worker",
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"manager",
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"team-leader",
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"team-manager",
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"team-lead",
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"researcher",
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"analyst",
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"architect",
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"developer",
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"tester",
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"qa",
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"designer",
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"product-owner",
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"scrum-master",
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"coordinator",
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"mentor",
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"trainer",
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"consultant",
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"specialist",
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"expert",
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"strategist",
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"planner",
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"executor",
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"reviewer",
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"auditor",
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]
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# print(agent_roles)
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@ -0,0 +1,580 @@
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"""
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TalkHier: A hierarchical multi-agent framework for content generation and refinement.
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Implements structured communication and evaluation protocols.
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"""
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import json
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import logging
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from dataclasses import dataclass
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from datetime import datetime
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from enum import Enum
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Union
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from swarms import Agent
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from swarms.structs.conversation import Conversation
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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class AgentRole(Enum):
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"""Defines the possible roles for agents in the system."""
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SUPERVISOR = "supervisor"
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GENERATOR = "generator"
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EVALUATOR = "evaluator"
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REVISOR = "revisor"
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@dataclass
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class CommunicationEvent:
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"""Represents a structured communication event between agents."""
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message: str
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background: Optional[str] = None
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intermediate_output: Optional[Dict[str, Any]] = None
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class TalkHier:
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"""
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A hierarchical multi-agent system for content generation and refinement.
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Implements the TalkHier framework with structured communication protocols
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and hierarchical refinement processes.
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Attributes:
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max_iterations: Maximum number of refinement iterations
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quality_threshold: Minimum score required for content acceptance
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model_name: Name of the LLM model to use
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base_path: Path for saving agent states
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"""
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def __init__(
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self,
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max_iterations: int = 3,
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quality_threshold: float = 0.8,
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model_name: str = "gpt-4",
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base_path: Optional[str] = None,
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return_string: bool = False,
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):
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"""Initialize the TalkHier system."""
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self.max_iterations = max_iterations
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self.quality_threshold = quality_threshold
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self.model_name = model_name
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self.return_string = return_string
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self.base_path = (
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Path(base_path) if base_path else Path("./agent_states")
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)
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self.base_path.mkdir(exist_ok=True)
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# Initialize agents
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self._init_agents()
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# Create conversation
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self.conversation = Conversation()
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def _safely_parse_json(self, json_str: str) -> Dict[str, Any]:
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"""
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Safely parse JSON string, handling various formats and potential errors.
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Args:
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json_str: String containing JSON data
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Returns:
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Parsed dictionary
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"""
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try:
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# Try direct JSON parsing
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return json.loads(json_str)
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except json.JSONDecodeError:
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try:
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# Try extracting JSON from potential text wrapper
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import re
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json_match = re.search(r"\{.*\}", json_str, re.DOTALL)
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if json_match:
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return json.loads(json_match.group())
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# Try extracting from markdown code blocks
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code_block_match = re.search(
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r"```(?:json)?\s*(\{.*?\})\s*```",
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json_str,
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re.DOTALL,
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)
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if code_block_match:
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return json.loads(code_block_match.group(1))
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except Exception as e:
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logger.warning(f"Failed to extract JSON: {str(e)}")
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# Fallback: create structured dict from text
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return {
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"content": json_str,
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"metadata": {
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"parsed": False,
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"timestamp": str(datetime.now()),
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},
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}
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def _init_agents(self) -> None:
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"""Initialize all agents with their specific roles and prompts."""
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# Main supervisor agent
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self.main_supervisor = Agent(
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agent_name="Main-Supervisor",
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system_prompt=self._get_supervisor_prompt(),
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model_name=self.model_name,
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max_loops=1,
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saved_state_path=str(
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self.base_path / "main_supervisor.json"
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),
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verbose=True,
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)
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# Generator agent
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self.generator = Agent(
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agent_name="Content-Generator",
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system_prompt=self._get_generator_prompt(),
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model_name=self.model_name,
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max_loops=1,
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saved_state_path=str(self.base_path / "generator.json"),
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verbose=True,
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)
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# Evaluators
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self.evaluators = [
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Agent(
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agent_name=f"Evaluator-{i}",
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system_prompt=self._get_evaluator_prompt(i),
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model_name=self.model_name,
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max_loops=1,
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saved_state_path=str(
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self.base_path / f"evaluator_{i}.json"
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),
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verbose=True,
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)
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for i in range(3)
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]
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# Revisor agent
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self.revisor = Agent(
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agent_name="Content-Revisor",
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system_prompt=self._get_revisor_prompt(),
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model_name=self.model_name,
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max_loops=1,
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saved_state_path=str(self.base_path / "revisor.json"),
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verbose=True,
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)
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def _get_supervisor_prompt(self) -> str:
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"""Get the prompt for the supervisor agent."""
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return """You are a Supervisor agent responsible for orchestrating the content generation process. Your role is to analyze tasks, develop strategies, and coordinate other agents effectively.
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You must carefully analyze each task to understand:
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- The core objectives and requirements
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- Target audience and their needs
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- Complexity level and scope
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- Any constraints or special considerations
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Based on your analysis, develop a clear strategy that:
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- Breaks down the task into manageable steps
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- Identifies which agents are best suited for each step
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- Anticipates potential challenges
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- Sets clear success criteria
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Output all responses in strict JSON format:
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{
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"thoughts": {
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"task_analysis": "Detailed analysis of requirements, audience, scope, and constraints",
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"strategy": "Step-by-step plan including agent allocation and success metrics",
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"concerns": "Potential challenges, edge cases, and mitigation strategies"
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},
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"next_action": {
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"agent": "Specific agent to engage (Generator, Evaluator, or Revisor)",
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"instruction": "Detailed instructions including context, requirements, and expected output"
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}
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}"""
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def _get_generator_prompt(self) -> str:
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"""Get the prompt for the generator agent."""
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||||||
|
return """You are a Generator agent responsible for creating high-quality, original content. Your role is to produce content that is engaging, informative, and tailored to the target audience.
|
||||||
|
|
||||||
|
When generating content:
|
||||||
|
- Thoroughly research and fact-check all information
|
||||||
|
- Structure content logically with clear flow
|
||||||
|
- Use appropriate tone and language for the target audience
|
||||||
|
- Include relevant examples and explanations
|
||||||
|
- Ensure content is original and plagiarism-free
|
||||||
|
- Consider SEO best practices where applicable
|
||||||
|
|
||||||
|
Output all responses in strict JSON format:
|
||||||
|
{
|
||||||
|
"content": {
|
||||||
|
"main_body": "The complete generated content with proper formatting and structure",
|
||||||
|
"metadata": {
|
||||||
|
"word_count": "Accurate word count of main body",
|
||||||
|
"target_audience": "Detailed audience description",
|
||||||
|
"key_points": ["List of main points covered"],
|
||||||
|
"sources": ["List of reference sources if applicable"],
|
||||||
|
"readability_level": "Estimated reading level",
|
||||||
|
"tone": "Description of content tone"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}"""
|
||||||
|
|
||||||
|
def _get_evaluator_prompt(self, evaluator_id: int) -> str:
|
||||||
|
"""Get the prompt for an evaluator agent."""
|
||||||
|
return f"""You are Evaluator {evaluator_id}, responsible for critically assessing content quality. Your evaluation must be thorough, objective, and constructive.
|
||||||
|
|
||||||
|
Evaluate content across multiple dimensions:
|
||||||
|
- Accuracy: factual correctness, source reliability
|
||||||
|
- Clarity: readability, organization, flow
|
||||||
|
- Coherence: logical consistency, argument structure
|
||||||
|
- Engagement: interest level, relevance
|
||||||
|
- Completeness: topic coverage, depth
|
||||||
|
- Technical quality: grammar, spelling, formatting
|
||||||
|
- Audience alignment: appropriate level and tone
|
||||||
|
|
||||||
|
Output all responses in strict JSON format:
|
||||||
|
{{
|
||||||
|
"scores": {{
|
||||||
|
"overall": "0.0-1.0 composite score",
|
||||||
|
"categories": {{
|
||||||
|
"accuracy": "0.0-1.0 score with evidence",
|
||||||
|
"clarity": "0.0-1.0 score with examples",
|
||||||
|
"coherence": "0.0-1.0 score with analysis",
|
||||||
|
"engagement": "0.0-1.0 score with justification",
|
||||||
|
"completeness": "0.0-1.0 score with gaps identified",
|
||||||
|
"technical_quality": "0.0-1.0 score with issues noted",
|
||||||
|
"audience_alignment": "0.0-1.0 score with reasoning"
|
||||||
|
}}
|
||||||
|
}},
|
||||||
|
"feedback": [
|
||||||
|
"Specific, actionable improvement suggestions",
|
||||||
|
"Examples of issues found",
|
||||||
|
"Recommendations for enhancement"
|
||||||
|
],
|
||||||
|
"strengths": ["Notable positive aspects"],
|
||||||
|
"weaknesses": ["Areas needing improvement"]
|
||||||
|
}}"""
|
||||||
|
|
||||||
|
def _get_revisor_prompt(self) -> str:
|
||||||
|
"""Get the prompt for the revisor agent."""
|
||||||
|
return """You are a Revisor agent responsible for improving content based on evaluator feedback. Your role is to enhance content while maintaining its core message and purpose.
|
||||||
|
|
||||||
|
When revising content:
|
||||||
|
- Address all evaluator feedback systematically
|
||||||
|
- Maintain consistency in tone and style
|
||||||
|
- Preserve accurate information
|
||||||
|
- Enhance clarity and flow
|
||||||
|
- Fix technical issues
|
||||||
|
- Optimize for target audience
|
||||||
|
- Track all changes made
|
||||||
|
|
||||||
|
Output all responses in strict JSON format:
|
||||||
|
{
|
||||||
|
"revised_content": {
|
||||||
|
"main_body": "Complete revised content incorporating all improvements",
|
||||||
|
"metadata": {
|
||||||
|
"word_count": "Updated word count",
|
||||||
|
"changes_made": [
|
||||||
|
"Detailed list of specific changes and improvements",
|
||||||
|
"Reasoning for each major revision",
|
||||||
|
"Feedback points addressed"
|
||||||
|
],
|
||||||
|
"improvement_summary": "Overview of main enhancements",
|
||||||
|
"preserved_elements": ["Key elements maintained from original"],
|
||||||
|
"revision_approach": "Strategy used for revisions"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}"""
|
||||||
|
|
||||||
|
def _evaluate_content(
|
||||||
|
self, content: Union[str, Dict]
|
||||||
|
) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
Coordinate the evaluation of content across multiple evaluators.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
content: Content to evaluate (string or dict)
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Combined evaluation results
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
# Ensure content is in correct format
|
||||||
|
content_dict = (
|
||||||
|
self._safely_parse_json(content)
|
||||||
|
if isinstance(content, str)
|
||||||
|
else content
|
||||||
|
)
|
||||||
|
|
||||||
|
# Collect evaluations
|
||||||
|
evaluations = []
|
||||||
|
for evaluator in self.evaluators:
|
||||||
|
try:
|
||||||
|
eval_response = evaluator.run(
|
||||||
|
json.dumps(content_dict)
|
||||||
|
)
|
||||||
|
|
||||||
|
self.conversation.add(
|
||||||
|
role=evaluator.agent_name,
|
||||||
|
content=eval_response,
|
||||||
|
)
|
||||||
|
|
||||||
|
eval_data = self._safely_parse_json(eval_response)
|
||||||
|
evaluations.append(eval_data)
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f"Evaluator error: {str(e)}")
|
||||||
|
evaluations.append(
|
||||||
|
self._get_fallback_evaluation()
|
||||||
|
)
|
||||||
|
|
||||||
|
# Aggregate results
|
||||||
|
return self._aggregate_evaluations(evaluations)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"Evaluation error: {str(e)}")
|
||||||
|
return self._get_fallback_evaluation()
|
||||||
|
|
||||||
|
def _get_fallback_evaluation(self) -> Dict[str, Any]:
|
||||||
|
"""Get a safe fallback evaluation result."""
|
||||||
|
return {
|
||||||
|
"scores": {
|
||||||
|
"overall": 0.5,
|
||||||
|
"categories": {
|
||||||
|
"accuracy": 0.5,
|
||||||
|
"clarity": 0.5,
|
||||||
|
"coherence": 0.5,
|
||||||
|
},
|
||||||
|
},
|
||||||
|
"feedback": ["Evaluation failed"],
|
||||||
|
"metadata": {
|
||||||
|
"timestamp": str(datetime.now()),
|
||||||
|
"is_fallback": True,
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
def _aggregate_evaluations(
|
||||||
|
self, evaluations: List[Dict[str, Any]]
|
||||||
|
) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
Aggregate multiple evaluation results into a single evaluation.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
evaluations: List of evaluation results
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Combined evaluation
|
||||||
|
"""
|
||||||
|
# Calculate average scores
|
||||||
|
overall_scores = []
|
||||||
|
accuracy_scores = []
|
||||||
|
clarity_scores = []
|
||||||
|
coherence_scores = []
|
||||||
|
all_feedback = []
|
||||||
|
|
||||||
|
for eval_data in evaluations:
|
||||||
|
try:
|
||||||
|
scores = eval_data.get("scores", {})
|
||||||
|
overall_scores.append(scores.get("overall", 0.5))
|
||||||
|
|
||||||
|
categories = scores.get("categories", {})
|
||||||
|
accuracy_scores.append(
|
||||||
|
categories.get("accuracy", 0.5)
|
||||||
|
)
|
||||||
|
clarity_scores.append(categories.get("clarity", 0.5))
|
||||||
|
coherence_scores.append(
|
||||||
|
categories.get("coherence", 0.5)
|
||||||
|
)
|
||||||
|
|
||||||
|
all_feedback.extend(eval_data.get("feedback", []))
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(
|
||||||
|
f"Error aggregating evaluation: {str(e)}"
|
||||||
|
)
|
||||||
|
|
||||||
|
def safe_mean(scores: List[float]) -> float:
|
||||||
|
return sum(scores) / len(scores) if scores else 0.5
|
||||||
|
|
||||||
|
return {
|
||||||
|
"scores": {
|
||||||
|
"overall": safe_mean(overall_scores),
|
||||||
|
"categories": {
|
||||||
|
"accuracy": safe_mean(accuracy_scores),
|
||||||
|
"clarity": safe_mean(clarity_scores),
|
||||||
|
"coherence": safe_mean(coherence_scores),
|
||||||
|
},
|
||||||
|
},
|
||||||
|
"feedback": list(set(all_feedback)), # Remove duplicates
|
||||||
|
"metadata": {
|
||||||
|
"evaluator_count": len(evaluations),
|
||||||
|
"timestamp": str(datetime.now()),
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
def run(self, task: str) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
Generate and iteratively refine content based on the given task.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
task: Content generation task description
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Dictionary containing final content and metadata
|
||||||
|
"""
|
||||||
|
logger.info(f"Starting content generation for task: {task}")
|
||||||
|
|
||||||
|
try:
|
||||||
|
# Get initial direction from supervisor
|
||||||
|
supervisor_response = self.main_supervisor.run(task)
|
||||||
|
|
||||||
|
self.conversation.add(
|
||||||
|
role=self.main_supervisor.agent_name,
|
||||||
|
content=supervisor_response,
|
||||||
|
)
|
||||||
|
|
||||||
|
supervisor_data = self._safely_parse_json(
|
||||||
|
supervisor_response
|
||||||
|
)
|
||||||
|
|
||||||
|
# Generate initial content
|
||||||
|
generator_response = self.generator.run(
|
||||||
|
json.dumps(supervisor_data.get("next_action", {}))
|
||||||
|
)
|
||||||
|
|
||||||
|
self.conversation.add(
|
||||||
|
role=self.generator.agent_name,
|
||||||
|
content=generator_response,
|
||||||
|
)
|
||||||
|
|
||||||
|
current_content = self._safely_parse_json(
|
||||||
|
generator_response
|
||||||
|
)
|
||||||
|
|
||||||
|
for iteration in range(self.max_iterations):
|
||||||
|
logger.info(f"Starting iteration {iteration + 1}")
|
||||||
|
|
||||||
|
# Evaluate current content
|
||||||
|
evaluation = self._evaluate_content(current_content)
|
||||||
|
|
||||||
|
# Check if quality threshold is met
|
||||||
|
if (
|
||||||
|
evaluation["scores"]["overall"]
|
||||||
|
>= self.quality_threshold
|
||||||
|
):
|
||||||
|
logger.info(
|
||||||
|
"Quality threshold met, returning content"
|
||||||
|
)
|
||||||
|
return {
|
||||||
|
"content": current_content.get(
|
||||||
|
"content", {}
|
||||||
|
).get("main_body", ""),
|
||||||
|
"final_score": evaluation["scores"][
|
||||||
|
"overall"
|
||||||
|
],
|
||||||
|
"iterations": iteration + 1,
|
||||||
|
"metadata": {
|
||||||
|
"content_metadata": current_content.get(
|
||||||
|
"content", {}
|
||||||
|
).get("metadata", {}),
|
||||||
|
"evaluation": evaluation,
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
# Revise content if needed
|
||||||
|
revision_input = {
|
||||||
|
"content": current_content,
|
||||||
|
"evaluation": evaluation,
|
||||||
|
}
|
||||||
|
|
||||||
|
revision_response = self.revisor.run(
|
||||||
|
json.dumps(revision_input)
|
||||||
|
)
|
||||||
|
current_content = self._safely_parse_json(
|
||||||
|
revision_response
|
||||||
|
)
|
||||||
|
|
||||||
|
self.conversation.add(
|
||||||
|
role=self.revisor.agent_name,
|
||||||
|
content=revision_response,
|
||||||
|
)
|
||||||
|
|
||||||
|
logger.warning(
|
||||||
|
"Max iterations reached without meeting quality threshold"
|
||||||
|
)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"Error in generate_and_refine: {str(e)}")
|
||||||
|
current_content = {
|
||||||
|
"content": {"main_body": f"Error: {str(e)}"}
|
||||||
|
}
|
||||||
|
evaluation = self._get_fallback_evaluation()
|
||||||
|
|
||||||
|
if self.return_string:
|
||||||
|
return self.conversation.return_history_as_string()
|
||||||
|
else:
|
||||||
|
return {
|
||||||
|
"content": current_content.get("content", {}).get(
|
||||||
|
"main_body", ""
|
||||||
|
),
|
||||||
|
"final_score": evaluation["scores"]["overall"],
|
||||||
|
"iterations": self.max_iterations,
|
||||||
|
"metadata": {
|
||||||
|
"content_metadata": current_content.get(
|
||||||
|
"content", {}
|
||||||
|
).get("metadata", {}),
|
||||||
|
"evaluation": evaluation,
|
||||||
|
"error": "Max iterations reached",
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
def save_state(self) -> None:
|
||||||
|
"""Save the current state of all agents."""
|
||||||
|
for agent in [
|
||||||
|
self.main_supervisor,
|
||||||
|
self.generator,
|
||||||
|
*self.evaluators,
|
||||||
|
self.revisor,
|
||||||
|
]:
|
||||||
|
try:
|
||||||
|
agent.save_state()
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(
|
||||||
|
f"Error saving state for {agent.agent_name}: {str(e)}"
|
||||||
|
)
|
||||||
|
|
||||||
|
def load_state(self) -> None:
|
||||||
|
"""Load the saved state of all agents."""
|
||||||
|
for agent in [
|
||||||
|
self.main_supervisor,
|
||||||
|
self.generator,
|
||||||
|
*self.evaluators,
|
||||||
|
self.revisor,
|
||||||
|
]:
|
||||||
|
try:
|
||||||
|
agent.load_state()
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(
|
||||||
|
f"Error loading state for {agent.agent_name}: {str(e)}"
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# Example usage
|
||||||
|
try:
|
||||||
|
talkhier = TalkHier(
|
||||||
|
max_iterations=1,
|
||||||
|
quality_threshold=0.8,
|
||||||
|
model_name="gpt-4o",
|
||||||
|
return_string=True,
|
||||||
|
)
|
||||||
|
|
||||||
|
task = "Write a comprehensive explanation of quantum computing for beginners"
|
||||||
|
result = talkhier.run(task)
|
||||||
|
print(result)
|
||||||
|
|
||||||
|
# print(f"Final content: {result['content']}")
|
||||||
|
# print(f"Quality score: {result['final_score']}")
|
||||||
|
# print(f"Iterations: {result['iterations']}")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.error(f"Error in main execution: {str(e)}")
|
||||||
Loading…
Reference in new issue