local-deep-research

local-deep-research is a RAG toolkit - ~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.

Overview

🧪 First open-source project — fully-local on a single RTX 3090 (Qwen3.6-27B) — to report ~95% SimpleQA (n=500) and 77% xbench-DeepSearch (n=100) on local hardware. See the r/LocalLLaMA announcement and the benchmark dataset.

AI research assistant you control. Run locally for privacy, use any LLM and build your own searchable knowledge base. You own your data and see exactly how it works.

Open http://localhost:5000 after ~30 seconds. For GPU setup, environment variables, and more, see the Docker Compose Guide.

You ask a complex question. LDR: - Does the research for you automatically - Searches across web, academic papers, and your own documents - Synthesizes everything into a report with proper citations

Choose from 20+ research strategies for quick facts, deep analysis, or academic research.

LangGraph Agent Strategy — An autonomous agentic research mode where the LLM decides what to search, which specialized engines to use (arXiv, PubMed, Semantic Scholar, etc.), and when to synthesize. It adaptively switches between search engines based on what it finds and collects significantly more sources than pipeline-based strategies — this is the strategy behind the ~95% SimpleQA result above. Select 'langgraph-agent' in Settings.

local-deep-research is open-source, written primarily in Python, with 8,401 GitHub stars under the MIT license. The latest release is v1.7.0 (2026-06-05).

Key capabilities

From the project's documentation:

• Does the research for you automatically
• Searches across web, academic papers, and your own documents
• Synthesizes everything into a report with proper citations
• Submit your own results on GitHub →
• Quick Summary - Get answers in 30 seconds to 3 minutes with citations
• Detailed Research - Comprehensive analysis with structured findings

Install

A quick way to get started (always check the official docs for the latest):

pip install "local-deep-research[mcp]"

How it fits a local-AI stack

local-deep-research runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related RAG toolkits in the directory:

• AnythingLLM
• Haystack
• Khoj
• Onyx

Sources

• Source code & docs: LearningCircuit/local-deep-research

Stats from GitHub, 2026-06-08.