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from typing import List, Dict, Any, Optional
from crewai import Agent
from crewai_tools import BraveSearchTool, ScrapeWebsiteTool
from tools import ContentAnalyzerTool, RateLimitedToolWrapper, TavilySearchTool, SearchRotationTool
def create_researcher_agent(llm=None, verbose=True) -> Agent:
"""
Creates a researcher agent responsible for query refinement and web search.
Args:
llm: Language model to use for the agent
verbose: Whether to log agent activity
Returns:
Configured researcher agent
"""
# Initialize search tools
brave_search_tool = BraveSearchTool(
n_results=5,
save_file=False
)
# Initialize Tavily search tool
# Requires a TAVILY_API_KEY in environment variables
tavily_search_tool = TavilySearchTool(
max_results=5,
search_depth="basic",
timeout=15 # Increase timeout for more reliable results
)
# Add minimal rate limiting to avoid API throttling
# Set delay to 0 to disable rate limiting completely
rate_limited_brave_search = RateLimitedToolWrapper(tool=brave_search_tool, delay=0)
rate_limited_tavily_search = RateLimitedToolWrapper(tool=tavily_search_tool, delay=0)
# Create the search rotation tool
search_rotation_tool = SearchRotationTool(
search_tools=[rate_limited_brave_search, rate_limited_tavily_search],
max_searches_per_query=5 # Limit to 5 searches per query as requested
)
return Agent(
role="Research Specialist",
goal="Discover accurate and relevant information from the web",
backstory=(
"You are an expert web researcher with a talent for crafting effective search queries "
"and finding high-quality information on any topic. Your goal is to find the most "
"relevant and factual information to answer user questions. You have access to multiple "
"search engines and know how to efficiently use them within the search limits."
),
# Use the search rotation tool
tools=[search_rotation_tool],
verbose=verbose,
allow_delegation=True,
memory=True,
llm=llm
)
def create_analyst_agent(llm=None, verbose=True) -> Agent:
"""
Creates an analyst agent responsible for content analysis and evaluation.
Args:
llm: Language model to use for the agent
verbose: Whether to log agent activity
Returns:
Configured analyst agent
"""
# Initialize tools
scrape_tool = ScrapeWebsiteTool()
content_analyzer = ContentAnalyzerTool()
return Agent(
role="Content Analyst",
goal="Analyze web content for relevance, factuality, and quality",
backstory=(
"You are a discerning content analyst with a keen eye for detail and a strong "
"commitment to factual accuracy. You excel at evaluating information and filtering "
"out irrelevant or potentially misleading content. Your expertise helps ensure that "
"only the most reliable information is presented."
),
tools=[scrape_tool, content_analyzer],
verbose=verbose,
allow_delegation=True,
memory=True,
llm=llm
)
def create_writer_agent(llm=None, verbose=True) -> Agent:
"""
Creates a writer agent responsible for synthesizing information into coherent responses.
Args:
llm: Language model to use for the agent
verbose: Whether to log agent activity
Returns:
Configured writer agent
"""
return Agent(
role="Research Writer",
goal="Create informative, factual, and well-cited responses to research queries",
backstory=(
"You are a skilled writer specializing in creating clear, concise, and informative "
"responses based on research findings. You have a talent for synthesizing information "
"from multiple sources and presenting it in a coherent and readable format, always with "
"proper citations. You prioritize factual accuracy and clarity in your writing."
),
verbose=verbose,
allow_delegation=True,
memory=True,
llm=llm
) |