Top AI Agents: A Comprehensive Guide to the Best Intelligent Agents

1import os
2import openai
3
4openai.api_key = os.getenv("OPENAI_API_KEY")
5
6def generate_response(prompt):
7    completions = openai.Completion.create(
8        engine="text-davinci-003",
9        prompt=prompt,
10        max_tokens=1024,
11        n=1,
12        stop=None,
13        temperature=0.5,
14    )
15
16    message = completions.choices[0].text
17    return message
18
19print(generate_response("What is Auto-GPT?"))
20

1// Example interaction (conceptual)
2async function runAgent(goal) {
3  const agent = new AgentGPT();
4  agent.setGoal(goal);
5  const result = await agent.execute();
6  console.log(result);
7}
8
9runAgent("Write a blog post about AI agents.");
10

1# Basic Python code for a simple AI agent
2class SimpleAgent:
3    def __init__(self, environment):
4        self.environment = environment
5        self.location = (0, 0)  # Starting location
6
7    def sense(self):
8        return self.environment.get_perception(self.location)
9
10    def act(self, action):
11        if action == "move_up":
12            self.location = (self.location[0], self.location[1] + 1)
13        elif action == "move_down":
14            self.location = (self.location[0], self.location[1] - 1)
15        # ... other actions ...
16        self.environment.update_state(self, action)
17
18
19# Example usage
20env = Environment()
21agent = SimpleAgent(env)
22agent.act("move_up")
23print(agent.location)
24

1# Example of agent training using TensorFlow/PyTorch (Conceptual)
2
3#Placeholder for a more complex training implementation.
4
5import tensorflow as tf
6
7model = tf.keras.models.Sequential([
8    tf.keras.layers.Dense(10, activation='relu', input_shape=(4,)), # Example Input Dimension
9    tf.keras.layers.Dense(num_actions) # Output layer
10])
11
12model.compile(optimizer='adam', loss='mse')
13
14# Example training loop (Conceptual)
15# Assuming 'states', 'actions', and 'rewards' are defined
16
17model.fit(states, actions, epochs=10)
18