On this tutorial, we’ll discover learn how to create sensible, multi-agent workflows utilizing the Mistral Brokers API’s Handoffs characteristic. This lets completely different brokers work collectively by passing duties to one another, enabling advanced issues to be solved in a modular and environment friendly method. We’ll construct a system the place brokers collaborate to reply inflation-related questions—performing calculations, fetching information on-line, and creating visualizations—to ship clear, correct, and dynamic responses.
Step 1: Establishing dependencies
Putting in the libraries
pip set up mistralai pydanticLoading the Mistral API Key
You will get an API key from https://console.mistral.ai/api-keys
from getpass import getpass
MISTRAL_API_KEY = getpass('Enter Mistral API Key: ')Step 2: Agent Conditions and Setup
Initializing the Agent
from mistralai import CompletionArgs, ResponseFormat, JSONSchema
from pydantic import BaseModel
from mistralai import Mistral
consumer = Mistral(MISTRAL_API_KEY)Creating the Customized Perform
The adjust_for_inflation perform calculates how a lot a given amount of cash can be value after accounting for inflation over time. It makes use of the compound method based mostly on the variety of years and the annual inflation charge. If the tip yr is earlier than the beginning yr, it returns an error. In any other case, it returns the adjusted worth together with the enter particulars. For instance, adjust_for_inflation(1000, 1899, 2025, 10) reveals what ₹1000 from 1899 can be value in 2025 at 10% inflation.
def adjust_for_inflation(quantity: float, start_year: int, end_year: int, annual_inflation_rate: float):
"""
Calculates inflation-adjusted worth utilizing compound method.
"""
if end_year < start_year:
return {"error": "Finish yr have to be better than or equal to start out yr."}
years = end_year - start_year
adjusted_value = quantity * ((1 + annual_inflation_rate / 100) ** years)
return {
"original_amount": quantity,
"start_year": start_year,
"end_year": end_year,
"inflation_rate": annual_inflation_rate,
"adjusted_value": spherical(adjusted_value, 2)
}
adjust_for_inflation(1000, 1899, 2025, 10)Creating Structured Output for Mathematical Reasoning
class CalcResult(BaseModel):
reasoning: str
end result: str
inflation_tool = {
"sort": "perform",
"perform": {
"identify": "adjust_for_inflation",
"description": "Calculate the worth of cash adjusted for inflation over a time interval.",
"parameters": {
"sort": "object",
"properties": {
"quantity": {
"sort": "quantity",
"description": "Unique amount of cash"
},
"start_year": {
"sort": "integer",
"description": "The beginning yr for inflation adjustment"
},
"end_year": {
"sort": "integer",
"description": "The ending yr for inflation adjustment"
},
"annual_inflation_rate": {
"sort": "quantity",
"description": "Annual inflation charge in %"
}
},
"required": ["amount", "start_year", "end_year", "annual_inflation_rate"]
}
}
}Step 3: Creating the Brokers
Defining the completely different brokers
On this setup, we outline a multi-agent system utilizing Mistral Brokers API to deal with inflation-related financial queries. The principle agent (economics-agent) acts as a coordinator that routes duties to specialised brokers. The inflation-agent performs inflation adjustment calculations utilizing a customized perform. If the inflation charge is lacking from the question, the websearch-agent fetches it from the web. The calculator-agent handles advanced numerical computations with step-by-step reasoning, whereas the graph-agent makes use of the code interpreter to visualise inflation traits over time. Collectively, these brokers collaborate through handoffs to ship correct, dynamic responses to financial queries.
# Important Agent
economics_agent = consumer.beta.brokers.create(
mannequin="mistral-large-latest",
identify="economics-agent",
description="Handles financial queries and delegates inflation calculations.",
)
# Inflation Perform Agent
inflation_agent = consumer.beta.brokers.create(
mannequin="mistral-large-latest",
identify="inflation-agent",
description="Agent that calculates inflation-adjusted worth utilizing a customized perform.",
instruments=[inflation_tool],
)
# Internet Search Agent
websearch_agent = consumer.beta.brokers.create(
mannequin="mistral-large-latest",
identify="websearch-agent",
description="Agent that may search the web for lacking financial information equivalent to inflation charges.",
instruments=[{"type": "web_search"}]
)
# Calculator Agent
from pydantic import BaseModel
class CalcResult(BaseModel):
reasoning: str
end result: str
calculator_agent = consumer.beta.brokers.create(
mannequin="mistral-large-latest",
identify="calculator-agent",
description="Agent used to make detailed calculations.",
directions="When doing calculations, clarify step-by-step.",
completion_args=CompletionArgs(
response_format=ResponseFormat(
sort="json_schema",
json_schema=JSONSchema(
identify="calc_result",
schema=CalcResult.model_json_schema(),
)
)
)
)
# Graph Agent
graph_agent = consumer.beta.brokers.create(
mannequin="mistral-large-latest",
identify="graph-agent",
description="Agent that generates graphs utilizing code interpreter.",
directions="Use code interpreter to attract inflation traits.",
instruments=[{"type": "code_interpreter"}]
)Defining the Handoffs Tasks
This configuration defines how brokers delegate duties amongst one another:
- The Important Agent (economics_agent) serves because the entry level and delegates queries both to the inflation_agent (for inflation calculations) or the websearch_agent (to fetch lacking information like inflation charges).
- The inflation_agent, after receiving both the consumer question or web-fetched information, can additional cross duties to the calculator_agent (for detailed math) or graph_agent (to visualise traits).
- The websearch_agent can cross management to the inflation_agent after retrieving required data, just like the inflation charge.
- calculator_agent and graph_agent are thought of terminal brokers. Nevertheless, elective mutual handoff is enabled in case one must do follow-up work (e.g., graphing a calculated end result or vice versa).
# Important Agent fingers off to inflation_agent and websearch_agent
economics_agent = consumer.beta.brokers.replace(
agent_id=economics_agent.id,
handoffs=[inflation_agent.id, websearch_agent.id]
)
# Inflation Agent can delegate to calculator_agent or graph_agent if deeper evaluation or visualization is required
inflation_agent = consumer.beta.brokers.replace(
agent_id=inflation_agent.id,
handoffs=[calculator_agent.id, graph_agent.id]
)
# Internet Search Agent can hand off to inflation_agent (after discovering the lacking charge)
websearch_agent = consumer.beta.brokers.replace(
agent_id=websearch_agent.id,
handoffs=[inflation_agent.id]
)
# Calculator and Graph brokers are terminal--they do not hand off additional
# But when wanted, we might allow them to hand off to one another:
calculator_agent = consumer.beta.brokers.replace(
agent_id=calculator_agent.id,
handoffs=[graph_agent.id] # Non-compulsory
)
graph_agent = consumer.beta.brokers.replace(
agent_id=graph_agent.id,
handoffs=[calculator_agent.id] # Non-compulsory
)Step 4: Operating the Agent
Instance A: What’s the present inflation charge in India?
On this instance, the immediate “What’s the present inflation charge in India?” is handed to the economics_agent, which is the principle entry level for dealing with financial queries. Because the query requires real-time information that isn’t included within the agent’s static information, the economics_agent robotically fingers off the question to the websearch_agent, which is provided with net search capabilities.
immediate = "What's the present inflation charge in India?"
response = consumer.beta.conversations.begin(
agent_id=economics_agent.id,
inputs=immediate
)
print(response.outputs[-1].content material[0].textual content)
Instance B: What’s the inflation-adjusted worth of 5,000 from the yr 2010 to 2023 with an annual inflation charge of 6.5%. Clarify calculation steps and plot a graph with information labels
This code block sends the immediate to an economics agent, checks if the agent triggers a selected perform name (adjust_for_inflation), executes that perform domestically with the offered arguments, after which returns the computed end result again to the agent. Lastly, it prints the agent’s response, which incorporates the inflation calculation rationalization, together with the Python code to plot the development.
import json
from mistralai.fashions import FunctionResultEntry
immediate = """What's the inflation-adjusted worth of 5,000 from the yr 2010 to 2023 with annual inflation charge of 6.5%.
Clarify calculation steps and plot a graph with information labels"""
response = consumer.beta.conversations.begin(
agent_id=economics_agent.id,
inputs=immediate
)
# Examine for perform name
if response.outputs[-1].sort == "perform.name" and response.outputs[-1].identify == "adjust_for_inflation":
args = json.masses(response.outputs[-1].arguments)
# Run native perform
function_result = json.dumps(adjust_for_inflation(**args))
# Return end result to Mistral
result_entry = FunctionResultEntry(
tool_call_id=response.outputs[-1].tool_call_id,
end result=function_result
)
response = consumer.beta.conversations.append(
conversation_id=response.conversation_id,
inputs=[result_entry]
)
print(response.outputs[-1].content material)
else:
print(response.outputs[-1].content material)The next code block was returned by the agent to plot the development of inflation-adjusted worth over time.
import matplotlib.pyplot as plt
import numpy as np
# Parameters
original_amount = 5000
start_year = 2010
end_year = 2023
inflation_rate = 6.5 / 100 # Convert share to decimal
# Calculate the variety of years
num_years = end_year - start_year + 1
# Calculate the adjusted worth for annually
years = np.arange(start_year, end_year + 1)
adjusted_values = original_amount * (1 + inflation_rate) ** (years - start_year)
# Plot the graph
plt.determine(figsize=(10, 6))
plt.plot(years, adjusted_values, marker="o", linestyle="-", coloration="b")
# Add information labels
for yr, worth in zip(years, adjusted_values):
plt.textual content(yr, worth, f'${worth:.2f}', ha="proper")
# Add titles and labels
plt.title('Inflation-Adjusted Worth Over Time')
plt.xlabel('12 months')
plt.ylabel('Adjusted Worth')
# Save the plot as a picture
plt.savefig('inflation_adjusted_value.png')
# Present the plot
plt.present()
Take a look at the Pocket book. All credit score for this analysis goes to the researchers of this undertaking. Additionally, be happy to comply with us on Twitter and don’t neglect to affix our 98k+ ML SubReddit and Subscribe to our Publication.
I’m a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I’ve a eager curiosity in Information Science, particularly Neural Networks and their utility in varied areas.
