# Your First Agent

[![Your first agent](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mozilla-ai/any-agent/blob/main/docs/cookbook/your_first_agent.ipynb)

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.

```python
%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.

```python
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: ")
```

```python
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.

```python
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.

```python
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}")
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.mozilla.ai/any-agent/cookbook/your-first-agent.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.