This example demonstrates how to use multiple LLM models within a single ControlFlow workflow. We’ll use GPT-4o-mini models for efficient classification tasks and GPT-4o for more complex synthesis. This approach allows us to optimize for both speed and quality in our AI-powered workflows. In this scenario, we’ll create a workflow that analyzes customer feedback for a product. The workflow will:
  1. Classify the sentiment of each piece of feedback (using GPT-4o-mini)
  2. Categorize the topic of each piece of feedback (using GPT-4o-mini)
  3. Generate a comprehensive summary of the feedback (using GPT-4o)

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Code

import controlflow as cf
from pydantic import BaseModel
from typing import Literal

# Create specialized agents
classifier = cf.Agent(name="Classifier", model="openai/gpt-4o-mini")
summarizer = cf.Agent(name="Summarizer", model="openai/gpt-4o")

# Define our data models
class Feedback(BaseModel):
    text: str
    sentiment: Literal["positive", "neutral", "negative"]
    topic: Literal["user interface", "performance", "features", "other"]

class FeedbackSummary(BaseModel):
    overall_sentiment: str
    key_points: list[str]
    recommendations: list[str]

@cf.flow
def analyze_customer_feedback(feedback_list: list[str]) -> FeedbackSummary:
    analyzed_feedback = []

    for feedback in feedback_list:

        # Classify sentiment
        sentiment = cf.run(
            "Classify the sentiment of this feedback",
            agents=[classifier],
            result_type=["positive", "neutral", "negative"],
            context={"feedback": feedback}
        )

        # Classify topic
        topic = cf.run(
            "Categorize this feedback into one of the predefined topics",
            agents=[classifier],
            result_type=["user interface", "performance", "features", "other"],
            context={"feedback": feedback}
        )

        analyzed_feedback.append(
            Feedback(text=feedback, sentiment=sentiment, topic=topic)
        )

    # Generate summary
    summary = cf.run(
        "Generate a comprehensive summary of the analyzed feedback",
        agents=[summarizer],
        result_type=FeedbackSummary,
        context={"feedback": analyzed_feedback}
    )

    return summary

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Example usage

feedback_list = [
    "The new user interface is intuitive and easy to use. Great job!",
    "The app crashes frequently when I try to save my work. This is frustrating.",
    "I love the new feature that allows collaboration in real-time.",
    "The performance has improved, but there's still room for optimization."
]

result = analyze_customer_feedback(feedback_list)
print(result)

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Key points

  1. Multiple LLM Models: We use GPT-4o-mini for quick classification tasks (sentiment and topic) and GPT-4o for the more complex task of summarization.
  2. Specialized Agents: We create separate agents for different tasks, each with its own LLM model. This allows us to optimize for both speed and quality.
  3. Structured Data: We use Pydantic models (Feedback and FeedbackSummary) to ensure type safety and consistent data structures throughout the workflow.
  4. Task-Specific Result Types: Each task has a specific result_type that matches the expected output, ensuring that the agents provide the correct type of information.
  5. Workflow Composition: The analyze_customer_feedback flow composes multiple tasks into a cohesive workflow, demonstrating how ControlFlow can manage complex, multi-step processes that include loops and conditional logic.
This example showcases how ControlFlow allows you to leverage the strengths of different LLM models within a single workflow. By using more efficient models for simpler tasks and more powerful models for complex analysis, you can create workflows that are both fast and capable of high-quality output.