KiroCrew

Kiro is a persistent workspace tool that aims to keep a developer's state across sessions and improve code quality through agentic engineering. The product turns natural language prompts into concrete specifications and uses parallel agents to implement, test, and validate the resulting code. It runs both locally via a CLI and on the cloud, and can be integrated into CI/CD pipelines to review pull requests without opening an editor.

You start Kiro by installing its CLI with a simple curl command, then launch a cloud session that stores your working context. From there you can feed a descriptive prompt into the tool. Kiro parses the prompt into an architectural design and a list of sequential tasks. Each task is handed to an autonomous agent that writes code, commits changes, or runs tests. The agents can pull context from external sources such as Figma or Terraform contracts, which is useful when working on large codebases that rely on design or infrastructure specifications.

Before code runs, Kiro checks the specification for contradictions and gaps using automated reasoning. After generation, it runs property‑based tests, similar to fuzzing, that assert rules that must hold for all inputs. This approach uncovers bugs that traditional unit tests might miss. Property tests are generated automatically when a new spec is created, and you can also write custom tests for specific endpoints. The results are available in the CLI, and a PR can be created automatically with the new changes and tests.

Kiro supports a wide model catalog. The default “auto” model picks the most efficient model for a task, balancing quality, latency, and price. You can also choose specific Anthropic Claude, OpenAI GPT, or open‑weight models like DeepSeek or Qwen. Pricing is handled via credits that reflect each model's cost relative to the auto baseline. The system tracks cost multipliers (for example, gpt‑5.6‑terra is 2.2x the auto base cost) and can be set per session. The CLI also supports rate limiting on the export endpoint to safeguard against runaway usage.

Key features

• Turns prompts into executable specs and architectural plans
• Parallel agents implement tasks and pull context from tools such as Figma and Terraform
• Automated reasoning checks for specification gaps and contradictions
• Property‑based testing that catches edge‑case bugs missed by unit tests