> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Groq Llama Finance Agent

> Llama 4 Scout on Groq uses the think tool as a scratchpad while writing a finance report.

> [!WARNING]
>
> For free and developer tiers, Groq will shut down `meta-llama/llama-4-scout-17b-16e-instruct` on July 17, 2026. Replace it with the vision-capable `qwen/qwen3.6-27b` before that date. See [Groq deprecations](https://console.groq.com/docs/deprecations).

```python groq_llama_finance_agent.py theme={null}
"""
Groq Llama Finance Agent
========================

Demonstrates this reasoning cookbook example.
"""

from textwrap import dedent

from agno.agent import Agent
from agno.models.groq import Groq
from agno.tools.reasoning import ReasoningTools
from agno.tools.websearch import WebSearchTools


# ---------------------------------------------------------------------------
# Create Example
# ---------------------------------------------------------------------------
def run_example() -> None:
    thinking_llama = Agent(
        model=Groq(id="meta-llama/llama-4-scout-17b-16e-instruct"),
        tools=[
            ReasoningTools(),
            WebSearchTools(),
        ],
        instructions=dedent("""\
        ## General Instructions
        - Always start by using the think tool to map out the steps needed to complete the task.
        - After receiving tool results, use the think tool as a scratchpad to validate the results for correctness
        - Before responding to the user, use the think tool to jot down final thoughts and ideas.
        - Present final outputs in well-organized tables whenever possible.

        ## Using the think tool
        At every step, use the think tool as a scratchpad to:
        - Restate the object in your own words to ensure full comprehension.
        - List the  specific rules that apply to the current request
        - Check if all required information is collected and is valid
        - Verify that the planned action completes the task\
        """),
        markdown=True,
    )
    thinking_llama.print_response("Write a report comparing NVDA to TSLA", stream=True)


# ---------------------------------------------------------------------------
# Run Example
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    run_example()
```

## Run the Example

<Snippet file="create-venv-step.mdx" />

**1. Install dependencies**

```bash theme={null}
uv pip install -U agno ddgs groq
```

**2. Export your Groq API key**

```bash Mac/Linux theme={null}
export GROQ_API_KEY="your_groq_api_key_here"
```

```bash Windows theme={null}
$Env:GROQ_API_KEY="your_groq_api_key_here"
```

**3. Replace Llama 4 Scout**

Replace `meta-llama/llama-4-scout-17b-16e-instruct` with `qwen/qwen3.6-27b` in the saved file.

**4. Run the example**

Save the code above as `groq_llama_finance_agent.py`, then run:

```bash theme={null}
python groq_llama_finance_agent.py
```

Full source: [cookbook/10\_reasoning/tools/groq\_llama\_finance\_agent.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/10_reasoning/tools/groq_llama_finance_agent.py)


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.