AI Agents
Tongyi Deep Research
Tongyi Deep Research - Alibaba's open-source deep research agent and 30B model
★ 20.0k starsApache-2.0
View guide →Deep research that stays on your machine — local or cloud LLMs, 10+ search engines, encrypted and private.
local-deep-research is an open-source ai agents project by LearningCircuit (LearningCircuit/local-deep-research on GitHub). It has 9,165 GitHub stars and 836 forks, is released under the MIT license, and was last updated on GitHub on 8 October 2026.
Choose your model path (a local model via Ollama is the private default) and which search engines and private document collections the researcher may use.
Clone LearningCircuit/local-deep-research from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.
The quickest path is Docker: run Ollama and SearXNG, then the Local Deep Research container — or install from pip if you prefer a native setup.
Open the web UI on port 5000, ask a research question, and review the cited report before running longer investigations.
Run the whole pipeline on your own machine with local models, with data kept local and encrypted.
Works with local and cloud LLMs alike — llama.cpp, Ollama, Google and more — so you can trade privacy against capability per run.
Searches across engines including arXiv, PubMed, Brave and SearXNG, plus your private documents.
It does the research for you: searching, reading and synthesising findings into a report with proper citations.
Build a local knowledge base from your own documents and research over it alongside web sources.
Operator controls govern which engine URLs the server may contact, with private/localhost engine URLs blocked by default unless explicitly allowed.
Get the source from the official GitHub repository for local-deep-research.
git clone https://github.com/LearningCircuit/local-deep-research.git
cd local-deep-researchFetch the compose file and start the stack as the README's Option 2 shows (Option 1 runs the individual containers).
curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -dInstall the package from PyPI and follow the README to configure your model and search engines, then open the web UI.
pip install local-deep-research
# Open http://localhost:5000 after starting — see READMELearningCircuit, through the LearningCircuit/local-deep-research repository.
Yes — the project is open source under the MIT license as recorded for this listing. You can use, modify and distribute it under the MIT terms.
No paid key is required with a local model and self-hosted SearXNG. Cloud models or hosted search engines you choose to enable use your own keys.
Hosted deep research runs on the vendor's cloud with their models and their data handling. Local Deep Research runs on your hardware with your choice of local or cloud models and encrypted local storage — you operate the stack, in exchange for control.
Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — open the web UI on port 5000, ask a research question, and review the cited report before running longer investigations.
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