Building Effective Agents with LangGraph

Introduction

This tutorial is designed to guide you through the process of building effective agents using LangGraph, based on the insights from Anthropic's blog post about the differences between agents and workflows. By following this guide, you will learn about various patterns for implementing workflows and agents, understand the benefits of using LangGraph, and apply these concepts through practical examples.

Step 1: Understand Workflows and Agents

• Definitions:

  • Workflows: A sequence of interconnected tasks that are processed in a specific order.
  • Agents: Autonomous entities that make decisions and perform tasks based on inputs and contexts.

• When to Use Each:

  • Use workflows for structured, sequential tasks.
  • Use agents for dynamic environments where decision-making is required.

Step 2: Explore the Benefits of Frameworks

Why Use LangGraph

• Simplifies the implementation of complex tasks.
• Provides built-in patterns and functionalities to streamline development.
• Enhances collaboration and scalability in agent development.

Step 3: Build the Augmented LLM

• Concept: The Augmented LLM serves as the foundational building block for both workflows and agents.

Implementation

• Set up your environment to include LangGraph.
• Create an instance of the Augmented LLM with necessary parameters.

from langgraph import AugmentedLLM

llm = AugmentedLLM(model='gpt-3.5-turbo', temperature=0.7)

Step 4: Implement Basic Prompt Chaining

• Pattern Overview: Connect multiple prompts to create a workflow.

• Steps:

  1. Define the first prompt and its expected output.
  2. Use the output of the first prompt as the input for the second prompt.

• Example:

first_prompt = "What is the capital of France?"
first_output = llm.generate(first_prompt)

second_prompt = f"What is the population of {first_output}?"
second_output = llm.generate(second_prompt)

Step 5: Explore Parallelization

• Pattern Overview: Execute multiple tasks simultaneously to improve efficiency.

Implementation Steps

1. Identify tasks that can run in parallel.
2. Use threading or asynchronous calls to handle multiple LLM requests at once.

Step 6: Implement Routing with LLMs

• Pattern Overview: Direct inputs to different workflows based on conditions.

Steps

1. Set up conditions to determine which workflow to execute.
2. Implement the routing logic to redirect inputs accordingly.

Step 7: Create the Orchestrator-Worker Pattern

• Pattern Overview: An orchestrator manages tasks while workers perform them.

Implementation

• Build an orchestrator function that delegates tasks to worker functions.

Step 8: Develop the Evaluator-Optimizer Workflow

• Pattern Overview: Evaluate outputs and optimize for better performance.

Steps

1. Create evaluation metrics for the outputs.
2. Implement optimization algorithms to improve results based on evaluations.

Step 9: Build a Basic Agent Loop

• Concept: An agent loop continuously checks for new inputs and processes them.

Implementation

1. Set up a loop that listens for inputs.
2. Process inputs using the defined workflows and return outputs.

while True

Conclusion

In this tutorial, you learned the fundamental differences between workflows and agents, explored the benefits of using LangGraph, and implemented various patterns for building effective agents. By mastering these concepts, you can effectively create scalable and efficient applications tailored to your specific needs. For further exploration, consider diving deeper into LangGraph's documentation and experimenting with more complex workflows and agents.