Hệ thống đa tác nhân hoàn toàn cục bộ với LangGraph

Following the release of OpenAI's new Agents SDK, we've seen a lot of interest in multi-agent workflows. Here, we discuss two different approaches for multi-agent systems - swarm and supervisor - and showcase two different LangGraph packages that make it easy to implement these approaches. We show that both can be run locally with Qwen2.5-14b (via Ollama), which excels at tool-calling. We also show LangGraph Studio and LangSmith traces to provide debugging and observability into these systems.

LangGraph-swarm:

https://github.com/langchain-ai/langgraph-swarm-py

LangGraph-supervisor:

https://github.com/langchain-ai/langgraph-supervisor-py

Video notes:

https://mirror-feeling-d80.notion.site/Fully-Local-Multi-Agent-1b5808527b178066bde0ed981b27998c?pvs=4

Chaters:

00:00 - Introduction to Multi-Agent Systems and Open Eye SDK

00:30 - Demo: Flight and Hotel Booking Multi-Agent System

01:10 - Running Locally with Qwen models

02:00 - What is an Agent? (Tool Calling in a Loop)

03:00 - Finding Local Models for Agent Development

04:00 - Berkeley Function Calling Leaderboard and Qwen Models

05:00 - Why Multi-Agent Systems Matter

06:00 - Supervisor vs. Swarm Architecture Explained

07:15 - Trade-offs Between Different Multi-Agent Approaches

08:00 - Building Multi-Agent Systems in a Notebook

09:00 - Understanding the Agent Implementation

10:00 - Setting up the Supervisor Architecture

11:00 - Tracing and Visualization with LangSmith

12:00 - Choosing the Right Local Models for Your Agents

12:45 - Conclusion and Final Thoughts