head to head · open source
local-deep-research vs Dograh
local-deep-research has 9,499 GitHub stars, 903 forks, 889 open issues and last shipped yesterday. Dograh has 6,024 stars, 1,541 forks, 77 open issues and last shipped 2 days ago. local-deep-research leads on adoption by 58% (9,499 vs 6,024 stars). local-deep-research is written in Python under MIT; Dograh is written in Python under BSD-2-Clause. local-deep-research has attracted 10% as many forks as stars, Dograh 26%. local-deep-research was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (local-llm), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| local-deep-research | Dograh | |
|---|---|---|
| GitHub stars | ★ 9.5K | ★ 6.0K |
| License | MIT | BSD-2-Clause |
| Written in | Python | Python |
| Last push | 2026-11-04 | 2026-11-03 |
| Forks | ⑂ 903 | ⑂ 1.5K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick local-deep-research if
- You weight community size — 9.5K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick Dograh if
- You want the Dograh feature set and don't need the biggest community
- You prefer the BSD-2-Clause license terms
- Your stack matches Python
- You evaluated both and Dograh fits your workflow better
About local-deep-research
Local Deep Research AI powered research assistant for deep, agentic research Performs deep, agentic research using multiple LLMs and search engines with proper citations 🧪 First open source project — fully local on a single RTX 3090 (Qwen3.6 27B) — to report 95% SimpleQA (n=500) and 77% xbench DeepSearch (n=100) on local hardware. See the r/LocalLLaMA announcement and the benchmark dataset. ▶️ Watch Review by The Art Of The Terminal 🚀 What is Local Deep Research? AI research assistant you control. Run locally for privacy, use any LLM and build your own searchable knowledge base. You own your data and see…
read the full local-deep-research overview →
About Dograh
Dograh AI The open source, self hostable alternative to Vapi & Retell — build production voice agents with a visual workflow builder, test them in minutes, and let AI coding assistants help design and edit them through MCP. 📖 Docs · 📜 BSD 2 Clause · 🌐 中文 · 🌐 日本語 100% open source , self hostable — no vendor lock in, unlike Vapi or Retell Full control & transparency — every line of code is open, with flexible LLM / TTS / STT integration Maintained by YC alumni and exit founders , committed to keeping voice AI open 🎥 Featured Featured by Better Stack — a hands on look at Dograh 📺 Prefer a 2 minute product…
read the full Dograh overview →
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Frequently asked questions
Is local-deep-research or Dograh more popular?
local-deep-research has 9,499 GitHub stars and Dograh has 6,024. local-deep-research has the larger community by that measure.
Are local-deep-research and Dograh free?
Both are open source. local-deep-research is licensed under MIT and Dograh under BSD-2-Clause. Neither carries a licence fee.
What is the difference between local-deep-research and Dograh?
local-deep-research is written in Python and Dograh in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, local-deep-research or Dograh?
Choose local-deep-research if you want the larger community (9,499 stars) or its MIT licence terms. Choose Dograh if its feature set, stack or BSD-2-Clause licence fits better. Both are self-hostable.