[FIX][Mcp logic file path name] [Improvement][Majority voting] [Improvement][Agent markdown formatter]
Kye Gomez
2 months ago
parent
cd75b19127
commit
7b6b7c4980
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from swarms import Agent
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from swarms.structs.majority_voting import MajorityVoting
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# System prompts for different coding expertise areas
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PYTHON_EXPERT_PROMPT = """You are a senior Python developer with expertise in clean, efficient code.
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Focus on:
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- Clean, readable code structure
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- Pythonic idioms and best practices
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- Efficient algorithms and data structures
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- Proper error handling and edge cases
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- Documentation and code comments"""
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GAME_DEV_EXPERT_PROMPT = """You are a game development specialist with focus on game mechanics.
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Focus on:
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- Smooth game loop implementation
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- Intuitive game controls and mechanics
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- Game state management
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- Performance optimization for games
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- User experience and gameplay flow"""
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SOFTWARE_ARCHITECT_PROMPT = """You are a software architect who designs robust, maintainable systems.
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Focus on:
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- Modular, scalable architecture
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- Separation of concerns
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- Design patterns and principles
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- Code maintainability and extensibility
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- Best practices for larger codebases"""
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CONSENSUS_EVALUATOR_PROMPT = """You are a senior code reviewer and technical lead who evaluates code quality comprehensively.
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Your evaluation criteria include:
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1. **Code Quality (25%)**:
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- Readability and clarity
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- Code structure and organization
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- Naming conventions
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- Documentation quality
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2. **Functionality (25%)**:
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- Correct game mechanics implementation
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- Complete feature set
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- Error handling and edge cases
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- Game stability and robustness
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3. **Performance & Efficiency (20%)**:
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- Algorithm efficiency
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- Memory usage optimization
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- Smooth gameplay performance
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- Resource management
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4. **Best Practices (15%)**:
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- Python conventions and idioms
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- Design patterns usage
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- Code reusability
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- Maintainability
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5. **Innovation & Creativity (15%)**:
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- Creative solutions
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- Unique features
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- Code elegance
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- Problem-solving approach
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Provide detailed analysis with scores for each criterion and a final recommendation.
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Compare implementations across multiple loops if applicable."""
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def create_code_agents():
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"""Create multiple coding agents with different expertise"""
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agents = []
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# Python Expert Agent
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python_expert = Agent(
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agent_name="Python-Code-Expert",
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agent_description="Senior Python developer specializing in clean, efficient code",
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system_prompt=PYTHON_EXPERT_PROMPT,
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model_name="gpt-4o",
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max_loops=1,
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max_tokens=4000,
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temperature=0.7,
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)
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agents.append(python_expert)
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# Game Development Expert Agent
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game_expert = Agent(
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agent_name="Game-Dev-Specialist",
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agent_description="Game development expert focusing on mechanics and user experience",
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system_prompt=GAME_DEV_EXPERT_PROMPT,
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model_name="gpt-4o",
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max_loops=1,
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max_tokens=4000,
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temperature=0.7,
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)
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agents.append(game_expert)
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return agents
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def create_consensus_agent():
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"""Create the consensus agent for code quality evaluation"""
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return Agent(
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agent_name="Code-Quality-Evaluator",
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agent_description="Senior code reviewer evaluating implementations across multiple criteria",
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system_prompt=CONSENSUS_EVALUATOR_PROMPT,
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model_name="gpt-4o",
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max_loops=1,
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max_tokens=5000,
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temperature=0.3, # Lower temperature for more consistent evaluation
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)
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def main():
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"""Main function to run the Snake game code quality voting example"""
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# Create agents
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coding_agents = create_code_agents()
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consensus_agent = create_consensus_agent()
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# Create Majority Voting system with multi-loop capability
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snake_game_voting = MajorityVoting(
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name="Snake-Game-Code-Quality-Voting",
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description="Multi-agent system for creating and evaluating Snake game implementations",
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agents=coding_agents,
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consensus_agent=consensus_agent,
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max_loops=1, # Enable multi-loop refinement
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verbose=True,
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)
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# Define the coding task
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coding_task = """
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Create a complete Snake game implementation in Python using the following requirements:
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**Core Requirements:**
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1. **Game Board**: 20x20 grid with borders
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2. **Snake**: Starts at center, grows when eating food
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3. **Food**: Randomly placed, appears after being eaten
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4. **Controls**: Arrow keys or WASD for movement
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5. **Game Over**: When snake hits wall or itself
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6. **Score**: Display current score and high score
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**Technical Requirements:**
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- Use Python standard library only (no external dependencies)
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- Clean, well-documented code
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- Proper error handling
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- Efficient algorithms
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- Modular design where possible
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**Advanced Features (Bonus):**
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- Increasing speed as score grows
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- Pause functionality
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- Game restart option
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- Score persistence
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- Smooth animations
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**Output Format:**
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Provide the complete, runnable Python code with proper structure and documentation.
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Make sure the code is immediately executable and includes a main function.
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"""
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# Run the multi-loop voting system
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result = snake_game_voting.run(coding_task)
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return result
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if __name__ == "__main__":
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main()
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@ -1,189 +0,0 @@
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import json
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import os
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import time
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from typing import Dict
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from swarms.utils.loguru_logger import initialize_logger
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from swarms.telemetry.main import (
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log_agent_data,
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)
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logger = initialize_logger(log_folder="onboarding_process")
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class OnboardingProcess:
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"""
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This class handles the onboarding process for users. It collects user data including their
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full name, first name, email, Swarms API key, and system data, then autosaves it in both a
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main JSON file and a cache file for reliability. It supports loading previously saved or cached data.
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"""
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def __init__(
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self,
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auto_save_path: str = "user_data.json",
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cache_save_path: str = "user_data_cache.json",
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) -> None:
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"""
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Initializes the OnboardingProcess with an autosave file path and a cache path.
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Args:
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auto_save_path (str): The path where user data is automatically saved.
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cache_save_path (str): The path where user data is cached for reliability.
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"""
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self.user_data: Dict[str, str] = {}
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self.auto_save_path = auto_save_path
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self.cache_save_path = cache_save_path
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self.load_existing_data()
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def load_existing_data(self) -> None:
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"""
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Loads existing user data from the auto-save file or cache if available.
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"""
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if os.path.exists(self.auto_save_path):
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try:
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with open(self.auto_save_path, "r") as f:
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self.user_data = json.load(f)
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logger.info(
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"Existing user data loaded from {}",
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self.auto_save_path,
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)
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return
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except json.JSONDecodeError as e:
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logger.error(
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"Failed to load user data from main file: {}", e
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)
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# Fallback to cache if main file fails
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if os.path.exists(self.cache_save_path):
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try:
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with open(self.cache_save_path, "r") as f:
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self.user_data = json.load(f)
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logger.info(
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"User data loaded from cache: {}",
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self.cache_save_path,
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)
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except json.JSONDecodeError as e:
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logger.error(
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"Failed to load user data from cache: {}", e
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)
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def save_data(self, retry_attempts: int = 3) -> None:
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"""
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Saves the current user data to both the auto-save file and the cache file. If the main
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save fails, the cache is updated instead. Implements retry logic with exponential backoff
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in case both save attempts fail.
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Args:
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retry_attempts (int): The number of retries if saving fails.
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"""
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attempt = 0
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backoff_time = 1 # Starting backoff time (in seconds)
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while attempt < retry_attempts:
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try:
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combined_data = {**self.user_data}
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log_agent_data(combined_data)
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return # Exit the function if saving was successful
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except Exception as e:
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logger.error(
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"Error saving user data (Attempt {}): {}",
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attempt + 1,
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e,
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)
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# Retry after a short delay (exponential backoff)
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time.sleep(backoff_time)
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attempt += 1
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backoff_time *= (
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2 # Double the backoff time for each retry
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)
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logger.error(
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"Failed to save user data after {} attempts.",
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retry_attempts,
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)
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def ask_input(self, prompt: str, key: str) -> None:
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"""
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Asks the user for input, validates it, and saves it in the user_data dictionary.
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Autosaves and caches after each valid input.
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Args:
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prompt (str): The prompt message to display to the user.
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key (str): The key under which the input will be saved in user_data.
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Raises:
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ValueError: If the input is empty or only contains whitespace.
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"""
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try:
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response = input(prompt)
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if response.strip().lower() == "quit":
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logger.info(
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"User chose to quit the onboarding process."
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)
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exit(0)
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if not response.strip():
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raise ValueError(
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f"{key.capitalize()} cannot be empty."
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)
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self.user_data[key] = response.strip()
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self.save_data()
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return response
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except ValueError as e:
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logger.warning(e)
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self.ask_input(prompt, key)
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except KeyboardInterrupt:
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logger.warning(
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"Onboarding process interrupted by the user."
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)
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exit(1)
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def collect_user_info(self) -> None:
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"""
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Initiates the onboarding process by collecting the user's full name, first name, email,
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Swarms API key, and system data. Additionally, it reminds the user to set their WORKSPACE_DIR environment variable.
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"""
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logger.info("Initiating swarms cloud onboarding process...")
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self.ask_input(
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"Enter your first name (or type 'quit' to exit): ",
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"first_name",
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)
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self.ask_input(
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"Enter your Last Name (or type 'quit' to exit): ",
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"last_name",
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)
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self.ask_input(
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"Enter your email (or type 'quit' to exit): ", "email"
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)
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workspace = self.ask_input(
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"Enter your WORKSPACE_DIR: This is where logs, errors, and agent configurations will be stored (or type 'quit' to exit). Remember to set this as an environment variable: https://docs.swarms.world/en/latest/swarms/install/quickstart/ || ",
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"workspace_dir",
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)
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os.environ["WORKSPACE_DIR"] = workspace
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logger.info(
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"Important: Please ensure you have set your WORKSPACE_DIR environment variable as per the instructions provided."
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)
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logger.info(
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"Additionally, remember to add your API keys for your respective models in your .env file."
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)
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logger.success("Onboarding process completed successfully!")
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def run(self) -> None:
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"""
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Main method to run the onboarding process. It handles unexpected errors and ensures
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proper finalization.
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"""
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try:
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self.collect_user_info()
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except Exception as e:
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logger.error("An unexpected error occurred: {}", e)
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finally:
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logger.info("Finalizing the onboarding process.")
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# if __name__ == "__main__":
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# onboarding = OnboardingProcess()
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# onboarding.run()
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@ -0,0 +1,129 @@
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#!/usr/bin/env python3
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"""Test script to verify the improved formatter markdown rendering."""
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from swarms.utils.formatter import Formatter
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def test_formatter():
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"""Test the formatter with various markdown content."""
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formatter = Formatter(md=True)
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# Test 1: Basic markdown with headers
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content1 = """# Main Title
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This is a paragraph with **bold** text and *italic* text.
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## Section 1
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- Item 1
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- Item 2
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- Item 3
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### Subsection
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This is another paragraph with `inline code`.
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"""
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formatter.print_panel(
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content1, title="Test 1: Basic Markdown", style="bold blue"
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)
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# Test 2: Code blocks with syntax highlighting
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content2 = """## Code Examples
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Here's a Python example:
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```python
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def hello_world():
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'''A simple hello world function.'''
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print("Hello, World!")
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return True
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```
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And here's some JavaScript:
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```javascript
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function greet(name) {
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console.log(`Hello, ${name}!`);
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}
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```
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Plain text code block:
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```
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This is just plain text
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without any syntax highlighting
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```
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"""
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formatter.print_panel(
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content2, title="Test 2: Code Blocks", style="bold green"
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)
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# Test 3: Mixed content
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content3 = """## Mixed Content Test
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This paragraph includes **various** formatting options:
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- Lists with `code`
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- Links [like this](https://example.com)
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- And more...
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```python
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# Python code with comments
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class Example:
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def __init__(self, name):
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self.name = name
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def greet(self):
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return f"Hello, {self.name}!"
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```
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### Table Example
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| Column 1 | Column 2 | Column 3 |
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|----------|----------|----------|
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| Data 1 | Data 2 | Data 3 |
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| Data 4 | Data 5 | Data 6 |
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"""
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formatter.print_panel(
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content3, title="Test 3: Mixed Content", style="bold magenta"
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)
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# Test 4: Edge cases
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content4 = """This content starts without a header
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It should still be formatted correctly.
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```
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No language specified
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```
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Single line content."""
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formatter.print_panel(
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content4, title="Test 4: Edge Cases", style="bold yellow"
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)
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# Test 5: Empty content
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formatter.print_panel(
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"", title="Test 5: Empty Content", style="bold red"
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)
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# Test 6: Using print_markdown method
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content6 = """# Direct Markdown Rendering
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This uses the `print_markdown` method directly.
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```python
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# Syntax highlighted code
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result = 42 * 2
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print(f"The answer is {result}")
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```
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"""
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formatter.print_markdown(
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content6, title="Test 6: Direct Markdown", border_style="cyan"
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)
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if __name__ == "__main__":
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test_formatter()
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Loading…
Reference in new issue