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<img src="docs/assets/wordmark.png" width="524" alt="ANT — Declared context. Auditable engineering."> Your agent's context is a declared list, not a search. The same runtime builds your product — PRD → design → code, every code job verifying itself — and runs your process, on work agents you define in files and put on a schedule. Self-hosted · Bring your own LLM key · Apache-2.0 <a href="LICENSE"><img alt="License" src="https://img.shields.io/badge/license-Apache%202.0-blue.svg"></a> <a href="https://github.com/to-nexus/ant/actions/workflows/ci.yml"><img alt="CI" src="https://github.com/to-nexus/ant/actions/workflows/ci.yml/badge.svg"></a> <a href="README.ko.md"><img alt="Korean" src="https://img.shields.io/badge/lang-한국어-red"></a> <a href="docs/local-mode/install.md"><img alt="Quickstart" src="https://img.shields.io/badge/docs-quickstart-success"></a> <img src="docs/assets/code-job.webp" width="880" alt="A directive decomposed into fourteen tasks on Ant's board, then documents and token files streaming into the workspace, then the board again with three tasks moved to Completed"> One directive → decomposed tasks running in parallel, each writing a real file at a real path — read it, diff it, argue with it. Not just a commit. > ⚠️ **Status: pre-alpha, solo-developed.** It works end-to-end, but the public > API and file layout may still move between releases, and building this > much breadth alone means some surfaces are thinner than others — see > **[Maturity](#maturity)** for an honest per-feature breakdown. Issues and PRs > are welcome; **[Contributing](#contributing)** says where help lands best. --- ## Why Ant? Most agent tools assemble the model's context by searching your files at run time — whatever the search happens to find is what the model reads. So every run reads slightly different context, costs a slightly different amount, and fails in ways you can't reproduce. Ant is built on the opposite premise: **context is declared, not discovered.** - **You see what enters the prompt, before the job runs.** Every job executes against a resolved set of artifacts — the PRD and system design handed to a code job, the documents handed to a work agent — and a binding runtime rule forbids wholesale-walking the project into that set. Anything the agent explores beyond it goes through logged tool calls under hard token budgets. - **Your chat is not the model's context.** The UI transcript and the LLM context are separate files by construction. A job receives a bounded distillation of prior work (4–12k tokens) — never the scrollback — and constraints you state in chat are carried forward verbatim in a ledger instead of being re-summarized until they disappear. - **Every stage is a durable file; every cost is itemized.** The same declared inputs produce the same prompt. Outputs land at real paths you can read, diff, and revise, and every job ends with a per-model token / cost / cache-hit breakdown. --- ## Two wings, one runtime Ant runs two kinds of project, and they are not two products. A **codespace** builds software: a PRD becomes a system design, a UI design, and code — each stage a durable file, every code job gated by its own verification task. A **workspace** runs your organization's work: you define an agent and its duties in files, connect the systems it may reach over MCP, and run it on demand or on a cron schedule. Both wings sit on the same four processes, the same Redis bus, the same agent loop, the same Handlebars prompt surface, and the same encrypted credential store. That is the whole point: the thing that builds your product and the thing that runs your process are one install, one set of keys, and one audit trail — not two tools you have to keep in sync. <img src="docs/assets/codespace-workspace.png" width="880" alt="Two kinds of space over one runtime — a codespace exposing the plan, design, code, visual, learn and ask jobs with a git anchor, feature worktrees, a kanban board and jobs a person starts, beside a workspace exposing only file-defined custom jobs with a universal artifacts and sessions tree, a checklist board and a cron pipeline that chains steps through a human gate, both converging on one runtime where projectType decides policy and never layout"> | | **Codespace** | **Workspace** *(experimental)* | |--------------------------|-----------------------------------------------|------------------------------------------------| | You build | a product | your organization's work agents | | Jobs it exposes | `plan` `design` `code` `visual` `learn` `ask` | your own, defined in files | | Unit of work | a **feature** — branch + worktree | an **(agent, job)** pair | | Progress surface | Kanban tasks, gated by a verification task | a checklist the agent writes as it works | | Runs when | you start a job | you start a job, or a **pipeline** fires on cron | | Git, live preview, browser IDE | yes | no — a workspace has no codebase | | On disk | `repo.git` + `features/{feature}/…` | `universal/{artifacts,sessions}/` | The kind is chosen at creation and fixed for the project's life; it is stored as `projectType` in `config.json`. The partition is strict in both directions — there is no job type that runs in both kinds — which is why it is a creation-time decision rather than a toggle. What the flag is *not* is a fork: `projectType` decides which jobs a project exposes, and never what the runtime does or how the disk is laid out. Further down: [the build loop](#the-build-loop-and-the-iteration-loop) for the codespace, [the work loop](#the-work-loop) for the workspace, and [Maturity](#maturity) for how far each one honestly is. Read more: [docs/concepts/spaces.md](docs/concepts/spaces.md). --- ## The build loop and the iteration loop Inside a codespace, what runs on those rails is a pipeline shaped like engineering, not like a chat. **The build loop** — greenfield, once per project: 1. Write the **PRD**. The `planner` agent helps you clarify it. 2. Generate the **system design** (architecture, API contracts, system docs). 3. Design the **UI** (or **game art** for the game domain) — generated from the PRD, or drop in your own Figma / Claude artifact (see [Bring your own design](#bring-your-own-design)). 4. Write **code** grounded in those designs. **The iteration loop** — every change after that: 5. Author a **spec** for the next unit of work, review it, and let a code job implement exactly that one spec — the spec is a persistent document you diff, revise, and keep (`gen-spec` → `gen-code-spec` → `rev-spec` → repeat). Verification is not a stage you schedule — it is a property of **every code job**: work decomposes into tasks that run in parallel, and a final verification task gates completion. Failed gates spawn error tasks that fix and re-verify. Each step is a separate job with its own prompt surface, its own tools, and its own durable artifacts. The result is a system you can audit, not a black box that occasionally writes code. Deciding between tools? **[docs/comparison.md](docs/comparison.md)** is an honest comparison table — including when to use spec-kit, OpenSpec, Claude Code, or Lovable *instead of* Ant. <img src="docs/assets/build-loop.png" width="880" alt="Two pipelines. The build loop, for greenfield or a major new feature, runs plan (plan/PRD.md) then system design (architecture/system/) then UI or game art (visual/ui/) then code (codebase/). The iteration loop, for every change after that, skips both plan and system design and runs spec (architecture/spec/) straight into code, with a rev-spec return arc"> --- ## The work loop A workspace has no PRD and no verification gate, because it isn't building anything. Its loop is shorter than the one above, and it repeats on a calendar rather than once per project: 1. Write the **agent**: who it is, in prose, plus the systems it may reach. 2. Write its **jobs** — one per duty — each with its own tool allowlist, and its own intents for the situations it has to tell apart. 3. **Run one** from the composer, or **activate a pipeline** that fires it on cron, chains it into other jobs, and stops for a human at an approval gate. 4. Read what it produced under `universal/artifacts/` — real files at real paths, announced with a manifest built from actual tool side-effects rather than from what the model said it did. Steps 1 and 2 are file operations: no code change, no new job type, no deploy. Definitions are read fresh at every run, so editing one and re-running it is the whole iteration cycle. Detail: [Custom agents & the universal runtime](#custom-agents--the-universal-runtime). --- ## Quickstart **Requires** Node.js >= 22.13 · pnpm 11.1.0 (`corepack enable && corepack prepare pnpm@11.1.0 --activate`) · Docker + Compose (for Redis) · an [LLM provider key](#providers). **macOS/Linux** — Windows only via WSL2, untested. ```bash git clone https://github.com/to-nexus/ant && cd ant pnpm install cp packages/ant-cli/.env.example.local packages/ant-cli/.env # edit packages/ant-cli/.env — one value is mandatory: # ANTHROPIC_API_KEY=sk-ant-... # (Redis URL and the encryption key have working defaults in local mode.) pnpm dev:infra:redis # Redis — the only required infra pnpm dev:all # API + Realtime + Worker + Preview + UI ``` `pnpm doctor` checks the install end-to-end (versions, Redis, process health, provider keys) whenever something looks off. Prefer containers? `cp .env.example .env`, set your key, and `docker compose up -d` boots the whole stack (Redis included) behind [http://localhost:4200](http://localhost:4200) — no Node or pnpm on the host. Open [http://localhost:4200](http://localhost:4200) and write your first directive — for example, *"Build a TODO app with React and Tailwind"*. Ant also has a second project kind — a **Workspace**, where you define your own work agents in files and run them on the same runtime ([Custom agents](#custom-agents--the-universal-runtime)). Create one in the same wizard and chat with the shipped `assistant` agent — no extra setup. `pnpm dev:infra` additionally starts ChromaDB and the visual-processor. Both are **optional**: the vector DB is off unless you set `ANT_VECTOR_DB_ENABLED=true` (RAG falls back to git-changes + keyword search), and the visual-processor is only used by the `visual` job. Full setup walkthrough: [docs/local-mode/install.md](docs/local-mode/install.md). Going to the cloud (managed or self-host)? See [docs/cloud-mode/install.md](docs/cloud-mode/install.md). --- ## Maturity Ant covers a lot of ground for a one-person project, and that breadth is uneven. This table is the honest version. Nothing here is hidden in a changelog footnote. | Surface | Status | What that means in practice | |---|---|---| | `code` / `design` / `plan` jobs — service domain | **Stable** | The supported path. This is what gets used daily. | | Live preview, browser IDE | **Stable** | Well covered by tests and exercised constantly. | | Service connections & virtualization | **Beta** | Connection config, auto-detection, and mock adapters all ship and are tested. What's missing is a **verification gate proving a generated adapter matches the real service** — so test generated integration code against your actual backend before trusting it. | | `creator` agent / `visual` job | **Experimental** | **Google Gemini only** — needs `GEMINI_API_KEY` no matter which provider you use elsewhere. Background removal needs an optional sidecar. **No mid-graph resume**: an interruption restarts the job. The graph nodes have no execution tests. | | Game domain | **Experimental** | **Greenfield only** — the game-art tier is suppressed on existing codebases. Phaser only (3D is the `enable3d` extension, not a separate engine). The sprite-atlas hand-off between the design and visual jobs isn't closed, so production art is user-placed. | | Vector DB / RAG (`learn` job) | **Experimental** | Wired end-to-end but **off by default — and we recommend leaving it off** (`ANT_VECTOR_DB_ENABLED=false`; needs a ChromaDB sidecar; hidden from the UI). The chunking / indexing strategy isn't tuned enough for indexing to pay off yet — the framework exists ahead of a future org-shared vector DB. Nothing degrades without it: retrieval is a 3-tier chain (vector → git-changes → keyword). The `learn` node itself still earns its keep with the DB off — it writes the LLM job summary and the session distillation every job ends with. | | Workspaces & custom agents (`universal` runtime) | **Experimental** | The runtime, the definition loader, the MCP overlay with its encrypted credential store, and the checklist board all ship and are covered by tests. Read-only work against an HTTP MCP server is the proven path (verified end to end). The gap that bites first is **interactive approval** — a gated write tool is refused with guidance, so a job writes only under an explicit `approval: never` grant. Also: MCP image results are dropped (text only), and an interrupted run can lose one checklist turn. | | Pipelines — scheduled agent chains | **Experimental** | Cron triggers and run-now, linear chains with `on: success / failure / always`, approval gates (chat card, inbox, or a timeout arm), clarify questions that park a run until you answer it, and per-project activation whose run history survives deactivation. Definitions can be promoted to an org scope. Not there yet: webhook and event triggers, Slack/email delivery, and concurrent branch execution (a fan-out expresses routing and failure isolation; steps still dispatch one at a time per run). **Workspace-only** — the Codespace toggle in the UI is reserved, not wired. | | Team / org sharing | **Beta** | Team organizations, the owner/admin/member ladder, invites, and domain claims all ship. Agent and pipeline definitions have a real org scope: promoting one moves it into the organization's directory, an ACL sidecar names the owner plus delegated editors, and authority is re-checked against live membership rather than a token claim. One sharp edge — **MCP credentials are per-user**, so every member running an org agent registers its `${secret:}` keys in their own store. | | Managed cloud (billing, credits, deploy quota, custom domains) | **Not open** | Inert no-op seams in a self-hosted deployment. | If something in the Experimental rows blocks you, say so in an issue — knowing what people actually hit is more useful than a roadmap guess. --- ## Providers Bring your own key. Configure one or more in `packages/ant-cli/.env`; models are selectable per job and per node. | Provider | Env var | Notes | |------------|---------------------|--------------------------------------------| | Anthropic | `ANTHROPIC_API_KEY` | Primary supported model | | OpenAI | `OPENAI_API_KEY` | | | Google | `GEMINI_API_KEY` | | | DeepSeek | `DEEPSEEK_API_KEY` | Third-party, China-hosted — consent-gated | | GLM | `GLM_API_KEY` | Third-party, China-hosted — consent-gated | | Kimi | `KIMI_API_KEY` | Third-party, China-hosted — consent-gated | Consent-gated providers require an explicit in-app data-privacy acknowledgement before they can be selected, because your prompts (which include your source code) leave for a third-party jurisdiction. Ant ships its own agent runtime, so there is **no router in the middle**. Three adapter families — Anthropic, OpenAI-compatible, and Gemini — speak to each vendor's API directly with your own key. You pay the provider's list price: no per-token markup, no request metering, and your prompts never transit a third party you didn't choose. Two caveats worth knowing before you pick: DeepSeek, GLM, and Kimi are wired through the OpenAI-compatible adapter rather than first-class clients, so provider-specific features may lag. And **image generation is Google-only** — the `visual` job needs `GEMINI_API_KEY` regardless of what you use elsewhere. **Local models (Ollama, llama.cpp, …) are not supported**, and that is a sizing fact rather than a policy: the code-job execute system prompt alone is ≈39k tokens, and the effective floor is **≈200K context plus reliable native tool calling** — a 32K local model fails on the system prompt before the first tool call. What *is* supported: routing the registered DeepSeek / GLM / Kimi model ids through your own OpenAI-compatible gateway (LiteLLM, vLLM, OpenRouter) via `ANT_{DEEPSEEK,GLM,KIMI}_BASE_URL` — see [docs/reference/env-vars.md](docs/reference/env-vars.md#local--self-hosted-models). --- ## Bring your own design Ant accepts **three kinds of design input** as first-class citizens. You don't pick a tool — you drop what you have. (This section, and the two that follow it, are the codespace wing.) <img src="docs/assets/design-input.png" width="880" alt="Three design inputs — Claude artifacts read observation-only from visual/ui/handoff/, a Figma URL fetched live over MCP, or nothing yet, in which case a design job authors the bundle — merging into one UI design contract that a code job builds against"> | Source | What you drop | When to use | |----------------------|-------------------------------------------------|--------------------------------------------------------------------------| | **Claude artifacts** | HTML/CSS/Markdown/PNG into `visual/ui/handoff/` | You've been iterating in Claude.ai. No license, no setup, no schema. | | **Figma** | A Figma URL into `visual/ui/figma/figma.json` | You have an existing Figma project. Live MCP exploration at prompt time. | | **Nothing yet** | Run a `design` job on your PRD | Greenfield. Ant authors the handoff bundle for you. | The three sources are hard-exclusive per feature and have different interpretation contracts (Claude handoff is observation-only / FPOP, Figma is live-fetched, ant-native JSON is schema-based). **What a `design` job emits depends on where you started.** From a PRD (`gen-ui-desc`) it writes a `DESIGN.md`-anchored bundle into `visual/ui/handoff/` (`styles.css`, `tokens/`, `components/`, `screens/`, `assets/`). From `figma.json` (`gen-ui-figma`) it writes the canonical trio `ui-tokens.json` + `ui-assets.json` + `ui-spec.json` into `visual/ui/ant/`. The full design-input guide is in [docs/guides/design-input/](docs/guides/design-input/). --- ## How it works <img src="docs/assets/architecture.png" width="880" alt="Ant's runtime topology — ant-ui and ant-site in the browser over one HTTP and SSE edge, then ant-api, ant-realtime and ant-preview, all talking through a Redis bus carrying Pub/Sub, BullMQ and state, with ant-job spawning one isolated job-runner child process per job, plus optional ChromaDB and visual-processor sidecars"> Four backend processes, one codebase, communicating over Redis only. Local mode and cloud (Kubernetes) mode share the same data plane — local is just "all processes on one machine". Each job runs an agent **LangGraph** state machine: `resolve` → `triage` → job-specific phases → `learn`. The UI draws that graph live, so you can watch which node is executing and how many parallel workers it fanned out. <img src="docs/assets/job-anatomy.png" width="880" alt="The graph a job runs — the architect, planner and creator agents with the jobs they own, then resolve, triage, detect and decompose, fanning out to setup, feature and ui tasks running in parallel, converging on a Final Verification task that can send work back before the job completes, then learn"> | Agent | Jobs it owns | |-------------|-----------------------------------------------| | `architect` | `code`, `design`, `learn`, `ask`, `inline-ask` | | `planner` | `plan` | | `creator` | `visual` | | `universal` | every custom job — see [Custom agents](#custom-agents--the-universal-runtime) | One `design` job, three surfaces — the intent picks one: system design (`gen-sys-*`), UI / game-art design (`gen-ui-*` / `gen-game-art-*`), or spec authoring (`gen-spec` / `rev-spec`). Custom jobs take a deliberately shorter graph — `resolve → agent ⇄ tool → respond`, with no triage and no decomposition. There is nothing to classify (you picked the job) and nothing to decompose into tasks, so both phases would be latency without a decision behind them. Read more: [docs/concepts/architecture.md](docs/concepts/architecture.md). --- ## Codespace layout A **project** has exactly one git repository: a hidden bare anchor at `{project}/repo.git`. Every **feature** is an equal linked worktree at `features/{feature}/codebase/`, and **the branch name is the feature name** — no prefix, no sanitising. Feature names may contain `/`, so `feature/base` and `release/1.0` work as you would expect. <img src="docs/assets/codespace-layout.png" width="880" alt="A project's hidden bare anchor at repo.git fanning out to three peer feature worktrees, each holding codebase/ plus plan/, architecture/, visual/ and assets/, with the branch name matching the feature name exactly"> A project with no features has no codebase. There is no privileged "main" worktree — features are peers. Alongside `codebase/`, each feature holds the artifacts the agents produce: `plan/` (PRD), `architecture/` (system design + spec), `visual/` (UI and game-art design), `assets/`, plus agent-internal `sessions/` and `meta/`. Because features are equal worktrees, you can run several in parallel — each with its own branch, its own checkout, its own preview server, and its own artifact set — and merge them like any other branches. --- ## What a codespace can build | Domain | Status | Examples | |--------------------------|----------------|----------------------------------------------| | **Service** (web/backend)| Stable | Full-stack SaaS, dashboards, REST APIs | | **Game** | Experimental | Phaser/Web games with sprites + HUD + audio | See [Maturity](#maturity) for what "Experimental" means here in concrete terms. The two domains share the same agents but ship different prompt overlays, different design templates, and different visual-tier catalogs. Adding new verticals is a domain-registry change — no fork required. A domain is a codespace concept, and there is deliberately no equivalent knob on the other side: workspaces are domain-less by construction — what they do is decided by the agents you write, not by a registry entry. --- ## Workspace layout A workspace has no features, so none of the codespace axes apply: no `repo.git`, no `codebase/`, no per-feature preview. Feature creation is refused outright — a feature without git is a contradiction, not a degraded mode. Everything lives under one container: ``` {project}/ config.json projectType: 'universal' universal/ artifacts/ the shared working tree — read-write plan/{agentId}/{jobId}/ reserved; `@plan` turns write here … free-form; whatever your jobs produce sessions/ per-(agent, job) checkpoints, chat, debug logs ``` Two ownership rules follow from that shape, and the asymmetry between them is what trips people up: - **Definitions are account-owned.** The agents and jobs live outside every project, so one `ops-team` agent serves every workspace you own — and can be promoted into your organization so the whole team shares it. - **Artifacts are project-owned.** One shared `universal/artifacts/` tree serves every agent and job in the project, so you upload a folder once and every custom job can read it. Read more: [docs/concepts/spaces.md](docs/concepts/spaces.md). --- ## Custom agents & the universal runtime > ⚠️ **Experimental**, and a headline capability rather than a side feature — > "experimental" here describes maturity, not importance. The > [Maturity](#maturity) table lists exactly what is and isn't there. Every organization runs work that is repetitive, judgement-heavy, and nobody's favourite part of the week: the incident write-up, the weekly ops report, the release note, the vendor-invoice reconciliation. That work is a poor fit for a coding agent and a poor fit for a chat window, because it is neither a codebase nor a conversation — it is a **role with duties**. So you write it down. An **agent** is a role; its **jobs** are that role's duties; both are plain files: ``` .ant/agents/ops-team/ agent.yaml # identity + shared MCP connections base/role.md # who this agent is — always in the prompt jobs/weekly-report/ job.yaml # the job contract: tools it may use, what needs approval base/system.md # how this job runs — always in the prompt intents/report/ # one folder per situation (intent) infer.md # when it applies (prose criterion) prompt.md # what to do while it is active (optional) hooks.yaml # optional completion contract for its turns ``` Adding, editing, or removing a job is a **file operation** — no code change, no new job type, no deploy. Definitions are read fresh at every run, and they live at the account level, so one `ops-team` agent serves every workspace you own. The shipped `assistant` agent is a read-only worked example you can study, and [`examples/`](examples/) has the `ops-team` agent above as runnable files alongside the MCP server it talks to — `pnpm build:example:mcp && MCP_AUTH_TOKEN=dev-token pnpm start:example:mcp` starts that server, and [authoring a custom agent](docs/guides/custom-agent-authoring.md) walks the two halves end to end. Think "Claude Projects / custom GPT, on infrastructure you control" — except what sits underneath is not a chat endpoint. Every custom job gets: - **An agentic loop** with tools, and context-window compaction so a long job doesn't fall off its own history. The conversation persists per (agent, job). - **A sandbox with two roots** — the project's shared `universal/artifacts/` tree read-write, and the agent's own definition read-only. The codespace plane is unreachable from a custom job under any configuration. - **MCP connections** declared in the definition, surfaced as `mcp__{server}__{tool}`. Credentials are `${secret:KEY}` references into an AES-256-GCM per-user store — rotate the value, never the file — and resolution reads only that store, so a definition cannot name one of Ant's own environment variables and exfiltrate it. A stdio server's child process sees the variables it declared and nothing else. - **Approval gates** on anything mutating. Today's behaviour is fail-closed: a gated call is *refused with guidance* rather than executed, so a job writes only where its author granted `approval: never`. - **`@intent:` and `@plan` in the composer.** An intent pulls its situational prose in verbatim, so rare-case rules arrive exactly when they apply. `@plan` makes the run produce a plan document instead of doing the work — enforced, not advisory: writes are confined to `plan/` and execution tools are rejected for the turn. - **A checklist board** the agent maintains itself when the work has several deliverables, and a **write manifest** built from real file writes rather than from what the model said it did. The boundary that makes this hold up in practice: > **Prompts specialize judgment; they cannot guarantee behavior.** Prose is the right home for "which incidents count as sev-1". It is the wrong home for a refund ceiling or a bulk-send limit — those go in an MCP server the definition merely connects to. The model decides *when* to call `refund_payment`; the server decides *whether this refund is allowed*. Ant owns the orchestration; each system owns its own guarantees. ### Pipelines — put it on a schedule A duty that runs every Monday shouldn't need someone to remember Monday. A **pipeline** is one more file: a cron trigger plus a list of steps, where a step is either a custom job or a gate that waits for a human. ```yaml version: 2 name: Weekly ops report on: schedule: cron: "0 9 * * 1" # Mondays, 09:00 tz: Asia/Seoul steps: - id: draft customJobRef: ops-team/weekly-report intent: report directive: "Draft last week's report. Run date: {{trigger.fireDate}}." - id: sign-off type: approval prompt: "Publish the weekly report?" timeout: { after: 24h, onTimeout: reject } - id: publish customJobRef: ops-team/weekly-report directive: "Publish the approved report." on: success ``` A definition is a **template** — project-free, and promotable to your organization. Binding it to a project is a separate act, so one pipeline can run on several projects at once, each keeping its own history. While a binding is live it *owns* that project: interactive jobs are refused, so a scheduled run and a person can never fight over the same artifacts. Two kinds of stop are first-class, and both are the reason this is a pipeline rather than a cron entry. An **approval gate** waits on a chat card, in the approvals inbox, or until its timeout arm fires. And a step that needs to *ask* something parks the whole run — **clarify** waits open-ended, resumes exactly where it stopped, and may ask again. Concepts: [docs/concepts/custom-agents.md](docs/concepts/custom-agents.md) · [docs/concepts/pipelines.md](docs/concepts/pipelines.md) · build one: [docs/guides/custom-agent-authoring.md](docs/guides/custom-agent-authoring.md). --- ## Features - **Spec-sized iteration.** Author a spec, review it, and a code job implements exactly that one spec — a persistent artifact you diff and revise, not a transient plan. - **Custom work agents.** Define an agent, its jobs, and the systems it may reach in files, and run them on the same runtime — no code change, no new job type. MCP for capability, an encrypted credential store for its secrets, a fail-closed approval gate for anything mutating. Promote a definition to your organization and the team shares it ([custom agents](#custom-agents--the-universal-runtime), experimental). - **Scheduled agent chains.** Put a work agent on a cron trigger, chain it across agents, and gate a step on a human — an approval card that waits, or a clarifying question that parks the run until you answer ([pipelines](#pipelines--put-it-on-a-schedule), experimental). - **Drop-in Claude designs.** Paste your Claude.ai artifact (HTML/CSS/MD) into `visual/ui/handoff/` and Ant treats it as observable-only design source. No conversion, no schema. Often the single biggest reason teams switch from prompt-only tools. - **Figma MCP.** Live exploration via the Figma MCP server at prompt time — nothing is snapshotted to disk. Design tokens are emitted into the canonical `visual/ui/ant/` trio. - **Multi-agent build pipeline.** Planner writes the PRD; architect generates the system design, the UI design, and the code. Every code job ends with a verification task that gates completion — failures spawn fix tasks and re-verify. - **5 execution tiers.** From one-shot Q&A to refs-grounded multi-task projects, dispatched automatically based on the request. - **Any stack.** Frontend, backend, or fullstack — the target language and framework are described by extensible tech tiers, not hard-coded into the prompts. - **Service connections & virtualization.** Declare the external services your app talks to and Ant generates toggleable mock adapters, so the app runs and demos before the real backend exists. - **Parallel features, live preview.** Each feature is a git worktree with its own branch and its own hot-reloading dev server — work several features at once, merge them like branches. - **Browser IDE.** Launch VSCode with your codebase in one click — a Docker container locally, a Kubernetes Pod when `ANT_K8S_NAMESPACE` is set. - **Interruptible & resumable.** Jobs checkpoint after every phase — stop it, crash it, or close the lid, and the job resumes where it stopped. - **Hackable prompt surface.** Every agent prompt is a Handlebars template on disk, auto-registered at startup — tune the agents for your codebase ([guide](docs/guides/custom-prompts.md)). - **Self-hosted, cost-transparent.** Bring your own LLM key from any of six providers — the runtime speaks Anthropic, OpenAI-compatible, and Gemini natively, straight to each vendor's endpoint with **no router markup** — and see per-model token / cost / cache-hit breakdowns for every job. Secrets for the services your agents reach live in a local AES-256-GCM store, not in a config file. ### What is cloud-only The repository contains the seams for the managed service — billing/credits, organizations, deploy, and custom domains. In a self-hosted deployment these are **inert**: capability gates leave no-op implementations in place, and you pay your LLM provider directly rather than buying credits. Nothing in the self-host path phones home. CI enforces this — the build fails if a cloud-only symbol reaches the open-source bundle. --- ## Documentation - **[Comparison](docs/comparison.md)** — Ant vs spec-kit, OpenSpec, Claude Code, OpenHands, Lovable — including when to use them instead - **[Local Mode](docs/local-mode/)** — install + develop on your own machine (Persona A) - **[Cloud Mode](docs/cloud-mode/)** — install + develop for managed (Persona B) or self-host cloud (Persona C) - **[Concepts](docs/concepts/)** — architecture, agents, jobs, execution tiers, the engineering-loop philosophy - **[Codespace & workspace](docs/concepts/spaces.md)** — the two project kinds and what each puts on disk - **[Custom agents](docs/concepts/custom-agents.md)** — the universal runtime, and [authoring a custom agent](docs/guides/custom-agent-authoring.md) - **[Pipelines](docs/concepts/pipelines.md)** — cron triggers, chained steps, approval and clarify gates - **[Guides](docs/guides/)** — design input, custom prompts, observability - **[Reference](docs/reference/)** — CLI, env vars, API, shared types - **[First feature](docs/getting-started/first-feature.md)** — PRD → Design → Code walkthrough - **[Troubleshooting](docs/getting-started/troubleshooting.md)** — install-time and runtime hiccups - **[한국어 README](README.ko.md)** — Korean readme (docs are English-only) For contributors: - **[CONTRIBUTING.md](CONTRIBUTING.md)** — dev setup, conventions, PR workflow - **[AGENTS.md](AGENTS.md)** — binding architectural rules for human and AI contributors - **[docs/internals/](docs/internals/)** — deep dives, SSOT policies, debug logging --- ## Stack **Backend** — Node.js 22+, TypeScript (strict), Express, LangGraph, Anthropic / OpenAI / Google SDKs, BullMQ, ioredis, Handlebars, Zod. **Frontend** — React 18, Vite, Zustand, Tailwind CSS, Radix UI, ReactFlow. The marketing site (`ant-site`) is a statically-exported Next.js app. **Infrastructure** — pnpm workspaces, Redis, Docker, Kubernetes, EFS for shared workspace volumes in cloud mode. --- ## Contributing **Ant is solo-developed.** One person wrote the agents, the prompts, the frontend, and the docs, which means reviews are best-effort rather than same-day, and the roadmap is a judgement call rather than a committee decision. If a PR or issue has had no reply after a week, bump it — that's helpful, not rude. It also means outside help is genuinely useful. These are the places where a contribution lands with the least required context: | Area | Why it's a good entry point | |---|---| | **Docs** | Reading a doc and fixing what confused you is the highest-value first PR there is. Docs are English-only — Korean exists only as the top-level [README.ko.md](README.ko.md). | | **LLM provider adapters** | Three of the six providers run through an OpenAI-compatible shim. Promoting one to a first-class client is well-scoped and self-contained. | | **Tests for the `visual` job** | The `creator` agent's graph has no execution tests. Anything here is net-new coverage. | | **Frontend tests** | `ant-ui` is thinly covered relative to the backend. | | **Custom agent definitions** | A definition is files, not code — writing a genuinely useful agent and reporting where the format got in your way needs no knowledge of the graph at all. | | **Bug reports with a reproduction** | Reproductions are worth more than patches. A report that lets the bug be re-created is already most of the fix. | Before a deep change to the agent graphs, open an issue first. The LangGraph core carries invariants that aren't visible in a diff — a design conversation up front saves a rewrite later. Read [CONTRIBUTING.md](CONTRIBUTING.md) for setup and the PR workflow, and [AGENTS.md](AGENTS.md) for the binding architectural rules. AGENTS.md matters more than it looks: most of those rules have regression-guard tests behind them, so violating one fails CI rather than review. --- ## License Apache-2.0 — see [LICENSE](LICENSE). Third-party dependency notices are in [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md). Contributions are accepted under the same license (Apache-2.0 §5). There is no CLA to sign and no DCO sign-off required. --- Ant is an open-source project. Star to follow along, file issues to shape it, send PRs to ship it.

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