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.

▶️ Watch the Introduction Video (8 min)

• 🛡️ 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).
• 🔎 Scalable Agent Discovery: Supports Prompt Mode for small teams (<15 agents) and Discovery Mode for unlimited scaling via vector-based agent lookup.
• 🤖 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.

• OpenAI (openai): Cloud models such as gpt-5 and gpt-5-nano.
• Anthropic (anthropic): Cloud models such as claude-4-5-sonnet and claude-4-5-opus.
• DeepSeek (deepseek): DeepSeek API models.
• Azure OpenAI (azure): Azure hosted OpenAI models.
• Google (google): Google Gemini models such as gemini-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.

• 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

1. Clone the repository:

   git clone https://github.com/mehrdadrad/radops.git
   cd radops

2. Install dependencies (using uv for speed):

   uv pip install -e .

For detailed guides on configuration, deployment, and features, please refer to the documentation.

We welcome contributions! Please follow these steps:

1. Fork the project on GitHub.
2. Create a new feature branch (git checkout -b feature/amazing-feature).
3. Commit your changes.
4. Push to the branch and open a Pull Request.

Built with LangGraph, Mem0, Top Vector Databases, and Passion.