AI Research Agent: Automated Literature Search and Summary Generation

The Research Bottleneck

A typical literature review involves searching multiple databases, skimming dozens of abstracts, reading a handful of full papers, extracting key claims, and weaving them into a coherent narrative. A single researcher might spend two to three weeks on this process. An AI research agent compresses the search-and-summarize loop from days to minutes while the human focuses on critical evaluation and synthesis decisions.

The agent we build here uses the Semantic Scholar API for paper discovery, LLM-powered summarization for each paper, and a synthesis step that identifies themes and contradictions across the collected literature.

Paper Search Tool

Semantic Scholar provides a free API that returns paper metadata, abstracts, citation counts, and more. The search tool wraps this API:

import httpx
from agents import Agent, Runner, function_tool

S2_BASE = "https://api.semanticscholar.org/graph/v1"

@function_tool
async def search_papers(query: str, limit: int = 10) -> str:
    """Search Semantic Scholar for papers matching a query.
    Returns titles, authors, year, citation count, and abstracts."""
    params = {
        "query": query,
        "limit": limit,
        "fields": "title,authors,year,abstract,citationCount,externalIds",
    }
    async with httpx.AsyncClient() as client:
        resp = await client.get(f"{S2_BASE}/paper/search", params=params)
        resp.raise_for_status()

    papers = resp.json().get("data", [])
    results = []
    for p in papers:
        authors = ", ".join(a["name"] for a in (p.get("authors") or [])[:3])
        doi = (p.get("externalIds") or {}).get("DOI", "N/A")
        abstract = (p.get("abstract") or "No abstract available.")[:500]
        results.append(
            f"Title: {p['title']}\n"
            f"Authors: {authors}\n"
            f"Year: {p.get('year', 'N/A')} | Citations: {p.get('citationCount', 0)}\n"
            f"DOI: {doi}\n"
            f"Abstract: {abstract}\n"
        )
    return "\n---\n".join(results) if results else "No papers found."

Citation Management Tool

Keeping track of references is essential. This tool stores papers the agent decides are relevant and outputs formatted citations:

_citation_store: list[dict] = []

@function_tool
def save_citation(title: str, authors: str, year: str, doi: str) -> str:
    """Save a paper to the citation list for the final bibliography."""
    entry = {"title": title, "authors": authors, "year": year, "doi": doi}
    _citation_store.append(entry)
    return f"Saved. Total citations: {len(_citation_store)}"

@function_tool
def get_bibliography() -> str:
    """Return all saved citations in APA-like format."""
    if not _citation_store:
        return "No citations saved yet."
    lines = []
    for i, c in enumerate(_citation_store, 1):
        lines.append(f"[{i}] {c['authors']} ({c['year']}). {c['title']}. DOI: {c['doi']}")
    return "\n".join(lines)

Assembling the Research Agent

The agent needs instructions that define a clear research workflow — search, filter, summarize, synthesize:

research_agent = Agent(
    name="Research Agent",
    instructions="""You are an academic research agent. When given a research topic:
1. Use search_papers to find the 10 most relevant papers.
2. Evaluate each abstract for relevance. Discard papers that do not directly
   address the topic.
3. For each relevant paper, save_citation to build the bibliography.
4. Summarize each relevant paper in 2-3 sentences focusing on methodology
   and key findings.
5. After reviewing all papers, write a synthesis section that identifies
   common themes, conflicting results, and open questions.
6. End with the full bibliography from get_bibliography.""",
    tools=[search_papers, save_citation, get_bibliography],
)

Running a Literature Search

import asyncio

async def main():
    result = await Runner.run(
        research_agent,
        "Survey the recent literature on retrieval-augmented generation "
        "for question answering systems. Focus on papers from 2024-2026.",
    )
    print(result.final_output)

asyncio.run(main())

The agent searches for RAG papers, filters by relevance and recency, saves citations for the strongest matches, summarizes each one, and produces a synthesis section identifying trends like the shift from sparse to dense retrieval and the emergence of hybrid chunking strategies.

Enhancing with Full-Text Analysis

Abstracts only tell part of the story. For deeper analysis, add a tool that fetches full paper text via open-access repositories:

@function_tool
async def fetch_paper_text(doi: str) -> str:
    """Fetch the full text of an open-access paper via Unpaywall."""
    async with httpx.AsyncClient() as client:
        resp = await client.get(
            f"https://api.unpaywall.org/v2/{doi}",
            params={"email": "your@email.com"},
        )
        if resp.status_code != 200:
            return "Paper not available open-access."
        data = resp.json()
        oa_url = data.get("best_oa_location", {}).get("url_for_pdf")
        if not oa_url:
            return "No open-access PDF URL found."
        return f"Full text available at: {oa_url}"

Handling Rate Limits and Errors

Academic APIs enforce rate limits. Wrap HTTP calls with exponential backoff:

import asyncio

async def resilient_get(client, url, params, max_retries=3):
    for attempt in range(max_retries):
        resp = await client.get(url, params=params)
        if resp.status_code == 429:
            wait = 2 ** attempt
            await asyncio.sleep(wait)
            continue
        resp.raise_for_status()
        return resp
    raise Exception("Max retries exceeded")

FAQ

Can this agent access papers behind paywalls?

No. The agent uses public APIs and open-access repositories. For paywalled content, you would need institutional access or an API key from a licensed database like IEEE Xplore or PubMed Central.

How accurate are the LLM-generated summaries?

LLM summaries of abstracts are generally reliable for capturing high-level findings. However, they can miss nuances in methodology sections. Always have a domain expert review the synthesis before using it in a formal publication.

How do I focus the search on a specific time range?

Add a year filter to the Semantic Scholar API request by appending &year=2024-2026 to the query parameters. You can also instruct the agent to discard papers outside the target date range during the filtering step.

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