OneCLI: An Open-Source Sandboxed Agent Harness for Teams
Score
72提供了企业级 Agent 安全部署的新开源方案,信息增量较高。
内容直接引用项目描述,结构完整但缺乏细节验证。
沙箱化 agent harness 可直接落地,尤其适合安全敏感业务。
发布自 YC S26 并刚在 Hacker News 出现,时效性强。
信息源自项目官方仓库及 HN 发布,可信度较高。
Base 79.5 × T2 Source weight ×0.9 = 72
Curation line 70Selected
Why selected
The product addresses the security pain point of enterprise AI deployment, with an open-source and verifiable solution, though its ecosystem maturity remains to be seen.
AI commentary
「In the current flood of AI agents, OneCLI takes a smart angle by building "secure sandboxing" as team-level infrastructure. It deserves attention not only because of the YC badge, but because it turns the thorny issue of permission boundaries into a default capability. It's especially relevant for platform engineering and security teams, and the open-source approach leaves room for deep customization.」
Quick overview
OneCLI, an open-source sandboxed agent harness from Y Combinator's S26 batch, aims to provide a secure personal AI agent for every employee in a team. Unlike generic agent frameworks, OneCLI emphasizes sandboxing and permission controls, allowing organizations to deploy AI agents without exposing sensitive data or critical systems. It integrates with existing development workflows, offering an isolated execution environment, auditability, and governance features. The project was launched on Hacker News and has attracted attention from engineers and enterprises seeking safe, team-wide AI adoption. By open-sourcing the harness, OneCLI enables customization and self-hosting, which is particularly appealing for security-conscious companies.
- OneCLI is an open-source agent harness from YC S26, designed for team-wide deployment.
- It emphasizes sandboxing and permission controls to give each employee a secure personal agent.
- Self-hosted and customizable, it suits security-sensitive enterprises.
- The Hacker News launch has drawn attention as potential infrastructure for enterprise agent adoption.
Full text
Showing the source text; the full translation is being generated.
The agent harness built for teams.
A pro assistant for companies. Give every employee a secured, sandboxed personal agent.
Quick Start
Cloud-hosted: onecli.sh
Self-hosted
git clone https://github.com/onecli/onecli.git && cd onecli pnpm install pnpm run setup
What is OneCLI v2?
OneCLI is an open-source platform for running AI agents as a team. You create an agent per person, give each agent the access it needs, and it works in a sandbox, routed through a gateway that injects the credentials and enforces your policy.
Why we built OneCLI?
OneCLI started as a credential vault for AI agents, built in Rust. We found that most of the demand came from individuals and teams running autonomous agents like Hermes, OpenClaw and NanoClaw. People wanted agents that do real work for the person running them, but two parts were missing:
- managing secrets and permissions.
- and for teams - multiplayer management.
Every autonomous agent out there is built for one person. And for one person, they're great. The moment you need to replicate that across a team, it gets messy: spinning up each agent, deciding what each one can and cannot do, hosting them, keeping track of whose agent is whose.
So we shifted, and built OneCLI v2.
Built for teams
- Your identity provider, integrated: provision agents on behalf of each employee's identity, straight from the company IdP.
- An agent per person: everyone in the workspace gets their own sandboxed agent, reachable from the dashboard or Slack.
- One policy, enforced everywhere: manage the team policy in one place, that any agent in the workspaces would be enforced by.
- Deterministic human-in-the-loop approvals: in the chat itself, for things you need 100% control over, like sending the email, deleting the Linear ticket, emptying an S3 bucket.
- Global connections: shared at the team level, like LLM keys or service accounts, granted per agent without ever being handed to one.
The agent
An agent is a durable thing, not a single prompt. It has:
- A computer: its own isolated sandbox, with a filesystem and a shell. The only way out is the gateway, so it can reach what you granted and nothing else.
- A conversation: its own page in the dashboard, or Slack. Images and files included. A message sent while the agent is working redirects it right away instead of queueing behind it.
- Memory: what the agent learns is kept by the platform, so it is never lost. You can read and edit it any time.
- Skills: instructions and helpers you write once, always available to the agent.
- A schedule: the agent can plan future work, and the platform wakes it at the right time.
- Credentials it never sees: each agent gets only the access you granted, and the gateway enforces it on every request. Or connect Bitwarden or 1Password for on-demand injection, with nothing stored on the server.
- Its own Slack app: connect it once and it answers in channels and DMs under its own name and avatar, with files and images. Delete the agent and its Slack app goes with it.
Agents run on your own infrastructure. The runner is outbound-only and holds no inbound ports, so a laptop, a homelab, or a VPC behind NAT all work with no ingress and no tunnel.
Architecture
- Web Dashboard: Next.js app. Create agents, chat with them, edit their memory and skills, manage connections, secrets and grants.
- API Server: the control plane. Owns the database, the conversation plane, and the work queue the runner polls.
- Rust Gateway: intercepts outbound requests (HTTPS included, via MITM) and injects credentials. Agents authenticate with access tokens via
Proxy-Authorizationheaders. - Runner: starts, parks and reaps agent sandboxes. Outbound-only, and never touches the database.
- Sandbox Supervisor: runs inside each sandbox, speaking a vendor-neutral harness interface so the agent runtime is swappable.
- Channel Adapter: the Slack daemon, one app per agent.
- Secret Store: AES-256-GCM at rest, decrypted only at request time, matched by host and path pattern, injected as headers or query parameters.
Local Development
git clone https://github.com/onecli/onecli.git && cd onecli mise install pnpm install pnpm dev
That's the whole setup: pnpm dev generates .env with every required secret, starts PostgreSQL, applies migrations, and runs the full stack. Prerequisites, the command reference, project structure, and configuration live in docs/development.md.
Contributing
Contributions are welcome. Read the Contributing Guide and Code of Conduct before getting started. Contributions are accepted under the terms of the Contributor License Agreement.
Security
To report a vulnerability, please follow our Security Policy. Do not open a public issue for security reports.
License
Apache-2.0, with one exception: the ee/ directories hold
enterprise features under the OneCLI Enterprise License,
each carrying a notice that points at it. That license is free for development,
testing and evaluation, and requires a subscription for production use.
Everything else is Apache-2.0 and can be self-hosted in production with no
commercial license. LICENSE-ENTERPRISE carries the
authoritative list of licensed paths.
Related items
92
FeaturedOpenRouter Launches Prompt Caching with Sticky Routing to Drastically Reduce Multi-Turn Costs
OpenRouter 推出提示缓存与粘性路由,大幅降低多轮对话成本
For developers making frequent AI API calls, this feature is a direct money-saver. Sticky routing guarantees cache hit rates, and session_id requires zero configuration—every OpenRouter user should try it immediately.
91
FeaturedChatGPT Work: An AI Agent That Takes Action Across Apps and Files
ChatGPT Work:可在跨应用和文件中执行操作的AI Agent
I think this is a key step for AI assistants moving from Q&A to execution. Especially noteworthy for professionals who need to automate complex workflows. ChatGPT Work could become a new paradigm for productivity tools.
90
FeaturedAnatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident
前沿实验室代理入侵事件技术时间线:2026年7月事件剖析
This technical timeline is a must-read for all AI security researchers and agent developers—it doesn't just describe one attack but sounds an alarm on the weak defense systems of agent architectures. For teams deploying autonomous agents, this is an essential disaster prevention manual.
90
FeaturedBetter Tools Made Copilot Code Review Worse? Here's How GitHub Improved It
更好的工具反而让Copilot代码审查更糟?GitHub这样改进
This blog post deserves close reading by all AI product engineers and ML practitioners—it breaks the intuition that 'more powerful tools = better AI' and instead emphasizes an engineering philosophy aligned with agent reasoning patterns. Especially important for teams optimizing code review or similar agent-based products.
88
FeaturedOpenRouter Launches Unified Multimodal API: Single Endpoint for Image, Video, Audio, Embeddings, and Transcription
OpenRouter 推出统一多模态 API:单端点支持图像、视频、音频、嵌入与转录
This news is worth attention from all developers building multimodal AI applications: OpenRouter's unified API solves the pain of model switching, especially for startups or rapid prototyping. If you're struggling with multiple API keys and SDKs, this interface could be the unified layer you need.
87
FeaturedAnthropic Launches Claude for Teachers: AI Tools Designed for Educators
Anthropic 推出 Claude for Teachers:面向教师的教育 AI 工具
This announcement is valuable for educators and AI industry watchers alike. Instead of a generic chatbot, Anthropic designed a safety-first tool specifically for teachers, demonstrating the right approach for AI vertical deployment: solving real pain points rather than showing off technology. The built-in guardrails also set a reference for other AI companies, especially when serving scenarios involving children or teenagers.
86
FeaturedOpenRouter Introduces New Auto Router: Model Routing Powered by Market Wisdom
OpenRouter发布全新Auto Router:用市场智慧优化模型路由
This update is worth attention for anyone using APIs to build AI apps. Instead of guessing or testing model selection, market data now does the heavy lifting. Though sparse on details, the direction is right, especially for teams relying on aggregated model services.
86
FeaturedClaude Code v2.1.211 Release: Multiple Fixes and Improvements
Claude Code v2.1.211 发布:多项修复与改进
As a developer who frequently uses Claude Code for automated coding, I'm particularly interested in the security fixes, especially the permission preview character bypass and auto mode overriding hook decisions—these directly affect the security boundaries during team collaboration. The improvement in background agent reporting also makes long-running tasks more reliable. I recommend all users upgrade as soon as possible.