Ai

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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: butterbase-ai
• Source: butterbase-ai/butterbase-skills
• License: MIT

Install and usage instructions live in the source repository linked above.