Is Tianji Safe? — Trust Score: 50.7/100
Why This Score
- Maintenance: 0/100 — low maintenance activity
- Documentation: 0/100 — limited documentation
- Popularity: 1/100 — 3,009 stars on pulsemcp
According to Nerq's independent analysis of Tianji, this automation has a trust score of 50.7 out of 100, earning a D grade. With 3,009 stars on pulsemcp, it is below the recommended threshold of 70. Data sourced from 13+ independent signals including GitHub, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-19. Machine-readable data (JSON).
Is Tianji safe?
CAUTION — Tianji has a Nerq Trust Score of 50.7/100 (D). It has moderate trust signals but shows some areas of concern that warrant attention. Suitable for development use — review security and maintenance signals before production deployment.
Trust Assessment
Caution — Tianji has below-average trust signals. There may be concerns around maintenance frequency, security practices, or ecosystem adoption. Proceed with care and conduct additional due diligence.
Trust Signal Breakdown
Details
| Author | https://github.com/msgbyte/tianji |
| Category | automation |
| Stars | 3,009 |
| Source | https://github.com/msgbyte/tianji |
Popular Alternatives in automation
Community Reviews
No reviews yet. Be the first to review Tianji.
What Is Tianji?
Tianji is a automation platform that connects AI capabilities with workflow orchestration. Bridges AI assistants with the Tianji platform for survey management.
As of March 2026, Tianji has 3,009 stars on pulsemcp, making it a notable tool in the AI ecosystem. But popularity alone does not equal safety — which is why Nerq independently analyzes every tool across 13+ trust signals.
How Nerq Assesses Tianji's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensions. Here is how Tianji performs in each:
- Maintenance (0/100): Tianji is potentially abandoned. We track commit frequency, release cadence, issue response times, and PR merge rates.
- Documentation (0/100): Documentation quality is insufficient. This includes README completeness, API documentation, usage examples, and contribution guidelines.
- Community (1/100): Community adoption is limited. Based on GitHub stars, forks, download counts, and ecosystem integrations.
The overall Trust Score of 50.7/100 (D) reflects the weighted combination of these signals. This is below the Nerq Verified threshold of 70. We recommend additional due diligence before production deployment.
Who Should Use Tianji?
Tianji is designed for:
- Teams automating repetitive workflows
- Organizations connecting multiple tools and services
- Developers building event-driven AI pipelines
Risk guidance: Tianji is suitable for development and testing environments. Before production deployment, conduct a thorough review of its security posture, review the specific trust signals above, and consider whether a higher-scored alternative meets your requirements.
How to Verify Tianji's Safety Yourself
While Nerq provides automated trust analysis, we recommend these additional steps before adopting any AI tool:
- Check the source code — Review the repository security policy, open issues, and recent commits for signs of active maintenance.
- Scan dependencies — Use tools like
npm audit,pip-audit, orsnykto check for known vulnerabilities in Tianji's dependency tree. - Review permissions — Understand what access Tianji requires. AI tools should follow the principle of least privilege.
- Test in isolation — Run Tianji in a sandboxed environment before granting access to production data or systems.
- Monitor continuously — Use Nerq's API to set up automated trust checks:
GET nerq.ai/v1/preflight?target=Tianji - Review the license — Confirm that Tianji's license is compatible with your intended use case. Pay attention to restrictions on commercial use, redistribution, and derivative works. Some AI tools use dual licensing or have separate terms for enterprise customers that differ from the open-source license.
- Check community signals — Look at the project's issue tracker, discussion forums, and social media presence. A healthy community actively reports bugs, contributes fixes, and discusses security concerns openly. Low community engagement may indicate limited peer review of the codebase.
Common Safety Concerns with Tianji
When evaluating whether Tianji is safe, consider these category-specific risks:
Understand how Tianji processes, stores, and transmits your data. Review the tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Tianji's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher security risk.
Regularly check for updates to Tianji. Security patches and bug fixes are only effective if you're running the latest version.
If Tianji connects to external APIs or services, each integration point is a potential attack surface. Audit all third-party connections, verify that data shared with external services is minimized, and ensure that integration credentials are rotated regularly.
Verify that Tianji's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Tianji in violation of its license can expose your organization to legal liability.
Best Practices for Using Tianji Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Tianji while minimizing risk:
Periodically review how Tianji is used in your workflow. Check for unexpected behavior, permissions drift, and compliance with your security policies.
Ensure Tianji and all its dependencies are running the latest stable versions to benefit from security patches.
Grant Tianji only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Tianji's security advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Tianji is used within your organization, including data handling guidelines and acceptable use cases.
When Should You Avoid Tianji?
Even promising tools aren't right for every situation. Consider avoiding Tianji in these scenarios:
- Production environments handling sensitive customer data
- Regulated industries (healthcare, finance, government) without additional compliance review
- Mission-critical systems where downtime has significant business impact
For each scenario, evaluate whether Tianji's trust score of 50.7/100 meets your organization's risk tolerance. We recommend running a manual security assessment alongside the automated Nerq score.
How Tianji Compares to Industry Standards
Nerq indexes over 204,000 AI agents and tools across dozens of categories. Among automation tools, the average Trust Score is 64/100. Tianji's score of 50.7/100 is below the category average of 64/100.
This suggests that Tianji trails behind many comparable automation tools. Organizations with strict security requirements should evaluate whether higher-scoring alternatives better meet their needs.
Industry benchmarks matter because they contextualize a tool's safety profile. A score that looks moderate in isolation may actually represent strong performance within a challenging category — or vice versa. Nerq's category-relative analysis helps teams make informed decisions by showing not just absolute quality, but how a tool ranks against its direct peers.
Trust Score History
Nerq continuously monitors Tianji and recalculates its Trust Score as new data becomes available. Our scoring engine ingests real-time signals from source repositories, vulnerability databases (NVD, OSV.dev), package registries, and community metrics. When a new CVE is published, a major release ships, or maintenance patterns change, Tianji's score is updated within 24 hours.
Historical trust trends reveal whether a tool is improving, stable, or declining over time. A tool that consistently maintains or improves its score demonstrates ongoing commitment to security and quality. Conversely, a downward trend may signal reduced maintenance, growing technical debt, or unresolved vulnerabilities. To track Tianji's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=Tianji&include=history
Nerq retains trust score snapshots at regular intervals, enabling trend analysis across weeks and months. Enterprise users can access detailed historical reports showing how each dimension — security, maintenance, documentation, compliance, and community — has evolved independently, providing granular visibility into which aspects of Tianji are strengthening or weakening over time.
Tianji vs Alternatives
In the automation category, Tianji scores 50.7/100. There are higher-scoring alternatives available. For a detailed comparison, see:
- Tianji vs Windows Desktop Control — Trust Score: 46.2/100
- Tianji vs baserow — Trust Score: 82.1/100
- Tianji vs gemma-7b — Trust Score: 62.2/100
Key Takeaways
- Tianji has a Trust Score of 50.7/100 (D) and is not yet Nerq Verified.
- Tianji shows moderate trust signals. Conduct thorough due diligence before deploying to production environments.
- Among automation tools, Tianji scores below the category average of 64/100, suggesting room for improvement relative to peers.
- Always verify safety independently — use Nerq's Preflight API for automated, up-to-date trust checks before integration.
Safer Alternatives
Higher-rated automation agents you may want to consider:
Frequently Asked Questions
Add This Badge to YOUR Project
Show users your project is trusted. Add this badge to your README:
[](https://nerq.ai/safe/tianji)
Click to copy. Works on GitHub, GitLab, and any markdown renderer.
pip install nerq && nerq scan
Scans all dependencies for trust scores and security issues.
Related Safety Checks
Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.