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Local Deep Research

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Local Deep Research's own site

What Local Deep Research is

Local Deep Research is a free AI research assistant that searches the web and connected sources, works with multiple language models, and produces reports with citations. It supports quick summaries, detailed research, report generation, and document analysis. Sources can be downloaded into an encrypted library, indexed, and searched alongside live web results. Listed search connections include arXiv, PubMed, Semantic Scholar, Wikipedia, SearXNG, GitHub, Elasticsearch, Wayback Machine, The Guardian, and Wikinews. Local models can run through Ollama, LM Studio, or llama.cpp, while hosted options include OpenAI, Anthropic Claude, Google Gemini, OpenRouter, Requesty, and compatible endpoints. Interfaces include a web UI, Python API, authenticated HTTP REST API, and an MCP server for Claude Desktop and Claude Code. Reports export to PDF or Markdown, and scheduled digests can be delivered as Markdown or structured summaries. Installation supports Docker, Docker Compose, and pip on Windows, macOS, and Linux.

Who it suits

Local Deep Research suits researchers who need cited reports from web, file, and connector sources. It also fits self-hosted users who want local models, APIs, encrypted storage, and scheduled research digests.

What it does well

  • Produces reports with source citations
  • Supports web, files, and connector-based research
  • Works with local and hosted language models
  • Offers web, Python, HTTP, and MCP interfaces
  • Exports PDF and Markdown reports

What to know first

  • Egress scopes are best-effort, not an air gap
  • MCP server has no built-in authentication or rate limiting
  • Requires installation through Docker, Compose, or pip
  • Free project has no free trial distinction

Limits

The free plan is the listed offering. Egress scopes are guard rails rather than an air gap, and the local-STDIO MCP server lacks built-in authentication and rate limiting.

Getting started

  1. Open the project website or repository
  2. Install with Docker, Docker Compose, or pip
  3. Run it on Windows, macOS, or Linux
  4. Connect a local or hosted language-model provider
  5. Configure search sources, retrievers, or uploaded files
  6. Use the web UI, APIs, or MCP server

Verdict

Local Deep Research offers a broad, free toolkit for cited investigation across live sources and stored documents. Its security controls have stated boundaries, particularly for egress and the local-STDIO MCP server, so deployment choices matter.

Questions

Is Local Deep Research free?

Yes. It has a free plan and no free trial is listed.

Which platforms are supported?

It supports API, Linux, macOS, self-hosted, web, and Windows use. Pip installation is described for Windows, macOS, and Linux.

Can it use local language models?

Yes. Local models can connect through Ollama, LM Studio, and llama.cpp.

Does it provide citations and exports?

Yes. It produces cited reports and can export them as PDF or Markdown.

Is it open source?

The repository states that it is distributed under the MIT License.

How is data protected?

The project describes isolated SQLCipher databases encrypted with AES-256 using a key derived from the login password, with no telemetry or tracking.

Facts from Local Deep Research's own pages

API and assistant integration
It provides authenticated HTTP and Python APIs and an MCP server for Claude Desktop and Claude Code. · github.com, 4 Oct 2026
Data security
The project says each user gets an isolated SQLCipher database encrypted with AES-256 using a key derived from their password, and that passwords are not stored. · github.com, 4 Oct 2026
Deployment
Installation options include Docker Compose, Docker, pip, and an Unraid template; the installation guide names CPU-only Compose support for all platforms and Windows PowerShell instructions. · github.com, 5 Oct 2026
Egress limitation
The project describes egress scopes as best-effort guard rails rather than an air gap or hard security boundary. · github.com, 5 Oct 2026
Egress limits
The project describes egress scopes as best-effort guard rails rather than an air gap or hard security boundary. · github.com, 4 Oct 2026
Exports and subscriptions
It supports PDF and Markdown exports and scheduled research digests delivered as Markdown reports or structured summaries. · github.com, 4 Oct 2026
Interfaces
The project provides a web UI, Python API, authenticated HTTP REST API, and an MCP server for Claude Desktop and Claude Code. · github.com, 5 Oct 2026
Knowledge base
It can index downloaded papers and web pages in an encrypted library so users can search those sources alongside the live web. · github.com, 5 Oct 2026
Knowledge base integrations
LangChain-compatible retrievers can connect sources including FAISS, Chroma, Pinecone, Weaviate and Elasticsearch. · github.com, 4 Oct 2026
Knowledge integrations
LangChain-compatible retrievers are supported, including FAISS, Chroma, Pinecone, Weaviate, and Elasticsearch. · github.com, 5 Oct 2026
License
The repository states that Local Deep Research is distributed under the MIT License. · github.com, 5 Oct 2026
LLM integrations
Supported model options include Ollama, LM Studio, llama.cpp, OpenAI, Anthropic Claude, Google Gemini, OpenRouter, Requesty, and compatible custom endpoints. · github.com, 5 Oct 2026
MCP limitation
The MCP server is intended for local STDIO use and has no built-in authentication or rate limiting. · github.com, 5 Oct 2026
MCP security limit
The MCP server is intended for local STDIO use and has no built-in authentication or rate limiting. · github.com, 4 Oct 2026
Privacy
The project says it has no telemetry, analytics, tracking, or crash reporting, and that network calls are initiated by the user’s configured search, model, or notification services. · github.com, 5 Oct 2026
Purpose
Local Deep Research is an AI-powered research assistant that searches multiple sources and synthesizes cited reports. · github.com, 5 Oct 2026
Research modes
It offers quick summaries, detailed research, report generation, and AI analysis of private documents. · github.com, 5 Oct 2026
Search integrations
Listed search sources include arXiv, PubMed, Semantic Scholar, Wikipedia, SearXNG, GitHub, Elasticsearch, Tavily, Google via SerpAPI or Programmable Search Engine, and Brave Search. · github.com, 5 Oct 2026
Security
The project says each user receives an isolated SQLCipher database encrypted with AES-256, with the key derived from the login password. · github.com, 5 Oct 2026
Support
The project directs users to Discord and Reddit for community help and GitHub Issues for bug reports. · github.com, 5 Oct 2026

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