gpt-researcher vs paperjury
gpt-researcher is much bigger: 29.3k stars against 1.1k. Over the days we have tracked them gpt-researcher moved +2.6% and paperjury +31%, so paperjury is growing faster right now.
They split the axes: gpt-researcher leads on model freedom, paperjury on context depth and noise control.
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
- 29.3k
- Tracked growth
- +2.6%
- Maturity
- ●●●●●
- Last commit
- 8d 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)
Bundles a bounded, audit-ready review→decide→patch→recheck loop with ledgered issues and edit-safety safeguards, exposed as a Claude Code skill.
- Stars
- 1.1k
- Tracked growth
- +31%
- Maturity
- ●●●●●
- Last commit
- 22d ago
- Language
- JavaScript
- License
- MIT
- Cost to run
- Runs in your Claude session — model usage billed by the provider
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 | paperjury |
|---|---|---|
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 | ●●●●● Whole-repo analysis |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Aggregation & filtering | ●●●●● Severity gating & dedup |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Extensive configs | ●●●●● Modes & personas |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Self-hostable | ●●●●● Cloud LLM via session |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Bring-your-own-key | ●●●●● Claude-first (fixed) |
Setup ease What it takes to get a first useful run out of it. | ●●●●● API key + config | ●●●●● Plugin install + session |
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.
- Model freedom: Bring-your-own-key (5/5 against 3/5)
Pick paperjury if…
Audit-ready review loop — pick PaperJury when you want a pre-submission, multi-round reviewer simulation that produces minimal, author-confirmed LaTeX patches and deterministic compile/compliance checks.
- Context depth: Whole-repo analysis (5/5 against 3/5)
- Noise control: Severity gating & dedup (5/5 against 3/5)
What people want from each one
assafelovic/gpt-researcher
u7079256/paperjury
Questions people ask
Is gpt-researcher better than paperjury?
They split the axes: gpt-researcher leads on model freedom, paperjury on context depth and noise control. 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 paperjury keeps my code private?
gpt-researcher: Self-hostable (4/5). paperjury: Cloud LLM via session (3/5).
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). paperjury: Runs in your Claude session — model usage billed by the provider.
Full profiles: assafelovic/gpt-researcher and u7079256/paperjury. Everything else in Research agents.