What is Ollama Deep Researcher?
Ollama Deep Researcher is a fully local web research and report writing assistant that leverages any LLM hosted by Ollama or LMStudio. It automates the entire research process: given a topic, it generates a web search query, gathers results, summarizes them, reflects on knowledge gaps, and iterates with new queries for a user-defined number of cycles. The final output is a comprehensive markdown summary with all sources cited.
Key features include full local execution, ensuring data privacy and offline capability. It supports multiple LLM backends (Ollama, LMStudio) and search tools (DuckDuckGo, SearXNG, Tavily, Perplexity). Configuration is straightforward via a .env file, allowing customization of models, search APIs, and cycle count. The tool uses LangGraph for workflow orchestration and can be run as a LangGraph server with a Studio UI.
Benefits include time savings from automated multi-step research, improved depth through iterative reflection, and transparency with source attribution. Use cases range from academic literature reviews to market analysis, competitive intelligence, and content creation. Technical details: it requires Python, LangGraph CLI, and either Ollama or LMStudio. It defaults to DuckDuckGo (no API key) but can integrate with paid search APIs. The architecture supports JSON mode or tool calling for structured outputs, with a warning that gpt-oss models on Ollama lack JSON mode.
Ideal for researchers, students, and professionals needing quick, thorough, and local web research without compromising privacy.
Use Cases
Key Features
local execution data privacy offline capability multi LLM support web search integration iterative research source citation markdown outputWho is it for?
researchers, students, content creators, market analysts, competitive intelligence analysts, academic writers
