gpt-researcher vs ScienceClaw
gpt-researcher is much bigger: 30.2k stars against 1.0k. Over the days we have tracked them gpt-researcher moved +5.7% and ScienceClaw +16.9%, so ScienceClaw is growing faster right now.
They split the axes: gpt-researcher leads on context depth and model freedom, ScienceClaw on noise control and 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
- 30.2k
- Tracked growth
- +5.7%
- Maturity
- ●●●●●
- Last commit
- 9d 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)
Self-evolving scientific skills plus a hard research protocol (SCIENCE.md) that enforces evidence-only citations and long-running, persistent research memory.
- Stars
- 1.0k
- Tracked growth
- +16.9%
- Maturity
- ●●●●●
- Last commit
- 3d ago
- Language
- TypeScript
- License
- MIT
- Cost to run
- Free, self-host
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 | ScienceClaw |
|---|---|---|
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 | ●●●●● Session-limited |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Aggregation & filtering | ●●●●● Protocol enforced |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Extensive configs | ●●●●● Extensive config & plugins |
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 | ●●●●● Single fixed provider |
Setup ease What it takes to get a first useful run out of it. | ●●●●● API key + config | ●●●●● One-click setup |
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 1/5)
Pick ScienceClaw if…
Zero-hallucination — pick ScienceClaw when you need a research assistant that enforces verified citations, supports long-duration sessions, and adapts to your field over time.
- Noise control: Protocol enforced (5/5 against 3/5)
- Setup ease: One-click setup (5/5 against 3/5)
What people want from each one
assafelovic/gpt-researcher
Questions people ask
Is gpt-researcher better than ScienceClaw?
They split the axes: gpt-researcher leads on context depth and model freedom, ScienceClaw on noise control and 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 ScienceClaw keeps my code private?
gpt-researcher: Self-hostable (4/5). ScienceClaw: 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). ScienceClaw: Free, self-host.
Full profiles: assafelovic/gpt-researcher and beita6969/ScienceClaw. Everything else in Research agents.