Is local-deep-research free to use? What license does it have?

local-deep-research is open source under the MIT license. Check the license terms before using it in commercial products.

Is local-deep-research still maintained?

Actively maintained. The last commit to local-deep-research was on Oct 19, 2026.

What can I use local-deep-research for?

Literature reviews that need searches across arXiv, PubMed and the open web with citations Querying confidential documents while keeping inference and data on your own hardware Building a searchable personal research library that grows with each research session Automating recurring topic digests from academic and news sources

How do I install local-deep-research?

Download the Docker Compose file and start the stack: curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -d Open the web UI in your browser after about 30 seconds

Where can I get local-deep-research?

The source code and installation instructions are on GitHub: https://github.com/LearningCircuit/local-deep-research

LearningCircuit/local-deep-research

Self-hosted AI research assistant that runs deep, multi-step searches across local and cloud LLMs and many search engines, with citations and encrypted storage.

Open-source alternative to:Perplexity ChatGPT Deep Research

View on GitHub

Stars: 9,331
Forks: 869
Open issues: 1,253
Last commit: yesterday

What is it?

Local Deep Research is a self-hosted research assistant. Given a question, it searches the web, academic databases such as arXiv and PubMed, and your own documents, then synthesizes the findings into a report with citations. It works with local models served by Ollama, LM Studio or llama.cpp, as well as cloud providers such as OpenAI, Anthropic, Google Gemini, OpenRouter and Requesty.

The project offers quick summaries, detailed research and full report generation, plus a LangGraph-based agent strategy in which the LLM decides which engines to query and when to stop. Downloaded sources can be indexed into a personal encrypted library and searched alongside live web results. Each user's data is kept in an SQLCipher database encrypted with a key derived from their password.

LDR also exposes a Python API, an HTTP API, an MCP server for Claude Desktop and Claude Code, and LangChain retriever integration. An egress scope setting controls which search engines and model providers a run may use, including a private-only mode that forces local inference.

Key features

01Multi-step agentic research that picks search engines and synthesizes cited reports
02Supports Ollama, LM Studio, llama.cpp, OpenAI, Anthropic, Gemini and OpenRouter models
03Searches arXiv, PubMed, Semantic Scholar, Wikipedia, SearXNG and other engines
04Indexes downloaded sources and your own documents into an encrypted local library
05Per-user SQLCipher databases with encryption keyed from the user's password
06Python API, HTTP API and MCP server for Claude integration

Use cases

Literature reviews that need searches across arXiv, PubMed and the open web with citations
Querying confidential documents while keeping inference and data on your own hardware
Building a searchable personal research library that grows with each research session
Automating recurring topic digests from academic and news sources

Quick start

1
Download the Docker Compose file and start the stack
curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -d
2
Open the web UI in your browser after about 30 seconds

Things to know

Full use needs a running LLM endpoint such as Ollama, plus a SearXNG search instance, which means self-hosting effort.
The quick Docker Run recipe only works on native Linux; Mac and Windows users must use Docker Compose.
Requires an AVX-capable x86-64 CPU or ARM64.

Topics

#ai #deep-research #rag #local-llm #ollama #self-hosted #search #academic-research #academia #anthropic

FAQ

What is local-deep-research?: Self-hosted AI research assistant that runs deep, multi-step searches across local and cloud LLMs and many search engines, with citations and encrypted storage. Local Deep Research is a self-hosted research assistant. Given a question, it searches the web, academic databases such as arXiv and PubMed, and your own documents, then synthesizes the findings into a report with citations. It works with local models served by Ollama, LM Studio or llama.cpp, as well as cloud providers such as OpenAI, Anthropic, Google Gemini, OpenRouter and Requesty.
Is local-deep-research free to use? What license does it have?: local-deep-research is open source under the MIT license. Check the license terms before using it in commercial products.
Is local-deep-research still maintained?: Actively maintained. The last commit to local-deep-research was on Oct 19, 2026.
What can I use local-deep-research for?: Literature reviews that need searches across arXiv, PubMed and the open web with citations Querying confidential documents while keeping inference and data on your own hardware Building a searchable personal research library that grows with each research session Automating recurring topic digests from academic and news sources
How do I install local-deep-research?: Download the Docker Compose file and start the stack: curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -d Open the web UI in your browser after about 30 seconds
Where can I get local-deep-research?: The source code and installation instructions are on GitHub: https://github.com/LearningCircuit/local-deep-research

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