Amazon Web Services (AWS) has introduced Strands Labs, a dedicated GitHub organization for its most experimental agentic AI projects. The initiative creates a separate space for frontier ideas that are not yet ready for the production version of the Strands Agents SDK.
Strands Labs will host projects developed by teams across Amazon. These experiments will sit outside the core SDK to ensure the main framework remains stable and production-ready.
Why a separate organization?
The Strands Agents SDK, open-sourced in May 2025, has recorded 14 million downloads. It is available in Python and TypeScript and is already used internally by AWS for production workloads.
Given that adoption, AWS decided to separate experimental work from the stable SDK. The goal is to allow rapid iteration where “the interfaces might change a lot,” without affecting the core API. Projects under Strands Labs will include documentation, working code, and basic tests, but users should expect breaking changes.
AI Functions: Generating code at runtime
One of the first projects under Strands Labs is AI Functions. This tool allows developers to describe a Python function in natural language, along with preconditions and postconditions that act as guardrails. At runtime, a coding agent generates the implementation.
If the output is incorrect, built-in deterministic checks ensure the agent retries and self-corrects.
For example, in parsing receipts where formats vary widely, a developer can specify that the function must return a vendor name, total price, and line items. The agent handles edge cases dynamically. From the application’s perspective, the result behaves like a normal function embedded in deterministic logic.
Over time, AWS sees potential to run such agentic functions at scale, analyze patterns, and eventually convert recurring behaviors into fully deterministic code.
Strands Robots: Bridging cloud AI and hardware
The second project, Strands Robots, connects large language models to physical hardware using vision-language-action models. Lightweight models run locally for low latency, while advanced LLMs in the cloud handle complex reasoning.
AWS is partnering with Nvidia and Hugging Face on the effort. A simulated environment is also being released so developers can experiment without physical robots.
The project has potential use cases in warehouse robotics, in-vehicle AI, and other edge scenarios requiring both fast local inference and long-term planning.
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