gpt-researcher vs MiroThinker
gpt-researcher is much bigger: 29.4k stars against 8.4k. Over the days we have tracked them gpt-researcher moved +3.1% and MiroThinker +1.3%, so gpt-researcher is growing faster right now.
They split the axes: gpt-researcher leads on context depth and model freedom, MiroThinker on setup ease.
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
An open-source multi-agent deep-research pipeline that combines web scraping, local document analysis, and MCP integrations to produce long, cited research reports.
- Stars
- 29.4k
- Tracked growth
- +3.1%
- Maturity
- ●●●●●
- Last commit
- 17d ago
- Language
- Python
- License
- Apache-2.0
- Cost to run
- Your API key; cloud model costs (≈$0.4 per deep research with o3-mini as documented)
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 | gpt-researcher | 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. | ●●●●● Multi-source context | ●●●●● Diff only |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Aggregation & filtering | ●●●●● Trace collection |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Extensive configs | ●●●●● Configurable workflows |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Self-hostable | ●●●●● Fully local |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Bring-your-own-key | ●●●●● HuggingFace weights |
Setup ease What it takes to get a first useful run out of it. | ●●●●● API key + config | ●●●●● Online demo |
Which one to pick
Pick gpt-researcher if…
Open and flexible — run locally or in Docker and connect your preferred LLM provider to get long, citation-backed research from web and local sources.
- Context depth: Multi-source context (3/5 against 1/5)
- Model freedom: Bring-your-own-key (5/5 against 3/5)
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.
- Setup ease: Online demo (5/5 against 3/5)
What people want from each one
assafelovic/gpt-researcher
Questions people ask
Is gpt-researcher better than MiroThinker?
They split the axes: gpt-researcher leads on context depth and model freedom, MiroThinker on setup ease. gpt-researcher is worth picking when open and flexible — run locally or in Docker and connect your preferred LLM provider to get long, citation-backed research from web and local sources.
Which of gpt-researcher and MiroThinker keeps my code private?
gpt-researcher: Self-hostable (4/5). MiroThinker: Fully local (5/5).
What does each one cost to run?
gpt-researcher: Your API key; cloud model costs (≈$0.4 per deep research with o3-mini as documented). MiroThinker: Free; self-host models or use HuggingFace.
Full profiles: assafelovic/gpt-researcher and MiroMindAI/MiroThinker. Everything else in Research agents.