Currently, AI is extremely popular, and for us engineers, the main focus is on multi-agent collaboration to complete vertical business tasks. Through exploration, it seems that the paradigm of AI does not deviate significantly from computational paradigms; its core collaboration logic still follows the classic control flow patterns in computer science. It represents a natural … Read more
As the LangChain ecosystem matures, the AI applications we build are evolving from simple “question-answering bots” to complex “agent collaboration systems.” LangGraph, as a powerful tool in the LangChain ecosystem for building stateful, multi-step AI applications, offers elegant support for multi-agent systems as one of its core values. Why Multi-Agent? What is an Agent? Essentially, … Read more
In the past year, Multi-Agent systems have been gaining significant traction, with continuous funding and increasing popularity. From AutoGPT to MetaGPT, from CrewAI to LangGraph, various multi-agent frameworks are emerging. The domestic phenomenon Manus, along with the recently open-sourced OpenManus by the MetaGPT team, are notable examples. OpenAI’s Swarm, Microsoft’s AutoGen, and Anthropic’s Claude Code … Read more
Hello everyone, this is AI Universe. In the current rapid evolution of artificial intelligence technology, the Multi-Agent architecture is becoming a core engine driving breakthroughs in AI applications due to its efficient collaboration capabilities for complex tasks. The explosive growth of this technology is closely related to the limitations of large language models — although … Read more
Introduction to LangGraph In the previous article, we explored the concept of multi-agent systems and how Clearwater Analytics pioneered this approach through CWIC Flow. We also demonstrated how to build a simple multi-agent system without using a specialized framework. Now, let’s explore LangGraph, a powerful library that provides a structured workflow for building complex multi-agent … Read more
As AI enters the “usable stage,” how can we build a truly efficient, flexible, and controllable intelligent agent system? Relying solely on a “smart large model” is clearly not enough. For genuinely complex tasks, a clearly defined “team operation” is essential. The Multi-Agent-Manus (MCP process) has emerged, which is not just a simple execution chain … Read more
Imagine asking an LLM, “Did Aristotle use a laptop?” It might seriously fabricate a reason: “There was already wireless internet in ancient Greece…” This phenomenon of “seriously nonsense” is what we call the LLM’s “hallucination.” Paper: Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Link: https://arxiv.org/pdf/2504.14520 The paper points out that meta-thinking— the ability … Read more
In today’s digital age, artificial intelligence (AI) is no longer just a tool; it is evolving into an intelligent workforce composed of numerous AI agents capable of autonomous planning, reasoning, and task execution. The rise of Multi-Agent Systems (MAS) is fundamentally changing the way businesses operate by enabling specialized AI agents to collaborate seamlessly, tackling … Read more
In the past two years, the most exciting development in the field of AI has been the rise of large language models (LLMs), which have demonstrated remarkable understanding and generation capabilities. Building on this foundation, a grander vision has emerged: to construct Multi-Agent Systems (MAS). Imagine not a single AI working alone, but a “dream … Read more