ai

Agent Building

Use when calling the app's AI gateway from agent tools — chat completions, embeddings, listing models, configuring defaults or BYOK, reading token/cost usage

QUICK START

How to use this skill

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Source SKILL.md: https://github.com/butterbase-ai/butterbase-skills/blob/HEAD/skills/ai/SKILL.md

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Butterbase AI Gateway

Every app has an LLM gateway with chat, embeddings, model listing, configuration, and usage reporting. One umbrella tool: manage_ai.

Action	What it does	Returns
`chat`	Synchronous chat completion (no streaming)	OpenAI-shaped `{ choices: [...] }`
`embed`	Vector embeddings for string or string[]	OpenAI-shaped `{ data: [{ embedding: [...] }] }`
`list_models`	Available models with capabilities	`{ models: AiModel[] }`
`get_config`	Current AI config (default model, BYOK key flag, etc.)	`AiConfig`
`update_config`	Set defaults, allowed models, max tokens, BYOK	`AiConfig`
`get_usage`	Token + cost aggregate over a window	usage record

1. Chat

manage_ai({
  action: "chat",
  app_id,
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user",   content: "What's RAG?" }
  ],
  model: "openai/gpt-4o-mini",     // optional — falls back to app's default
  temperature: 0.2,                // optional
  max_tokens: 500                  // optional
})

This action sets stream: false deliberately — agent tools don't stream. If you need partial-token deltas, drive the SDK's ai.chatStream(…) from inside a function or DO instead.

messages[].content can be a string or an array of content parts ({ type: "text", text }, { type: "image_url", image_url: {...} }, { type: "video_url", video_url: {...} }).

2. Embed

manage_ai({
  action: "embed",
  app_id,
  input: "hello world",            // or ["a", "b", "c"]
  model: "openai/text-embedding-3-small",   // optional
  encoding_format: "float"          // or "base64"
})

3. List models

manage_ai({ action: "list_models", app_id })
// → { models: [{ id, provider, capabilities: ["chat", "embed", ...], context_window, pricing }, ...] }

Use this to discover what the app can call — capabilities + context window matter when picking a model.

4. Configure

manage_ai({
  action: "update_config",
  app_id,
  config: {
    defaultModel: "openai/gpt-4o-mini",
    allowedModels: ["openai/gpt-4o-mini", "anthropic/claude-haiku-4-5"],
    maxTokensPerRequest: 4000,
    byokKey: "..." // optional — rotates the customer-supplied OpenRouter / Anthropic key
  }
})

maxTokensPerRequest is server-clamped to 1–100000.
allowedModels is a whitelist — empty means all models the provider exposes.
Setting byokKey switches the app to route through that customer key. Clear it by passing byokKey: "" (returns to platform pool).

5. Usage

manage_ai({
  action: "get_usage",
  app_id,
  startDate: "2026-05-01",
  endDate:   "2026-05-31"
})

Returns aggregate token counts + cost. Useful for billing reconciliation, spending-cap diagnostics, and showing dashboards.

6. Common pitfalls

Trying to stream from a tool — manage_ai is synchronous. Use the SDK inside a function for streamed deltas.
Sending stream: true in the body — the tool ignores it; always wired to false.
Hardcoding model — better to omit, let the app's defaultModel win, and surface that knob via update_config.
Skipping list_models before suggesting one — model availability shifts; verify before recommending.

7. What this skill does NOT cover

Streaming chat — use the SDK (ai.chatStream) inside a function or DO.
Vector storage / retrieval — see butterbase-skills:rag-dev (RAG collections wrap embeddings + search together).
AI in deployed functions — they import @butterbase/sdk and call client.ai.* directly; no MCP needed at runtime.

If a docs/butterbase/00-state.md exists in the working directory, prefer invoking via /butterbase-skills:journey-ai so the journey orchestrator stays in sync.