Orb
Summary
Orb is a closed-source agent framework for building and running LLM-powered multi-agent systems.
Orb handles orchestration of agents that call large language models, manage tool use, and maintain state across steps. It targets teams that want to ship agent workflows without assembling and hosting their own stack from open-source components. No public pricing is listed, so cost requires direct contact. The largest drawback is the lack of code access, which prevents inspection, forking, or migration away from the vendor.
Bottom line: *Choose Orb when speed of deployment outweighs the need for code control or pricing transparency.*
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Orb Backend is the server-side engine for Orb, an AI assistant designed to initiate contact rather than wait for it. The core workflow chains a brain module, an intent router, a proactive engine, and a tool loop: the proactive engine scans for relevant context on its own schedule, the intent router decides whether to surface an alert or execute a task, and the tool registry handles the actual work. An MCP-compatible HTTP layer (mcp_http.py, orb_mcp.py) means external tools plug in without forking the core. Voice input is handled locally via stt.py, and push notifications route through an APNS integration — so the assistant can interrupt you on your phone when something warrants it.
The standout architectural decision is the proactive mind process in mind.py. Unlike request-response assistants that are inert between prompts, this loop runs continuously, maintains situation awareness across projects via a persistent memory store, and can delegate tasks to background agents tracked in tasks_store.py. For a developer running this on a home server or a dedicated box, that means the assistant can notice things — a build that finished, a deadline approaching, a pattern in recent notes — without being asked.
Orb fits tightly defined personal or small-team deployments where data sovereignty matters and the iOS ecosystem is the target client surface. The SELF_HOSTING.md and SETUP.md docs lower the barrier for a solo developer, and the MIT license means the code is yours to modify. Where it breaks: the client layer is iOS-first by the vendor’s own framing, the GitHub repo shows 7 stars and 8 commits at the time of curation, and there is no documented path for multi-user or multi-tenant deployments. Teams needing a web interface, Android support, or horizontal scaling will find the current codebase a starting sketch rather than a finished foundation.
Technically, the repo separates concerns across agent.py, supervisor.py, connectors.py, personas.py, and a screen_bridge.py that suggests screen-context awareness as an input channel. The .env.example file and requirements.txt indicate a Python stack with configuration-driven setup. API surface is documented in API.md, and AGENTS.md describes agent behavior expectations — which matters if you plan to extend the tool loop with custom agents.