SLOPSHOPPER

token-weather

A live forecast of the context window, drawn above the prompt.

newband
A shopper browsing a rack in a slop shop
Preview · a replayed session in a sandbox
claude · ~/work/app · token-weather
› fix the failing auth test and add an audit log call ⏺ Read(src/auth.ts) ⎿ Read 6 lines ⏺ Update(src/auth.ts) ⎿ Added 2 lines, removed 1 line ⏺ Bash(bun test) ⎿ 3 pass, 1 fail ● Done. refresh now rejects expired claims and logs an audit event. ✻ Worked for 42s · done 4:20 PM ☁ Cloudy 49% of context 97.4k / 200k last turns ██ steady ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── › ? for shortcuts

Draws

Band
☁ Cloudy 49% of context 97.4k / 200k last turns ██ steady
README

Karaka

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Karaka is a self-hosted server for adding persistent AI agents to an application. Connect a backend or browser frontend to named agents, stream their responses, and let them call selected application functions as tools.

Each named agent is an Agent Preset: a Cordis plugin composition that defines its prompt, tools, skills, and model behavior. Karaka runs as a separate process and keeps durable chat state; your application owns user authentication and business authorization.

What Karaka provides

  • Durable chats bound to an application, tenant, and user.
  • Independent Agent Presets with their own prompts, model behavior, skills, and allowed tools.
  • A backend SDK for chat streams and authenticated MCP tool hosting.
  • A browser client that uses expiring credentials issued by your backend.
  • A profile-based Agent runtime launched through the Karaka CLI.

How it fits

Application backend                         Karaka process
@karaka-ai/sdk chat client  -- HTTP / SSE -->  named Agent Preset
@karaka-ai/sdk MCP tools     <--    MCP    --  selected application tools

Browser frontend                            Karaka process
@karaka-ai/agent/browser    -- HTTP / SSE -->  authenticated chat and events

Backend integrations use @karaka-ai/sdk for chat over HTTP/SSE and for hosting authenticated MCP tools on the application's existing HTTP server. The backend supplies trusted tenant and user identifiers. Karaka binds those identifiers to the chat and forwards them to tool callbacks, where the application enforces business authorization. Installing the SDK starts no process and opens no port.

Browser integrations use @karaka-ai/agent/browser with expiring credentials issued by the application backend. Each credential binds the application, tenant, and user identity; browser requests cannot choose another identity. See the browser client setup for authentication and server configuration.

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Start here

  1. Create an agent workspace with the Karaka CLI.
  2. Configure the runtime and each agent's plugins, then start the server.
  3. Connect your application backend or browser frontend.
  4. Expose application tools and select the tools each agent can use.

Read the runtime implementation guide for composition, builds, and deployment limitations. The CLI and SDK are maintained in their own repositories.

Foundation

Karaka builds on the open-source DeepSeek Harness developed by DeepSeek AI and retains its everything-is-a-plugin architecture powered by Cordis. Karaka has its own @karaka-ai/* packages and karaka CLI.

Developer preview

Karaka and its inherited Harness runtime are in developer preview and may make compatibility-breaking changes. Review the safety notice before running the project.

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Development

Start with the development guide and architecture documentation.

See CONTRIBUTING.md before proposing a change.

For agents, follow AGENTS.md.

License

MIT

Third-party dependencies and their licenses are disclosed in THIRD_PARTY_NOTICES.md.

Source 2 files
hooks/token-weather.mjs 88 lines
1// Token Weather, from "Getting started with Claude Code mods"
2// (https://claude.dev/blog/getting-started-with-claude-code-mods/, Anthropic, 2026-10-01).
3// The published module, unchanged.
4
5// Token Weather: a live forecast of the context window, above the prompt.
6
7const HISTORY = 12;
8const BARS = "▁▂▃▄▅▆▇█";
9const FORECAST = [
10  { upTo: 25, icon: "☀", word: "Clear", color: "yellow" },
11  { upTo: 50, icon: "☁", word: "Cloudy", color: "cyan" },
12  { upTo: 75, icon: "☂", word: "Showers", color: "blue" },
13  { upTo: 90, icon: "☇", word: "Storm", color: "magenta" },
14  { upTo: Infinity, icon: "↯", word: "Compact soon", color: "red" },
15];
16
17// Held by the host, so the history survives a hot reload of this file.
18const readings = { plugin: "token-weather", key: "readings" };
19
20export function register(on) {
21  on("session.start", async ($, e, next) => {
22    const result = await next(e);
23    await takeReading($);
24    return result;
25  });
26
27  on("turn.complete", async ($, e, next) => {
28    const result = await next(e);
29    if (!e.agentId) {
30      await takeReading($); // main-loop turns only, not subagents
31    }
32    return result;
33  });
34
35  on("ui.render", { component: "AbovePrompt" }, async ($, e, next) => {
36    const { value: history = [] } = await $.state.get(readings);
37    if (e.props.hasSurvey || history.length === 0) {
38      return next(e);
39    }
40    const { Box, Text } = $.ui.resolve(e);
41    return band(Box, Text, history, e.props.bodyColumns);
42  });
43}
44
45async function takeReading($) {
46  const { context } = await $.session.usage();
47  if (!context?.window) return;
48  const tokens = context.tokens ?? 0;
49  const percent = context.percent ?? Math.round((tokens / context.window) * 100);
50  const { value: history = [] } = await $.state.get(readings);
51  await $.state.set(readings, [...history, { tokens, window: context.window, percent }].slice(-HISTORY));
52}
53
54function band(Box, Text, history, columns) {
55  const now = history[history.length - 1];
56  const f = FORECAST.find((b) => now.percent < b.upTo);
57  const parts = [
58    Text({ color: f.color, bold: true, children: `${f.icon}  ${f.word}` }),
59    Text({ children: `  ${now.percent}% of context` }),
60    Text({ dimColor: true, children: `  ${short(now.tokens)} / ${short(now.window)}` }),
61  ];
62  if (columns >= 60) {
63    parts.push(Text({ dimColor: true, children: "   last turns " }));
64    parts.push(Text({ color: f.color, children: sparkline(history) }));
65    if (history.length > 1) {
66      parts.push(Text({ dimColor: true, children: trend(history) }));
67    }
68  }
69  return Box({ flexDirection: "row", paddingX: 1, children: parts });
70}
71
72function sparkline(history) {
73  const top = Math.max(...history.map((r) => r.tokens), 1);
74  return history.map((r) => BARS[Math.floor((r.tokens / top) * (BARS.length - 1))]).join("");
75}
76
77function trend(history) {
78  const delta = history[history.length - 1].tokens - history[history.length - 2].tokens;
79  if (delta === 0) return "  steady";
80  return delta > 0 ? `  ▲ +${short(delta)} last turn` : `  ▼ ${short(-delta)} last turn`;
81}
82
83function short(n) {
84  if (n >= 1_000_000) return `${+(n / 1_000_000).toFixed(1)}M`;
85  if (n >= 1_000) return `${+(n / 1_000).toFixed(1)}k`;
86  return String(n);
87}
88
types/index.d.ts 11 lines
1// Token Weather's type contract, from https://claude.dev/blog/getting-started-with-claude-code-mods/
2// (Anthropic, 2026-10-01). The published file, unchanged.
3
4export type TokenWeatherReading = { tokens: number; window: number; percent: number }
5
6declare module 'claude-code' {
7  interface PluginState {
8    'token-weather': { readings: TokenWeatherReading[] }
9  }
10}
11