Structuring Inputs & Outputs in Multi Agent systems Using CrewAI

Optimizing Agent-Based Systems: Structuring Inputs and Outputs for Enhanced Performance

Want to boost the performance of your agent-based systems? A key strategy is meticulously structuring both inputs and intermediate outputs exchanged between agents. This article details how to organize inputs, utilize placeholders for data transfer, and structure outputs to ensure each agent delivers the expected results. By optimizing these elements, you'll achieve more consistent and reliable outcomes from your agentic systems. Agentic systems leverage multiple agents collaborating, communicating, and problem-solving—capabilities exceeding those of individual LLMs. This guide uses CrewAI, Pydantic models, and JSON to structure outputs (and inputs) in a multi-agent context.

Key Considerations

1. Well-structured inputs and outputs are essential for optimal agent-based system performance.
2. Pydantic models provide data validation and organization for inter-agent communication.
3. CrewAI facilitates agent integration, task execution, and input/output data management.
4. Structured data prevents data loss and inconsistencies, promoting seamless collaboration.
5. Defining expected outputs using models or JSON enhances precision and reliability.
6. Effective data management optimizes multi-agent systems for complex tasks.

Table of Contents

• Key Considerations
• Leveraging Pydantic Models
• Pydantic Model Example
• Installation
• Structuring Inputs
• Structuring Outputs
• Conclusion
• Frequently Asked Questions

Leveraging Pydantic Models

Pydantic models, provided by the Pydantic library, are Python objects designed for data parsing and validation. They enable the creation of Python classes (models) that automatically validate data upon instantiation, ensuring input data matches expected types and constraints. This ensures reliable structured data handling.

Key Features:

Feature	Description
Data Validation	Verifies input data against expected types (e.g., int, str, list) and custom rules.
Automatic Type Conversion	Automatically converts compatible data types (e.g., "2024-10-27" to datetime.date).
Data Serialization	Serializes data into formats like JSON, simplifying API interactions.
Default Values	Allows optional fields or default values for flexible input handling.

Pydantic Model Example

Let's create a UserModel inheriting from Pydantic's BaseModel. The instantiated class requires an integer id, a string name, and an email address.

from pydantic import BaseModel
class UserModel(BaseModel):
    id: int
    name: str
    email: str

# Valid input
valid_user = UserModel(id=1, name="Vidhya", email="vidhya@example.com")
print(valid_user)

# Invalid input (raises a validation error)
try:
    invalid_user = UserModel(id="one", name="Vidhya", email="vidhya@example.com")
except ValueError as e:
    print(f"Validation Error: {e}")

This demonstrates Pydantic's error handling when incorrect data types are provided.

Let's explore optional, date, and default value features:

from pydantic import BaseModel
from typing import Optional
from datetime import date
class EventModel(BaseModel):
    event_name: Optional[str] = None  # Optional field
    event_loc: str = "India"  # Default value
    event_date: date

# Automatic conversion
event = EventModel(event_date="2024-10-27")
print(event)

This showcases optional fields and automatic type conversion.

Installation

Install CrewAI:

pip install crewai

Structuring Inputs

Inputs are formatted within curly braces {} using variable names when defining Agents and Tasks. Setting human_input=True prompts the user for output feedback. Here's an example of an agent and task for answering physics questions:

from crewai import Agent, Task, Crew
import os
os.environ['OPENAI_API_KEY'] = '' # Replace with your key
os.environ['OPENAI_MODEL_NAME'] = 'gpt-4o-mini-2024-07-18' # Or your preferred model

# ... (Agent and Task definitions as in the original example) ...

Inputs are passed via the inputs parameter in crew.kickoff().

Structuring Outputs

Let's create agents to collect user details (name, email, phone, job). Structuring outputs as Pydantic models or JSON defines the expected output format, ensuring subsequent agents receive structured data.

from pydantic import BaseModel
from typing import List

# ... (Pydantic model definitions as in the original example) ...

# ... (Agent and Task definitions as in the original example, using output_pydantic and output_json) ...

The final agent combines all details, saving the output to a file using output_file.

Conclusion

This article highlighted the importance of structuring inputs and outputs in multi-agent systems using Pydantic and CrewAI. Well-structured data enhances performance, reliability, and prevents errors. These strategies build more robust agentic systems for complex tasks.

Frequently Asked Questions

Q1. What are agent-based systems? Agent-based systems use multiple agents collaborating to solve problems, exceeding the capabilities of single LLMs.

Q2. What is CrewAI? CrewAI is a framework for managing agentic systems, streamlining agent collaboration and data handling.

Q3. How to input images in CrewAI? One method is to provide the image URL as an input variable.

Q4. What are Pydantic models? Pydantic models validate and serialize data, ensuring data integrity in agent-based systems.

Q5. How to structure outputs using Pydantic? Define expected output fields within Pydantic models to ensure consistent data formatting for subsequent agents.

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