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Prerequisites
  • Python 3.10 or higher
  • PraisonAI Agents package installed
  • arxiv package installed

​
arXiv Tools

Use arXiv Tools to search and analyze research papers with AI agents.
1

Install Dependencies

First, install the required packages:
pip install praisonaiagents arxiv
2

Import Components

Import the necessary components:
from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.tools import search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category
3

Create Agent

Create a research agent:
research_agent = Agent(
    name="ResearchAgent",
    role="Scientific Literature Specialist",
    goal="Find and analyze relevant scientific papers.",
    backstory="Expert in academic research and literature review.",
    tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category],
    self_reflect=False
)
4

Define Task

Define the research task:
research_task = Task(
    description="Search for recent papers on 'quantum machine learning' and summarize key findings.",
    expected_output="List of relevant papers with summaries.",
    agent=research_agent,
    name="quantum_research"
)
5

Run Agent

Initialize and run the agent:
agents = PraisonAIAgents(
    agents=[research_agent],
    tasks=[research_task],
    process="sequential"
)
agents.start()

​
Understanding arXiv Tools

What are arXiv Tools?

arXiv Tools provide scientific paper search capabilities for AI agents:
  • Paper search functionality
  • Author-based search
  • Category filtering
  • Abstract retrieval
  • PDF download options

​
Available Functions

from praisonaiagents.tools import search_arxiv
from praisonaiagents.tools import get_arxiv_paper
from praisonaiagents.tools import get_papers_by_author
from praisonaiagents.tools import get_papers_by_category

​
Function Details

​
search_arxiv(query: str, max_results: int = 10, sort_by: str = “relevance”, sort_order: str = “descending”, include_fields: Optional[List[str]] = None)

Search arXiv for papers:
  • Flexible query support
  • Customizable results
  • Multiple sorting options
  • Field selection
  • Error handling
# Basic search
papers = search_arxiv("quantum computing")

# Advanced search
papers = search_arxiv(
    query="quantum computing",
    max_results=5,
    sort_by="submittedDate",
    sort_order="descending",
    include_fields=["title", "authors", "summary"]
)

# Returns: List[Dict[str, Any]]
# Example output:
# [
#     {
#         "arxiv_id": "2401.00123",
#         "title": "Quantum Computing: A Survey",
#         "authors": ["John Doe", "Jane Smith"],
#         "summary": "This paper reviews...",
#         "published": "2024-01-01T00:00:00",
#         "updated": "2024-01-02T00:00:00"
#     },
#     ...
# ]

​
get_arxiv_paper(paper_id: str, include_fields: Optional[List[str]] = None)

Get specific paper details:
  • Direct ID lookup
  • Full paper metadata
  • Customizable fields
  • PDF/Abstract links
# Get paper by ID
paper = get_arxiv_paper("2401.00123")

# Get specific fields
paper = get_arxiv_paper(
    paper_id="2401.00123",
    include_fields=["title", "authors", "pdf_url"]
)

# Returns: Dict[str, Any]
# Example output:
# {
#     "arxiv_id": "2401.00123",
#     "title": "Quantum Computing: A Survey",
#     "authors": ["John Doe", "Jane Smith"],
#     "pdf_url": "https://arxiv.org/pdf/2401.00123",
#     "abstract_url": "https://arxiv.org/abs/2401.00123",
#     "published": "2024-01-01T00:00:00",
#     "updated": "2024-01-02T00:00:00"
# }

​
get_papers_by_author(author: str, max_results: int = 10, sort_by: str = “submittedDate”, sort_order: str = “descending”, include_fields: Optional[List[str]] = None)

Search papers by author:
  • Author-specific search
  • Publication timeline
  • Sort options
  • Field selection
# Get author's papers
papers = get_papers_by_author("Yoshua Bengio")

# Recent papers first
papers = get_papers_by_author(
    author="Yoshua Bengio",
    max_results=5,
    sort_by="submittedDate",
    sort_order="descending"
)

# Returns: List[Dict[str, Any]]
# Example output:
# [
#     {
#         "arxiv_id": "2401.00124",
#         "title": "Deep Learning Advances",
#         "authors": ["Yoshua Bengio", "Other Author"],
#         "published": "2024-01-01T00:00:00"
#     },
#     ...
# ]

​
get_papers_by_category(category: str, max_results: int = 10, sort_by: str = “submittedDate”, sort_order: str = “descending”, include_fields: Optional[List[str]] = None)

Search papers by category:
  • Category-specific search
  • Latest publications
  • Sort options
  • Field selection
# Get papers in category
papers = get_papers_by_category("cs.AI")

# Recent machine learning papers
papers = get_papers_by_category(
    category="cs.LG",
    max_results=5,
    sort_by="submittedDate",
    sort_order="descending"
)

# Returns: List[Dict[str, Any]]
# Example output:
# [
#     {
#         "arxiv_id": "2401.00125",
#         "title": "New ML Algorithm",
#         "categories": ["cs.LG", "cs.AI"],
#         "published": "2024-01-01T00:00:00"
#     },
#     ...
# ]

​
Dependencies

The arXiv tools require the following package:
  • arxiv: For accessing the arXiv API
This will be automatically installed when needed.

​
Available Fields

When using include_fields, you can select from:
  • title: Paper title
  • authors: List of authors
  • summary: Abstract text
  • comment: Author comments
  • journal_ref: Journal reference
  • doi: Digital Object Identifier
  • primary_category: Main arXiv category
  • categories: All arXiv categories
  • links: PDF and abstract URLs

​
Common Use Cases

  1. Literature Review:
# Search multiple related topics
topics = ["machine learning", "neural networks", "deep learning"]
all_papers = []
for topic in topics:
    papers = search_arxiv(
        topic,
        max_results=5,
        sort_by="relevance"
    )
    all_papers.extend(papers)
  1. Author Research:
# Get recent papers by multiple authors
authors = ["Yoshua Bengio", "Geoffrey Hinton", "Yann LeCun"]
author_papers = {}
for author in authors:
    papers = get_papers_by_author(
        author,
        max_results=3,
        sort_by="submittedDate"
    )
    author_papers[author] = papers
  1. Category Monitoring:
# Monitor new papers in AI categories
categories = ["cs.AI", "cs.LG", "cs.CL"]
new_papers = {}
for category in categories:
    papers = get_papers_by_category(
        category,
        max_results=5,
        sort_by="submittedDate"
    )
    new_papers[category] = papers

​
Examples

​
Basic Research Agent

from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.tools import search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category

# Create research agent
research_agent = Agent(
    name="PaperSearcher",
    role="Scientific Literature Specialist",
    goal="Find relevant scientific papers on specified topics.",
    backstory="Expert in academic research and literature analysis.",
    tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category],
    self_reflect=False
)

# Define research task
research_task = Task(
    description="Search for papers on 'transformer models in NLP' from the last year.",
    expected_output="List of relevant papers with abstracts and key findings.",
    agent=research_agent,
    name="nlp_research"
)

# Run agent
agents = PraisonAIAgents(
    agents=[research_agent],
    tasks=[research_task],
    process="sequential"
)
agents.start()

​
Advanced Research with Multiple Agents

# Create research agent
research_agent = Agent(
    name="Researcher",
    role="Literature Specialist",
    goal="Gather comprehensive scientific literature.",
    tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category],
    self_reflect=False
)

# Create analysis agent
analysis_agent = Agent(
    name="Analyzer",
    role="Research Analyst",
    goal="Analyze and synthesize research findings.",
    backstory="Expert in scientific literature analysis.",
    self_reflect=False
)

# Define tasks
research_task = Task(
    description="Search for papers on quantum computing applications in cryptography.",
    agent=research_agent,
    name="quantum_research"
)

analysis_task = Task(
    description="Analyze the papers and identify key trends and breakthroughs.",
    agent=analysis_agent,
    name="research_analysis"
)

# Run agents
agents = PraisonAIAgents(
    agents=[research_agent, analysis_agent],
    tasks=[research_task, analysis_task],
    process="sequential"
)
agents.start()

​
Best Practices

Configure agents with clear research focus:
Agent(
    name="Researcher",
    role="Literature Specialist",
    goal="Find relevant scientific papers",
    tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category]
)
Define specific research objectives:
Task(
    description="Find papers on 'deep learning in healthcare' from top authors",
    expected_output="Curated list of papers with impact analysis"
)

​
Common Patterns

​
Literature Review

# Literature search agent
searcher = Agent(
    name="Searcher",
    role="Literature Specialist",
    tools=[search_arxiv, get_arxiv_paper, get_papers_by_author, get_papers_by_category]
)

# Review agent
reviewer = Agent(
    name="Reviewer",
    role="Research Reviewer"
)

# Define tasks
search_task = Task(
    description="Find papers on AI ethics",
    agent=searcher
)

review_task = Task(
    description="Review and summarize findings",
    agent=reviewer
)

# Run workflow
agents = PraisonAIAgents(
    agents=[searcher, reviewer],
    tasks=[search_task, review_task]
)
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