> ## Documentation Index
> Fetch the complete documentation index at: https://docs.a1facts.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Stock Analysis Agent

> This example demonstrates how to use `a1facts` to build a simple financial analyst agent that can answer questions about companies.

This agent uses a knowledge graph and external data sources to provide informed answers about companies, their competitors, and their products.

## How it Works

The agent is built using the following components, which you can find in the `cookbook/stock_analysis` directory:

<CardGroup>
  <Card title="`infoagent.py`" icon="robot">
    The main script that creates and runs the agent. It initializes the `a1facts` tool, which acts as the bridge between the agent and the knowledge graph.
  </Card>

  <Card title="`company.yaml`" icon="sitemap">
    This file defines the schema of our knowledge graph. It specifies the types of entities (e.g., `Company`, `Product_Service`) and the relationships between them (e.g., `competes_with`).
  </Card>

  <Card title="`sources.yaml`" icon="cloud-download">
    This file configures the external data sources. In this case, it's set up to use the Financial Modeling Prep (FMP) API and yfinance.
  </Card>

  <Card title="`fmp_functions.py` & `yfinance_functions.py`" icon="code">
    These files contain the Python functions that interact with the FMP and yfinance APIs to fetch data.
  </Card>
</CardGroup>

When you run `infoagent.py`, it initializes an agent with the `a1facts` tool. The agent is then asked a question: `"what do you know about how UnitedHealth competes with CVS?"`. The `a1facts` tool uses the information from `company.yaml` and `sources.yaml` to understand the query, fetch relevant data from the external APIs, and then provide a precise answer.

## Running the Example

<Steps>
  <Step title="Set up your environment">
    Create a `.env` file in the `a1facts/cookbook/stock_analysis` directory and add your API keys:

    ```dotenv .env theme={null}
    OPENAI_API_KEY="your_openai_api_key"
    EXA_API_KEY="your_exa_api_key"
    FMP_API_KEY="your_fmp_api_key"
    ```

    <Warning>
      You will need to sign up for a free API key from [Financial Modeling Prep](https://site.financialmodelingprep.com/developer/docs) to run this example.
    </Warning>
  </Step>

  <Step title="Run the agent">
    Navigate to the `a1facts/cookbook/stock_analysis` directory and run the following command:

    ```bash theme={null}
    uv run python infoagent.py
    ```
  </Step>

  <Step title="Expected Output">
    The agent will run the query and print the answer to the console. The output will be a detailed explanation of how UnitedHealth competes with CVS, based on the data retrieved from the knowledge graph and external sources.
  </Step>
</Steps>