LearningCircuit/local-deep-research
Local Deep Research
Local Deep Research is an autonomous research system consisting of an LLM research agent, a local model orchestrator, and a multi-engine search aggregator. It is designed to execute deep research by decomposing complex questions into atomic facts and synthesizing cited reports from academic, technical, and private document sources.
The system features an encrypted research workspace that ensures zero-knowledge privacy through isolated, per-user encrypted databases. It utilizes a local RAG knowledge base to index research sources into searchable vector stores, allowing for retrieval-augmented generation while maintaining data privacy via local language model integration.
The project covers autonomous research synthesis and academic research, including tools for journal quality scoring and adaptive search strategies. It provides capabilities for multi-engine querying, automated research monitoring through scheduled digests, and the export of findings into PDF and Markdown formats.
The system provides a research analytics dashboard for monitoring usage and performance, and offers a REST API for authenticated access to its research capabilities.
Features
- Autonomous Agent Orchestrators - Coordinates autonomous cycles of retrieval and synthesis by managing plans and invoking research tools.
- Research Synthesis - Uses LLMs to analyze raw data and synthesize cited reports from web, academic, and private sources.
- Autonomous Research Agents - Decomposes complex questions into atomic facts and synthesizes cited reports through autonomous research cycles.
- LLM Orchestrators - Provides a framework to coordinate workflows between various local and cloud language model deployments.
- LLM Provider Integrations - Provides configurations and adapters to connect the research system to both local and cloud-based LLM providers.
- Local Model Integrations - Integrates with locally-hosted language models to maintain data privacy and eliminate cloud API costs.
- Provider-Agnostic Model Interfaces - Uses standardized API bridges to allow flexible swapping between different local and cloud LLM providers.
- RAG Knowledge Management - Implements a local library that indexes research sources into vector stores to optimize retrieval augmented generation.
- Adaptive Search Strategies - Implements a dynamic strategy to select the most appropriate search engines based on the nature of the research query.
- Search Aggregators - Consolidates search results from academic, technical, and private sources into a single research interface.
- Knowledge Management - Provides a private, self-hosted system for organizing and retrieving indexed research findings.
- Local Knowledge Bases - Maintains a local library that parses and indexes extracted research sources for private retrieval.
- Recursive Query Decomposition - Decomposes complex research questions into smaller, atomic sub-queries to enable targeted multi-engine searches.
- Local Knowledge Base Indexers - Indexes research sources into a local vector library for efficient embedding-based semantic retrieval.
- Academic Research Syntheses - Synthesizes detailed reports from academic papers and web sources to answer complex research questions.
- Encrypted Database Managers - Provides a secure workspace with managed encrypted database files for individual user privacy.
- Multi-User Runtime Isolation - Ensures zero-knowledge privacy by maintaining isolated, encrypted data stores for every individual user.
- Zero-Knowledge Research Storage - Ensures zero-knowledge privacy for sensitive research information through isolated encrypted databases.
- MCP Server Integrations - Exposes research tools to AI assistants using the Model Context Protocol for seamless local integration.
- Scheduled Research Digests - Provides AI-powered summaries and updates on specific topics via a customizable delivery schedule.
- Topic Monitoring - Automatically tracks specific academic topics and updates through scheduled AI digests.
- Cross-Engine Retrieval - Gathers information from a diverse variety of academic, general, technical, and news sources.
- Vector Database Integrations - Integrates with specialized vector databases to enable high-dimensional similarity search across knowledge bases.
- Source Quality Assessments - Provides a tool to evaluate academic source reputation and identify predatory journals using indexed metadata.
- Data Encryption - Secures individual user research data in separate encrypted databases to ensure zero-knowledge privacy.
- Research APIs - Offers a REST API that allows external applications to programmatically access the autonomous research and synthesis engine.
- AI Agents and Automation - Local AI research assistant that searches web, papers, and documents.
- RAG Frameworks - Local-first agentic framework for multi-source research and retrieval.
- Research Agents - Local deep research using multiple LLMs.
- Research Assistants - Tool for iterative deep research.
- Research and Academia - Deep research tool for searching academic and private sources.
