Building a multi-agent researcher with llms.txt

LangChain · Beginner ·🤖 AI Agents & Automation ·1y ago
A common pattern with Deep Research agents is the ability to ask clarifying questions in a planing phase followed by iterative research in a research phase. Here, we show that a multi-agent architecture (via Swarm) is well-suited for this talk, with a planner agent asking clarifying questions and a research agent performing iterative research. We build a simple multi-agent system that can answer questions about LangGraph documentation using our new llms.txt file (https://langchain-ai.github.io/langgraph/llms.txt) along with a simple URL loader tool. Code: https://github.com/langchain-ai/lang…
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Chapters (14)

Introduction to Deep Research Agents
0:27 Demo of Planner-Researcher System
1:00 Multi-agent System for Research Tasks
1:34 Introduction to Swarm Architecture
2:06 Exploring the Code Implementation
2:30 Defining Agent Handoff Tools
3:00 The Planner Agent's Prompt and Role
3:34 The Researcher Agent's Responsibilities
4:00 Setting Up the Swarm Framework
4:17 Testing the System in a Notebook
4:40 Tracing Through the Execution Process
5:22 Verifying the Generated Implementation
5:40 Key Benefits of Multi-agent Research
6:00 Conclusion and Final Thoughts

Playlist

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