Best AI Agent Framework: A Comprehensive Comparison

1from langchain.llms import OpenAI
2from langchain.agents import initialize_agent, Tool
3from langchain.agents import AgentType
4
5llm = OpenAI(temperature=0)
6
7def search_google(query: str) -> str:
8    # Placeholder for Google search functionality
9    return f"Search results for: {query}"
10
11tools = [
12    Tool(
13        name = "Search",
14        func=search_google,
15        description="useful for when you need to answer questions about current events"
16    )
17]
18
19agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
20
21print(agent.run("What is the current weather in London?"))
22

1import autogen
2
3config_list = [
4    {
5        'model': 'gpt-4',
6        'api_key': '<your OpenAI API key here>',
7    }
8]
9
10llm_config={
11    "seed": 42,
12    "config_list": config_list,
13    "temperature": 0,
14}
15
16user_proxy = autogen.UserProxyAgent(
17    name="User_proxy",
18    human_input_mode="TERMINATE",
19    max_consecutive_auto_reply=10,
20    is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
21    code_execution_config=False,
22    llm_config=llm_config,
23    system_message="""Reply TERMINATE if the task has been solved at full satisfaction.
24Otherwise, reply CONTINUE, or the reason why the task is not solved yet.""",
25)
26
27assistant = autogen.AssistantAgent(
28    name="Assistant",
29    llm_config=llm_config,
30)
31
32user_proxy.initiate_chat(
33    assistant,
34    message="Tell me a joke.",
35)
36

1from langchain.agents import create_csv_agent
2from langchain.llms import OpenAI
3
4# Load the CSV data into a LangChain agent.
5agent = create_csv_agent(
6    OpenAI(temperature=0), "./path/to/your/data.csv", verbose=True
7)
8
9# Ask the agent a question about the data.
10query = "What is the average value of column X?"
11
12# Run the query and print the result.
13response = agent.run(query)
14print(response)
15
16
17# Load the CSV data into a LangChain agent.
18agent = create_csv_agent(
19    OpenAI(temperature=0), "./path/to/your/data.csv", verbose=True
20)
21
22# Ask the agent a question about the data.
23query = "What is the average value of column X?"
24
25# Run the query and print the result.
26response = agent.run(query)
27print(response)
28

1from langchain.llms import OpenAI
2from langchain.chains import LLMChain
3from langchain.prompts import PromptTemplate
4
5# Initialize the language model
6llm = OpenAI(temperature=0.7)
7
8# Create a prompt template
9template = "You are a helpful assistant. Respond to the user's query: {query}"
10prompt = PromptTemplate(template=template, input_variables=["query"])
11
12# Create an LLMChain
13llm_chain = LLMChain(prompt=prompt, llm=llm)
14
15# Get user input
16query = input("Enter your query: ")
17
18# Run the chain and print the response
19response = llm_chain.run(query)
20print(response)
21