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 & Encrypted.
Stars: 4033
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
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.
Docker Run (Linux):
# Step 1: Pull and run Ollama
docker run -d -p 11434:11434 --name ollama ollama/ollama
docker exec ollama ollama pull gpt-oss:20b
# Step 2: Pull and run SearXNG for optimal search results
docker run -d -p 8080:8080 --name searxng searxng/searxng
# Step 3: Pull and run Local Deep Research
docker run -d -p 5000:5000 --network host \
--name local-deep-research \
--volume 'deep-research:/data' \
-e LDR_DATA_DIR=/data \
localdeepresearch/local-deep-researchExemplary Docker Compose:
- Mac and no Nvidia-GPU: Docker Compose File
# download and up -d
curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -d- With NVIDIA GPU (Linux):
# download and up -d
curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && \
curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.gpu.override.yml && \
docker compose -f docker-compose.yml -f docker-compose.gpu.override.yml up -dOpen http://localhost:5000 after ~30 seconds.
pip install (for developers):
pip install local-deep-research
⚠️ Docker is preferred for most users. SQLCipher installation can be difficult — if you don't need database encryption, setexport LDR_ALLOW_UNENCRYPTED=trueto skip it. API keys and data will be stored unencrypted. For encryption setup, see SQLCipher 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.
flowchart LR
R[Research] --> D[Download Sources]
D --> L[(Library)]
L --> I[Index & Embed]
I --> S[Search Your Docs]
S -.-> REvery research session finds valuable sources. Download them directly into your encrypted library—academic papers from ArXiv, PubMed articles, web pages. LDR extracts text, indexes everything, and makes it searchable. Next time you research, ask questions across your own documents and the live web together. Your knowledge compounds over time.
flowchart LR
U1[User A] --> D1[(Encrypted DB)]
U2[User B] --> D2[(Encrypted DB)]Your data stays yours. Each user gets their own isolated SQLCipher database encrypted with AES-256 (Signal-level security). No password recovery means true zero-knowledge—even server admins can't read your data. Run fully local with Ollama + SearXNG and nothing ever leaves your machine.
In-memory credentials: Like all applications that use secrets at runtime — including password managers, browsers, and API clients — credentials are held in plain text in process memory during active sessions. This is an industry-wide accepted reality, not specific to LDR: if an attacker can read process memory, they can also read any in-process decryption key. We mitigate this with session-scoped credential lifetimes and core dump exclusion. Ideas for further improvements are always welcome via GitHub Issues. See our Security Policy for details.
Supply Chain Security: Docker images are signed with Cosign, include SLSA provenance attestations, and attach SBOMs. Verify with:
cosign verify localdeepresearch/local-deep-research:latestDetailed Architecture → | Security Policy →
~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 →
- 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
- Automated Research Digests - Subscribe to topics and receive AI-powered research summaries
- Customizable Frequency - Daily, weekly, or custom schedules for research updates
- Smart Filtering - AI filters and summarizes only the most relevant developments
- Multi-format Delivery - Get updates as markdown reports or structured summaries
- Topic & Query Support - Track specific searches or broad research areas
- Academic: arXiv, PubMed, Semantic Scholar
- General: Wikipedia, SearXNG
- Technical: GitHub, Elasticsearch
- Historical: Wayback Machine
- News: The Guardian, Wikinews
- 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
docker run -d -p 5000:5000 --network host \
--name local-deep-research \
--volume 'deep-research:/data' \
-e LDR_DATA_DIR=/data \
localdeepresearch/local-deep-researchLDR uses Docker compose to bundle the web app and all its dependencies so you can get up and running quickly.
Default: CPU-only base (works on all platforms)
The base configuration works on macOS (M1/M2/M3/M4 and Intel), Windows, and Linux without requiring any GPU hardware.
Quick Start Command:
Note: curl -O will overwrite existing docker-compose.yml files in the current directory.
Linux/macOS:
curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -dWindows (PowerShell required):
curl.exe -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml
if ($?) { docker compose up -d }Use with a different model:
curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && MODEL=gpt-oss:20b docker compose up -dFor users with NVIDIA GPUs who want hardware acceleration.
Prerequisites:
Install the NVIDIA Container Toolkit first (Ubuntu/Debian):
# Install NVIDIA Container Toolkit (for GPU support)
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install nvidia-container-toolkit -y
sudo systemctl restart docker
# Verify installation
nvidia-smiVerify: The nvidia-smi command should display your GPU information. If it fails, check your NVIDIA driver installation.
Note: For RHEL/CentOS/Fedora, Arch, or other Linux distributions, see the NVIDIA Container Toolkit installation guide.
Quick Start Commands:
Note: curl -O will overwrite existing files in the current directory.
curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && \
curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.gpu.override.yml && \
docker compose -f docker-compose.yml -f docker-compose.gpu.override.yml up -dOptional: Create an alias for convenience
alias docker-compose-gpu='docker compose -f docker-compose.yml -f docker-compose.gpu.override.yml'
# Then simply use: docker-compose-gpu up -dOpen 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-researchCookiecutter 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 upNote: For most users, Docker is preferred as it handles all dependencies automatically. pip install is best suited for developers or users who want to integrate LDR into existing Python projects. SQLCipher installation can be difficult — see the note below for how to skip it.
# 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: Start the web interface
python -m local_deep_research.web.app
⚠️ SQLCipher Note: For database encryption (AES-256), install system-level SQLCipher libraries — see SQLCipher Guide. If you don't need encryption, setexport LDR_ALLOW_UNENCRYPTED=trueto use standard SQLite. API keys and data will be stored unencrypted. Docker includes encryption out of the box.
Note: For development from source, see the Development Guide.
VLLM support (for running transformer models directly):
pip install "local-deep-research[vllm]"This installs torch, transformers, and vllm for advanced local model hosting. Most users running Ollama or LlamaCpp don't need this.
For Unraid users:
Local Deep Research is fully compatible with Unraid servers!
- Navigate to Docker tab → Docker Repositories
- Add template repository:
https://github.com/LearningCircuit/local-deep-research - Click Add Container → Select LocalDeepResearch from template
- Configure paths (default:
/mnt/user/appdata/local-deep-research/) - Click Apply
If you prefer using Docker Compose on Unraid:
- Install "Docker Compose Manager" from Community Applications
- Create a new stack with the compose file from this repo
- Update volume paths to Unraid format (
/mnt/user/appdata/...)
Features on Unraid:
- ✅ Pre-configured template with sensible defaults
- ✅ Automatic SearXNG and Ollama integration
- ✅ NVIDIA GPU passthrough support (optional)
- ✅ Integration with Unraid shares for document search
- ✅ Backup integration with CA Appdata Backup plugin
from local_deep_research.api import LDRClient, quick_query
# Option 1: Simplest - one line research
summary = quick_query("username", "password", "What is quantum computing?")
print(summary)
# Option 2: Client for multiple operations
client = LDRClient()
client.login("username", "password")
result = client.quick_research("What are the latest advances in quantum computing?")
print(result["summary"])The code example below shows the basic API structure - for working examples, see the link below
import requests
from bs4 import BeautifulSoup
# Create session and authenticate
session = requests.Session()
login_page = session.get("http://localhost:5000/auth/login")
soup = BeautifulSoup(login_page.text, "html.parser")
login_csrf = soup.find("input", {"name": "csrf_token"}).get("value")
# Login and get API CSRF token
session.post("http://localhost:5000/auth/login",
data={"username": "user", "password": "pass", "csrf_token": login_csrf})
csrf = session.get("http://localhost:5000/auth/csrf-token").json()["csrf_token"]
# Make API request
response = session.post("http://localhost:5000/api/start_research",
json={"query": "Your research question"},
headers={"X-CSRF-Token": csrf})🚀 Ready-to-use HTTP API Examples → examples/api_usage/http/
- ✅ Automatic user creation - works out of the box
- ✅ Complete authentication with CSRF handling
- ✅ Result retry logic - waits until research completes
- ✅ Progress monitoring and error handling
# 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 resetConnect 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 →
Track costs, performance, and usage with detailed metrics. Learn more →
- 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
- Installation Guide
- Frequently Asked Questions
- API Quickstart
- Configuration Guide
- Full Configuration Reference
"Local Deep Research deserves special mention for those who prioritize privacy... tuned to use open-source LLMs that can run on consumer GPUs or even CPUs. Journalists, researchers, or companies with sensitive topics can investigate information without queries ever hitting an external server."
- Korben.info - French tech blog ("Sherlock Holmes numérique")
- Roboto.fr - "L'alternative open-source gratuite à Deep Research d'OpenAI"
- KDJingPai AI Tools - AI productivity tools coverage
- AI Sharing Circle - AI resources coverage
- Hacker News - 190+ points, community discussion
- LangChain Twitter/X - Official LangChain promotion
- LangChain LinkedIn - 400+ likes
- Juejin (掘金) - Developer community
- Cnblogs (博客园) - Developer blogs
- GitHubDaily (Twitter/X) - Influential tech account
- Zhihu (知乎) - Tech community
- A姐分享 - AI resources
- CSDN - Installation guide
- NetEase (网易) - Tech news portal
- note.com: 調査革命：Local Deep Research徹底活用法 - Comprehensive tutorial
- Qiita: Local Deep Researchを試す - Docker setup guide
- LangChainJP (Twitter/X) - Japanese LangChain community
- PyTorch Korea Forum - Korean ML community
- GeekNews (Hada.io) - Korean tech news
- BSAIL Lab: How useful is Deep Research in Academia? - Academic review by contributor @djpetti
- The Art Of The Terminal: Use Local LLMs Already! - Comprehensive review of local AI tools, featuring LDR's research capabilities (embeddings now work!)
- SearXNG LDR-Academic - Academic-focused SearXNG fork with 12 research engines (arXiv, Google Scholar, PubMed, etc.) designed for LDR
- DeepWiki Documentation - Third-party documentation and guides
Note: Third-party projects and articles are independently maintained. We link to them as useful resources but cannot guarantee their code quality or security.
- Discord - Get help and share research techniques
- Reddit - Updates and showcases
- GitHub Issues - Bug reports
We welcome contributions of all sizes — from typo fixes to new features. The key rule: keep PRs small and atomic (one change per PR). For larger changes, please open an issue or start a discussion first — we want to protect your time and make sure your effort leads to a successful merge rather than a misaligned PR. See our Contributing Guide to get started.
MIT License - see LICENSE file.
Dependencies: All third-party packages use permissive licenses (MIT, Apache-2.0, BSD, etc.) - see allowlist
Built with: LangChain, Ollama, SearXNG, FAISS
Support Free Knowledge: Consider donating to Wikipedia, arXiv, or PubMed.
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Paper-ai is a tool that helps you write papers using artificial intelligence. It provides features such as AI writing assistance, reference searching, and editing and formatting tools. With Paper-ai, you can quickly and easily create high-quality papers.
paper-qa
PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and follows a process of embedding docs and queries, searching for top passages, creating summaries, scoring and selecting relevant summaries, putting summaries into prompt, and generating answers. Users can customize prompts and use various models for embeddings and LLMs. The tool can be used asynchronously and supports adding documents from paths, files, or URLs.
ChatData
ChatData is a robust chat-with-documents application designed to extract information and provide answers by querying the MyScale free knowledge base or uploaded documents. It leverages the Retrieval Augmented Generation (RAG) framework, millions of Wikipedia pages, and arXiv papers. Features include self-querying retriever, VectorSQL, session management, and building a personalized knowledge base. Users can effortlessly navigate vast data, explore academic papers, and research documents. ChatData empowers researchers, students, and knowledge enthusiasts to unlock the true potential of information retrieval.
noScribe
noScribe is an AI-based software designed for automated audio transcription, specifically tailored for transcribing interviews for qualitative social research or journalistic purposes. It is a free and open-source tool that runs locally on the user's computer, ensuring data privacy. The software can differentiate between speakers and supports transcription in 99 languages. It includes a user-friendly editor for reviewing and correcting transcripts. Developed by Kai Dröge, a PhD in sociology with a background in computer science, noScribe aims to streamline the transcription process and enhance the efficiency of qualitative analysis.
AIStudyAssistant
AI Study Assistant is an app designed to enhance learning experience and boost academic performance. It serves as a personal tutor, lecture summarizer, writer, and question generator powered by Google PaLM 2. Features include interacting with an AI chatbot, summarizing lectures, generating essays, and creating practice questions. The app is built using 100% Kotlin, Jetpack Compose, Clean Architecture, and MVVM design pattern, with technologies like Ktor, Room DB, Hilt, and Kotlin coroutines. AI Study Assistant aims to provide comprehensive AI-powered assistance for students in various academic tasks.
data-to-paper
Data-to-paper is an AI-driven framework designed to guide users through the process of conducting end-to-end scientific research, starting from raw data to the creation of comprehensive and human-verifiable research papers. The framework leverages a combination of LLM and rule-based agents to assist in tasks such as hypothesis generation, literature search, data analysis, result interpretation, and paper writing. It aims to accelerate research while maintaining key scientific values like transparency, traceability, and verifiability. The framework is field-agnostic, supports both open-goal and fixed-goal research, creates data-chained manuscripts, involves human-in-the-loop interaction, and allows for transparent replay of the research process.
k2
K2 (GeoLLaMA) is a large language model for geoscience, trained on geoscience literature and fine-tuned with knowledge-intensive instruction data. It outperforms baseline models on objective and subjective tasks. The repository provides K2 weights, core data of GeoSignal, GeoBench benchmark, and code for further pretraining and instruction tuning. The model is available on Hugging Face for use. The project aims to create larger and more powerful geoscience language models in the future.