DeepInfra Basic

Run DeepSeek V3 through DeepInfra with synchronous, asynchronous, and streaming agent responses.

The preserved source selects meta-llama/Llama-2-70b-chat-hf. Use the currently documented DeepSeek V3 model by applying the substitution below before running.

basic.py

"""
Deepinfra Basic
===============

Cookbook example for `deepinfra/basic.py`.
"""

from agno.agent import Agent, RunOutput  # noqa
from agno.models.deepinfra import DeepInfra  # noqa
import asyncio

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

agent = Agent(
    model=DeepInfra(id="meta-llama/Llama-2-70b-chat-hf"),
    markdown=True,
)

# Get the response in a variable
# run: RunOutput = agent.run("Share a 2 sentence horror story")
# print(run.content)

# Print the response in the terminal

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    # --- Sync ---
    agent.print_response("Share a 2 sentence horror story")

    # --- Sync + Streaming ---
    agent.print_response("Share a 2 sentence horror story", stream=True)

    # --- Async ---
    asyncio.run(agent.aprint_response("Share a 2 sentence horror story"))

    # --- Async + Streaming ---
    asyncio.run(agent.aprint_response("Share a 2 sentence horror story", stream=True))

Run the Example

Set up your virtual environment

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

Install dependencies

uv pip install -U agno openai

Export your DeepInfra API key

export DEEPINFRA_API_KEY="your_deepinfra_api_key_here"

Use a current DeepInfra model

Replace meta-llama/Llama-2-70b-chat-hf with deepseek-ai/DeepSeek-V3 in the saved file.

Run the example

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

python basic.py

Full source: cookbook/90_models/deepinfra/basic.py