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twelvelabs_tools.py
"""
TwelveLabs Tools
=============================

Demonstrates using TwelveLabs video understanding tools with an agent.

`analyze_video` answers questions about a video using the Pegasus model.
`embed_text` generates a multimodal (Marengo) embedding that lives in the same
latent space as TwelveLabs video/audio/image embeddings.

Set your API key first: `export TWELVELABS_API_KEY=...`
Grab a free key at https://twelvelabs.io.

Install dependencies: `pip install twelvelabs`
"""

from agno.agent import Agent
from agno.tools.twelvelabs import TwelveLabsTools

# Example 1: Enable all tools
agent = Agent(
    tools=[TwelveLabsTools(all=True)],
    markdown=True,
)

agent.print_response(
    "What is happening in this video? https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
)

# Example 2: Enable only text embedding (useful for embedding search queries
# against a TwelveLabs video index)
embedding_agent = Agent(
    tools=[
        TwelveLabsTools(
            enable_analyze_video=False,
            enable_embed_text=True,
        )
    ],
    markdown=True,
)

embedding_agent.print_response(
    "Embed the text 'a cat playing piano' and tell me how many dimensions it has."
)

​
Run the Example

1

Set up your virtual environment

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 twelvelabs
3

Export your API keys

export TWELVELABS_API_KEY="your_twelvelabs_api_key_here"

$Env:TWELVELABS_API_KEY="your_twelvelabs_api_key_here"
4

Run the example

Save the code above as twelvelabs_tools.py, then run:
python twelvelabs_tools.py
Full source: cookbook/91_tools/twelvelabs_tools.py