Is LlamaIndex Safe? — Trust Score: 88.7/100
According to Nerq's independent analysis of run-llama/llama_index, this coding has a trust score of 88.7 out of 100, earning a A grade. With 47,102 stars on github, it is recommended for production use. Security score: 1/100. Compliance: 100/100 across 52 jurisdictions. Data sourced from 13+ independent signals including GitHub, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-19. Machine-readable data (JSON).
Is LlamaIndex safe?
YES — LlamaIndex has a Nerq Trust Score of 88.7/100 (A). It meets Nerq's trust threshold with strong signals across security, maintenance, and community adoption. Recommended for production use — review the full report below for specific considerations.
Trust Assessment
Highly Trusted — run-llama/llama_index ranks among the top AI agents with exceptional trust signals across security, maintenance, and ecosystem metrics. It has been independently assessed by Nerq and demonstrates consistently strong quality indicators.
Trust Signal Breakdown
Details
| Author | run-llama |
| Category | coding |
| Stars | 47,102 |
| Source | https://github.com/run-llama/llama_index |
| Frameworks | langchain · llamaindex · openai · huggingface |
| Protocols | rest |
Regulatory Compliance
| EU AI Act Risk Class | Not assessed |
| Compliance Score | 100/100 |
| Jurisdictions | Assessed across 52 jurisdictions |
Popular Alternatives in coding
Deep Analysis: run-llama/llama_index
Executive Summary
run-llama/llama_index is a coding tool with a Nerq Trust Score of 88.7/100 (A). No known vulnerabilities. 47,102 GitHub stars. LlamaIndex is the leading document agent and OCR platform
Security
No known CVEs. run-llama/llama_index has a clean security record in the Nerq database.
Maintenance Health
- GitHub stars: 47,102
- Activity score: 1/100
Ecosystem Position
- Compatible frameworks: langchain, llamaindex, openai
Cost Analysis
- Cost per code_review: $0.0300
- Cost per code_generation: $0.0450
- Cost per chat_response: $0.0075
- Cost per document_analysis: $0.0450
- Cost per data_extraction: $0.0225
Trust Score Breakdown
Strongest: Compliance (100/100). Weakest: Documentation (1/100).
How to Improve This Score
Frequently Asked Questions
Is llama_index safe to use in production?
Yes. llama_index has a Nerq Trust Score of 88.7/100 (A). This is a high trust score, indicating strong security, maintenance, and community signals.
Does llama_index have any known vulnerabilities?
As of March 2026, llama_index has no known CVEs in the Nerq database.
What license does llama_index use?
License information is not yet available in the Nerq database.
How does llama_index compare to alternatives?
In the coding category, llama_index scores 88.7/100. Use the Nerq comparison API to compare directly: curl nerq.ai/v1/compare/llama_index/vs/[alternative]
How often is llama_index updated?
Check the maintenance health section above for the latest activity data. Nerq tracks commit frequency, release cadence, and issue response times.
Community Reviews
No reviews yet. Be the first to review run-llama/llama_index.
What Is LlamaIndex?
LlamaIndex is a AI tool in the coding category. LlamaIndex is the leading document agent and OCR platform
As of March 2026, LlamaIndex has 47,102 stars on github, making it one of the most popular tools in its category 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 LlamaIndex's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensions. Here is how LlamaIndex performs in each:
- Security (1/100): LlamaIndex's security posture is poor. This score factors in known CVEs, dependency vulnerabilities, security policy presence, and code signing practices.
- Maintenance (1/100): LlamaIndex is potentially abandoned. We track commit frequency, release cadence, issue response times, and PR merge rates.
- Documentation (1/100): Documentation quality is insufficient. This includes README completeness, API documentation, usage examples, and contribution guidelines.
- Compliance (100/100): LlamaIndex is broadly compliant. Assessed against regulations in 52 jurisdictions including the EU AI Act, CCPA, and GDPR.
- Community (1/100): Community adoption is limited. Based on GitHub stars, forks, download counts, and ecosystem integrations.
The overall Trust Score of 88.7/100 (A) reflects the weighted combination of these signals. This exceeds the Nerq Verified threshold of 70, indicating the tool meets our standards for production use.
Who Should Use LlamaIndex?
LlamaIndex is designed for:
- Developers and teams working with coding tools
- Organizations evaluating AI tools for their stack
- Researchers exploring AI capabilities in this domain
Risk guidance: LlamaIndex is well-suited for production environments. Its high trust score indicates robust security, active maintenance, and strong community support. Standard security practices (dependency pinning, access controls, monitoring) are still recommended.
How to Verify LlamaIndex'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's 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 LlamaIndex's dependency tree. - Review permissions — Understand what access LlamaIndex requires. AI tools should follow the principle of least privilege.
- Test in isolation — Run LlamaIndex 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=run-llama/llama_index - Review the license — Confirm that LlamaIndex'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 LlamaIndex
When evaluating whether LlamaIndex is safe, consider these category-specific risks:
Understand how LlamaIndex processes, stores, and transmits your data. Review the tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check LlamaIndex's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher security risk.
Regularly check for updates to LlamaIndex. Security patches and bug fixes are only effective if you're running the latest version.
If LlamaIndex 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 LlamaIndex's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using LlamaIndex in violation of its license can expose your organization to legal liability.
Best Practices for Using LlamaIndex Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from LlamaIndex while minimizing risk:
Periodically review how LlamaIndex is used in your workflow. Check for unexpected behavior, permissions drift, and compliance with your security policies.
Ensure LlamaIndex and all its dependencies are running the latest stable versions to benefit from security patches.
Grant LlamaIndex only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to LlamaIndex's security advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how LlamaIndex is used within your organization, including data handling guidelines and acceptable use cases.
When Should You Avoid LlamaIndex?
Even well-trusted tools aren't right for every situation. Consider avoiding LlamaIndex in these scenarios:
- Scenarios where LlamaIndex's specific capabilities exceed your actual needs — simpler tools may be safer
- Air-gapped environments where the tool cannot receive security updates
- Projects with strict regulatory requirements that haven't been explicitly validated
For each scenario, evaluate whether LlamaIndex's trust score of 88.7/100 meets your organization's risk tolerance. The Nerq Verified status indicates general production readiness, but sector-specific requirements may apply.
How LlamaIndex Compares to Industry Standards
Nerq indexes over 204,000 AI agents and tools across dozens of categories. Among coding tools, the average Trust Score is 62/100. LlamaIndex's score of 88.7/100 is significantly above the category average of 62/100.
This places LlamaIndex in the top tier of coding tools that Nerq tracks. Tools scoring this far above average typically demonstrate mature security practices, consistent release cadence, and broad community adoption.
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 LlamaIndex 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, LlamaIndex'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 LlamaIndex's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=run-llama/llama_index&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 LlamaIndex are strengthening or weakening over time.
LlamaIndex vs Alternatives
In the coding category, LlamaIndex scores 88.7/100. It ranks among the top tools in its category. For a detailed comparison, see:
- LlamaIndex vs AutoGPT — Trust Score: 74.7/100
- LlamaIndex vs ollama — Trust Score: 73.8/100
- LlamaIndex vs langchain — Trust Score: 87.6/100
Key Takeaways
- LlamaIndex has a Trust Score of 88.7/100 (A) and is Nerq Verified.
- LlamaIndex demonstrates strong trust signals and is well-suited for production use with standard security precautions.
- Among coding tools, LlamaIndex scores significantly above the category average of 62/100, demonstrating above-average reliability.
- Always verify safety independently — use Nerq's Preflight API for automated, up-to-date trust checks before integration.
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.