upstash/ahi

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Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/upstash/ahi.

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# Ahi If you're running AI agents for your users, you're probably building an app server — routes, handlers, orchestration code, tenant isolation. Ahi replaces all of that. Ahi is an open-source framework for running each agent in its own isolated container with tools, skills, durable data, and a schedule. No orchestration. No deployment. > The name comes from the _Ahilik_ tradition — independent craftsmen in 13th-century Anatolia who each had their own trade, their own tools, and a shared code of conduct. An Ahi agent works the same way. Powered by [Upstash Box](https://upstash.com/docs/box/overall/quickstart). Read the [launch blog post](https://upstash.com/blog/ahi). ## Why Not Just Use an App Server? You could build all of this on a traditional app server. But here's what you'd actually be building on top of your agent logic — and what an agent server gives you for free: | | App Server | Agent Server | | ----------------- | --------------------------------------- | -------------------------------------------- | | **Isolation** | Software-defined (RBAC, scoped queries) | Container-isolated (one container per agent) | | **State** | External DB (Postgres, Redis) | Local filesystem (durable files) | | **Cost model** | Pay for running processes | Sleep/wake — pay only when active | | **Orchestration** | Hand-written glue code | Zero — agent reads skills, calls tools | | **Deployment** | Full CI/CD for any change | File-based sync (update one file) | ## Five Primitives Ahi implements the **agent server** architecture. Instead of app code, you get five primitives: ![Agent Server Primitives](https://upstash.com/blog/agent-server/primitives-light.png) | Primitive | What it is | | ------------ | ----------------------------------------------- | | **Agent** | The LLM that reasons and acts | | **Tools** | TypeScript scripts the agent executes via shell | | **Skills** | Markdown instructions (system prompt) | | **Data** | Durable JSON/MD files that persist across runs | | **Schedule** | Cron that sends prompts, not code | Each primitive has its own lifecycle. Fix a bug in a tool — `ahi apply`, only that file updates remotely. Rewrite the agent's strategy — update the skill file, `ahi apply`. Same tools, same data, new behavior. Switch from Claude to GPT — change the model in `ahi.yaml`, `ahi apply`. Tools, skills, and data stay untouched. ## Install ``` npm install -g @upstash/ahi ``` ## Quick Start ``` ahi init # scaffold project structure cp .env.example .env # add your API keys ahi dev "remember to buy milk" # run agent locally ahi apply # apply project state to the box ahi run "list my notes" # run remotely ``` ## Project Structure ``` my-project/ ├── ahi.yaml # agent definitions ├── CLAUDE.md # local Claude development guidance ├── AGENTS.md # local Codex/OpenCode development guidance ├── .env # API keys ├── tools/ # scripts agents execute ├── skills/ # reusable workflow instructions and native skill source └── data/ # durable files managed by the agent ``` The folder structure is the convention. `tools/` maps to Tools, `skills/` maps to Skills, `data/` maps to Data. Root `CLAUDE.md` and `AGENTS.md` are local-only development guidance for coding agents working in the repo. The `skills:` entry in `ahi.yaml` is the deployed runtime instruction entrypoint. On apply, Ahi mirrors valid skill packages from `skills/` into harness-native discovery paths based on the configured runner: `claude-code` uses `.claude/skills`, while `codex` and `opencode` use `.agents/skills`. The `ahi.yaml` defines Agents and Schedules. ## ahi.yaml ```yaml tools: ./tools/ skills: ./skills/SKILL.md env: BRAVE_API_KEY: # forwarded from local .env NODE_ENV: production # explicit value setup: - npm install agents: - name: my-agent harness: claude-code model: anthropic/claude-sonnet-4-6 schedules: - cron: "0 9 * * *" prompt: "Do your daily task" timeout: 300000 # optional; defaults to /workspace/home folder: /workspace/home ``` ## Skills The `skills:` field in `ahi.yaml` points to the primary runtime skill file for the project: ```yaml skills: ./skills/SKILL.md ``` This is the deployed runtime instruction entrypoint. For local `ahi dev` runs, Ahi prefers the harness-specific root file when present: - `CLAUDE.md` for `claude-code` local development - `AGENTS.md` for `codex` and `opencode` local development - otherwise the path configured in `skills:` Those root files are optional. They help coding agents work on the repo locally, but they do not define deployed runtime behavior. Before each `ahi dev`, Ahi prepares the local harness-specific skill setup: - nested skill packages under `skills/` are mirrored into `.claude/skills/` or `.agents/skills/` That means you can keep multiple files and folders under `skills/`, but local runs still do not auto-pick one of them for you. Use `CLAUDE.md` or `AGENTS.md` for repo-specific development guidance, and treat `skills/SKILL.md` as the runtime behavior you are developing. If local root files disagree with `skills/SKILL.md`, the `skills:` path remains the source of truth for what gets deployed. On `ahi apply`, the configured `skills:` file is projected into the harness root filename on the box, and the entire `skills/` directory is uploaded. Any directory under `skills/` that contains a `SKILL.md` file is treated as a skill package and mirrored into the harness-native skill directory, along with any helper files in that folder. Local root files like `CLAUDE.md` and `AGENTS.md` are not uploaded. Recommended structure for multiple skills: ```text skills/ ├── SKILL.md ├── notes/ │ ├── SKILL.md │ └── templates/ └── research/ ├── SKILL.md └── references/ ``` In this layout: - `skills/SKILL.md` is the primary router referenced by `ahi.yaml` - `skills/notes/SKILL.md` and `skills/research/SKILL.md` are reusable native skill packages - helper files inside each package are uploaded with that package If you use nested skill packages, keep the root `skills/SKILL.md` as the router. Do not rely on it also being mirrored as a native default package when nested skill directories are present. ### Config Fields | Field | Required | Description | | -------- | -------- | ------------------------------------------------------------------------------ | | `tools` | yes | Path to tools directory | | `skills` | yes | Path to skill file | | `env` | no | Env vars to write inside the box. Empty value = forward from local environment | | `setup` | no | Commands to run on the box after file upload (e.g. `npm install`) | | `agents` | yes | List of agent definitions | ### Agent Fields | Field | Required | Description | | ----------- | -------- | ------------------------------------------------------------------------ | | `name` | yes | Agent name (also the box name) | | `model` | yes | Model identifier (for example `anthropic/claude-sonnet-4-6`, `openai/gpt-5.4`, `openrouter/google/gemini-3.1-pro-preview`) | | `harness` | yes | Runner to use for the agent: `claude-code`, `codex`, or `opencode` | | `schedules` | no | Cron schedules with prompt, optional timeout, and optional folder override | ## Commands ### `ahi init` Scaffolds a minimal project with a notes agent, tool, runtime skill, and local development instruction files for Claude and Codex-compatible agents. ### `ahi dev <prompt>` Runs the agent locally using the installed CLI (Claude Code, Codex, or OpenCode). By default, `ahi dev` picks a single local development agent based on your local instruction files and installed CLI. It does not require or fan out across the agents defined in `ahi.yaml`. Before launch, Ahi prepares local harness-specific skill files from your project: - `CLAUDE.md` or `AGENTS.md` is used when present - if that root file is missing, Ahi falls back to the path in `skills:` - nested skill packages under `skills/` are mirrored into the local native skill directory for the selected harness Use `--agent <name>` only when you explicitly want to emulate a configured agent from `ahi.yaml`. Local root files are optional and local-only. They are meant to help coding agents work on the project; they are not uploaded by `ahi apply`. ``` ahi dev "save a note about the meeting" ahi dev "list my notes" --agent my-agent ``` ### `ahi apply` Applies tools, skills, env vars, setup commands, and schedules to every agent box. Creates boxes if they don't exist. The configured `skills:` file is projected into the harness root instruction filename inside each box, and valid skill packages from `skills/` are mirrored into `.claude/skills/` or `.agents/skills/` based on the explicit `harness`. Scheduled prompts default to the project workspace at `/workspace/home` unless a schedule `folder` is explicitly configured. Local root files like `CLAUDE.md` and `AGENTS.md` are not uploaded. Agent API keys are picked up from your environment based on the `model` prefix, not the `harness`. `anthropic/...` uses `ANTHROPIC_API_KEY`, `openai/...` uses `OPENAI_API_KEY`, `openrouter/...` uses `OPENROUTER_API_KEY`, and `opencode/...` uses `OPENCODE_API_KEY`. ### `ahi run <prompt>` Runs the agent remotely on the box and streams output back. ``` ahi run "analyze today's data" ahi run "generate report" --agent my-agent ``` ### `ahi pull-data` Downloads the agent's `data/` directory from the box to your local project. ``` ahi pull-data ahi pull-data --agent my-agent ``` ### `ahi push-data` Uploads your local `data/` directory to the box. ``` ahi push-data ahi push-data --agent my-agent ahi push-data --all ``` ## Environment Variables | Variable | Used for | | --------------------- | ----------------------------------------------------- | | `UPSTASH_BOX_API_KEY` | Required for `apply`, `run`, `pull-data`, `push-data` | | `ANTHROPIC_API_KEY` | Passed when the model starts with `anthropic/...` | | `OPENAI_API_KEY` | Passed when the model starts with `openai/...` | | `OPENROUTER_API_KEY` | Passed when the model starts with `openrouter/...` | | `OPENCODE_API_KEY` | Passed when the model starts with `opencode/...` | ## Example: Botstreet [Botstreet](https://botstreet.vercel.app/) is three agents competing as stock traders. Same tools, same skills, different models. Each runs in its own Upstash Box. ```yaml tools: ./tools/ skills: ./skills/SKILL.md agents: - name: botstreet-claude harness: claude-code model: anthropic/claude-opus-4-6 schedules: - cron: "30 14 * * 1-5" prompt: "Run daily trading analysis, research market news, make trades, and save a snapshot" timeout: 600000 - name: botstreet-gemini harness: claude-code model: openrouter/google/gemini-3.1-pro-preview schedules: - cron: "30 14 * * 1-5" prompt: "Run daily trading analysis, research market news, make trades, and save a snapshot" timeout: 600000 - name: botstreet-openai harness: codex model: openai/gpt-5.4 schedules: - cron: "30 14 * * 1-5" prompt: "Run daily trading analysis, research market news, make trades, and save a snapshot" timeout: 600000 ``` Every weekday at 9:30 AM ET, each agent wakes up, reads the news, analyzes its portfolio, makes trades, and saves a snapshot. Between runs, the box sleeps. The data — portfolio state, trade history, diary entries — persists as files inside each box. [Live dashboard](https://botstreet.vercel.app) · [Source](https://github.com/upstash/botstreet) ## Links - [Upstash Box docs](https://upstash.com/docs/box/overall/quickstart) - [Launch blog post](https://upstash.com/blog/ahi) - [Botstreet live dashboard](https://botstreet.vercel.app) - [Botstreet source](https://github.com/upstash/botstreet)

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Skills في هذا المستودع

موظفو المراسلات

١ فئات مهنية · 100% مصنفة

skill

المهنة

الوصف

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daily-news-researcher

موظفو المراسلات

Research one news topic and save a one-sentence summary each day

لغة النص الأصلي: الإنجليزية

2026-04-12

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