gpt-researcher vs k-dense-byok
gpt-researcher is much bigger: 29.3k stars against 1.1k. Over the days we have tracked them gpt-researcher moved +2.6% and k-dense-byok +23.5%, so k-dense-byok is growing faster right now.
k-dense-byok leads on context depth and setup ease. 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)
A local-first, bring-your-own-keys AI research assistant that runs on your computer and bundles domain-specific scientific skills, workflows, and a living lab notebook.
- Stars
- 1.1k
- Tracked growth
- +23.5%
- Maturity
- ●●●●●
- Last commit
- 13h ago
- Language
- TypeScript
- License
- MIT
- Cost to run
- Your API key (pay-per-use) or free 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 | k-dense-byok |
|---|---|---|
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-project access |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Aggregation & filtering | ●●●●● Clarifying prompts & review |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Extensive configs | ●●●●● Configurable skills & presets |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Self-hostable | ●●●●● Fully local optional |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Bring-your-own-key | ●●●●● BYOK + local models |
Setup ease What it takes to get a first useful run out of it. | ●●●●● API key + config | ●●●●● Quick local start |
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 k-dense-byok if…
Privacy-first — run a full scientific assistant locally or use your own OpenRouter key while keeping projects and data on your machine, with many prebuilt scientific skills and workflows.
- Context depth: Whole-project access (5/5 against 3/5)
- Setup ease: Quick local start (5/5 against 3/5)
What people want from each one
assafelovic/gpt-researcher
K-Dense-AI/k-dense-byok
Questions people ask
Is gpt-researcher better than k-dense-byok?
k-dense-byok leads on context depth and setup ease. 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 k-dense-byok keeps my code private?
gpt-researcher: Self-hostable (4/5). k-dense-byok: Fully local optional (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). k-dense-byok: Your API key (pay-per-use) or free local models.
Full profiles: assafelovic/gpt-researcher and K-Dense-AI/k-dense-byok. Everything else in Research agents.