OpenPilot Research：你的本地 AI 研究助理｜Hacktoberfest 2026

🎯 This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.

🛠️ What I Built

Meet OpenPilot Research — an AI-powered research assistant designed to make online research faster, simpler, and more organized. 🤖📚

Researching a topic often means searching the web, opening multiple pages, comparing information, and writing summaries manually. What if AI could help bring those steps together?

✨ With OpenPilot Research, you can enter a research question, retrieve relevant web-search results, and generate an AI-powered research report using a locally running Gemma 3 4B model.

📌 Current features:

🔎 Web search using SerpApi
🧠 AI-generated research reports using Gemma 3 4B
⚡ FastAPI backend for the research workflow
💻 Next.js and TypeScript dashboard
💾 SQLite storage for generated reports and research history

🚧 A Knowledge Base and detailed agent-tracing features are planned for future development.

🎬 Demo

🔗 GitHub Repository: OpenPilot Research

The application currently runs locally. A short walkthrough can demonstrate the research workflow, generated report, sources, and saved history.

🎥 Demo video:

💻 Code

🌟 Explore the source code, review the implementation, and follow the project's progress:

👉 github.com/Snehal20Koli/OpenPilot-Research

Contributions, suggestions, and feedback are welcome! 🤝

⚙️ How I Built It

OpenPilot Research combines web technologies, web search, and local AI inference.

🧠 Gemma 3 4B: Generates research summaries from retrieved search snippets.
🦙 Ollama: Runs the model locally without requiring a hosted LLM API for inference.
🔎 SerpApi: Retrieves relevant web-search results.
🐍 FastAPI: Provides the backend API and connects the research workflow.
⚛️ Next.js + TypeScript: Power the interactive web dashboard.
🗄️ SQLite: Stores generated reports, questions, and sources for later retrieval.

🔄 The workflow:

💬 Enter a research question.
🔍 Retrieve relevant web-search results.
🤖 Generate a report using the local Gemma model.
💾 Save the report and sources in research history.
📖 Revisit previous investigations when needed.

🌍 Why Does Open Innovation Matter?

Open innovation gives developers more freedom to experiment, learn, and build on shared technology.

By using an open-weight model such as Gemma, I can experiment with local inference and build an AI application without depending entirely on a proprietary hosted LLM API.

🔐 Local model inference can help keep prompts and model inputs on the local machine. However, OpenPilot Research uses SerpApi for web searches, so search queries are sent to that service.

🚀 Open tools also make it easier for developers to inspect the implementation, adapt it, experiment with different models, and contribute improvements. That's what makes building in the open valuable!

⏳ The Lazy Payoff

The goal is simple: less time collecting information, more time understanding it. 💡

Instead of repeatedly switching between search results and a blank document, users can generate a starting report and revisit saved investigations.

I haven't measured exact time savings yet, so I see the current benefit as reducing repetitive research steps rather than claiming a specific productivity improvement.

🏆 Prize Categories

🧠 Best Use of Gemma
🔎 Best Use of SerpApi

🙌 Final Thoughts

OpenPilot Research is a work in progress, and this is just the beginning!

I'm excited to keep improving it, explore more open-source AI capabilities, and make research workflows more accessible to developers and curious learners.

⭐ Check out the repository, share your feedback, and let's build in the open!

🔗 Project: https://github.com/Snehal20Koli/OpenPilot-Research