ollama-deep-researcher
The agent performs AI-driven research by generating queries, gathering web data, summarizing findings, and refining results through iterative knowledge gap analysis.
Example requests
beeai run ollama-deep-researcher "Advancements in quantum computing"
Description
This agent automates deep web research by generating queries, gathering relevant sources, summarizing key information, and iterating on knowledge gaps to refine the results. It enables structured, efficient research through a configurable workflow.
How It Works
The agent follows a structured workflow to perform iterative web research and summarization:
- Query Generation: Uses an LLM to generate a precise search query based on the given research topic.
- Web Research: Searches the web using different APIs (Tavily, Perplexity, DuckDuckGo) to retrieve relevant sources.
- Summarization: Extracts key insights from search results and integrates them into an evolving summary.
- Reflection & Iteration: Identifies knowledge gaps and generates follow-up queries for deeper research.
- Finalization: Consolidates all gathered insights and sources into a structured summary.
The agent loops through steps 2–4 until the research loop limit is reached.
Input Parameters
- prompt (string) – The topic to research.
Key Features
- Iterative Research Process – Automatically refines queries and expands knowledge.
- Multi-Source Information Gathering – Supports Tavily, DuckDuckGo, and Perplexity APIs.
- LLM-Powered Summarization – Generates coherent and well-structured summaries.
- Automated Query Refinement – Identifies knowledge gaps and adjusts queries dynamically.
Use Cases
- Market Research – Automates data gathering for competitive analysis.
- Academic Research – Summarizes recent findings on a specific topic.
- Content Creation – Gathers background information for articles, blogs, and reports.
- Technical Deep Dives – Explores emerging technologies with structured, iterative research.