I Built a Super Agent in n8n
AI Summary
Building a Super Agent in NA10
Overview
- Introduction to creating a super agent using NA10.
- Explanation of a layout with multiple set nodes and an AI agent.
Key Components
- Set Nodes
- Each set node holds a unique prompt.
- Reduces input tokens for cost efficiency.
- AI Agent Functionality
- Processes various queries (e.g., fetching the latest AI news).
- Utilizes a text classifier to determine the task.
- Dynamic memory allows learning from previous interactions.
Demonstration
- Example Query: Getting the latest AI news.
The system identifies the type of query (search web task).
Relevant prompts and memories are used efficiently.
- Memory Structure
- Memory adapts based on previous searches (e.g., Reddit, web searches).
- Enhances learning for more accurate future queries.
Advantages
- Reduces complexity by minimizing workflows.
- Cost-effective due to shorter prompts.
- Avoids latency issues and hallucinations.
- Encourages efficient searching methods for different data sources.
Additional Components
- Discussed using Telegram and text classifier models (e.g., 4.1 nano, 4.1 mini).
- Touches on using Zep and Base Row for data management.
- Explanation of creating and managing events and contacts using prompts.
Conclusion
- Encourages users to build and customize their super agents.
- Mentions future plans for more powerful tools and community resources for members.