kvsecure.com

What It Is

kv from kvsecure is a local encrypted secrets manager designed for AI-assisted development. It is not aimed at the traditional problem of “humans copying keys around.” Instead, it addresses the way secrets can spread from .env files, chat logs, script inputs, stdout, or generated files once an AI agent can read files, run commands, retry tasks, and produce logs. The idea behind kv is to keep secrets inside a locally encrypted vault and daemon-controlled execution path: the agent requests that a task be completed, while kv calls the API, queries the database, or runs SSH on its behalf, returning only the result to the agent.

Core Features and Integrations

The product consists of modules such as vault, unlock, broker, scope, audit, and guide. Vault handles local encrypted storage; a passphrase is the main unlock barrier, with optional 2FA. Brokered tools include kv_api, kv_query, and kv_ssh, which are intended for scenarios where raw credentials should not be handed over. kv run can inject selected secrets into a subprocess, which is useful when a local program genuinely needs environment variables, but this has a weaker security boundary. On the editor side, the documentation mentions support for Claude Code, Cursor, VS Code, Codex, and other MCP-compatible clients, while Claude Code also gets additional hook guidance.

Open Source, Pricing, and Maturity

The page clearly states that the local CLI, daemon, and MCP server are open source, with links to GitHub, a Security policy, and a PyPI package. There is no commercial pricing table. The FAQ only says that the local components are open source; team vaults, cloud sync, roles, hosted sharing, and billing should still be treated as future or private beta work rather than publicly available production features.

Pros, Cons, and Best Fit

Its strengths are precise positioning and honest security boundaries: it does not claim to make malicious scripts safe, but rather reduces the chances of an agent directly receiving secrets. It is highly valuable for solo developers or small teams using local AI coding workflows, especially developers who frequently interact with real infrastructure such as OpenAI, databases, or SSH. The downsides are that team-level features have not yet been publicly released, and there is limited detail on supported providers, database types, and enterprise audit capabilities. In addition, kv run still passes secrets to a subprocess, so users must approve such use carefully.

Access from China and Alternatives

The documentation does not provide information on network availability in mainland China, payment methods, or mirrors, so access status can only be marked as unknown. For mature team secrets management, alternatives include 1Password, Doppler, Infisical, HashiCorp Vault, or cloud provider Secret Manager products. If the main focus is the local credential boundary for AI agents, kv’s brokered model is more targeted.