Deep-Research-skills vs MiroThinker
MiroThinker is much bigger: 8.4k stars against 2.2k. Over the days we have tracked them Deep-Research-skills moved +82.3% and MiroThinker +1.3%, so Deep-Research-skills is growing faster right now.
MiroThinker leads on privacy and setup ease. Deep-Research-skills does not take any axis by a clear margin.
Stars and commit dates come from our own daily tracking. The six axes are read off each project's documentation by our review pipeline, so they describe what a project says about itself, not what we measured in its code.
Where they stand today
Provides a structured two-phase (outline then deep investigation) human-in-the-loop research workflow packaged specifically as skills for Claude Code / OpenCode / Codex, including parallel web research modules and report generation.
- Stars
- 2.2k
- Tracked growth
- +82.3%
- Maturity
- ●●●●●
- Last commit
- 22d ago
- Language
- Python
- License
- MIT
- Cost to run
- Depends on model provider
Provides open-source deep research agents with extremely long (256K) context windows and very high tool-call budgets, enabling long‑horizon, verifiable multi-step research workflows.
- Stars
- 8.4k
- Tracked growth
- +1.3%
- Maturity
- ●●●●●
- Last commit
- 69d ago
- Language
- Python
- License
- Apache-2.0
- Cost to run
- Free; self-host models or use HuggingFace
Six axes, head to head
Each axis runs 0 to 5. The label under a score is what that project's own docs claim, not a category average.
| Axis | Deep-Research-skills | MiroThinker |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Web-only | ●●●●● Diff only |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Human-in-the-loop | ●●●●● Trace collection |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Config file options | ●●●●● Configurable workflows |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Cloud APIs | ●●●●● Fully local |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Specific providers | ●●●●● HuggingFace weights |
Setup ease What it takes to get a first useful run out of it. | ●●●●● Manual install steps | ●●●●● Online demo |
Which one to pick
Pick Deep-Research-skills if…
Claude/OpenCode/Codex integration — choose this when you run those agent clients and want an extensible, human-in-the-loop two-phase workflow (outline + deep web research) that outputs organized markdown reports.
Pick MiroThinker if…
Long‑context research — choose MiroThinker when you need an open-source agent with 256K context and high tool‑call capacity for deep, verifiable multi‑step research tasks.
- Privacy: Fully local (5/5 against 3/5)
- Setup ease: Online demo (5/5 against 3/5)
What people want from each one
Questions people ask
Is Deep-Research-skills better than MiroThinker?
MiroThinker leads on privacy and setup ease. Deep-Research-skills does not take any axis by a clear margin. Deep-Research-skills is worth picking when claude/OpenCode/Codex integration — choose this when you run those agent clients and want an extensible, human-in-the-loop two-phase workflow (outline + deep web research) that outputs organized markdown reports.
Which of Deep-Research-skills and MiroThinker keeps my code private?
Deep-Research-skills: Cloud APIs (3/5). MiroThinker: Fully local (5/5).
What does each one cost to run?
Deep-Research-skills: Depends on model provider. MiroThinker: Free; self-host models or use HuggingFace.
Full profiles: Weizhena/Deep-Research-skills and MiroMindAI/MiroThinker. Everything else in Research agents.