LangChain is revolutionizing the way we build applications with LLMs (Large Language Models). One of its standout features is the ability to chain agents together. This capability allows developers to create complex workflows where multiple models can cooperate and share information to achieve more intricate tasks.
Chained agents can be used to split a task into smaller subtasks that can be handled by different models or functions. This not only enhances modularity but also improves the overall efficiency and effectiveness of the application.
from langchain import AgentExecutor, create_openai_tools
from langchain.agents import initialize_agent
# Initialize tools and models
tools = create_openai_tools()
first_agent = initialize_agent(llm="openai", tools=tools[:1], verbose=True)
second_agent = initialize_agent(llm="openai", tools=tools[1:], verbose=True)
# Define a function to chain the agents together
def chained_agents(input_text):
first_response = first_agent.run(input_text)
second_response = second_agent.run(first_response)
return second_response
# Usage
result = chained_agents("What can I do on a rainy day?")
print(result)
This simple example showcases the process of chaining two agents where the output of the first agent becomes the input to the second. This allows for complex queries to be broken down and processed effectively!
As you explore LangChain, consider how chaining agents can enhance your application's capabilities.