gpt-researcher vs llm-for-zotero
gpt-researcher is much bigger: 29.3k stars against 2.9k. Over the days we have tracked them gpt-researcher moved +2.6% and llm-for-zotero +56.2%, so llm-for-zotero is growing faster right now.
llm-for-zotero leads on context depth. gpt-researcher 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
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)
Deep Zotero-native integration that provides grounded, citation-linked paper chat and library-wide agent actions (reads, writes, tagging, notes) not offered by generic agents.
- Stars
- 2.9k
- Tracked growth
- +56.2%
- Maturity
- ●●●●●
- Last commit
- 1d ago
- Language
- TypeScript
- License
- AGPL-3.0
- Cost to run
- Your API key or local models
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 | llm-for-zotero |
|---|---|---|
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-library access |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Aggregation & filtering | ●●●●● Citation gating & confirmations |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Extensive configs | ●●●●● Custom skills & settings |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Self-hostable | ●●●●● Local model support |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Bring-your-own-key | ●●●●● Bring-your-own model/key |
Setup ease What it takes to get a first useful run out of it. | ●●●●● API key + config | ●●●●● Easy install + API key |
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.
Pick llm-for-zotero if…
Zotero-native — pick this when you want a research assistant tightly integrated with your local Zotero library for grounded citations, file-based notes, and library-wide agent workflows.
- Context depth: Whole-library access (5/5 against 3/5)
What people want from each one
assafelovic/gpt-researcher
yilewang/llm-for-zotero
Questions people ask
Is gpt-researcher better than llm-for-zotero?
llm-for-zotero leads on context depth. gpt-researcher does not take any axis by a clear margin. 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 llm-for-zotero keeps my code private?
gpt-researcher: Self-hostable (4/5). llm-for-zotero: Local model support (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). llm-for-zotero: Your API key or local models.
Full profiles: assafelovic/gpt-researcher and yilewang/llm-for-zotero. Everything else in Research agents.