diff --git a/README.md b/README.md
index edb951b8..a732bc9b 100644
--- a/README.md
+++ b/README.md
@@ -135,6 +135,83 @@ for i in range(epoch):
 
 Then, we can use the familiar PyTorch-like syntax to conduct the optimization.
 
+Here is another example of a simple sales agent:
+
+```python
+from opto import trace
+
+@trace.model
+class Agent:
+
+    def __init__(self, system_prompt):
+        self.system_prompt = system_prompt
+        self.instruct1 = trace.node("Decide the language", trainable=True)
+        self.instruct2 = trace.node("Extract name if it's there", trainable=True)
+
+    def __call__(self, user_query):
+        response = trace.operators.call_llm(self.system_prompt,
+                                            self.instruct1, user_query)
+        en_or_es = self.decide_lang(response)
+
+        user_name = trace.operators.call_llm(self.system_prompt,
+                                             self.instruct2, user_query)
+        greeting = self.greet(en_or_es, user_name)
+
+        return greeting
+
+    @trace.bundle(trainable=True)
+    def decide_lang(self, response):
+        """Map the language into a variable"""
+        return
+
+    @trace.bundle(trainable=True)
+    def greet(self, lang, user_name):
+        """Produce a greeting based on the language"""
+        greeting = "Hola"
+        return f"{greeting}, {user_name}!"
+```
+
+Imagine we have a feedback function (like a reward function) that tells us how well the agent is doing. We can then optimize this agent online:
+
+```python
+from opto.optimizers import OptoPrime
+
+def feedback_fn(generated_response, gold_label='en'):
+    if  gold_label == 'en' and 'Hello' in generated_response:
+        return "Correct"
+    elif gold_label == 'es' and 'Hola' in generated_response:
+        return "Correct"
+    else:
+        return "Incorrect"
+
+def train():
+    epoch = 3
+    agent = Agent("You are a sales assistant.")
+    optimizer = OptoPrime(agent.parameters())
+
+    for i in range(epoch):
+        print(f"Training Epoch {i}")
+        try:
+            greeting = agent("Hola, soy Juan.")
+            feedback = feedback_fn(greeting.data, 'es')
+        except trace.ExecutionError as e:
+            greeting = e.exception_node
+            feedback, terminal, reward = greeting.data, False, 0
+
+        optimizer.zero_feedback()
+        optimizer.backward(greeting, feedback)
+        optimizer.step(verbose=True)
+
+        if feedback == 'Correct':
+            break
+
+    return agent
+
+agent = train()
+```
+
+Defining and training an agent through Trace will give you more flexibility and control over what the agent learns. 
+
 ## Tutorials
 
 | **Level** | **Tutorial**                                                                              | **Run in Colab**                                                                                                                                                                                       | **Description**                                                                                                                                                                       |
diff --git a/examples/greeting.py b/examples/greeting.py
new file mode 100644
index 00000000..787afdfe
--- /dev/null
+++ b/examples/greeting.py
@@ -0,0 +1,95 @@
+from opto import trace
+from opto.trace import node, bundle, model, ExecutionError
+from opto.optimizers import OptoPrime
+
+
+@trace.model
+class Agent:
+
+    def __init__(self, system_prompt):
+        self.system_prompt = system_prompt
+        self.instruct1 = trace.node("Decide the language", trainable=True)
+        self.instruct2 = trace.node("Extract name if it's there", trainable=True)
+
+    def __call__(self, user_query):
+        response = trace.operators.call_llm(self.system_prompt,
+                                            self.instruct1, user_query)
+        en_or_es = self.decide_lang(response)
+
+        user_name = trace.operators.call_llm(self.system_prompt,
+                                             self.instruct2, user_query)
+        greeting = self.greet(en_or_es, user_name)
+
+        return greeting
+
+    @trace.bundle(trainable=True)
+    def decide_lang(self, response):
+        """Map the language into a variable"""
+        return
+
+    @trace.bundle(trainable=True)
+    def greet(self, lang, user_name):
+        """Produce a greeting based on the language"""
+        greeting = "Hola"
+        return f"{greeting}, {user_name}!"
+
+
+def feedback_fn(generated_response, gold_label='en'):
+    if  gold_label == 'en' and 'Hello' in generated_response:
+        return "Correct"
+    elif gold_label == 'es' and 'Hola' in generated_response:
+        return "Correct"
+    else:
+        return "Incorrect"
+
+
+def train():
+    epoch = 3
+    agent = Agent("You are a sales assistant.")
+    optimizer = OptoPrime(agent.parameters())
+
+    for i in range(epoch):
+        print(f"Training Epoch {i}")
+        try:
+            greeting = agent("Hola, soy Juan.")
+            feedback = feedback_fn(greeting.data, 'es')
+        except ExecutionError as e:
+            greeting = e.exception_node
+            feedback, terminal, reward = greeting.data, False, 0
+
+        optimizer.zero_feedback()
+        optimizer.backward(greeting, feedback)
+        optimizer.step(verbose=True)
+
+        if feedback == 'Correct':
+            break
+
+    return agent
+
+
+class CorrectAgent:
+
+    def __init__(self, system_prompt):
+        self.system_prompt = system_prompt
+        self.instruct1 = node("Decide the language: es or en?", trainable=True)
+        self.instruct2 = node("Extract name if it's there", trainable=True)
+
+    def __call__(self, user_query):
+        response = trace.operators.call_llm(self.system_prompt, self.instruct1, user_query)
+        en_or_es = self.decide_lang(response)
+
+        user_name = trace.operators.call_llm(self.system_prompt, self.instruct2, user_query)
+        greeting = self.greet(en_or_es, user_name)
+
+        return greeting
+
+    @bundle(trainable=True)
+    def decide_lang(self, response):
+        """Map the language into a variable"""
+        return 'es' if 'es' or 'spanish' in response.lower() else 'en'
+
+    @bundle(trainable=True)
+    def greet(self, lang, user_name):
+        """Produce a greeting based on the language"""
+        greeting = "Hola" if lang.lower() == "es" else "Hello"
+        return f"{greeting}, {user_name}!"
diff --git a/opto/trace/__init__.py b/opto/trace/__init__.py
index d370a42c..b0bb9067 100644
--- a/opto/trace/__init__.py
+++ b/opto/trace/__init__.py
@@ -3,6 +3,7 @@
 from opto.trace.containers import NodeContainer
 from opto.trace.broadcast import apply_op
 import opto.trace.propagators as propagators
+import opto.trace.operators as operators
 
 from opto.trace.nodes import Node, GRAPH
 from opto.trace.nodes import node