u14app/deep-research
Deep Research
Deep research is an automated research generation system that uses large language models and web search engines to synthesize comprehensive reports and deep-dive analyses on complex topics. It combines real-time web search results with uploaded local documents to ground generated content in specific factual data.
The system employs an iterative research workflow to refine reports through a step-by-step process of editing, updating, and restarting specific research stages. It can transform unstructured report data into knowledge graph visualizations to map relationships between different findings and organize the structure of a research project.
Capability areas include the integration of multiple AI models and search engines, the use of specialized templates to define research scope, and the management of research history. The system also supports the Model Context Protocol to connect research capabilities and data streams to external tools.
Features
- Structured Research Reports - Synthesizes comprehensive analysis reports by combining large language models with web search engines to aggregate information.
- Iterative Research Tools - Implements a multi-step web research system that generates queries and refines searches based on identified knowledge gaps.
- Web-Augmented Retrievers - Combines real-time web search results with uploaded local documents to ground model generations in specific facts.
- Local Document Indexing - Processes uploaded documents and files into searchable vector stores for RAG pipelines.
- AI Model Integrations - Connects multiple large language models and search engines to balance retrieval speed with analysis depth.
- Automated Research Generation - Uses large language models and web search engines to synthesize comprehensive reports and deep-dive analysis on complex topics.
- Research Report Drafting - Provides a process for iteratively retrieving and refining information to draft detailed research reports.
- Hybrid Knowledge Integration - Unifies user-provided documents and local resources as supplementary data sources for automated research.
- Language Model Connectivity - Integrates various language models and search engines to balance analysis depth with retrieval speed.
- Retrieval Iteration Loops - Repeatedly queries language models and search engines until a comprehensive information threshold is met.
- Document Search Grounding - Grounds AI generated reports in specific factual data from a combination of local documents and web search.
- Research Synthesis - Uses LLMs to synthesize raw scraped web data and uploaded local documents into structured research reports.
- Model Context Protocol Integrations - Uses a standardized communication protocol to connect core logic to external tools and disparate data sources.
- MCP Protocol Integrations - Uses the Model Context Protocol to connect research capabilities and data streams to external AI tools.
- Research Data Management - Stores previous results for review and triggers further in-depth study based on past reports.
- Process State Recovery - Tracks research progress in stages to allow restarting the synthesis loop from a specific saved checkpoint.
- Template-Driven Reporters - Guides the research process by applying pre-defined structural schemas for specific report types like market maps.
- Knowledge Graph Visualizations - Transforms unstructured report data into a structured network of nodes and edges to visualize conceptual relationships.
- Investigation Scoping - Sets the initial topic and applies specialized templates for market maps or technical deep dives to guide investigations.
Star history
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