Browsing: multiagent

Claude Flow is an open-source orchestration framework designed to run multiple Claude agents in coordinated workflows. Instead of relying on a single LLM prompt chain, it…

In this tutorial, we build an advanced multi-agent communication system using a structured message bus architecture powered by LangGraph and Pydantic. We define a strict ACP-style…

def executor_agent(step: Dict[str, Any], context: Dict[str, Any]) -> StepResult: step_id = int(step.get(“id”, 0)) title = step.get(“title”, f”Step {step_id}”) tool = step.get(“tool”, “llm”) ctx_compact = { “goal”:…

For the past year, AI devs have relied on the ReAct (Reasoning + Acting) pattern—a simple loop where an LLM thinks, picks a tool, and executes.…

A junior loan officer handling data intake, risk screening, and final decisions alone is prone to mistakes because the role demands too much at once. The…

My first attempt at building a travel planning agent looked exactly like most early prototypes: one big model, a few tools, and a long system prompt.…

Enterprises are managing ever-growing volumes of content, ranging from product catalogs and support articles to knowledge bases and technical documentation. Ensuring this information remains accurate, relevant,…

@tool def sql_investigate(query: str) -> dict: try: df = con.execute(query).df() head = df.head(30) return { “rows”: int(len(df)), “columns”: list(df.columns), “preview”: head.to_dict(orient=”records”) } except Exception as e:…

Modern AI applications rely on intelligent agents that think, cooperate, and execute complex workflows, while single-agent systems struggle with scalability, coordination, and long-term context. AgentScope AI…

Multi-agent solutions, in which networks of agents collaborate, coordinate, and reason together, are changing how we approach real-world challenges. Enterprises manage environments with multiple data sources,…