Notes on CrewAI task structured outputs

The ability to generate structured outputs has become increasingly important for practical applications. This post explores structured outputs, their significance, use cases, implementation strategies, and best practices for leveraging them in real-world applications.

What Are Structured Outputs?

Structured outputs are formatted, predictable responses that follow specific patterns or schemas. Unlike free form responses, structured outputs enable easy parsing and integration into downstream applications.

Why Are Structured Outputs Important?

• Reliability: Ensures consistent formatting for downstream processing.
• Interoperability: Allows seamless integration with APIs, databases, and automation workflows.
• Interpretability: Makes it easier to extract meaningful insights from LLM responses.
• Validation & Safety: Reduces risks of hallucinations by enforcing constraints on responses.

Use Cases for Structured Outputs

1. Information Extraction

• Extracting structured data from unstructured text (e.g., extracting names, dates, and locations from documents).

2. API Responses

• Generating responses in a structured format that can be directly consumed by applications.

3. AI Agents & Autonomous Systems

• Structuring intermediate reasoning steps in AI workflows.

4. Knowledge Graphs & Databases

• Generating structured data that can be stored and queried efficiently.

5. Workflow Automation

• Integrating AI outputs into business processes via structured formats like JSON for automation tools.

Structured CrewAI tasks output

CrewAI provides 2 ways to get structured outputs from tasks, output_pydantic and output_json.

Using output_pydantic

This property allows you to define a Pydantic model that will be used to structure and validate the task output. You can read more on the CrewAI output_pydantic docs.

from pydantic import BaseModel

class UserData(BaseModel):
    name: str
    age: int
    location: str
    
 ...
 task1 = Task(
    description="""Extract the user details from the paragraph: {paragraph}""",
    expected_output="A structured dict with the user details",
    agent=data_agent,
    output_pydantic=UserData,
)
...

name = result.pydantic.name
print("Name ", name)

output_dict = result.to_dict()
location = output_dict["location"]
print("Location ", location)

Using output_json

This property allows you to define the expected output in JSON format. You still need to define the JSON structure using a Pydantic model. You can read more on the CrewAI output_json docs.

from pydantic import BaseModel

class UserData(BaseModel):
    name: str
    age: int
    location: str
    
 ...
 task1 = Task(
    description="""Extract the user details from the paragraph: {paragraph}""",
    expected_output="A structured json with the user details",
    agent=data_agent,
    output_json=UserData,
)
...

name = result["name"]
print("Name:", name)

Best Practices

• Be Explicit in Prompts: Clearly define the expected format.
• Use Schema Validation: Enforce correctness via JSON Schema or Pydantic.
• Test with Edge Cases: Ensure robust handling of unexpected inputs.
• Combine LLMs with Deterministic Logic: Use AI for creativity but validate with rule based checks.

Conclusion

Structured outputs are essential for making LLMs and AI agents more reliable, interpretable, and useful in production environments. By leveraging structured formats, function calling, and validation techniques, developers can build AI systems that seamlessly integrate with real-world applications.

I'd love to hear about your experiences implementing task guardrails! Connect with me on X (formerly Twitter) or LinkedIn to share your thoughts and questions.

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