// HACKER NEWS — CYBERSECURITY
Strands Harness
Strands harness is a fully assembled, customizable, state-of-the-art agent you run locally or deploy anywhere.
We noticed builders often wished their Claude Code or Codex setup could run in the cloud because locally their agent idea just “worked” with those harnesses.
But the moment you build your own agent, you’re on your own. It’s tricky wiring up the right primitives just well enough to match that “it just worked” feeling.
Today we’re releasing Strands harness: a fully assembled state-of-the-art agent harness you can easily run locally or deploy to your favorite provider. It’s built to be a general-purpose agent rather than a coding agent. All you need is one line of Python or TypeScript to get it working with your choice of model. Strands harness beats other agent harnesses on cost efficiency while maintaining equal or better accuracy. It’s available under an Apache 2.0 license.
Strands harness costs 28% less when using the same Claude or GPT models across six benchmarks. We found Strands harness showed better token-efficiency and nearly equal benchmark scores compared to Claude Code, Codex, and other popular harnesses. That’s important because we never want to sacrifice accuracy for lower cost. Deepseek Harness proved to be the most token-efficient overall, however, it typically reported the lowest accuracy scores. Our testing setup was distributed benchmarking on EC2 with Harbor.
With Fable 5, Strands harness cost 77% less than Claude Code and scored higher on Terminal Bench 2.1. We’re also pleased to see two other open source harnesses perform similarly well on cost/accuracy against Claude Code.
Strands harness, built on the Strands Harness SDK, contains defaults for prompt caching and context management. Our default context management largely drove the token-efficiency and accuracy: tool results over ~1500 tokens get truncated, summarization (compaction) triggers when the context window hits above 85%, and context recovery runs within the loop if there’s an overflow.
Keep an eye out for a follow-up paper from our researchers regarding these benchmarks. In the meantime, we’re excited everyone gets to use these defaults in Strands harness.
Strands harness runs on the latest models across Amazon Bedrock, Anthropic, OpenAI, and Google. Pick one by name, or point it at a local Ollama model:
Out of the box, create_harness() returns an agent that: