--- layout: blog title: Building a Research Agent with RSS Feed Support date: 2025-04-20T00:00:00-06:00 description: How I created a comprehensive research assistant that combines web search, academic papers, RSS feeds, and document analysis to revolutionize information discovery. categories: ["AI", "LLM", "Research", "Technology", "Agents"] coverImage: "https://images.unsplash.com/photo-1507842217343-583bb7270b66?q=80&w=2290&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" readingTime: 5 published: true --- In the age of information overload, finding the right data efficiently has become increasingly challenging. Whether you're conducting academic research, staying updated on industry trends, or investigating specific topics, the process often involves juggling multiple tools and platforms. This fragmentation inspired me to create a comprehensive solution: a research agent with RSS feed support that brings together multiple information sources in one unified interface. ## Why Build a Research Agent? As someone who regularly conducts research across different domains, I've experienced the frustration of switching between search engines, academic databases, news aggregators, and document analysis tools. Each context switch breaks concentration and slows down the discovery process. I wanted a tool that could: - Search across multiple information sources simultaneously - Analyze uploaded documents in the context of web information - Provide transparent reasoning about its research process - Deliver structured, well-cited reports The result is the [Research Agent](https://huggingface.co/spaces/mafzaal/AIE6-ResearchAgent) - an LLM-powered assistant that brings together web search, academic papers, RSS feeds, and document analysis into a single, coherent workflow. ## Multi-Source Research Architecture The agent's strength comes from its ability to tap into various information streams: ### Web Search Integration For real-time information and general knowledge, the agent leverages both Tavily and DuckDuckGo APIs to perform semantic searches across the web. This provides access to current events, recent developments, and general information that might not be available in academic sources. ### Academic Research Pipeline Research often requires scholarly sources. The agent connects to arXiv's extensive database of scientific papers, allowing it to retrieve relevant academic articles complete with titles, authors, and abstracts. This is particularly valuable for technical topics that require peer-reviewed information. ### RSS Feed Aggregation For targeted news monitoring and industry updates, the RSS feed reader component allows the agent to retrieve content from specific publications and blogs. This is ideal for tracking industry trends or following particular news sources relevant to your research topic. ### Document Analysis Engine Perhaps the most powerful feature is the document analysis capability, which uses Retrieval Augmented Generation (RAG) to process uploaded PDFs or text files. By breaking documents into semantic chunks and creating vector embeddings, the agent can answer questions specifically about your documents while incorporating relevant information from other sources. ## Behind the Scenes: LangGraph Workflow What makes this agent particularly powerful is its LangGraph-based architecture, which provides a structured framework for reasoning and tool orchestration: ![Research Agent Graph](/images/building-research-agent-01.png) This workflow provides several key advantages: 1. **Contextual Awareness**: The agent maintains context throughout the research process 2. **Dynamic Tool Selection**: It intelligently chooses which information sources to query based on your question 3. **Transparent Reasoning**: You can see each step of the research process 4. **Consistent Output Structure**: Results are formatted into comprehensive reports with proper citations ## The Technology Stack Building the Research Agent required integrating several cutting-edge technologies: - **LangChain**: Provides the foundation for LLM application development - **LangGraph**: Enables sophisticated workflow orchestration - **Chainlit**: Powers the interactive chat interface - **Qdrant**: Serves as the vector database for document embeddings - **OpenAI**: Supplies the GPT-4o language model and embeddings - **Tavily/DuckDuckGo**: Delivers web search capabilities - **arXiv API**: Connects to academic paper repositories - **Feedparser**: Handles RSS feed processing ## The Research Process in Action When you ask the Research Agent a question, it follows a systematic process: 1. **Query Analysis**: It first analyzes your question to determine which information sources would be most relevant 2. **Multi-Tool Research**: Depending on the query, it executes searches across selected tools 3. **Context Retrieval**: If you've uploaded documents, it retrieves relevant passages from them 4. **Research Transparency**: It shows each step of its research process for full transparency 5. **Information Synthesis**: It analyzes and combines information from all sources 6. **Structured Reporting**: It delivers a comprehensive response with proper citations ## Real-World Applications The Research Agent has proven valuable across various use cases: - **Academic Research**: Gathering information across multiple scholarly sources - **Competitive Analysis**: Staying updated on industry competitors - **Technical Deep Dives**: Understanding complex technical topics - **News Monitoring**: Tracking specific events across multiple sources - **Document Q&A**: Asking questions about specific documents in broader context ## Lessons Learned and Future Directions Building this agent taught me several valuable lessons about LLM application development: 1. **Tool Integration Complexity**: Combining multiple data sources requires careful consideration of data formats and query patterns 2. **Context Management**: Maintaining context across different research steps is critical for coherent outputs 3. **Transparency Matters**: Users trust AI more when they can see how it reached its conclusions 4. **LangGraph Power**: The graph-based approach to LLM workflows provides significant advantages over simpler chains Looking ahead, I'm exploring several enhancements: - Expanded academic database integration beyond arXiv - More sophisticated document analysis with multi-document reasoning - Improved citation formats and bibliographic support - Enhanced visualization of research findings ## Try It Yourself The Research Agent is available as an open-source project, and you can try it directly on Hugging Face Spaces: - **Live Demo**: [Hugging Face Space](https://huggingface.co/spaces/mafzaal/AIE6-ResearchAgent) - **Source Code**: [GitHub Repository](https://github.com/mafzaal/AIE6-ResearchAgent) If you're interested in deploying your own instance, the GitHub repository includes detailed setup instructions for both local development and Docker deployment. --- *Have you used the Research Agent or built similar tools? I'd love to hear about your experiences and any suggestions for improvements. Feel free to reach out through the contact form or connect with me on social media!*