loom

name	loom
description	Build server-based web applications where Claude Code CLI (`claude -p`) is the backend runtime — a Node/Express server spawns Claude processes to power a custom browser interface with streaming output.
when_to_use	Use when building a deployed or multi-user web app on a server, handling per-user Anthropic OAuth, rate limiting, or persistent sessions. Triggers: "build an app that uses Claude", "Claude as backend/runtime", "wrap claude -p in a server", "streaming Claude output to browser". NOT for local single-user tools (use loom-local), desktop apps (use loom-desktop), or direct Anthropic API usage.

Loom: Applications on the Claude Code Runtime

You're helping someone build an application where Claude Code is the runtime — not a helper writing React components, but the actual engine that powers the application's intelligence. The interface talks to a server that spawns claude -p processes or uses the Agent SDK, streaming results back to the browser.

This is a different posture than normal web development. Normally, the backend is a database and some business logic. Here, the backend is Claude — an agent that can read files, run commands, search code, and reason about complex tasks. The interface's job is to give that agent a form — to decide what the output looks like and how someone interacts with it.

Why This Matters

Most "AI-powered" web apps just wrap a chat API. They put a text box on screen, send messages to an LLM, and show the response. That's a chat replica.

Claude Code is not a chat API. It's an agentic runtime with filesystem access, tool use, multi-turn sessions, structured output, and streaming. Building an interface on top of Claude Code means designing something that exposes these capabilities in ways that make sense — not just conversations, but whatever interaction paradigm fits what the person is trying to do.

The question to help users explore is: what would you build if your backend could read files, run code, search the web, coordinate multiple agents, and stream its reasoning to the browser in real time?

The Architecture

Every Loom app has the same basic shape:

┌──────────────┐     HTTP/WS      ┌──────────────┐    stdio/SDK    ┌──────────────┐
│   Interface  │ ◄──────────────► │  Node Server  │ ◄────────────► │  claude -p   │
│  (React/HTML)│                  │  (Express/etc) │                │  (Agent SDK) │
└──────────────┘                  └──────────────┘                └──────────────┘
     UI layer                      Bridge layer                    Runtime layer

Interface: The custom UI. Whatever makes sense for what's being built.

Node Server: The bridge. Receives requests from the interface, spawns Claude processes, parses output, streams results back. This is where you handle Anthropic OAuth authentication, rate limiting, session management, and the mapping between web concepts and Claude invocations. The server verifies credentials exist before spawning Claude and shows a setup screen if they're missing (see references/oauth-reference.md).

Claude Runtime: The intelligence. claude -p with the right flags, or the Agent SDK for programmatic control. This is where the agentic work happens — reading files, running commands, generating structured output. Before the runtime can start, the user needs valid Anthropic credentials. See the OAuth setup in the Building It section below.

Communication Patterns

Pattern	When to Use	How
REST + JSON	One-shot requests, data extraction	POST → claude -p --output-format json → JSON response
SSE (Server-Sent Events)	Streaming text to browser	claude -p --output-format stream-json --verbose → SSE stream
WebSocket	Bidirectional, multi-turn sessions	WS connection → claude -p --input-format stream-json
Background job	Long-running tasks	Queue → claude -p process → poll for result or push via WS
HTTP Hooks	Tool visibility, permission approval, lifecycle events	Configure in .claude/settings.local.json, server receives POSTs at lifecycle points

For most apps, start with REST + SSE: REST for triggering tasks, SSE for streaming progress and results. Add WebSockets only if you need true bidirectional communication (e.g., the user can interrupt or steer while Claude is working). Add HTTP Hooks when you need structured tool-lifecycle events or UI-driven permission approval (see references/advanced-patterns.md#http-hooks).

When a run can outlive the page — long tasks, or users who refresh or open a second tab — don't tie the stream to a single request. A plain per-request SSE stream dies on reload (the most common thing a user does), losing the run. Put a server-side event log between Claude and the browser so clients can reconnect and replay what they missed (see references/server-patterns.md#pattern-reconnect-safe-streaming).

The Conversation

When someone comes to you with an idea, walk through these design questions. Don't dump them all at once — have a natural conversation. But cover this ground before you start building:

1. What does the person see and do?

Get concrete about the interface, not the AI. What's the layout? What does someone click? What appears when Claude is working? What does the final output look like?

Ask: "Walk me through the screen. What does someone see when they open this? What do they do first? What happens next?"

2. What's the interaction model?

How does the person's action translate to a Claude invocation?

Interaction	Claude Pattern
Click a button, get a result	One-shot claude -p, REST response
Watch progress in real-time	Stream-JSON → SSE to browser
Multi-step workflow with state	Session-based (--session-id first turn, --resume <id> after), or a persistent duplex bridge (references/advanced-patterns.md#persistent-session-long-lived-process)
Concurrent analysis of multiple items	Parallel claude -p processes
Person steers while Claude works	Persistent stdin bridge: write user messages mid-run; stop with an interrupt (references/server-patterns.md#pattern-interrupting-a-run)

3. What does Claude actually do?

Map each action to what Claude needs behind the scenes:

• What tools? Read-only analysis (Read,Glob,Grep) vs. modification (Write,Edit,Bash) vs. no tools at all (--tools "" for pure reasoning)
• What persona? --system-prompt "You are..." replaces the default system prompt entirely — use this for character personas, branded assistants, or any app where Claude should NOT inherit the user's CLAUDE.md settings. --append-system-prompt adds to the default prompt (preserving user settings, skills, etc.) — use this when Claude should still act as a general assistant with extra instructions.
• What data? Files on the server? User-uploaded content? Piped from other services? Claude's Read tool natively handles images and PDFs — save uploaded binary files to a temp directory and pass the path (see references/server-patterns.md#handling-file-uploads-and-drops).
• What output shape? Free text for display? Structured JSON for rendering UI components? Both (use --json-schema for structured + result for narrative)?

4. How should the output render?

This is where custom interfaces shine over CLIs. You can render Claude's structured output as rich UI:

• JSON schema with typed arrays → render as cards, lists, timelines, visualizations
• Schema with nodes and edges → render as an interactive graph
• Schema with sections and status → render as a progress view
• Stream-JSON events → animate a progress indicator or live log

Design the JSON schema to match the UI components you want to render. The schema IS your API contract between Claude and the frontend.

For richer session UIs — live tool calls with running/done status, streaming tool input progress, diffs, plan/todo checklists, reasoning panels, and a context-window meter — see references/session-ux.md.

5. What are the safety boundaries?

Web apps add security concerns that CLIs don't have:

• Auth (required): Every Loom app needs valid Anthropic credentials before Claude can start. The standard pattern is Anthropic OAuth — users authenticate with their own Anthropic account via a one-time setup screen (see references/oauth-reference.md). For multi-user apps, each user gets their own server-side session with tokens stored in memory. The server injects CLAUDE_CODE_OAUTH_TOKEN into each spawned Claude process — no shared credential file. This ensures each user's Claude processes use only their own Anthropic subscription.
• Sandboxing: Claude has filesystem access — what directory should it be scoped to?
• Rate limiting: Do you need to throttle requests per user or globally?
• Permissions: Use the tightest --permission-mode and --allowedTools that work. Prefer dontAsk (auto-denies unallowed tools) over bypassPermissions (skips all checks). Only use bypassPermissions when --allowedTools fully constrains Claude's capabilities and you need unattended execution in a trusted environment (CI/CD, local dev tools).
• Securing tool access: An approval gate alone is not enough — some built-in tools can bypass it. Narrow the surface with --allowedTools and name dangerous ones in --disallowedTools too (see references/advanced-patterns.md#securing-tool-access-a-gate-is-not-enough).
• Multi-tenancy: If multiple users, each needs isolated sessions (see the multi-user pattern in references/oauth-reference.md). Each user authenticates independently, gets a session cookie, and their Claude processes receive their own token via env var. Consider separate working directories per user if they have persistent file operations.

6. Model and performance

Need	Model	Why
Fast responses (<3s)	haiku	Classification, extraction, routing
Good quality, reasonable speed	sonnet	Default for most apps
Best reasoning	opus	Complex analysis, code generation
Hardest agentic work	fable	Most capable — enable when available on your plan
Reliability	--fallback-model sonnet,haiku	Auto-fallback on overload (comma-separated, tried in order; print-mode only)

Aliases resolve to the latest generation of each tier — prefer them over pinned model IDs, which go stale as new generations ship.

Reasoning depth is a separate axis from model choice: --effort takes low | medium | high | xhigh | max. xhigh is Claude Code's own default for coding/agentic work; use low/medium for routing and extraction. The fable alias resolves to Anthropic's most capable model — the CLI recognizes it, so swap it in for opus on the hardest tasks once it's available on your plan.

For web UIs, perceived speed matters. Use streaming to show partial results immediately, even when using slower models.

Building It

Default to Node.js/TypeScript with Express for the server and plain HTML/CSS/JS or React for the frontend, unless the person prefers otherwise.

Choosing the runtime: CLI vs Agent SDK

Loom spawns the claude -p CLI rather than calling the Claude Agent SDK (@anthropic-ai/claude-agent-sdk) as a library, and the reason is authentication. The CLI authenticates with the user's Claude subscription via OAuth through the CLAUDE_CODE_OAUTH_TOKEN env var, and the server injects each user's token into their own spawned process. For a deployed multi-user app, those tokens come from the in-app PKCE flow in references/oauth-reference.md — short-lived access tokens with rotating refresh tokens, which is why the server refreshes before every spawn (see Authentication Setup). (claude setup-token also mints a one-year inference-scoped token, but it's an interactive terminal flow — right for a single-user tool or CI, not something a web app can run for its users.) No API key, no per-call billing — every user brings their own subscription.

The Agent SDK, used as a library, authenticates only with ANTHROPIC_API_KEY, and Anthropic's policy is explicit that third-party products built on the SDK should not offer claude.ai/subscription login. (The SDK can reach a subscription only by pointing pathToClaudeCodeExecutable at a real claude binary running under the user's own HOME — i.e. driving the CLI anyway — which is fine for a single-user local tool but not a deployed multi-user server.)

So for a deployed Loom app, use the CLI. Reach for the SDK only when the app is deliberately API-key-based — an internal or billed tool where you own the key and per-call cost is acceptable. Everything the SDK is praised for has a CLI equivalent that keeps subscription OAuth:

SDK convenience	CLI equivalent (keeps subscription OAuth)
canUseTool browser approval	PreToolUse HTTP hook → browser (references/advanced-patterns.md#http-hooks)
Queued steering / mid-turn input	Duplex --input-format stream-json on stdin (references/advanced-patterns.md#persistent-session-long-lived-process)
sessionStore / resume	--session-id first turn, --resume <id> after, plus your own transcript store
Programmatic mcpServers	--mcp-config / --strict-mcp-config
Typed partial-message stream	--include-partial-messages
Structured output + retry	--json-schema

Authentication Setup

Before any server pattern works, each user needs valid Anthropic credentials. For multi-user apps, tokens are stored in an in-memory session store — NOT in a shared file. Your server should:

1. Use requireAuth middleware on protected endpoints
2. Show the OAuth setup screen if no valid session cookie exists
3. After token exchange, fetch the user's profile from https://api.anthropic.com/v1/me using the new access_token as a Bearer token, and store {name, email} in the session. Without this, the frontend can't show who's logged in — it falls back to a generic "CONNECTED" label. This is the most commonly omitted step.
4. Call refreshSessionIfNeeded() before spawning Claude processes
5. Inject the user's token via CLAUDE_CODE_OAUTH_TOKEN env var on each spawn

Always log OAuth errors server-side. The exchange endpoint calls Anthropic's token endpoint over HTTPS — this is the most failure-prone path (DNS issues on fresh VMs, transient network errors, expired codes). Both the !resp.ok branch and the catch block must console.error the actual error, not silently return a generic message. Without this, journalctl shows nothing when the exchange fails and you're debugging blind.

Read references/oauth-reference.md for the complete implementation — PKCE utilities, server endpoints, session store, requireAuth middleware, token refresh, and the ready-to-use React <SetupScreen> component.

The Server Layer

The server's job is simple: receive HTTP requests, spawn Claude, return results.

Read references/cli-runtime-reference.md for the full claude -p flag reference.

Safety Defaults

Every pattern runs Claude in a server — no human sitting at a terminal to approve tool use. These defaults are non-negotiable:

--permission-mode dontAsk — In a server context, there's nobody to click "approve." Print mode never shows an interactive prompt: a tool call that would need approval is auto-denied, and in the default mode Claude responds by asking for permission in its result text — a dead-end question no one can answer. dontAsk tells Claude the denial is final, so it works within the allowed tools or reports plainly that it couldn't: predictable, unattended execution.

Pair it with --allowedTools or --tools. Under dontAsk, read-only tools (Read, Glob, Grep) still work — they never need approval — but anything that writes or shells out (Write, Edit, Bash) is auto-denied unless explicitly allowed. The run then finishes with is_error: false and the work not done. Detect this server-side: every denial lands in the result event's permission_denials array ({tool_name, tool_use_id, tool_input}) — log it and surface a warning instead of treating an empty-handed result as success.

--max-turns — Prevents conversational loops where Claude keeps trying approaches that won't work. 5 for one-shot tasks, 10-15 for streaming, 20 for multi-turn sessions.

--max-budget-usd (recommended for unattended runs) — A hard dollar ceiling per invocation. --max-turns bounds the loop count; --max-budget-usd bounds the spend. For a server running Claude processes nobody is watching, set both so a runaway task can't burn turns or budget. Print-mode only — silently ignored outside -p.

Isolate the spawn from the host. A spawned claude -p inherits the server account's user-level configuration by default — CLAUDE.md, hooks, plugins, MCP servers. Whatever happens to be installed on the box quietly becomes part of your app. Pass --setting-sources "" (app-owned config still loads via --settings), and set cwd on every spawn to a per-user or per-run directory so users can't read each other's files. See references/server-patterns.md#spawn-isolation.

Every pattern also handles three failure modes:

• stderr — Claude writes warnings, errors, and diagnostics here. Always capture it; it's your best debugging signal when something goes wrong.
• Non-zero exit codes — Model overloaded, permission denied, timeout hit. Listen on the close event and check the code (all patterns spawn asynchronously — a synchronous spawn would freeze the whole server).
• Malformed output — A killed or timed-out process may emit partial JSON. Always wrap JSON.parse in try/catch and check parsed.is_error before using structured_output.

Shared Utilities

Three helpers used by every pattern: cleanEnv() (remove nesting guards), createStreamParser() (buffer stdout into JSON lines), and spawnEnvForUser() (inject OAuth token into spawn env).

See references/server-patterns.md#shared-utilities for the full implementations with explanatory prose.

Patterns at a Glance

Pattern	When to Use	Reference
REST + JSON	One-shot requests, data extraction	references/server-patterns.md#pattern-rest-endpoint
SSE Streaming	Streaming text to browser	references/server-patterns.md#pattern-sse-streaming
WebSocket	Bidirectional, multi-turn	references/server-patterns.md#pattern-websocket-session
Background Job	Long-running tasks	references/server-patterns.md#pattern-background-job-with-progress
Parallel	Batch analysis	references/server-patterns.md#pattern-parallel-analysis
Reconnect-Safe Stream	Survive browser reload, multi-tab, runs that outlive a request	references/server-patterns.md#pattern-reconnect-safe-streaming
Interrupt	Stop an in-flight run from the UI	references/server-patterns.md#pattern-interrupting-a-run
Structured Extraction	Fast async data extraction (Haiku)	references/advanced-patterns.md#structured-extraction-async-haiku
Persistent Session	Long-lived process, lower latency	references/advanced-patterns.md#persistent-session-long-lived-process
Action Markers	Mid-stream structured events	references/advanced-patterns.md#action-markers
HTTP Hooks	Tool lifecycle events, browser permission approval	references/advanced-patterns.md#http-hooks
Validate Preview	Avoid white-screening on mid-stream writes	references/server-patterns.md#pattern-validate-before-reloading-a-preview
Honest Progress	Show truthful progress when tokens aren't flowing	references/server-patterns.md#pattern-honest-progress-for-slow-turns
Durable Sessions	Survive a restart; persist transcript + session_id	references/advanced-patterns.md#durable-sessions-survive-a-restart

Read references/server-patterns.md when implementing a specific server endpoint or wiring up the frontend. Read references/advanced-patterns.md when the basic patterns aren't enough for your use case.

Stream-JSON Event Types

When using --output-format stream-json --verbose --include-partial-messages, Claude emits newline-delimited JSON events:

Event Type	Shape	Forward?
system	`{type:"system", subtype:"init"	"compact_boundary"
stream_event	{type:"stream_event", event:{delta:{text:"..."}}}	Yes (live text)
assistant	{type:"assistant", message:{content:[...]}}	Tool use only (text already streamed)
user	{type:"user", message:{content:[{type:"tool_result", tool_use_id, content}]}}	Optional (tool outcomes — match tool_use_id to the earlier tool_use block's id)
rate_limit_event	{type:"rate_limit_event", rate_limit_info:{...}}	No (log it)
result	`{type:"result", subtype:"success"	"error_max_turns", is_error, permission_denials}`

There is no top-level tool_result event type — tool results arrive as user messages containing tool_result content blocks, and those blocks carry only tool_use_id (never the tool name). Ignore unrecognized system subtypes (status, thinking_tokens, hook_started, …) rather than erroring.

See references/server-patterns.md#stream-json-event-types for complete notes, extended thinking behavior, code samples for extracting text/tool use, and max-turns detection.

Frontend Integration

Use fetch() + ReadableStream for POST-based SSE (CSRF-safe). Parse data: lines, dispatch on event type (token, done, error). For quick prototyping, EventSource works for GET-based SSE.

See references/server-patterns.md#frontend-integration for the complete streaming text display and structured result rendering code.

Error Handling

Every pattern in references/server-patterns.md handles errors inline — you won't find a separate error handling block to copy-paste because it doesn't belong in one. Here's the mental model behind the three failure modes:

stderr fires first. Claude writes diagnostics, warnings, and model errors here before the process exits. Always pipe it somewhere — console.error at minimum. In production, send it to your logging stack. This is your primary debugging signal when a request fails silently.

Non-zero exit codes mean Claude didn't complete successfully. Common causes: model overloaded (503 from upstream), permission denied (tool not in --allowedTools), or process killed by your timeout. Listen on the close event and check the code.

Malformed output happens when a process is killed mid-stream (timeout, client disconnect, OOM). The stdout buffer contains partial JSON that won't parse. Always wrap JSON.parse in try/catch, and always check parsed.is_error before reaching for structured_output — Claude sets this flag when it couldn't complete the task (tool failures, turn limit exceeded, etc.).

See references/server-patterns.md#error-surfacing-checklist for the three-channel checklist with code samples.

What to Generate

When you build the app, produce:

1. server.ts — Express server using the Express baseline from references/server-patterns.md#server-setup (express, cookie-parser, express.static). Includes session store, requireAuth middleware, OAuth endpoints (/api/oauth/start, /api/oauth/exchange, /api/health, /api/logout), and your app's endpoint pattern(s). The exchange endpoint must fetch the user's profile from https://api.anthropic.com/v1/me and store it in the session. Each spawn uses spawnEnvForUser() to inject the requesting user's token.
2. public/index.html — The frontend, starting with the <SetupScreen> component (shown when no session exists) and your app's main UI (shown after authentication). Include a user indicator showing the user's email (from /api/health → user.email) and a logout button that calls POST /api/logout. Use a fallback label like "CONNECTED" if the profile has no email. Protected endpoints check for 401 responses and redirect to the setup screen (in-memory sessions are wiped on server restart). If the app has both a chat input and a setup screen input, use input.setup-input selectors (not .setup-input) to avoid CSS specificity conflicts with global input[type="text"] rules — see references/oauth-reference.md.
3. package.json — Dependencies (express, cookie-parser, uuid, plus ws and cookie if using WebSockets, and cors if frontend/server are separate origins) and a start script
4. A one-liner to run it — so the person can verify it works immediately

First-Run Reliability Checklist

These are the silent-failure modes — things that break with NO error message. The patterns in references/server-patterns.md demonstrate correct handling for each, but they're easy to miss or deviate from when generating a new app.

• --include-partial-messages is on every streaming spawn — without it, text dumps as a single block instead of streaming token-by-token
• Text is forwarded from stream_event only, NOT from assistant text blocks — otherwise every token appears twice
• spawnEnvForUser() is called on every spawn — this removes nesting guards AND injects the user's OAuth token; bare cleanEnv() omits the token and causes silent auth failure
• --permission-mode dontAsk is paired with --allowedTools or --tools — write/exec tools are otherwise auto-denied while the run still ends is_error: false; check result.permission_denials to catch it
• subtype === "error_max_turns" is checked before is_error on result events — max-turns sets is_error: true (verified on CLI v2.1.x), so an if (is_error) … else if (subtype === …) ordering makes the max-turns branch dead code and surfaces "incomplete" as a hard error
• express.json() middleware is applied before any route that reads req.body — without it, req.body is undefined and the spawn gets an empty prompt
• 401 responses in the frontend redirect to the setup screen — in-memory sessions are wiped on server restart, and a 401 fed to the SSE parser fails silently
• cookie-parser middleware is applied before any route that reads req.cookies — without it, requireAuth sees undefined and every request returns 401
• Profile fetch happens in the /api/oauth/exchange handler after token exchange — without it, the frontend shows "CONNECTED" instead of the user's email
• /api/health does NOT use requireAuth — it must return {needsSetup: true} for unauthenticated users, not 401
• WebSocket upgrade validates the session cookie from req.headers.cookie — Express middleware does not run on WebSocket handshakes

For simple apps, a single server.ts serving a static index.html is ideal. For complex UIs, scaffold a React frontend with a separate server. For the frontend itself, a no-build-step approach (UMD React + Babel-in-browser, or plain HTML/JS) matches the thin-bridge ethos and ships faster — reach for a bundled build only when the UI complexity justifies it. All five reference apps shipped no-build frontends.

When deploying behind a trusted reverse proxy (exe.dev, Cloudflare Access) that handles user identity, the proxy-header auth alternative avoids the full OAuth flow for the user-identity layer — see references/oauth-reference.md#reverse-proxy-header-auth.

After generating, offer to start the server and open it in the browser together. Then iterate based on what the person sees.

Deployment Verification

When deploying to a remote VM (exe.dev, etc.), verify outbound connectivity after starting the service. Fresh VMs can have transient DNS or network issues that cause the first OAuth exchange to fail:

# Verify the app can reach Anthropic's token endpoint
curl -s -o /dev/null -w "%{http_code}" https://platform.claude.com/v1/oauth/token
# Should return 405 (Method Not Allowed for GET) — confirms connectivity

If this returns a network error, wait a few seconds and retry. Don't declare deployment complete until the VM can reach Anthropic's servers.

The Possibility Space

The most interesting Loom apps are not chat interfaces in a browser. They're things that couldn't exist without an agentic runtime — applications where the backend can read, reason, and act on context that traditional APIs can't access.

Help people think about what they actually want to make. The question isn't "how do I put a chat box in a browser?" but "what would this look like if there were an intelligence behind it?"