Building Multi-Agent RAG Workflows
Design and implement sophisticated multi-agent systems where different agents collaborate on retrieval and generation tasks.
Beyond Single Agents
In the previous lesson, we explored individual LangChain Agents and their tools. But what happens when tasks become too complex for one agent?
This is where Multi-Agent RAG Workflows come in. They involve several LLM agents collaborating to achieve a common, often complex, goal.
When One Agent Isn't Enough
Multi-agent systems shine when dealing with tasks that are:
- Complex: Requiring multiple steps, perspectives, or deep reasoning.
- Specialized: Different parts of the task need different 'expertise' or tool sets (e.g., search vs. summarize).
- Iterative: Benefitting from feedback loops, review, or refinement steps.
Think of it like a team tackling a project, rather than a single person.
All lessons in this course
- LangChain Agents and Tool Concepts
- Building Multi-Agent RAG Workflows
- Integrating External APIs as Tools
- Memory and State in Agentic RAG