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LearningCircuit/local-deep-research

~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.

Python ◇ academia MIT
★9.2KSTARS
⑂833FORKS
!1,143ISSUES
🏆#1,148GLOBAL RANK
🔥1DAYS TRENDING
🚀
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Momentum

+229

STARS · LAST 30 DAYS

8

PER DAY

#188

MOST-STARRED Python

Window7 days30 days90 days
Stars gained+56+229+720
Per day888
Forks gained+4+17+42

local-deep-research gained 229 stars in the last 30 days, about 8 a day, and now has 9.2K. It is about 2 years old and has averaged roughly 4.6K stars a year. It ranks #188 among Python repositories and #1,148 across all languages on GitHubRepo.

Trending Record

local-deep-research has maintained a continuous presence across global trending indexes, peaking at #3310. Below is the 30-day activity profile:

💡 Overview

local-deep-research is an open-source project written in Python: ~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.

Engineered for speed, consistency, and developer ease, it solves common hurdles in academia, anthropic, arxiv. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.

⚡ Key Features

1

Optimized execution pipeline written in Python for predictable speed.

2

Zero-friction configuration with comprehensive sensible defaults out of the box.

3

Cross-platform runtime support across Linux, macOS, and Windows environments.

4

Strong typing and modular architecture designed for easy extension and maintainability.

5

Standardized CLI and API interfaces for smooth integration into CI/CD workflows.

6

Active community maintenance with regular dependency updates and security patches.

📥 Installation

terminal
$ pip install local-deep-research

⚙ System Requirements

Platforms

  • • macOS
  • • Linux
  • • Windows

Runtime & Dependencies

Python >= 3.9, pip, virtualenv

Architecture

x86_64, ARM64 (Apple Silicon & Graviton)

🧠 How It Works

local-deep-research coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.

🎯 Production Use Cases

Autonomous AI Agents

Orchestrate intelligent workflows and tool-calling routines with local-deep-research.

Model Inference & Prompting

Integrate fast, local or cloud-hosted generative AI models directly into production code.

Context Memory & RAG

Augment language models with dynamic vector retrieval and structured project memory.

Developer Productivity

Automate repetitive engineering tasks, code generation, and test creation using AI agents.

🚀 Getting Started

1

Install local-deep-research using your package manager: `pip install local-deep-research`

2

Initialize your project workspace or configuration file for local-deep-research.

3

Import local-deep-research into your codebase or invoke it directly from your terminal.

4

Execute your test suite or run `local-deep-research --help` to verify successful setup.

👍 Strengths

Active community backing with 9,154 GitHub stars and verified adoption.
Permissive open-source distribution under the MIT license.
Built in Python for high execution speed and developer familiarity.
Cross-platform compatibility across modern Linux, macOS, and Windows environments.
Clean modular design allowing flexible configuration and pipeline integration.

⚠️ Considerations

Requires familiarity with Python and modern CLI workflows.
Ecosystem extensions may require manual configuration depending on environment constraints.
Active development roadmap means breaking API changes may occur across major versions.

⇄ Alternatives & Direct Competitors

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👥 Who Should Use This

Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.

🏆 Nearby in the Rankings

LearningCircuit/local-deep-research is currently ranked #1,148 by stars across every repository tracked on GitHubRepo. These are adjacent projects:

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Frequently Asked Questions

What does local-deep-research do? +

~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.

What language is local-deep-research written in? +

The primary language is Python. Topics include: academia, anthropic, arxiv, brave, deep-research.

Is local-deep-research actively maintained? +

Yes, the last recorded push was on Oct 6, 2026 with 1,143 open issues being tracked.

How many stars does local-deep-research have? +

local-deep-research has 9,154 stars and 833 forks on GitHub.

How does local-deep-research rank among GitHub repositories? +

With 9,154 stars, LearningCircuit/local-deep-research is ranked #1,148 globally across all repositories tracked on GitHubRepo and #188 among Python projects.

What license is local-deep-research distributed under? +

The repository reports a MIT license. Always verify the repository LICENSE file for legal terms.

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