Introduction: Bringing AI Video Understanding to Agent Workflows

The AI landscape is rapidly evolving from standalone models to sophisticated agent-driven systems that can perform complex, multi-step tasks. We are excited to announce that TwelveLabs' industry-leading video understanding technology is now natively integrated with Strands Agents, marking another milestone in our commitment to democratizing video intelligence across the developer ecosystem.

This partnership represents more than just technical integration—it's a strategic expansion of how developers can harness the power of multimodal video understanding within their agent workflows. By bringing our Marengo and Pegasus models directly into the Strands ecosystem, we are making it effortless for developers to add sophisticated video capabilities to their AI applications without wrestling with complex APIs or infrastructure concerns.

What This Integration Offers

The integration delivers two powerful tools that seamlessly blend TwelveLabs' cutting-edge video foundation models with Strands' intuitive agent framework:

search_video Tool - Powered by TwelveLabs Marengo

This tool transforms how agents discover and retrieve video content using natural language queries. Whether you're searching for "people discussing AI" or specific visual elements, Marengo's multimodal understanding delivers precise results with configurable confidence thresholds and grouping options.

chat_video Tool - Powered by TwelveLabs Pegasus

This enables agents to engage in sophisticated conversations about video content, extracting insights, generating summaries, and answering complex questions about what's happening in your videos. From analyzing meeting recordings to understanding educational content, Pegasus brings conversational intelligence to video data.

Developer-First Experience

The integration exemplifies our developer-first philosophy with minimal setup requirements—just three environment variables get you started. No complex authentication flows, no lengthy SDK installations, just straightforward configuration that lets developers focus on building rather than integrating.

This seamless integration into the AWS partner ecosystem demonstrates our commitment to meeting developers where they are, while contributing meaningfully to the open-source community through the Strands Agents Tools repository. By lowering the barrier to entry for video understanding capabilities, we are empowering the next generation of intelligent applications that can truly understand and interact with visual content.

Code Example: Simple Yet Powerful

Building powerful video-enabled agents has never been more straightforward. The integration transforms complex video AI capabilities into simple, natural language interactions that any developer can implement in minutes, not days.

from strands import Agent
from strands_tools import search_video, chat_video

# Create an agent with video understanding superpowers
agent = Agent(tools=[search_video, chat_video])

# Search for video content using natural language  
result = agent.tool.search_video(
    query="people discussing AI technology",
    threshold="high",
    group_by="video",
    page_limit=5
)

# Chat with existing video (no index_id needed)
result = agent.tool.chat_video(
    prompt="What are the main topics discussed in this video?",
    video_id="existing-video-id"
)

# Chat with new video file (index_id required for upload)
result = agent.tool.chat_video(
    prompt="Describe what happens in this video",
    video_path="/path/to/video.mp4",
    index_id="your-index-id"
)

This elegant simplicity showcases our developer-first philosophy—three environment variables, two tools, and unlimited possibilities. By abstracting away the complexity of multimodal AI, we're enabling developers to focus on building innovative applications rather than wrestling with infrastructure. The clean API design ensures that video understanding capabilities integrate seamlessly into existing Strands Agents workflows while maintaining the robustness our enterprise customers demand.

Real-World Use Cases & Benefits

The integration unlocks transformative applications across industries by making sophisticated video understanding as accessible as calling any standard function.

• Content Discovery & Management: Transform vast video libraries into searchable knowledge bases where agents can instantly find "quarterly revenue discussions" or "product demonstration moments" using natural language queries. Organizations can now build intelligent content management systems that understand context, not just metadata.

• Automated Video Analysis: Deploy agents that automatically extract key insights, generate summaries, and identify action items from meetings, training sessions, or customer interactions. This capability turns passive video content into actionable business intelligence, driving efficiency across departments from sales to compliance.

• Interactive Learning Systems: Create educational agents that can answer specific questions about video lessons, generate study guides, or provide personalized explanations based on visual content. The integration enables adaptive learning experiences that respond to both spoken and visual elements in educational materials.

• Customer Support Enhancement: Build support agents that can analyze video submissions, understand technical demonstrations, and provide contextual assistance based on visual troubleshooting content. This transforms static knowledge bases into dynamic, video-aware support systems.

These use cases demonstrate how our AWS partnership momentum creates compound value—each integration strengthens both ecosystems while expanding the total addressable market for video AI applications. By contributing this functionality to the open-source community through Strands, we're not just building tools; we're establishing video understanding as fundamental infrastructure for the next generation of intelligent applications.

Conclusion: Start Building Today

Getting started with TwelveLabs video AI in Strands Agents is refreshingly straightforward. Simply set three environment variables—TWELVELABS_API_KEY, TWELVELABS_MARENGO_INDEX_ID, and TWELVELABS_PEGASUS_INDEX_ID—and you're ready to integrate powerful video understanding into your agent workflows.

Explore the complete integration in our merged Pull Request and get hands-on with the Strands Agents Tools repository. The comprehensive documentation provides everything you need to start building video-enabled agents today.

Ready to transform how your agents understand video content? Install the tools, set your API credentials, and discover what becomes possible when sophisticated video AI meets elegant developer experience. The future of video-enabled AI agents starts with your next pip install strands-agents-tools.