fast-llm-security-guardrails
The fastest Trust Layer for AI Agents
GraphCanon updated Sep 14, 2026 · GitHub synced Sep 14, 2026
60views this month
Decision brief
fast-llm-security-guardrails, also known as ZenGuard, is designed for the rapid deployment of security measures into AI agent systems to ensure they operate within defined trust constraints.
Good fit when
- Fast integration of privacy and security guardrails in environments where real-time evaluation and observability are critical.
- In development ecosystems that already support integrations such as LangChain or LlamaIndex, leveraging ZenGuard's pre-built connectors for seamless integration.
Avoid when
- If your project requires a more customizable solution than what fast-llm-security-guardrails offers in its out-of-the-box configurations.
- For teams that operate without established runtime environments like LangChain or LlamaIndex, as this may necessitate significant adaptation of ZenGuard.
Observed Jul 16, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Slowing (222d since push)
- As of Sep 14, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 14, 2026
- Security (OSV)
- 12 low (12 low)
- As of Jul 15, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install fast-llm-security-guardrailsSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A tool designed to quickly integrate security guardrails into AI agent systems.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Sep 14, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 14, 2026)
Integration with [LangChain](https://python.langchain.com/v0.2/docs/integrations/tools/zenguard/) <a href="Source link
Source: README excerpt (regex_v1, Sep 14, 2026)
Integration with [LangChain](https://python.langchain.com/v0.2/docs/integrations/tools/zenguard/) <a href="https://colab.reSource link
Tags
README
Installation Start by installing ZenGuard package: Using pip: Using poetry: Getting Started Jump into our Quickstart Guide to easily integrate ZenGuard with your AI Agents. Integration with LangChain <a href="https://colab.research.google.com/github/langchain ai/langchain/blob/ma...
For agents
This page has a .md twin and JSON over the API.