AI Agents Guide

This guide explains diffrent types of AI agents approach from Antropic. Each agent is designed for specific use cases and can help you solve various problems.

MultiStepAgent

This is the base agent that all other agents inherit from. It works by:

• Taking a task
• Breaking it down into steps
• Using tools to complete each step
• Keeping track of what its done

Its like having a worker who breaks down a big job into smaller tasks and completes them one by one.

ManagedAgent

Think of this as a supervisor for other agents. It:

• Has a name and description
• Can handle specific types of tasks
• Provides additional instructions to other agents
• Can give summaries of what other agents have done

Its useful when you need to coordinate multiple agents working together.

ChainedPromptAgent

This agent works like an assembly line. It:

• Has predefined steps that must be done in order
• Validates the output of each step
• Only moves forward when a step is completed successfully
• Uses the output of previous steps as input for next steps

Perfect for tasks that need to be done in a specific sequence!

RoutingAgent and RouteDefinition

These work together like a traffic controller:

RouteDefinition:

• Defines where different types of tasks should go
• Includes rules for classifying tasks
• Specifies which agent should handle each type of task

RoutingAgent:

• Looks at incoming tasks
• Decides which route they should take
• Sends them to the right handler
• Can use either rules or AI to make decisions

OrchestratorAgent and WorkerDefinition

Think of this as a project manager with specialized workers:

WorkerDefinition:

• Defines what each worker is good at
• Specifies what tools they can use
• Has instructions for how they should work

OrchestratorAgent:

• Breaks down big tasks into smaller pieces
• Assigns work to the right specialists
• Combines everyones work into final result
• Makes sure everything works together

EvaluatorOptimizerAgent

This is like having a writer and an editor working together:

• One part generates solutions
• Another part evaluates and suggests improvements
• They work in loops to make the solution better
• Stops when the solution is good enough or after maximum tries

When to Use Each Agent?

• Use MultiStepAgent for basic step-by-step tasks
• Use ManagedAgent when you need to supervise other agents
• Use ChainedPromptAgent for tasks that need strict order
• Use RoutingAgent when you need to sort tasks to different handlers
• Use OrchestratorAgent for complex projects needing multiple specialists
• Use EvaluatorOptimizerAgent when you need high-quality output with feedback loops

Example Usage

from agents import MultiStepAgent, RoutingAgent

# Create a basic agent
agent = MultiStepAgent(tools=my_tools, model=my_model)

# Run a task
result = agent.run("Calculate the square root of 16")

Remember, each agent is designed for specific use cases, so choose the one that best matches your needs!

Credits

This project is inspired by and builds upon concepts from SmoLAGents, a lightweight framework for building language agents. We extend our gratitude to the SmoLAGents team for their foundational work in this space.