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Local-deep-research Alternatives
Similar projects and alternatives to local-deep-research
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ollama
Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.
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ScholarRef
A professional Python tool and modern desktop GUI for converting academic citations and document formatting (APA 7, Harvard, Vancouver) in Microsoft Word (.docx) files.
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openai-edge-tts
Free, high-quality text-to-speech API endpoint to replace OpenAI, Azure, or ElevenLabs
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chatterbox-tts-api
Local, OpenAI-compatible text-to-speech (TTS) API using Chatterbox, enabling users to generate voice cloned speech anywhere the OpenAI API is used (e.g. Open WebUI, AnythingLLM, etc.)
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agentops
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI
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cleverbee
Discontinued CleverBee - The Open Source Deep Researcher Tool [GET https://api.github.com/repos/SureScaleAI/cleverbee: 404 - Not Found // See: https://docs.github.com/rest/repos/repos#get-a-repository]
local-deep-research discussion
local-deep-research reviews and mentions
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Local Deep Research: Run Your Own AI Research Assistant, Fully Private
Local Deep Research is a self-hosted AI research assistant. You give it a question. It searches across multiple sources — web, arXiv, PubMed, Wikipedia, GitHub, your own local documents — iterates on what it finds, and produces a structured report with citations.
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Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch
I love this one: https://github.com/LearningCircuit/local-deep-research
I tied it together with qwen3 30b thinking. Very easy to get it up and running, but lots of the numbers are shockingly low. You need to boost iterations and context. Especially easy if you already run searxng locally.
I havent finished tuning the actual settings, but for the detailed report it'll take ~20 minutes and so far has given pretty good results. Similar to openai's deep research. Mine often has ~100 sources.
But something I have noticed. It didnt seem to me the model was important. The magic was moreso in the project. Getting deep with higher iterations and more results.
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Local Deep Research – ArXiv, wiki and other searches included
(maybe the final report could be an interactive HTML file that the user can ask questions, or edit themselves).
[1] https://github.com/LearningCircuit/local-deep-research/blob/...
[2] "In-Browser Graph RAG with Kuzu-WASM and WebLLM" https://news.ycombinator.com/item?id=43321523
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Show HN: A new fork of OpenDeepResearcher with DeepSeek R1
Thanks, I saw someone building local researcher on YT, found 1 repo https://github.com/LearningCircuit/local-deep-research . Local ones are with ollama mostly
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A note from our sponsor - SaaSHub
www.saashub.com | 20 Jul 2026
Stats
LearningCircuit/local-deep-research is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of local-deep-research is Python.
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