Build an ARMY of AI Agents on Autopilot with Archon, Here's How

Key Concepts

• Archon: An open-source AI agent that builds other AI agents (an "agenteer").
• MCP (Multi-Chain Protocol) Server: A server that provides access to various services and tools for AI agents.
• AI Agent Army: A collection of specialized sub-agents, each leveraging an MCP server, coordinated by a primary agent.
• Specialized Agents: AI agents designed for specific tasks, reducing the burden on the primary agent and improving performance.
• Pyantic AI: A Python AI framework used to build Archon and its agents.
• Langraph: Another Python AI framework being integrated into Archon.
• RAG (Retrieval-Augmented Generation): A technique for enhancing AI agents with external knowledge.
• Vectorize: A platform for simplifying RAG pipelines.
• Agentic Techniques: Advanced methods for building and managing AI agents.
• Tool Library: A collection of pre-built tools, examples, and MCP servers for Archon to use.
• Advisor Agent: An agent within Archon that intelligently selects examples, pre-built tools, and MCP servers based on the user's request.

AI Agent Army Built with Archon

Overview

The video showcases an AI agent army built using Archon, an open-source AI agent builder. This army consists of specialized sub-agents, each connected to a specific MCP server, coordinated by a primary agent. This architecture allows for complex tasks to be broken down and handled efficiently, avoiding overwhelming the primary agent.

Benefits of Specialized Agents

• Reduced Prompt Size: By distributing tasks among specialized agents, the prompts sent to the LLMs are smaller, leading to better performance.
• Simplified Decision-Making: The primary agent only needs to decide which sub-agent to use, rather than choosing from a large number of individual tools.
• Scalability: The system can be easily extended with additional MCP servers and sub-agents without significantly impacting performance.

Example Use Case

The video demonstrates the AI agent army's capabilities with a complex request:

1. Search the web for top AI agent frameworks.
2. Add the frameworks to an AirTable base, including the source URL.
3. Get the AirTable base URL and send it to a Slack channel.

This task involves multiple sub-agents:

• Brave Agent: Searches the web using the Brave MCP server.
• AirTable Agent: Interacts with the AirTable base.
• Slack Agent: Sends the message to the Slack channel.

The primary agent routes the requests to the appropriate sub-agents, which then perform the specific tasks using their respective MCP servers.

Demonstration

The demonstration shows the agent successfully:

• Listing GitHub repositories.
• Searching the web for AI agent frameworks.
• Adding the frameworks to an AirTable base.
• Posting a link to the AirTable base in a Slack channel.

This highlights the agent's ability to handle complex, multi-step tasks by leveraging the specialized sub-agents.

Building the AI Agent Army with Archon

MCP Integration with Pyantic AI

The AI agent army is built using the new MCP integration in Pyantic AI. This integration simplifies the process of connecting agents to MCP servers.

Code Structure

The code for the AI agent army consists of:

• Configuration files: Define the MCP servers and their corresponding credentials.
• Sub-agent definitions: Each sub-agent is responsible for interacting with a specific MCP server.
• Primary agent definition: The primary agent coordinates the sub-agents and routes requests to the appropriate ones.

Step-by-Step Process

1. Define MCP servers: Configure the connections to the desired MCP servers (e.g., Brave, AirTable, Slack).
2. Create sub-agents: For each MCP server, create a specialized agent that uses the tools provided by that server.
3. Define tools for sub-agents: Specify the tools that each sub-agent can use.
4. Create the primary agent: Define the primary agent and give it tools to call each of the sub-agents.
5. Define tools for the primary agent: Specify the tools that the primary agent can use to interact with the sub-agents.

Using Archon to Generate Code

The video demonstrates how Archon can be used to generate the code for the AI agent army. By providing Archon with a prompt describing the desired functionality, it can generate a starting point for the code. The generated code may require some refinement, but it significantly reduces the amount of manual coding required.

Prompt Example

The prompt used to kick off the AI agent building process with Archon is available in the GitHub repository.

Archon's Architecture and Future Development

Tool Library and MCP Integration

Archon's architecture includes a tool library and MCP integration, which allows it to easily access and utilize various tools and services.

• Agent Resources Folder: Contains pre-built tools, examples, and MCP server configurations.
• Advisor Agent: Intelligently selects the appropriate resources based on the user's request.

Upcoming Features

The video highlights several upcoming features for Archon:

• Langraph Integration: Archon will be able to build agents using both Pyantic AI and Langraph.
• Autonomous Refinement: Archon will be able to spin up isolated environments, run the agents, and autonomously refine them based on the results.

Version 6: Tool Library and MCP Integration

This version introduces pre-built tools, examples, and MCP servers within the agent resources folder. The advisor agent intelligently selects resources based on user requests.

Version 9: Autonomous Refinement

This upcoming version will enable Archon to create isolated environments, run agents, and autonomously refine them based on performance and error analysis.

Vectorize: Simplifying RAG Pipelines

Overview

Vectorize is a platform that simplifies the process of building RAG pipelines. It automates the data processing steps required to ingest documents into a vector database.

Key Features

• Automated Data Processing: Vectorize automates the steps of extracting text, embedding it, and inserting it into a vector database.
• Support for Multiple Sources: Vectorize supports a variety of data sources, including Google Drive.
• Support for Multiple Vector Databases: Vectorize supports a variety of vector databases, including Pinecone, Weaviate, and Superbase.
• RAG Sandbox: Vectorize provides a sandbox environment for testing RAG pipelines.

Benefits

• Simplified Data Ingestion: Vectorize simplifies the process of ingesting data for RAG, making it easier to build AI agents that can access external knowledge.
• Reduced Development Time: Vectorize reduces the amount of time required to build RAG pipelines.

Notable Quotes

• "archon is the first of its kind it's an open- source AI agent that builds other AI agents"
• "...specialized agents are powerful because LLMs get overwhelmed very quickly if you give them too many tools..."

Conclusion

Archon is a powerful tool for building AI agents, particularly those that leverage MCP servers. Its open-source nature and educational framework make it a valuable resource for developers looking to learn more about advanced agentic systems. The AI agent army demonstrates the potential of specialized agents to handle complex tasks efficiently. The integration with Vectorize further simplifies the process of building AI agents by automating the data ingestion process for RAG. The upcoming features for Archon promise to make it an even more powerful and versatile tool for AI agent development.