Developer Builds Multi-Agent AI System Using LangGraph for Automated Task Execution

A developer has shared a multi-agent AI system built with LangGraph that automates the pipeline from user query to task execution. The system consists of four components: a search agent that retrieves up to five web results using the DDGS library, a planning agent that uses an LLM to devise an execution strategy, an execution agent that carries out tasks such as creating project folders or installing libraries, and an orchestrator that routes workflow between agents. Shared state, defined via a custom MultiAgentState class, allows agents to pass data including search results, task queues, and completion flags to one another. The orchestrator drives the workflow sequentially — search, plan, execute — until no pending tasks remain, at which point the process ends. The project uses GPT-class models served through Groq's API for fast, free-tier LLM inference during the planning stage.