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Introduction

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What is RadarOS?

RadarOS is a TypeScript-native agent orchestration framework for Node.js. It provides a complete toolkit for building AI-powered applications—from simple chatbots to complex multi-agent systems—with zero meta-framework dependency. Write pure TypeScript, plug in your preferred LLM provider, and ship production-ready agent applications. RadarOS is designed for developers who want:
  • Full control over their agent architecture
  • Native TypeScript without Python runtime or transpilation layers
  • Flexibility to swap models, storage, and transport layers without rewriting code
  • Production readiness with built-in session management, memory, RAG, and background job queues

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Key Features

Model Agnostic

Use OpenAI, Anthropic, Google Gemini, or Ollama with a unified interface. Switch providers with a single line change.

Multi-Agent Teams

Coordinate, route, broadcast, and collaborate across multiple agents. Build sophisticated agent hierarchies.

Stateful Workflows

Define agent steps, conditions, and parallel execution. Orchestrate complex multi-step pipelines.

Tools & Function Calling

Zod-validated tool definitions with type-safe execution. Full support for OpenAI-compatible function calling.

RAG & Knowledge Base

Vector stores, embeddings, and retrieval. Build context-aware agents with your own documents.

Multi-Modal

Handle images, audio, and files. Pass rich content to vision and audio-capable models.

Transport Layer

Express REST API and Socket.IO WebSocket gateway. Expose agents as HTTP or real-time endpoints.

Background Jobs

BullMQ queue and worker support. Process long-running tasks asynchronously.

Storage Drivers

In-memory, SQLite, PostgreSQL, and MongoDB. Choose the right persistence for your scale.

Memory & Sessions

Session history, LLM-powered long-term summaries, and cross-session user memory. Three complementary layers.

Voice / Realtime Agents

Speech-to-speech conversations via OpenAI Realtime and Google Gemini Live. Same tools, same memory, real-time audio.

Browser Agents

Vision-based autonomous browser automation. An agent sees screenshots, decides actions, and operates web pages via Playwright.

Sandbox Execution

Run tools in isolated subprocesses with timeout and memory limits. Fully optional — off by default.

Human-in-the-Loop

Pause the agent loop for human approval before executing sensitive tools. CLI, event-driven, or Socket.IO.

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Why RadarOS?

LangGraph.js is a powerful low-level orchestration framework, but it requires you to model everything as a directed state graph — defining nodes, edges, reducers, and conditional routing. It’s explicitly described as “very low-level, focused entirely on agent orchestration.” RadarOS takes a higher-level, declarative approach: define an Agent with tools, a Team with members, or a Workflow with steps — no graph theory required.Both frameworks support human-in-the-loop, streaming, and state persistence. Where they diverge:
  • Voice agents: LangGraph has no built-in voice support — you’d need to integrate LiveKit or another real-time layer yourself. RadarOS ships VoiceAgent with OpenAI Realtime and Google Live providers out of the box.
  • Browser automation: Not part of LangGraph. RadarOS includes BrowserAgent with Playwright, stealth mode, credential vaulting, and video recording.
  • Multi-agent teams: LangGraph supports multi-agent via handoffs and supervisor patterns, but you wire the graph manually. RadarOS provides Team with four built-in modes (coordinate, route, broadcast, collaborate) — one config object.
  • Transport: LangGraph relies on LangGraph Platform (paid) for deployment. RadarOS ships free Express, Socket.IO, and A2A transport out of the box.
The Vercel AI SDK (v6) is a solid choice for single-agent tool-calling loops, especially if you’re already on Vercel. It has good model provider support, tool validation via Zod, and a ToolLoopAgent class. However, it’s designed primarily for individual agents inside Vercel’s ecosystem.Where RadarOS goes further:
  • Multi-agent teams: The AI SDK has no built-in team coordination. RadarOS provides Team with coordinator, router, broadcast, and collaborate modes.
  • Workflows: The AI SDK offers workflow “patterns” (docs examples), but no first-class Workflow class with typed state, parallel steps, retry policies, and conditions. RadarOS does.
  • Voice agents: The AI SDK has speech and transcription utilities, but no real-time bidirectional voice agent. RadarOS has VoiceAgent with OpenAI Realtime and Google Live.
  • Browser agents: Not part of the AI SDK. RadarOS includes BrowserAgent.
  • Background jobs: The AI SDK relies on Vercel’s Fluid Compute (waitUntil). RadarOS provides a standalone @radaros/queue package with BullMQ — runs anywhere, not just Vercel.
  • Framework lock-in: The AI SDK is optimized for Vercel + React. RadarOS is framework-agnostic — Express, Fastify, Socket.IO, or headless.
LangChain.js is a large ecosystem with integrations for nearly every vector store, memory backend, and LLM provider. It’s great if you need breadth. However, the chain-based abstraction layer sits between you and the LLM, and the framework is actively steering users toward LangGraph for anything beyond simple chains (AgentExecutor is deprecated, sunset December 2026).RadarOS takes a different philosophy:
  • Direct control: You work with Agent, Team, and Workflow directly — no chain composition or AgentExecutor to reason about.
  • Batteries-included: Session management, user memory, hybrid RAG (BM25 + vector), voice agents, browser automation, sandbox execution, and human-in-the-loop are all first-class — not spread across langchain, @langchain/community, and langgraph.
  • Simpler mental model: One framework, one set of docs. LangChain.js users currently need to decide between LangChain, LangGraph, LangSmith, and LangServe — each with its own API surface.
CrewAI is Python-only (98% Python on GitHub). Autogen supports Python and .NET, with more languages “coming.” Neither has native TypeScript support. If your stack is Node.js, you’d need to run a separate Python service, manage two runtimes, and deal with inter-process communication.RadarOS keeps everything in TypeScript — same language, same process, same deployment. It also provides capabilities that these frameworks don’t:
  • Real-time voice agents with OpenAI Realtime and Google Live
  • Autonomous browser agents with Playwright and stealth mode
  • Transport gateways (Express REST, Socket.IO, Voice Gateway, Browser Gateway)
  • Background job queues with BullMQ
  • Sandbox execution for isolated tool running

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Get Started

Ready to build your first agent? Head to the Quickstart to install RadarOS and run a simple example in under five minutes.

Quickstart

Install, configure, and run your first RadarOS agent in minutes.