No. 16 of 28 ·AI Research Assistants

Local Deep Research

Web · Windows · Mac · Linux · Self-hosted · API

Freedom report

Two barsScore 5.6

Free tierNo free tier on record
Open codeNo open-source code on record
Runs widely4 of 6 device platforms
DocumentedPlans, terms and facts published

Local Deep Research is ranked #16 of 28 in AI research assistants on Freedom251. It runs on API, Linux, macOS, Self-hosted, Web, Windows.

Compared on AI research assistants

Free plan: Yes
Source citations: Yes
Deep research mode: Yes
Source access: web+files+connectors
File uploads: Yes

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The record

Purpose: Local Deep Research is an AI research assistant that performs agentic research with multiple LLMs and search engines, then produces cited reports.
Research modes: It offers quick summaries, detailed research, report generation and document analysis.
Knowledge base: It can download sources into an encrypted library, index them and search them alongside the live web.
Search integrations: Listed sources include arXiv, PubMed, Semantic Scholar, Wikipedia, SearXNG, GitHub, Elasticsearch, Wayback Machine, The Guardian and Wikinews.
LLM integrations: It supports local models through Ollama, LM Studio and llama.cpp, plus OpenAI, Anthropic Claude, Google Gemini, OpenRouter, Requesty and compatible endpoints.
Knowledge base integrations: LangChain-compatible retrievers can connect sources including FAISS, Chroma, Pinecone, Weaviate and Elasticsearch.
API and assistant integration: It provides authenticated HTTP and Python APIs and an MCP server for Claude Desktop and Claude Code.
Exports and subscriptions: It supports PDF and Markdown exports and scheduled research digests delivered as Markdown reports or structured summaries.
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.
Privacy: The project says it has no telemetry, analytics, tracking, crash reporting or phone-home calls; network requests are for user-initiated searches, model calls and configured notifications.
Egress limits: The project describes egress scopes as best-effort guard rails rather than an air gap or hard security boundary.
MCP security limit: The MCP server is intended for local STDIO use and has no built-in authentication or rate limiting.
Deployment: Installation options include Docker, Docker Compose and pip; the README says pip installation works on Windows, macOS and Linux.
Support: The project points users to Discord, Reddit and GitHub Issues for help, updates and bug reports.

Where it ranks on Freedom251

Best AI Research Assistants in 2026#16 of 28

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Sources

github.com/LearningCircuit/local-deep-research· checked 4 Oct 2026