Multi-Agent-Research-Planner vs gpt_academic — Trust Score Comparison

Side-by-side trust comparison of Multi-Agent-Research-Planner and gpt_academic. Scores based on security, compliance, maintenance, popularity, and ecosystem signals.

Multi-Agent-Research-Planner scores 66.1/100 (D) while gpt_academic scores 0.0/100 (C) on the Nerq Trust Score. Multi-Agent-Research-Planner leads by 66.1 points. Multi-Agent-Research-Planner is a research agent with 0 stars. gpt_academic is a research agent with 70,114 stars.
66.1
D
Categoryresearch
Stars0
Sourcegithub
Security0
Compliance100
Maintenance1
Documentation1
vs
0.0
C
Categoryresearch
Stars70,114
Sourcegithub
Security0
Compliance79
Maintenance1
Documentation0

Detailed Metric Comparison

Metric Multi-Agent-Research-Planner gpt_academic
Trust Score66.1/1000.0/100
GradeDC
Stars070,114
Categoryresearchresearch
Security00
Compliance10079
Maintenance11
Documentation10
EU AI Act Riskminimalminimal
VerifiedNoNo

Verdict

Multi-Agent-Research-Planner leads with a trust score of 66.1/100 compared to gpt_academic's 0.0/100 (a 66.1-point difference). Multi-Agent-Research-Planner scores higher on compliance (100 vs 79). However, gpt_academic has stronger community adoption (70,114 vs 0 stars). Both agents should be evaluated based on your specific requirements.

Detailed Analysis

Security

Multi-Agent-Research-Planner leads on security with a score of 0/100 compared to gpt_academic's 0/100. This score reflects dependency vulnerability analysis, known CVE exposure, and security best practices. A higher security score means fewer known vulnerabilities and better security hygiene in the codebase.

Maintenance & Activity

Multi-Agent-Research-Planner demonstrates stronger maintenance activity (1/100 vs 1/100). This metric captures commit frequency, issue response times, and release cadence. Actively maintained tools receive faster security patches and are less likely to accumulate technical debt.

Documentation

Multi-Agent-Research-Planner has better documentation (1/100 vs 0/100). Good documentation reduces onboarding time and helps teams adopt the tool safely. This score evaluates README completeness, API documentation, code examples, and tutorial availability.

Community & Adoption

Multi-Agent-Research-Planner has 0 GitHub stars while gpt_academic has 70,114. gpt_academic has significantly broader community adoption, which typically means more Stack Overflow answers, more third-party tutorials, and faster ecosystem development.

When to Choose Each Tool

Choose Multi-Agent-Research-Planner if you need:

  • Higher overall trust score — more reliable for production use
  • Better documentation for faster onboarding

Choose gpt_academic if you need:

  • Larger community (70,114 vs 0 stars)

Switching from Multi-Agent-Research-Planner to gpt_academic (or vice versa)

When migrating between Multi-Agent-Research-Planner and gpt_academic, consider these factors:

  1. API Compatibility: Multi-Agent-Research-Planner (research) and gpt_academic (research) share similar interfaces since they are in the same category.
  2. Security Review: Run a security audit after migration. Check the Multi-Agent-Research-Planner safety report and gpt_academic safety report for known issues.
  3. Testing: Ensure your test suite covers all integration points before switching in production.
  4. Community Support: Multi-Agent-Research-Planner has 0 stars and gpt_academic has 70,114. Larger communities typically mean better Stack Overflow answers and migration guides.
Multi-Agent-Research-Planner Safety Report gpt_academic Safety Report Multi-Agent-Research-Planner Alternatives gpt_academic Alternatives

Related Pages

Frequently Asked Questions

Which is safer, Multi-Agent-Research-Planner or gpt_academic?
Based on Nerq's independent trust assessment, Multi-Agent-Research-Planner has a trust score of 66.1/100 (D) while gpt_academic scores 0.0/100 (C). The 66.1-point difference suggests Multi-Agent-Research-Planner has a stronger trust profile. Trust scores are based on security, compliance, maintenance, documentation, and community adoption.
How do Multi-Agent-Research-Planner and gpt_academic compare on security?
Multi-Agent-Research-Planner has a security score of 0/100 and gpt_academic scores 0/100. Both have comparable security profiles. Multi-Agent-Research-Planner's compliance score is 100/100 (EU risk: minimal), while gpt_academic's is 79/100 (EU risk: minimal).
Should I use Multi-Agent-Research-Planner or gpt_academic?
The choice depends on your requirements. Multi-Agent-Research-Planner (research, 0 stars) and gpt_academic (research, 70,114 stars) serve similar use cases. On trust, Multi-Agent-Research-Planner scores 66.1/100 and gpt_academic scores 0.0/100. Review the full KYA reports for each agent before making a decision. Consider factors like integration requirements, documentation quality (1 vs 0), and maintenance activity (1 vs 1).

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Last updated: 2026-09-18 | Data refreshed weekly
Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.

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