
local-deep-research
Local Deep Research achieves ~95% on SimpleQA benchmark (tested with GPT-4.1-mini). Supports local and cloud LLMs (Ollama, Google, Anthropic, ...). Searches 10+ sources - arXiv, PubMed, web, and your private documents. Everything Local.
Stars: 3352

Local Deep Research is a powerful AI-powered research assistant that performs deep, iterative analysis using multiple LLMs and web searches. It can be run locally for privacy or configured to use cloud-based LLMs for enhanced capabilities. The tool offers advanced research capabilities, flexible LLM support, rich output options, privacy-focused operation, enhanced search integration, and academic & scientific integration. It also provides a web interface, command line interface, and supports multiple LLM providers and search engines. Users can configure AI models, search engines, and research parameters for customized research experiences.
README:
AI-powered research assistant for deep, iterative research
Performs deep, iterative research using multiple LLMs and search engines with proper citations
LDR is an AI research assistant that performs systematic research by:
- Breaking down complex questions into focused sub-queries
- Searching multiple sources in parallel (web, academic papers, local documents)
- Verifying information across sources for accuracy
- Creating comprehensive reports with proper citations
It aims to help researchers, students, and professionals find accurate information quickly while maintaining transparency about sources.
- Privacy-Focused: Run entirely locally with Ollama + SearXNG
- Flexible: Use any LLM, any search engine, any vector store
- Comprehensive: Multiple research modes from quick summaries to detailed reports
- Transparent: Track costs and performance with built-in analytics
- Open Source: MIT licensed with an active community
~95% accuracy on SimpleQA benchmark (preliminary results)
- Tested with GPT-4.1-mini + SearXNG + focused-iteration strategy
- Comparable to state-of-the-art AI research systems
- Local models can achieve similar performance with proper configuration
- Join our community benchmarking effort β
- Quick Summary - Get answers in 30 seconds to 3 minutes with citations
- Detailed Research - Comprehensive analysis with structured findings
- Report Generation - Professional reports with sections and table of contents
- Document Analysis - Search your private documents with AI
- LangChain Integration - Use any vector store as a search engine
- REST API - Authenticated HTTP access with per-user databases
- Benchmarking - Test and optimize your configuration
- Analytics Dashboard - Track costs, performance, and usage metrics
- Real-time Updates - WebSocket support for live research progress
- Export Options - Download results as PDF or Markdown
- Research History - Save, search, and revisit past research
- Adaptive Rate Limiting - Intelligent retry system that learns optimal wait times
- Keyboard Shortcuts - Navigate efficiently (ESC, Ctrl+Shift+1-5)
- Per-User Encrypted Databases - Secure, isolated data storage for each user
- Academic: arXiv, PubMed, Semantic Scholar
- General: Wikipedia, SearXNG
- Technical: GitHub, Elasticsearch
- Historical: Wayback Machine
- News: The Guardian
- Tavily - AI-powered search
- Google - Via SerpAPI or Programmable Search Engine
- Brave Search - Privacy-focused web search
- Local Documents - Search your files with AI
- LangChain Retrievers - Any vector store or database
- Meta Search - Combine multiple engines intelligently
# Step 1: Pull and run SearXNG for optimal search results
docker run -d -p 8080:8080 --name searxng searxng/searxng
# Step 2: Pull and run Local Deep Research (Please build your own docker on ARM)
docker run -d -p 5000:5000 --network host --name local-deep-research --volume 'deep-research:/data' -e LDR_DATA_DIR=/data localdeepresearch/local-deep-research
LDR uses Docker compose to bundle the web app and all it's dependencies so you can get up and running quickly.
Linux:
curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -d
Windows:
curl.exe -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml; docker compose up -d
Use with a different model:
MODEL=gemma:1b curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -d
Open http://localhost:5000 after ~30 seconds. This starts LDR with SearXNG and all dependencies.
See docker-compose.yml for a docker-compose file with reasonable defaults to get up and running with ollama, searxng, and local deep research all running locally.
Things you may want/need to configure:
- Ollama GPU driver
- Ollama context length (depends on available VRAM)
- Ollama keep alive (duration model will stay loaded into VRAM and idle before getting unloaded automatically)
- Deep Research model (depends on available VRAM and preference)
- Docker
- Docker Compose
-
cookiecutter
: Runpip install --user cookiecutter
Clone the repository:
git clone https://github.com/LearningCircuit/local-deep-research.git
cd local-deep-research
Cookiecutter will interactively guide you through the process of creating a
docker-compose
configuration that meets your specific needs. This is the
recommended approach if you are not very familiar with Docker.
In the LDR repository, run the following command to generate the compose file:
cookiecutter cookiecutter-docker/
docker compose -f docker-compose.default.yml up
# Step 1: Install the package
pip install local-deep-research
# Step 2: Setup SearXNG for best results
docker pull searxng/searxng
docker run -d -p 8080:8080 --name searxng searxng/searxng
# Step 3: Install Ollama from https://ollama.ai
# Step 4: Download a model
ollama pull gemma3:12b
# Step 5: Build frontend assets (required for Web UI)
# Note: If installed via pip and using the Web UI, you need to build assets
# Navigate to the installation directory first (find with: pip show local-deep-research)
npm install
npm run build
# Step 6: Start the web interface
python -m local_deep_research.web.app
Important for pip users: If you installed via pip and want to use the web UI, you must run npm install
and npm run build
in the package installation directory to generate frontend assets (icons, styles). Without this, the UI will have missing icons and styling issues. For programmatic API usage only, these steps can be skipped.
from local_deep_research.api import quick_summary
from local_deep_research.settings import CachedSettingsManager
from local_deep_research.database.session_context import get_user_db_session
# Authentication required - use with user session
with get_user_db_session(username="your_username", password="your_password") as session:
settings_manager = CachedSettingsManager(session, "your_username")
settings_snapshot = settings_manager.get_all_settings()
# Simple usage with settings
result = quick_summary(
query="What are the latest advances in quantum computing?",
settings_snapshot=settings_snapshot
)
print(result["summary"])
import requests
# Create session and authenticate
session = requests.Session()
session.post("http://localhost:5000/auth/login",
json={"username": "user", "password": "pass"})
# Get CSRF token
csrf = session.get("http://localhost:5000/auth/csrf-token").json()["csrf_token"]
# Make API request
response = session.post(
"http://localhost:5000/research/api/start",
json={"query": "Explain CRISPR gene editing"},
headers={"X-CSRF-Token": csrf}
)
# Run benchmarks from CLI
python -m local_deep_research.benchmarks --dataset simpleqa --examples 50
# Manage rate limiting
python -m local_deep_research.web_search_engines.rate_limiting status
python -m local_deep_research.web_search_engines.rate_limiting reset
Connect LDR to your existing knowledge base:
from local_deep_research.api import quick_summary
# Use your existing LangChain retriever
result = quick_summary(
query="What are our deployment procedures?",
retrievers={"company_kb": your_retriever},
search_tool="company_kb"
)
Works with: FAISS, Chroma, Pinecone, Weaviate, Elasticsearch, and any LangChain-compatible retriever.
Early experiments on small SimpleQA dataset samples:
Configuration | Accuracy | Notes |
---|---|---|
gpt-4.1-mini + SearXNG + focused_iteration | 90-95% | Limited sample size |
gpt-4.1-mini + Tavily + focused_iteration | 90-95% | Limited sample size |
gemini-2.0-flash-001 + SearXNG | 82% | Single test run |
Note: These are preliminary results from initial testing. Performance varies significantly based on query types, model versions, and configurations. Run your own benchmarks β
Track costs, performance, and usage with detailed metrics. Learn more β
- Llama 3, Mistral, Gemma, DeepSeek
- LLM processing stays local (search queries still go to web)
- No API costs
- OpenAI (GPT-4, GPT-3.5)
- Anthropic (Claude 3)
- Google (Gemini)
- 100+ models via OpenRouter
- Discord - Get help and share research techniques
- Reddit - Updates and showcases
- GitHub Issues - Bug reports
We welcome contributions! See our Contributing Guide to get started.
MIT License - see LICENSE file.
Built with: LangChain, Ollama, SearXNG, FAISS
Support Free Knowledge: Consider donating to Wikipedia, arXiv, or PubMed.
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