LLM Agent vs SerpApi: Which Works Better for Searching Public Resumes in 2026

Introduction

Recruiters and HR professionals increasingly rely on automated tools to find public resumes on the web. Two popular approaches have emerged: using a Large Language Model (LLM) agent to autonomously browse and extract data, or using a dedicated search API like SerpApi to retrieve structured results from search engines. A recent technical analysis published on Habr compares these methods in a real-world project aimed at collecting public resumes from platforms like LinkedIn and GitHub. The article highlights the strengths, weaknesses, and practical trade-offs between LLM agents and SerpApi for this specific task. This article summarizes the key findings and offers actionable insights for developers and recruiters.

The Core Problem: Finding Public Resumes at Scale

Public resumes are scattered across multiple platforms — LinkedIn, GitHub, personal websites, and job boards. Manually searching and parsing each profile is time-consuming. Automating this process requires a system that can query search engines, navigate pages, extract relevant text, and structure it into a usable format. The project described in the source article aimed to build a resume aggregator that could handle hundreds of queries daily. The authors tested two approaches: an LLM agent (using GPT-4o and custom browsing tools) and the SerpApi service, which provides a clean JSON interface to Google search results.

Approach 1: LLM Agent for Resume Discovery

An LLM agent is a software system that uses a large language model to reason, plan, and execute actions — such as sending HTTP requests, parsing HTML, and extracting text. The authors configured an agent with access to a headless browser and a set of instructions: search for specific job titles and locations, visit each profile link, extract the resume content, and return a structured summary.

How It Worked

• The agent received a query like "software engineer resume New York"
• It used a browser automation tool (similar to Playwright) to search Google
• For each result, it visited the page, waited for content to load, and extracted text from relevant sections
• The LLM then summarized the extracted data into a JSON object with fields: name, experience, skills, education

Results and Challenges

• Success rate: Approximately 60% of queries returned at least one valid resume
• Average time per query: 45–90 seconds, depending on page complexity
• Major issues: The agent frequently encountered CAPTCHAs, dynamic page loading, and inconsistent HTML structures. It also had a tendency to get stuck on irrelevant pages (e.g., company homepages instead of personal profiles)
• Cost: Each query cost about $0.05–$0.10 in API calls (GPT-4o tokens + browser execution)
• Hallucination: The LLM sometimes invented resume details when the actual page was empty or blocked

Expert Take

LLM agents are flexible and can adapt to new website layouts without manual configuration. However, they are slow, expensive, and unreliable for high-volume scraping. The authors noted that the agent required constant monitoring and fallback logic to handle errors.

Approach 2: SerpApi for Structured Search Results

SerpApi is a commercial API that scrapes Google (and other search engines) and returns structured JSON with titles, snippets, links, and sometimes additional metadata. The authors used SerpApi to get a list of links and snippets for each query, then wrote a lightweight script to visit each link and extract text using a simple HTML parser (BeautifulSoup). No LLM was involved in the extraction step.

How It Worked

• A query was sent to SerpApi's /search endpoint with parameters like q=software+engineer+resume+New+York
• The API returned up to 100 results with title, URL, snippet, and site name
• A Python script filtered results for likely resume pages (e.g., URLs containing "linkedin.com/in/" or "github.com/")
• For each filtered URL, the script fetched the page and extracted visible text using a basic parser
• The extracted data was saved as plain text without LLM summarization

Results and Challenges

• Success rate: 85% of queries returned relevant links; 70% of those links contained extractable resume text
• Average time per query: 5–10 seconds (API call + page fetch)
• Major issues: SerpApi sometimes returns outdated or irrelevant results. The API also has rate limits (100 queries per month on the free plan, higher on paid plans)
• Cost: $0.01 per query on the basic plan (plus page fetch costs)
• Data quality: No hallucination, but the extracted text was raw and required additional parsing to extract structured fields

Expert Take

SerpApi is faster, cheaper, and more reliable than an LLM agent for the search part. However, it requires additional logic to filter and parse the results. The authors found that combining SerpApi with a simple rule-based parser was more efficient than using an LLM agent for the entire pipeline.

Head-to-Head Comparison

The following table summarizes the key differences based on the project's experience:

Practical Recommendations

Based on the article, here are actionable recommendations for developers and recruiters:

1. Use SerpApi for the search layer: It provides clean, structured search results without the overhead of browser automation. This reduces cost and latency significantly.
2. Use a lightweight parser for extraction: Instead of an LLM, use rules or a small ML model to extract name, experience, and skills from raw text. This avoids hallucination and reduces API costs.
3. Reserve LLM agents for complex cases: If you need to extract data from highly dynamic or unstructured pages (e.g., PDF resumes, personal blogs), an LLM agent may be necessary. But use it sparingly and with fallback logic.
4. Combine both approaches: For example, use SerpApi to get candidate links, then use an LLM agent only for the top 10% of promising profiles to extract detailed information.
5. Monitor CAPTCHA and rate limits: Both approaches face challenges with anti-bot measures. SerpApi abstracts CAPTCHA handling, while LLM agents often fail. Consider using proxy rotation or dedicated scraping services for high-volume tasks.

Case Study: A Resume Aggregator in Production

The authors built a prototype aggregator that ran 1,000 queries per day. With SerpApi, the system processed all queries in under 3 hours at a cost of ~$10/day. With the LLM agent, the same workload took over 12 hours and cost ~$80/day, with a 15% failure rate due to CAPTCHAs and timeouts. The final production system used SerpApi for search and a custom parser for extraction, with a small LLM agent as a fallback for pages that the parser couldn't handle.

Conclusion

The choice between an LLM agent and SerpApi depends on your specific requirements for speed, cost, accuracy, and flexibility. For most resume searching tasks, SerpApi offers a faster, cheaper, and more reliable solution. LLM agents are best reserved for complex, low-volume extraction tasks where adaptability is critical. The project described in the source article demonstrates that a hybrid approach — using SerpApi for search and a lightweight parser for extraction — delivers the best balance of performance and cost. For those looking to integrate resume search capabilities into their own systems, ASI Biont supports connecting to SerpApi via API — learn more at asibiont.com/courses.

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