Simplify Agent Development with Strands: An Open-Source SDK for Intelligent Models

Unlock the Power of Conversational AI: How Strands, an Open-Source SDK, Revolutionizes Agent Development with Zero Scaffolding and Unlimited Model Options.

  • 1. Stand is an open-source SDK with the goal of making agents as simple as possible without any scaffolding.
  • 2. The only requirements for using Stand are a tool and a model; there is no need for predetermined agent actions or complex background processes.
  • 3. Stand integrates with third-party providers like Langfuse, LightLM, and Lama, allowing users to choose their preferred models.
  • 4. Stand utilizes an "agentic loop" consisting of two components: a model and a tool, denoted by the two strands in the Stand logo.
  • 5. In this presentation, a simple demo will be shown on how to create a Stand agent that reads a file, generates a summary, writes the summary to a local drive, and verbalizes the result using default
  • 6. To use Stand, you first need to install the Stand agent, followed by the installation of Stand tools through pip.
  • 7. After installing Stand tools, out-of-the-box tools become available for use, but custom tools can also be created.
  • 8. The example presented will involve importing Stand, creating an instance with a default bedrock model (which uses cloud 3.7), and defining the tools to be used by the agent.
  • 9. A system prompt is included in this example to provide context for how things work under the hood, but it can be deleted without affecting functionality.
  • 10. The agent is then asked to read chapter 10, summarize it, write the summary in a markdown file, and verbalize the result using the default tools provided by Stand.
  • 11. Another demo will involve using Stand with an MCP (Multi-Computer Project) server to generate videos using Manim, similar to those seen on the Three Blue One Brown YouTube channel.
  • 12. The Manim library allows for easy visualization of complex concepts like vectors and SVD, making it suitable for science and mathematics backgrounds.
  • 13. To use Stand with MCP, an agent is created that imports both the stance agent and the MCP client, followed by creating the MCP client and specifying the server path where the Manim code is located
  • 14. Instead of using default tools like read file, write file, or speak, a custom tool (the MCP server) is used to broadcast all available tools from the server to the client.
  • 15. The agent then generates a visualization for a cubic equation within the range of x=-3 to x=3 based on the provided prompt.
  • 16. To use Stand with MCP, it's essential that the server is running before starting the agent and app.py code.
  • 17. Stand allows you to define the duration of the generated video (e.g., 30 seconds or 10 seconds).
  • 18. The integration between Stand and MCP enables the model to reason about prompts without requiring explicit system prompts or scaffolding.
  • 19. Custom tools can be created by wrapping a function with an MCP decorator, allowing it to be used as a tool in agents.
  • 20. For more information on Stand, visit their GitHub repository, documentation website (strandsagent.com), and the provided GitHub link.
  • 21. Users are encouraged to ask questions, share feedback, or contribute to the project through raising pull requests.
  • 22. The presenter mentions that there will be booths available with demos on using Stand, Lambda, and MCP.
  • 23. Stand is an open-source project, inviting users to explore, build upon, and share their contributions via PRs in the samples code repository.
  • 24. Users are welcome to submit feedback or requests for new features in the Stand SDK through various channels mentioned at the end of the presentation.

Source: AI Engineer via YouTube

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