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tasks.py

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+from crewai import Task
+
+def get_research_task(agent, topic):
+    return Task(
+        description=f"""Research the topic "{topic}" extensively using PMC and Google Scholar.
+                      
+                      Follow these steps:
+                      1. First search PMC using the pmc_search tool
+                      2. Then search Google Scholar using the google_scholar_search tool
+                      3. For each source, carefully extract and analyze:
+                         - Title
+                         - Authors (full names for APA citation)
+                         - Publication year
+                         - Journal name (for APA citation)
+                         - Volume/Issue (if available)
+                         - DOI or URL (if available)
+                         - Key findings from abstract
+                         - Methodology used
+                         - Outcomes reported
+                      4. Combine and synthesize the information:
+                         - Identify common themes
+                         - Note conflicting findings
+                         - Highlight strongest evidence
+                      5. Organize findings by:
+                         - Study type (RCT, cohort, etc.)
+                         - Sample size
+                         - Follow-up duration
+                         - Outcome measures
+                      
+                      If search results are limited:
+                      1. Try alternative search terms
+                      2. Broaden the search scope
+                      3. Include related conditions/treatments
+                      
+                      Output Format:
+                      1. Summary of search strategy
+                      2. Overview of findings
+                      3. Detailed analysis of key studies
+                      4. Evidence synthesis
+                      5. Knowledge gaps identified
+                      6. Complete reference list in APA format for ALL sources
+                         Format: Author(s) (Year). Title. Journal, Volume(Issue), Pages. DOI/URL
+                         Example: Smith, J. D., & Jones, M. R. (2023). Study findings. Journal of Medicine, 45(2), 123-145. https://doi.org/10.1000/med.2023""",
+        agent=agent,
+        expected_output="A comprehensive research summary with detailed analysis and APA-formatted references for all sources."
+    )
+
+def get_writing_task(agent, topic):
+    return Task(
+        description=f"""Write a comprehensive scientific paper about "{topic}" based on the research provided.
+                      
+                      Paper Structure:
+                      1. Introduction (500-750 words):
+                         - Current state of knowledge
+                         - Clinical significance
+                         - Research gaps
+                         - Clear objectives
+                         - Include at least 5 in-text citations
+                      
+                      2. Materials and Methods (500-750 words):
+                         - Search strategy
+                         - Inclusion criteria
+                         - Data extraction process
+                         - Quality assessment
+                         - Include methodology citations
+                      
+                      3. Results (MINIMUM 1000 words):
+                         - Study characteristics
+                         - Patient demographics
+                         - Primary outcomes
+                         - Secondary outcomes
+                         - Safety data
+                         - Meta-analysis if applicable
+                         - Include specific data from at least 10 studies
+                         - Use in-text citations for every finding
+                      
+                      4. Discussion (MINIMUM 1000 words):
+                         - Summary of main findings
+                         - Comparison with existing literature
+                         - Clinical implications
+                         - Strengths and limitations
+                         - Future research directions
+                         - Include at least 15 in-text citations
+                         - Compare and contrast findings from different studies
+                         - Address conflicting results
+                         - Discuss potential mechanisms
+                      
+                      5. Conclusion (250-500 words):
+                         - Key takeaways
+                         - Clinical recommendations
+                         - Research priorities
+                      
+                      6. References:
+                         - MUST list ALL cited articles (minimum 20)
+                         - Use proper APA format
+                         - Include DOI/URL when available
+                         - Alphabetical order
+                      
+                      Writing Guidelines:
+                      - Use clear, scientific language
+                      - Support EVERY claim with citations
+                      - Include relevant statistics
+                      - Maintain objectivity
+                      - Address conflicting findings
+                      - Consider clinical relevance
+                      
+                      Citation Format:
+                      - In-text: (Author et al., Year) or Author et al. (Year)
+                      - For 2 authors: (Smith & Jones, 2023)
+                      - For 3+ authors: (Smith et al., 2023)
+                      - Multiple citations: (Smith et al., 2023; Jones et al., 2022)
+                      
+                      IMPORTANT:
+                      - Results and Discussion MUST be at least 1000 words each
+                      - EVERY paragraph should include at least one citation
+                      - ALL sources from the research must be cited and listed
+                      - Check that all in-text citations have corresponding references""",
+        agent=agent,
+        expected_output="A well-structured scientific paper with comprehensive analysis, proper citations, and complete reference list."
+    )

tools.py

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+from langchain.tools import tool
+from bs4 import BeautifulSoup
+import requests
+import datetime
+import json
+import time
+import re
+
+def clean_text(text):
+    """Clean text from HTML tags and extra whitespace"""
+    if not text:
+        return ""
+    text = re.sub(r'<[^>]+>', '', text)
+    text = re.sub(r'\s+', ' ', text)
+    return text.strip()
+
+@tool
+def pmc_search(query: str) -> str:
+    """Search PubMed Central (PMC) for articles"""
+    try:
+        # Base URLs for PubMed APIs
+        search_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
+        fetch_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi"
+        
+        # Search parameters
+        search_params = {
+            "db": "pmc",
+            "term": query,
+            "retmax": 20,
+            "retmode": "json",
+            "sort": "relevance"
+        }
+        
+        # Get article IDs
+        response = requests.get(search_url, params=search_params)
+        if not response.ok:
+            return json.dumps([{"error": "PubMed search failed"}])
+            
+        try:
+            search_data = response.json()
+            article_ids = search_data.get("esearchresult", {}).get("idlist", [])
+        except:
+            # Fallback to XML parsing if JSON fails
+            soup = BeautifulSoup(response.text, 'xml')
+            article_ids = [id.text for id in soup.find_all('Id')]
+        
+        articles = []
+        for pmid in article_ids:
+            try:
+                # Fetch article details
+                fetch_params = {
+                    "db": "pmc",
+                    "id": pmid,
+                    "retmode": "xml"
+                }
+                article_response = requests.get(fetch_url, params=fetch_params)
+                if not article_response.ok:
+                    continue
+                    
+                article_soup = BeautifulSoup(article_response.text, 'xml')
+                
+                # Extract article data
+                title_elem = article_soup.find("article-title")
+                title = clean_text(title_elem.text if title_elem else "No title")
+                
+                abstract_elem = article_soup.find("abstract")
+                abstract = clean_text(abstract_elem.text if abstract_elem else "No abstract")
+                
+                authors = []
+                for author in article_soup.find_all(["author", "contrib"]):
+                    surname = author.find(["surname", "last-name"])
+                    given_name = author.find(["given-names", "first-name"])
+                    if surname:
+                        author_name = surname.text
+                        if given_name:
+                            author_name = f"{given_name.text} {author_name}"
+                        authors.append(clean_text(author_name))
+                
+                year_elem = article_soup.find(["pub-date", "year"])
+                year = year_elem.find("year").text if year_elem and year_elem.find("year") else "Unknown"
+                
+                journal_elem = article_soup.find(["journal-title", "source"])
+                journal = clean_text(journal_elem.text if journal_elem else "Unknown Journal")
+                
+                articles.append({
+                    "id": pmid,
+                    "title": title,
+                    "authors": authors,
+                    "year": year,
+                    "journal": journal,
+                    "abstract": abstract
+                })
+                
+                # Add delay to avoid rate limiting
+                time.sleep(0.5)
+                
+            except Exception as e:
+                continue
+        
+        return json.dumps(articles, indent=2)
+        
+    except Exception as e:
+        return json.dumps([{"error": f"PMC search failed: {str(e)}"}])
+
+@tool
+def google_scholar_search(query: str) -> str:
+    """Search alternative sources for medical literature"""
+    try:
+        # Use alternative medical literature sources
+        search_urls = [
+            f"https://europepmc.org/webservices/rest/search?query={query}&format=json&pageSize=20",
+            f"https://api.semanticscholar.org/graph/v1/paper/search?query={query}&limit=20&fields=title,abstract,year,authors,venue"
+        ]
+        
+        results = []
+        
+        for url in search_urls:
+            try:
+                headers = {
+                    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
+                }
+                response = requests.get(url, headers=headers)
+                
+                if not response.ok:
+                    continue
+                
+                data = response.json()
+                
+                # Handle Europe PMC response
+                if "resultList" in data:
+                    for result in data["resultList"].get("result", []):
+                        pub = {
+                            "title": clean_text(result.get("title", "No title")),
+                            "authors": [clean_text(author.get("fullName", "Unknown")) for author in result.get("authorList", {}).get("author", [])],
+                            "year": result.get("pubYear", "Unknown"),
+                            "journal": clean_text(result.get("journalTitle", "Unknown Journal")),
+                            "abstract": clean_text(result.get("abstractText", "No abstract")),
+                            "source": "Europe PMC"
+                        }
+                        if pub["title"] != "No title" and pub["abstract"] != "No abstract":
+                            results.append(pub)
+                
+                # Handle Semantic Scholar response
+                elif "data" in data:
+                    for paper in data["data"]:
+                        pub = {
+                            "title": clean_text(paper.get("title", "No title")),
+                            "authors": [clean_text(author.get("name", "Unknown")) for author in paper.get("authors", [])],
+                            "year": paper.get("year", "Unknown"),
+                            "journal": clean_text(paper.get("venue", "Unknown Journal")),
+                            "abstract": clean_text(paper.get("abstract", "No abstract")),
+                            "source": "Semantic Scholar"
+                        }
+                        if pub["title"] != "No title" and pub["abstract"] != "No abstract":
+                            results.append(pub)
+                
+                time.sleep(1)  # Rate limiting
+                
+            except Exception as e:
+                continue
+        
+        return json.dumps(results, indent=2)
+        
+    except Exception as e:
+        return json.dumps([{"error": f"Literature search failed: {str(e)}"}])
+
+@tool
+def today_tool() -> str:
+    """Get today's date"""
+    return str(datetime.date.today())