Is Hashbrown Safe? — Trust Score: 77.4/100
According to Nerq's independent analysis of hashbrown, this agent_framework has a trust score of 77.4 out of 100, earning a B grade. With 614 stars on github, it is recommended for production use. Security score: 0/100. 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 Hashbrown safe?
YES — Hashbrown has a Nerq Trust Score of 77.4/100 (B). 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
Trusted — hashbrown demonstrates strong trust signals. It meets the threshold for Nerq Verified status, indicating solid security practices, active maintenance, and a healthy ecosystem presence.
Trust Signal Breakdown
Details
| Author | liveloveapp |
| Category | agent_framework |
| Stars | 614 |
| Source | https://github.com/liveloveapp/hashbrown |
| Frameworks | openai · anthropic · ollama |
| Protocols | rest |
Regulatory Compliance
| EU AI Act Risk Class | Not assessed |
| Compliance Score | 100/100 |
| Jurisdictions | Assessed across 52 jurisdictions |
Popular Alternatives in agent_framework
Community Reviews
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What Is Hashbrown?
Hashbrown is a framework for building autonomous AI agents that can reason and take actions. Hashbrown is an open-source framework for building agents that run in the browser, supporting Angular and React.
As of March 2026, Hashbrown has 614 stars on github, making it an emerging 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 Hashbrown's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensions. Here is how Hashbrown performs in each:
- Security (0/100): Hashbrown's security posture is poor. This score factors in known CVEs, dependency vulnerabilities, security policy presence, and code signing practices.
- Maintenance (1/100): Hashbrown 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): Hashbrown 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 77.4/100 (B) 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 Hashbrown?
Hashbrown is designed for:
- AI engineers building autonomous agent systems
- Research teams experimenting with multi-agent architectures
- Companies creating AI-powered automation workflows
Risk guidance: Hashbrown meets the minimum threshold for production use, but we recommend monitoring for security advisories and keeping dependencies up to date. Consider implementing additional guardrails for sensitive workloads.
How to Verify Hashbrown'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 Hashbrown's dependency tree. - Review permissions — Understand what access Hashbrown requires. AI tools should follow the principle of least privilege.
- Test in isolation — Run Hashbrown 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=hashbrown - Review the license — Confirm that Hashbrown'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 Hashbrown
When evaluating whether Hashbrown is safe, consider these category-specific risks:
Agent frameworks like Hashbrown can take actions autonomously — executing code, calling APIs, modifying files. Always implement guardrails and human-in-the-loop controls for production deployments.
AI agents built with Hashbrown may be vulnerable to prompt injection attacks where malicious input causes the agent to take unintended actions. Test for adversarial inputs before deploying.
Autonomous agents can incur unexpected API costs or resource usage. Set budget limits and monitoring alerts when deploying Hashbrown-based agents.
When using Hashbrown to orchestrate multiple agents, failures in inter-agent communication can lead to cascading errors, duplicated actions, or deadlocks. Implement circuit breakers and timeout mechanisms to prevent runaway agent loops that can consume resources indefinitely.
Agents built with Hashbrown that persist memory across sessions can have their context poisoned by adversarial inputs. Once corrupted, the agent may make consistently poor decisions in future interactions. Implement memory validation and periodic context resets for long-running agents.
Best Practices for Using Hashbrown Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Hashbrown while minimizing risk:
Configure Hashbrown agents to require human approval for high-impact actions like payments, data deletion, or external API calls.
Autonomous agents built with Hashbrown can incur unexpected costs through API calls and resource usage. Set hard spending limits and rate caps.
Log all actions taken by agents built with Hashbrown. Use observability tools to detect anomalous behavior patterns that could indicate prompt injection or logic errors.
Before deploying Hashbrown-based agents in production, test with adversarial prompts designed to bypass guardrails and cause unintended actions.
Each agent should have the minimum permissions required. Never give an agent root access, admin credentials, or unrestricted API keys.
When Should You Avoid Hashbrown?
Even well-trusted tools aren't right for every situation. Consider avoiding Hashbrown in these scenarios:
- Scenarios where Hashbrown'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 Hashbrown's trust score of 77.4/100 meets your organization's risk tolerance. The Nerq Verified status indicates general production readiness, but sector-specific requirements may apply.
How Hashbrown Compares to Industry Standards
Nerq indexes over 204,000 AI agents and tools across dozens of categories. Among agent frameworks, the average Trust Score is 65/100. Hashbrown's score of 77.4/100 is significantly above the category average of 65/100.
This places Hashbrown in the top tier of agent frameworks 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 Hashbrown 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, Hashbrown'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 Hashbrown's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=hashbrown&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 Hashbrown are strengthening or weakening over time.
Hashbrown vs Alternatives
In the agent_framework category, Hashbrown scores 77.4/100. It ranks among the top tools in its category. For a detailed comparison, see:
- Hashbrown vs crewAI — Trust Score: 92.1/100
- Hashbrown vs AutoAgents — Trust Score: 82.1/100
- Hashbrown vs LoongFlow — Trust Score: 86.4/100
Key Takeaways
- Hashbrown has a Trust Score of 77.4/100 (B) and is Nerq Verified.
- Hashbrown meets the minimum threshold for production deployment, though monitoring and additional guardrails are recommended.
- Among agent frameworks, Hashbrown scores significantly above the category average of 65/100, 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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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.