RadOps - AI-Powered Operations
RadOps is an AI-powered, multi-agent platform that automates DevOps workflows with human-level reasoning. Unlike traditional chatbots, RadOps remembers context, validates its own work, and executes complex multi-step operations across your entire infrastructure—autonomously.
🚀 Key Highlights
- 🛡️ Guardrailed Orchestration: Uses a Supervisor-Worker architecture with strict sequential logic to prevent execution errors.
- 🧠 3-Tier Cognitive Memory: Distinguishes between Working Memory, Ephemeral Facts , and Core Knowledge (Permanent Architecture rules).
- 🤖 Config-Driven Specialists: Instantly spin up specialized agents (e.g., Network, Security) by defining personas and toolsets in YAML — no new code required.
- **👨💻 Hum…
RadOps - AI-Powered Operations
RadOps is an AI-powered, multi-agent platform that automates DevOps workflows with human-level reasoning. Unlike traditional chatbots, RadOps remembers context, validates its own work, and executes complex multi-step operations across your entire infrastructure—autonomously.
🚀 Key Highlights
- 🛡️ Guardrailed Orchestration: Uses a Supervisor-Worker architecture with strict sequential logic to prevent execution errors.
- 🧠 3-Tier Cognitive Memory: Distinguishes between Working Memory, Ephemeral Facts , and Core Knowledge (Permanent Architecture rules).
- 🤖 Config-Driven Specialists: Instantly spin up specialized agents (e.g., Network, Security) by defining personas and toolsets in YAML — no new code required.
- 👨💻 Human-in-the-Loop: Seamlessly pause workflows for user approval or input before executing sensitive actions.
- 🔄 Multi-Step Workflows: Automatically decomposes complex requests into logical steps, executing them sequentially with state tracking and plan enforcement.
- ✅ Trust-but-Verify Auditing: A dedicated QA Auditor Node verifies actual tool outputs against the user request to catch hallucinations before they reach you.
- 📂 Declarative RAG & BYODB: "Bring Your Own Database." Supports top vector databases with zero-code, config-driven knowledge tool generation.
- 🔌 Resilient Connectivity: Built on the Model Context Protocol (MCP) with self-healing clients that survive server restarts.
- 👀 Deep Observability: Full tracing of Agent Logic, Tool Execution, and LLM Streaming via OpenTelemetry.
🧠 Supported Providers
LLM Providers
- OpenAI (
openai): Cloud models such asgpt-5andgpt-5-nano. - Anthropic (
anthropic): Cloud models such asclaude-4-5-sonnetandclaude-4-5-opus. - DeepSeek (
deepseek): DeepSeek API models. - Azure OpenAI (
azure): Azure hosted OpenAI models. - Google (
google): Google Gemini models such asgemini-3-pro-preview. - Groq (
groq): Groq Cloud models. - Mistral (
mistral): Mistral AI models. - AWS Bedrock (
bedrock): AWS managed models. - Ollama (
ollama): Local models. If used for agents, the model must support tool calling.
Vector Databases
- Weaviate Hybrid search, GraphQL API, multi-tenancy
- Qdrant High performance (Rust), advanced filtering
- Pinecone Managed cloud, serverless, auto-scaling
- Milvus Open source, horizontal scaling, GPU support
- Chroma Lightweight, embedded, perfect for dev/test
📦 Installation
Clone the repository:
git clone https://github.com/mehrdadrad/radops.git
cd radops
Install dependencies (using uv for speed):
uv pip install -e .
📚 Documentation
For detailed guides on configuration, deployment, and features, please refer to the documentation.
🤝 Contribute
We welcome contributions! Please follow these steps:
- Fork the project on GitHub.
- Create a new feature branch (
git checkout -b feature/amazing-feature). - Commit your changes.
- Push to the branch and open a Pull Request.
Built with LangGraph, Mem0, Top Vector Databases, and Passion.