Deep-Research-skills vs gpt-researcher
gpt-researcher is much bigger: 29.3k stars against 2.1k. Over the days we have tracked them Deep-Research-skills moved +107% and gpt-researcher +2.6%, so Deep-Research-skills is growing faster right now.
gpt-researcher leads on context depth and model freedom. Deep-Research-skills 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
Provides a structured two-phase (outline then deep investigation) human-in-the-loop research workflow packaged specifically as skills for Claude Code / OpenCode / Codex, including parallel web research modules and report generation.
- Stars
- 2.1k
- Tracked growth
- +107%
- Maturity
- ●●●●●
- Last commit
- 13d ago
- Language
- Python
- License
- MIT
- Cost to run
- Depends on model provider
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)
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 | Deep-Research-skills | gpt-researcher |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Web-only | ●●●●● Multi-source context |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Human-in-the-loop | ●●●●● Aggregation & filtering |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Config file options | ●●●●● Extensive configs |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Cloud APIs | ●●●●● Self-hostable |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Specific providers | ●●●●● Bring-your-own-key |
Setup ease What it takes to get a first useful run out of it. | ●●●●● Manual install steps | ●●●●● API key + config |
Which one to pick
Pick Deep-Research-skills if…
Claude/OpenCode/Codex integration — choose this when you run those agent clients and want an extensible, human-in-the-loop two-phase workflow (outline + deep web research) that outputs organized markdown reports.
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)
What people want from each one
assafelovic/gpt-researcher
Questions people ask
Is Deep-Research-skills better than gpt-researcher?
gpt-researcher leads on context depth and model freedom. Deep-Research-skills does not take any axis by a clear margin. Deep-Research-skills is worth picking when claude/OpenCode/Codex integration — choose this when you run those agent clients and want an extensible, human-in-the-loop two-phase workflow (outline + deep web research) that outputs organized markdown reports.
Which of Deep-Research-skills and gpt-researcher keeps my code private?
Deep-Research-skills: Cloud APIs (3/5). gpt-researcher: Self-hostable (4/5).
What does each one cost to run?
Deep-Research-skills: Depends on model provider. gpt-researcher: Your API key; cloud model costs (≈$0.4 per deep research with o3-mini as documented).
Full profiles: Weizhena/Deep-Research-skills and assafelovic/gpt-researcher. Everything else in Research agents.