Worker bees
Turn data into action — running code, processing workloads, and executing automation wherever it’s needed
Worker bees are designed to execute workloads within the platform rather than communicate directly with external services. They act as the distributed execution layer of BeeOS, transforming incoming data into actions, insights, or automated processes. Unlike connector bees, which focus on protocol integration, worker bees specialize in processing, orchestration, and runtime execution.
Depending on their configuration and role, worker bees can perform a wide range of tasks, including running custom code scripts, executing automation workflows, processing data streams, and managing containerized applications such as Docker workloads. This flexibility allows organizations to deploy logic exactly where it is needed — at the edge for low-latency processing or in centralized environments for larger-scale operations.
Worker bees enable advanced scenarios such as real-time data transformation, rule-based automation, AI inference, and workload orchestration across distributed infrastructures. By running workloads close to the data source, they reduce latency, minimize network dependency, and allow systems to continue operating even in environments with limited or intermittent connectivity.
Because worker bees operate as independent yet coordinated components, they can scale horizontally to handle increasing workloads while remaining fully manageable through the centralized control plane. This distributed model ensures that computation, automation, and intelligence are not limited to a single location but can be executed wherever they deliver the most value.
Types of workers
Worker bees can be broadly categorized into several functional types.
Script Workers
Script workers execute custom code or lightweight programs that process data, apply transformations, or trigger automated actions. They allow developers to embed business logic directly into the data flow without requiring external services.
Container Workers
Container workers manage and execute containerized workloads, such as Docker-based applications or microservices. This allows complex processing pipelines, integrations, or custom runtime environments to run as part of the distributed system.
Automation Workers
Automation workers orchestrate workflows based on rules or events. They can react to incoming messages, trigger sequences of actions, or coordinate multiple agents to perform complex operational tasks.
AI Workers
AI workers enable advanced analytics and intelligent processing by running machine learning inference or AI-driven workloads close to the data source. This makes it possible to analyze data in real time, even in remote or constrained environments where sending data to the cloud is not always feasible.
The documentation is currently a work in progress and is not yet completed
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