William-Lu-stackOps & cloud

Flawless

An enterprise Agentic SRE platform for Kubernetes and cloud infrastructure that automates fault discovery, evidence gathering, diagnosis, controlled execution, and recovery verification.

  • DevOps
  • Backend
  • Security
  • Automation
  • Monitoring / Observability
  • Testing
  • Web
  • Linux
  • macOS
  • Windows
  • Browser
  • Self-hostable
  • Runs locally
  • Docker supported
  • Needs API key
Flawless screenshot
Popularity
777 Stars
GitHub stars
Recent activity
8/14/2026
Maintenance status unclear
License
NOASSERTION
Review the terms yourself

Why it matters

We look beyond stars: what problem it solves, whether it creates real utility, and what makes its approach worth noticing.

Last 90 days

Problem

The platform addresses the lack of trust in models, bypassed approval gates, and insufficient evidence chains in IT infrastructure operations.

Practical value

It provides an auditable closed loop from risk discovery and diagnosis to controlled change and verification, ensuring practical applicability.

Innovation / differentiation

It introduces a plugin-first architecture and domain skill routing, decoupling LLM reasoning from deterministic execution boundaries.

Leverage potential

Through standardized plugin manifests and typed actions, it allows infrastructure teams to deliver ops capabilities without core modifications.

Why now

It arrives as enterprises adopt cloud-native operations and AI-assisted troubleshooting while maintaining strict security boundaries.

Community activity

In the last 90 days there were 5 new issues and 0 pull requests; the bounded issue/PR samples include 5 issue authors and 0 PR contributors, with 0 releases.

Maintainer responsiveness

The 5-issue window sample had a 0% close rate, and the 0-pull-request sample had a — merge rate. Maintainer-comment observations covered only 100% of that issue sample, so response rate and first-response speed are not reported.

0 contributors in PR sample0 releases

Key highlights

  • Closed-loop SRE workflow: Unifies risk discovery, evidence collection, root-cause diagnosis, controlled changes, and recovery verification into an audited loop.
  • Domain Agents & plugin architecture: Features dedicated agents for Kubernetes, databases, VMs, and storage, extensible via manifests, providers, and skills.
  • Strict safety gates & approvals: Blocks arbitrary command injection by requiring typed actions, blast radius evaluation, and human approval.

Quick start

How it is installed, how hard it is, and where to start.

Where it runs

Self-hosted (your own server)

Difficulty

Medium — some setup needed

Docker supportedSelf-hostableRuns locally
  1. 01Set up and activate a Python virtual environment: python3 -m venv .venv && source .venv/bin/activate
  2. 02Install backend dependencies: python -m pip install -r requirements.txt
  3. 03Run backend test suite to verify kernel configuration: python -m pytest tests
  4. 04Start the backend local stack: python scripts/runlocalstack.py --host 127.0.0.1 --api-port 8080
  5. 05Navigate to the frontend directory, install dependencies, and start the development server: cd frontend/modern && npm ci && npm run dev

Best for

  • Teams that want data on their own servers
  • Developers who want to try it on their machine
  • People who prefer Docker deploys

More about it

CISRE (Cloud Infrastructure Site Reliability Engine) is an enterprise-grade AgenticOps platform built for Kubernetes and cloud infrastructure. It connects risk discovery, evidence gathering, root-cause diagnosis, skill routing, change approval, controlled execution, and recovery verification into an audited workflow, overcoming typical AI ops pitfalls such as unsafe executions or unverified resolution claims.

The architecture adopts a "Everything is a Plugin" philosophy inspired by Harness orchestrations. Resource domains like Kubernetes, databases, VMs, storage, and networking integrate via modular Domain Agents, read-only Providers, domain Skills, and typed action catalogs without altering the core engine. AI models handle reasoning and planning while actual state mutations require strict typed action mapping and mandatory human approvals.

For safety and accountability, external plugins cannot gain direct high-privilege credentials or execute arbitrary Shell/SQL commands. CISRE uses append-only event journals, snapshot stores, and hash-chained session logs for full auditability, while Agent Trace redacts raw credentials and unmasked prompts to satisfy enterprise compliance requirements.

Sources

Each field shows its status and source — expand to review.

11 · Expand
  • capability tags

    Verified

    automation, monitoring, testing, security

    Source: admin_cms · cms editor · 8/15/2026

  • License

    Verified

    NOASSERTION

    Source: GitHub API · license.spdx_id=NOASSERTION · 10/1/2026

  • needs api key

    Verified

    Yes

    Source: admin_cms · cms editor · 8/15/2026

  • One-liner

    Verified

    {"en":"An enterprise Agentic SRE platform for Kubernetes and cloud infrastructure that automates fault discovery, evidence gathering, diagnosis, controlled execution, and recovery verification.","zh":"面向 Kubernetes 与云基础设施的企业级 Agentic SRE 运维平台,贯穿风险发现、取证诊断、受控变更与恢复验证全闭环。"}

    Source: admin_cms · cms editor · 8/15/2026

  • Platforms

    Verified

    linux, macos, windows, browser

    Source: admin_cms · cms editor · 8/15/2026

  • Category hint

    Inferred from materials

    ai-apps

    Source: Project README · hint=ai-apps · 8/15/2026

  • product forms

    Verified

    web

    Source: admin_cms · cms editor · 8/15/2026

  • role tags

    Verified

    devops, backend, security

    Source: admin_cms · cms editor · 8/15/2026

  • supports docker

    Verified

    Yes

    Source: Repository file · dockerfile=true; compose=true · 10/1/2026

  • supports local

    Verified

    Yes

    Source: admin_cms · cms editor · 8/15/2026

  • supports self host

    Verified

    Yes

    Source: admin_cms · cms editor · 8/15/2026

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