Groq Llama Finance Agent

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

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.

groq_llama_finance_agent.py

"""
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

1. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
2. Install dependencies

   uv pip install -U agno ddgs groq
   
3. Export your Groq API key

   Mac/Linux

   export GROQ_API_KEY="your_groq_api_key_here"
   

   $Env:GROQ_API_KEY="your_groq_api_key_here"
   
4. Replace Llama 4 Scout

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

5. Run the example

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

   python groq_llama_finance_agent.py
   

Full source: cookbook/10_reasoning/tools/groq_llama_finance_agent.py