Building a Terminal-based Intelligent Research Assistant: A Practical Guide to Claude-based AI Web Search Agent

This article introduces an open-source intelligent web research agent project based on Python and Anthropic Claude—AI Web Research Agent. Combining large language model reasoning capabilities with automated web scraping technology, this project can understand user intent, autonomously plan search strategies, deeply read information from multiple sources, and generate structured research reports with citations. It aims to solve the pain point of traditional search engines returning scattered results that require manual filtering and integration. The core adopts the ReAct loop architecture and tool calling mechanism, supporting direct operation in the terminal.

Traditional search models have obvious limitations: when facing complex questions (such as "latest breakthroughs in nuclear fusion energy"), search engines only return a list of scattered links. Users need to manually click, read, compare, and summarize, which is time-consuming and easy to miss key information. The design goal of AI Web Research Agent is to solve this pain point—it is not just a search tool, but a complete intelligent agent system that can understand intent, autonomously plan searches, integrate information, and generate reports.

The project adopts the ReAct (Reason-Act-Observe-Repeat) agent loop architecture, with core components including: 1. Agent Core (agent/researcher.py): Coordinates thinking and actions, maintains conversation history, and performs reasoning, action, observation, and termination judgment; 2. Tool Layer (tools/): Includes a search module (integrates DuckDuckGo API, returns web page titles, summaries, and links) and a scraping module (uses requests + BeautifulSoup4 to extract and clean web page content); 3. Tool Calling Mechanism: Leverages Claude's tool usage capabilities, by providing tool definition schemas, allowing the model to autonomously decide which tools and parameters to use to actively obtain external information.

Taking the question "What are the latest breakthroughs in nuclear fusion energy?" as an example, the system workflow is as follows: After startup, first call the search_web tool (parameters: query="nuclear fusion energy breakthroughs 2024", num_results=5) to obtain candidate links; then call the scrape_url tool to crawl relevant web page content; after multiple iterations (e.g., 4 rounds) and using multiple sources (e.g., 3), generate the final report. The process is adaptive—if the first round of results is not ideal, it will adjust the query terms and search again.

The project's technical highlights include: 1. Prompt Engineering Strategy: Carefully designed SYSTEM_PROMPT to clarify role positioning, behavioral guidelines (citing sources, objectivity) and output format; 2. Multi-turn Dialogue Management: Maintains a messages list to record interactions (user questions, model thinking, tool calls/returns), giving the model "memory"; 3. Report Formatting: The utils/formatter.py module converts output into terminal reports with timestamps and separators, supports saving as txt files, balancing interaction and archiving needs.

Usage Methods: Supports multiple modes: interactive mode (run python main.py to input questions), command-line parameters (directly pass in questions), save report (--save flag), quiet mode (--quiet to turn off logs), iteration control (--max-iter to limit rounds).

Expansion Possibilities: Can add new tools such as calculators and Wikipedia API; implement persistent memory via JSON/database; build a Web interface with Streamlit; integrate reportlab to generate PDF reports; combine with the schedule library to implement scheduled tasks for daily summaries.

AI Web Research Agent demonstrates the typical pattern of modern AI agents: large language models as reasoning engines, tool calls as action means, and ReAct loops as coordination frameworks. This architecture is not only suitable for research scenarios but also provides a reusable template for complex autonomous agent systems. For developers, it is an excellent learning resource to understand tool calling, prompt engineering, and agent design. The clear code structure and documentation facilitate secondary development. As large model capabilities improve, such intelligent agents will play a more important role in knowledge acquisition, information integration, and decision support.