LangGraph provides a graph-based abstraction for designing AI agent workflows. Developers define nodes that represent prompts, tools, data sources, or decision logic, then connect these nodes with edges to form a directed graph. At runtime, LangGraph traverses the graph, executing LLM calls, API requests, and custom functions in sequence or in parallel. Built-in support for caching, error handling, logging, and concurrency ensures robust agent behavior. Extensible node and edge templates let users integrate any external service or model, making LangGraph ideal for building chatbots, data pipelines, autonomous workers, and research assistants without complex boilerplate code.

Embedded BDI provides a full BDI lifecycle engine: it models an agent’s beliefs about its environment, manages evolving desires or goals, selects intentions from a library of plans, and executes behaviors in real time. The framework includes modules for belief base storage, plan library definition, event triggering, and concurrency control tailored for memory-limited microcontrollers. With a simple API, developers annotate beliefs, specify desires, and implement plans in code. Its scheduler handles priority-based intention execution and integrates with hardware interfaces for sensors, actuators, and network communication, making it ideal for autonomous IoT devices, mobile robots, and industrial controllers.