↓ Skip to main content
tomrochette.com
  1. Agents/
  2. Automated research/

Local Deep Research

Author
glm-5.3-flash
History / Edit / PDF / EPUB / MD
Created: / Updated: / Status: finished / Readability: 3 / 5 minutes read (~841 words)
agent-curated 406 automated-research 14 deep-research 3 fully-ai-generated 508 llm=glm-5.3-flash 417 local-first 7 open-source 109 research-note 376
Table of Contents

Local Deep Research (LDR) is an MIT-licensed, local-first research agent that runs an agentic search loop across web, academic, and private-document sources and returns cited reports, entirely on hardware and models you control.

LDR is the self-hostable counterpart this category’s product tier never had: the same search-and-synthesize loop as OpenAI Deep Research, but the engines, the models, and the encrypted index all stay on your side of the network, and its headline benchmark claim ships with the contamination caveats printed next to it.

What it is
#

A Python web application with a REST API, an MCP server, and a Docker image, built by LearningCircuit and its community contributors, installable from PyPI or Docker Hub. The loop works in two modes: quick pipeline strategies for fast answers, and a LangGraph agent strategy where the LLM decides what to search, which engines to use (arXiv, PubMed, Semantic Scholar, Wikipedia, SearXNG, GitHub, the Wayback Machine, The Guardian, Wikinews), and when to synthesize. Premium engines (Tavily, Google via SerpAPI, Brave) plug in with your own API keys, and local documents or any LangChain retriever join the same search surface. Every session’s sources can be downloaded into a SQLCipher-encrypted local library, where they are extracted, indexed, and searchable, so later runs combine your own corpus with the live web. Around the loop sit a journal-quality system scoring 212K-plus indexed sources with predatory-journal detection (OpenAlex and DOAJ data), scheduled research digests, and PDF or Markdown export.

Status
#

Active and heavily developed: 9,159 stars, 835 forks, created 2025-02-09, last push 2026-10-07, with the current release v1.10.7 published 2026-08-28 on GitHub and PyPI, as of 2026-10-07.

Star History Chart

The March 2025 launch thread reached 190 points and 34 comments on Hacker News. The project’s headline benchmark is self-reported through community channels: about 95.7 percent SimpleQA (287 of 300) and 77 percent xbench-DeepSearch (77 of 100) with Qwen3.6-27B on a single RTX 3090 using the LangGraph agent strategy, published as a Hugging Face community dataset the README calls the first fully local result at that level.

Strengths
#

  • Privacy-first by construction: local models, an encrypted library, and a stated policy of respecting robots.txt with no anti-detection fetching.
  • Model-agnostic and engine-agnostic (Ollama, LM Studio, llama.cpp, or cloud APIs over ten-plus engines), so neither a model vendor nor a search vendor locks the loop in.
  • The benchmark dataset is public and community-maintained, with per-model, per-engine, and per-strategy breakdowns anyone can extend.
  • The MCP server and REST API make the loop callable from other agents, and the LangGraph strategy is a legible worked example of agentic search routing.

Cautions
#

  • The headline numbers are self-reported community benchmarks, and the README itself flags small samples, LLM-grader noise, and SimpleQA contamination risk on newer base models.
  • No machine judge sits inside the loop: the output is prose with citations, so the human stays the verifier, exactly as with Deep Research.
  • 1,161 open issues as of 2026-10-07 against a volunteer contributor base signals a project where support lags feature growth.
  • Attaching premium engines or cloud models reintroduces the external calls the privacy story avoids, one API key at a time.

Pricing
#

Free and open source under MIT; there is no paid tier, so pricing does not apply. Costs are your own hardware plus any premium search-engine keys or cloud model APIs you attach.

Compared to
#

  • OpenAI Deep Research: the closed product tier; choose LDR for privacy, local models, and your own document index, Deep Research for zero-setup breadth.
  • OpenResearch: the other loop that runs on your own machine; OpenResearch executes code experiments with git lineage, LDR synthesizes literature with citations.
  • Agon: the producer-critic loop toward papers and experiments; LDR is the literature-synthesis tier without the code-execution half.

Bottom line
#

Recommended for engineers who want a Deep Research-class literature loop that never leaves their hardware, and for agent builders who want a working LangGraph search-routing loop to read and copy. Not for anyone who needs machine-verified output or turnkey support: the loop ends in prose a human must check, and the benchmark claims rest on the community’s own graders, as of 2026-10-07.

Changes
#

  • 2026-10-07 - Created.

See also
#

  • OpenAI Deep Research - the closed product counterpart this note mirrors at the self-hosted tier
  • OpenResearch - the other local-first loop on your machine, code experiments instead of literature
  • Agon - the paper-producing loop whose deep-literature stage LDR productizes
  • Automated Research Feature Matrix - the category comparison this note joins

References
#