Creating your first agent¶

If you're looking to build your first agent using a few simple tools, this is a great place to start. In this cookbook example, we will create and run a simple agent that has access to a few web tools. This can be easily expanded to add more advanced tools and features. 🚀

Install Dependencies¶

any-agent uses the python asyncio module to support async functionality. When running in Jupyter notebooks, this means we need to enable the use of nested event loops. We'll install any-agent and enable this below using nest_asyncio.

In [ ]:

%pip install 'any-agent' --quiet
%pip install ddgs --quiet

import warnings

import nest_asyncio

# Suppress technical warnings to reduce noise for the user
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=RuntimeWarning)

nest_asyncio.apply()

%pip install 'any-agent' --quiet %pip install ddgs --quiet import warnings import nest_asyncio # Suppress technical warnings to reduce noise for the user warnings.filterwarnings("ignore", category=DeprecationWarning) warnings.filterwarnings("ignore", category=RuntimeWarning) nest_asyncio.apply()

Configure the Agent¶

Pick an LLM¶

For this tutorial, we'll use Mistral's mistral-small-latest (fast and affordable). You could also use:

• gpt-4o-mini
• claude-3-5-sonnet-latest
• Any other model supported by any-agent

Pick Tools¶

We'll use search_web(DuckDuckGo), which provides Duck Duck Go search for free with no API key required.

In [ ]:

import os
from getpass import getpass

if "MISTRAL_API_KEY" not in os.environ:
    os.environ["MISTRAL_API_KEY"] = getpass("Enter your Mistral API Key: ")

import os from getpass import getpass if "MISTRAL_API_KEY" not in os.environ: os.environ["MISTRAL_API_KEY"] = getpass("Enter your Mistral API Key: ")

In [ ]:

from any_agent import AgentConfig, AnyAgent
from any_agent.tools import search_web

# We use the 'mistral-small-latest' model we promised in the text
agent = AnyAgent.create(
    "tinyagent",
    AgentConfig(model_id="mistral:mistral-small-latest", tools=[search_web]),
)

from any_agent import AgentConfig, AnyAgent from any_agent.tools import search_web # We use the 'mistral-small-latest' model we promised in the text agent = AnyAgent.create( "tinyagent", AgentConfig(model_id="mistral:mistral-small-latest", tools=[search_web]), )

Run the Agent¶

Now we've configured our agent, so it's time to run it! Let's give it a simple task: find 5 trending new TV shows that were released recently.

In [ ]:

agent_trace = agent.run(
    "What are 5 tv shows that are trending in 2025? Check a few sites, and provide the name of the show, the exact release date, the genre, and a brief description of the show."
)

agent_trace = agent.run( "What are 5 tv shows that are trending in 2025? Check a few sites, and provide the name of the show, the exact release date, the genre, and a brief description of the show." )

View the results¶

The agent.run method returns an AgentTrace object, which has a few convenient attributes for displaying some interesting information about the run.

In [ ]:

print(agent_trace.final_output)  # Final answer
print(f"Duration: {agent_trace.duration.total_seconds():.2f} seconds")
print(f"Usage: {agent_trace.tokens.total_tokens:,}")
print(f"Cost (USD): {agent_trace.cost.total_cost:.6f}")

print(agent_trace.final_output) # Final answer print(f"Duration: {agent_trace.duration.total_seconds():.2f} seconds") print(f"Usage: {agent_trace.tokens.total_tokens:,}") print(f"Cost (USD): {agent_trace.cost.total_cost:.6f}")