Update all examples

docs/examples/features/private-flows.mdx

@@ -1,5 +1,5 @@
 ---
-title: Private flows
+title: Private Flows
 description: Create isolated execution environments within your workflows.
 icon: lock
 ---

docs/examples/features/tools.mdx

@@ -1,5 +1,5 @@
 ---
-title: Custom tools
+title: Custom Tools
 description: Provide tools to expand agent capabilities.
 icon: wrench
 ---

docs/examples/seinfeld-conversation.mdx

@@ -0,0 +1,127 @@
+---
+title: Seinfeld Conversation
+description: Simulate a conversation between Seinfeld characters using multiple AI agents.
+icon: comments
+---
+
+This example demonstrates how to use ControlFlow to create a multi-agent conversation simulating the characters from the TV show Seinfeld. It showcases the use of multiple agents with distinct personalities, a task-based conversation flow, and command-line interaction.
+
+## Code
+
+The following code creates a conversation between Jerry, George, Elaine, Kramer, and Newman, discussing a given topic:
+
+```python
+import sys
+from controlflow import Agent, Task, flow
+
+jerry = Agent(
+    name="Jerry",
+    description="The observational comedian and natural leader.",
+    instructions="""
+    You are Jerry from the show Seinfeld. You excel at observing the quirks of
+    everyday life and making them amusing. You are rational, often serving as
+    the voice of reason among your friends. Your objective is to moderate the
+    conversation, ensuring it stays light and humorous while guiding it toward
+    constructive ends.
+    """,
+)
+
+george = Agent(
+    name="George",
+    description="The neurotic and insecure planner.",
+    instructions="""
+    You are George from the show Seinfeld. You are known for your neurotic
+    tendencies, pessimism, and often self-sabotaging behavior. Despite these
+    traits, you occasionally offer surprising wisdom. Your objective is to
+    express doubts and concerns about the conversation topics, often envisioning
+    the worst-case scenarios, adding a layer of humor through your exaggerated
+    anxieties.
+    """,
+)
+
+elaine = Agent(
+    name="Elaine",
+    description="The confident and independent thinker.",
+    instructions="""
+    You are Elaine from the show Seinfeld. You are bold, witty, and unafraid to
+    challenge social norms. You often take a no-nonsense approach to issues but
+    always with a comedic twist. Your objective is to question assumptions, push
+    back against ideas you find absurd, and inject sharp humor into the
+    conversation.
+    """,
+)
+
+kramer = Agent(
+    name="Kramer",
+    description="The quirky and unpredictable idea generator.",
+    instructions="""
+    You are Kramer from the show Seinfeld. Known for your eccentricity and
+    spontaneity, you often come up with bizarre yet creative ideas. Your
+    unpredictable nature keeps everyone guessing what you'll do or say next.
+    Your objective is to introduce unusual and imaginative ideas into the
+    conversation, providing comic relief and unexpected insights.
+    """,
+)
+
+newman = Agent(
+    name="Newman",
+    description="The antagonist and foil to Jerry.",
+    instructions="""
+    You are Newman from the show Seinfeld. You are Jerry's nemesis, often
+    serving as a source of conflict and comic relief. Your objective is to
+    challenge Jerry's ideas, disrupt the conversation, and introduce chaos and
+    absurdity into the group dynamic.
+    """,
+)
+
+@flow
+def demo(topic: str):
+    task = Task(
+        "Discuss a topic",
+        agents=[jerry, george, elaine, kramer, newman],
+        completion_agents=[jerry],
+        result_type=None,
+        context=dict(topic=topic),
+        instructions="Every agent should speak at least once. only one agent per turn. Keep responses 1-2 paragraphs max.",
+    )
+    task.run()
+
+if __name__ == "__main__":
+    if len(sys.argv) > 1:
+        topic = sys.argv[1]
+    else:
+        topic = "sandwiches"
+
+    print(f"Topic: {topic}")
+    demo(topic=topic)
+```
+
+## Key concepts
+
+This implementation showcases several important ControlFlow features:
+
+1. **Multiple agents**: We create five distinct agents, each with their own personality and objectives, mirroring the characters from Seinfeld.
+
+2. **Agent instructions**: Each agent has detailed instructions that guide their behavior and responses, ensuring they stay in character.
+
+3. **Task-based conversation**: The conversation is structured as a task, with specific instructions for how the agents should interact.
+
+4. **Completion agent**: Jerry is designated as the completion agent, giving him the role of moderating and concluding the conversation.
+
+5. **Command-line interaction**: The script accepts a topic as a command-line argument, allowing for easy customization of the conversation subject.
+
+## Running the example
+
+You can run this example with a custom topic:
+
+```bash
+python examples/seinfeld.py "coffee shops"
+```
+
+Or use the default topic ("sandwiches") by running it without arguments:
+
+```bash
+python examples/seinfeld.py
+```
+
+This example demonstrates how ControlFlow can be used to create complex, multi-agent interactions that simulate realistic conversations between distinct personalities. It's a fun and engaging way to showcase the capabilities of AI in generating dynamic, character-driven dialogues.

docs/mint.json

@@ -101,8 +101,9 @@
             "pages": [
                 "examples/language-tutor",
                 "examples/rock-paper-scissors",
-                "examples/agent-engineer",
-                "examples/call-routing"
+                "examples/seinfeld-conversation",
+                "examples/call-routing",
+                "examples/agent-engineer"
             ]
         },
         {

examples/anonymization.py

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+from pydantic import BaseModel, Field
+
+import controlflow as cf
+
+
+class AnonymizationResult(BaseModel):
+    original: str
+    anonymized: str
+    replacements: dict[str, str] = Field(
+        description=r"The replacements made during anonymization, {original} -> {placeholder}"
+    )
+
+
+def anonymize_text(text: str) -> AnonymizationResult:
+    return cf.run(
+        "Anonymize the given text by replacing personal information with generic placeholders",
+        result_type=AnonymizationResult,
+        context={"text": text},
+    )
+
+
+if __name__ == "__main__":
+    original_text = "John Doe, born on 05/15/1980, lives at 123 Main St, New York. His email is [email protected]."
+
+    result = anonymize_text(original_text)
+    print(f"Original: {result.original}")
+    print(f"Anonymized: {result.anonymized}")
+    print("Replacements:")
+    for original, placeholder in result.replacements.items():
+        print(f"  {original} -> {placeholder}")

examples/call_routing.py

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+import random
+
+import controlflow as cf
+
+DEPARTMENTS = [
+    "Sales",
+    "Support",
+    "Billing",
+    "Returns",
+]
+
+
+@cf.flow
+def routing_flow():
+    target_department = random.choice(DEPARTMENTS)
+
+    print(f"\n---\nThe target department is: {target_department}\n---\n")
+
+    customer = cf.Agent(
+        name="Customer",
+        instructions=f"""
+            You are training customer reps by pretending to be a customer
+            calling into a call center. You need to be routed to the
+            {target_department} department. Come up with a good backstory.
+            """,
+    )
+
+    trainee = cf.Agent(
+        name="Trainee",
+        instructions=""",
+            You are a trainee customer service representative. You need to
+            listen to the customer's story and route them to the correct
+            department. Note that the customer is another agent training you.
+            """,
+    )
+
+    with cf.Task(
+        "Route the customer to the correct department.",
+        agents=[trainee],
+        result_type=DEPARTMENTS,
+    ) as main_task:
+        while main_task.is_incomplete():
+            cf.run(
+                "Talk to the trainee.",
+                instructions=(
+                    "Post a message to talk. In order to help the trainee "
+                    "learn, don't be direct about the department you want. "
+                    "Instead, share a story that will let them practice. "
+                    "After you speak, mark this task as complete."
+                ),
+                agents=[customer],
+                result_type=None,
+            )
+
+            cf.run(
+                "Talk to the customer.",
+                instructions=(
+                    "Post a message to talk. Ask questions to learn more "
+                    "about the customer. After you speak, mark this task as "
+                    "complete. When you have enough information, use the main "
+                    "task tool to route the customer to the correct department."
+                ),
+                agents=[trainee],
+                result_type=None,
+                tools=[main_task.get_success_tool()],
+            )
+
+    if main_task.result == target_department:
+        print("Success! The customer was routed to the correct department.")
+    else:
+        print(
+            f"Failed. The customer was routed to the wrong department. "
+            f"The correct department was {target_department}."
+        )
+
+
+if __name__ == "__main__":
+    routing_flow()

examples/code_explanation.py

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+from pydantic import BaseModel
+
+import controlflow as cf
+
+
+class CodeExplanation(BaseModel):
+    code: str
+    explanation: str
+    language: str
+
+
+def explain_code(code: str, language: str = None) -> CodeExplanation:
+    return cf.run(
+        f"Explain the following code snippet",
+        result_type=CodeExplanation,
+        context={"code": code, "language": language or "auto-detect"},
+    )
+
+
+if __name__ == "__main__":
+    code_snippet = """
+    def fibonacci(n):
+        if n <= 1:
+            return n
+        else:
+            return fibonacci(n-1) + fibonacci(n-2)
+    """
+
+    result = explain_code(code_snippet, "Python")
+    print(f"Code:\n{result.code}\n")
+    print(f"Explanation:\n{result.explanation}")