Understanding the Target Audience
The primary audience for this tutorial includes AI developers, business analysts, and product managers interested in leveraging AI to enhance business operations. Typically, these professionals are tech-savvy and possess a solid understanding of programming and data analysis concepts. The key pain points they face include:
- Difficulty in integrating multiple AI agents for collaborative tasks.
- Challenges in optimizing workflows for efficiency and accuracy.
- The need for clear documentation and examples to implement AI solutions effectively.
Their goals often involve creating scalable and efficient multi-agent systems, improving response accuracy in AI-driven applications, and enhancing the ability to visualize and interpret data-driven insights. Interests generally span AI technology advancements, workflow optimization strategies, and practical applications in business management. Communication preferences lean toward concise, technical documentation with clear examples and step-by-step guides.
Creating Smart Multi-Agent Workflows Using the Mistral Agents API’s Handoffs Feature
This tutorial explores how to create smart, multi-agent workflows utilizing the Mistral Agents API’s handoffs feature. This functionality enables different agents to collaborate by passing tasks to one another, solving complex problems in a modular and efficient manner. Here, we will construct a system where agents collaborate to address inflation-related questions, conducting calculations, fetching data online, and generating visualizations to provide clear, accurate, and dynamic responses.
Step 1: Setting Up Dependencies
Installing the Libraries
To begin, install the necessary libraries using the following command:
pip install mistralai pydanticLoading the Mistral API Key
Next, retrieve your API key from the Mistral API Console:
from getpass import getpass
MISTRAL_API_KEY = getpass('Enter Mistral API Key: ')Step 2: Agent Prerequisites and Setup
Initializing the Agent
Initialize the agent using the following code:
from mistralai import CompletionArgs, ResponseFormat, JSONSchema
from pydantic import BaseModel
from mistralai import Mistral
client = Mistral(MISTRAL_API_KEY)Creating the Custom Function
The adjust_for_inflation function calculates the value of a given amount adjusted for inflation over time. Here’s an implementation example:
def adjust_for_inflation(amount: float, start_year: int, end_year: int, annual_inflation_rate: float):
if end_year < start_year:
return {"error": "End year must be greater than or equal to start year."}
years = end_year - start_year
adjusted_value = amount * ((1 + annual_inflation_rate / 100) ** years)
return {
"original_amount": amount,
"start_year": start_year,
"end_year": end_year,
"inflation_rate": annual_inflation_rate,
"adjusted_value": round(adjusted_value, 2)
}Creating Structured Output for Mathematical Reasoning
Define a class for structured output:
class CalcResult(BaseModel):
reasoning: str
result: strStep 3: Creating the Agents
Defining the Different Agents
We will define a multi-agent system using the Mistral Agents API to handle inflation-related economic queries:
# Main Agent
economics_agent = client.beta.agents.create(
model="mistral-large-latest",
name="economics-agent",
description="Handles economic queries and delegates inflation calculations.",
)
# Inflation Function Agent
inflation_agent = client.beta.agents.create(
model="mistral-large-latest",
name="inflation-agent",
description="Agent that calculates inflation-adjusted value using a custom function.",
tools=[inflation_tool],
)
# Web Search Agent
websearch_agent = client.beta.agents.create(
model="mistral-large-latest",
name="websearch-agent",
description="Agent that can search the internet for missing economic data such as inflation rates.",
tools=[{"type": "web_search"}]
)
# Calculator Agent
calculator_agent = client.beta.agents.create(
model="mistral-large-latest",
name="calculator-agent",
description="Agent used to make detailed calculations.",
instructions="When doing calculations, explain step by step.",
completion_args=CompletionArgs(
response_format=ResponseFormat(
type="json_schema",
json_schema=JSONSchema(
name="calc_result",
schema=CalcResult.model_json_schema(),
)
)
)
)
# Graph Agent
graph_agent = client.beta.agents.create(
model="mistral-large-latest",
name="graph-agent",
description="Agent that generates graphs using code interpreter.",
instructions="Use code interpreter to draw inflation trends.",
tools=[{"type": "code_interpreter"}]
)Defining the Handoffs Responsibilities
Configure how agents delegate tasks among each other:
economics_agent = client.beta.agents.update(
agent_id=economics_agent.id,
handoffs=[inflation_agent.id, websearch_agent.id]
)
inflation_agent = client.beta.agents.update(
agent_id=inflation_agent.id,
handoffs=[calculator_agent.id, graph_agent.id]
)
websearch_agent = client.beta.agents.update(
agent_id=websearch_agent.id,
handoffs=[inflation_agent.id]
)
calculator_agent = client.beta.agents.update(
agent_id=calculator_agent.id,
handoffs=[graph_agent.id]
)
graph_agent = client.beta.agents.update(
agent_id=graph_agent.id,
handoffs=[calculator_agent.id]
)Step 4: Running the Agents
Example A: What is the Current Inflation Rate in India?
In this example, the prompt “What is the current inflation rate in India?” is sent to the economics_agent, which serves as the main entry point for handling economic queries. The economics_agent hands off the query to the websearch_agent since real-time data is required:
prompt = "What is the current inflation rate in India?"
response = client.beta.conversations.start(
agent_id=economics_agent.id,
inputs=prompt
)
print(response.outputs[-1].content[0].text)Example B: What is the Inflation-Adjusted Value of 5,000 from 2010 to 2023 with an Annual Inflation Rate of 6.5%?
This code block sends a prompt to the economics_agent and checks if a specific function call is triggered:
import json
from mistralai.models import FunctionResultEntry
prompt = """What is the inflation-adjusted value of 5,000 from the year 2010 to 2023 with annual inflation rate of 6.5%.
Explain calculation steps and plot a graph with data labels"""
response = client.beta.conversations.start(
agent_id=economics_agent.id,
inputs=prompt
)
if response.outputs[-1].type == "function.call" and response.outputs[-1].name == "adjust_for_inflation":
args = json.loads(response.outputs[-1].arguments)
function_result = json.dumps(adjust_for_inflation(**args))
result_entry = FunctionResultEntry(
tool_call_id=response.outputs[-1].tool_call_id,
result=function_result
)
response = client.beta.conversations.append(
conversation_id=response.conversation_id,
inputs=[result_entry]
)
print(response.outputs[-1].content)
else:
print(response.outputs[-1].content)Visualizing the Inflation-Adjusted Value
The following code block can be used to visualize the inflation-adjusted value:
import matplotlib.pyplot as plt
import numpy as np
# Parameters
original_amount = 5000
start_year = 2010
end_year = 2023
inflation_rate = 6.5 / 100
# Calculate the number of years
num_years = end_year - start_year + 1
years = np.arange(start_year, end_year + 1)
adjusted_values = original_amount * (1 + inflation_rate) ** (years - start_year)
# Plot the graph
plt.figure(figsize=(10, 6))
plt.plot(years, adjusted_values, marker='o', linestyle='-', color='b')
# Add data labels
for year, value in zip(years, adjusted_values):
plt.text(year, value, f'${value:.2f}', ha='right')
# Add titles and labels
plt.title('Inflation-Adjusted Value Over Time')
plt.xlabel('Year')
plt.ylabel('Adjusted Value')
# Save the plot as an image
plt.savefig('inflation_adjusted_value.png')
# Show the plot
plt.show()Check out the Notebook. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 98k+ ML SubReddit and subscribe to our Newsletter.
