Terminal.skills
Skills/portkey
>

portkey

You are an expert in Portkey, the AI gateway that sits between your app and LLM providers. You help developers add caching, fallbacks, load balancing, request retries, guardrails, semantic caching, budget limits, and observability to LLM calls — using a single unified API that works with 200+ models from OpenAI, Anthropic, Google, and open-source providers.

#llm#gateway#observability#routing#caching#guardrails#production
terminal-skillsv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source

Usage

$
✓ Installed portkey v1.0.0

Getting Started

  1. Install the skill using the command above
  2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
  3. Reference the skill in your prompt
  4. The AI will use the skill's capabilities automatically

Example Prompts

  • "Review the open pull requests and summarize what needs attention"
  • "Generate a changelog from the last 20 commits on the main branch"

Information

Version
1.0.0
Author
terminal-skills
Category
AI & Machine Learning
License
Apache-2.0

Use Cases

→Build a Production RAG System with Automated Evaluation

Documentation

You are an expert in Portkey, the AI gateway that sits between your app and LLM providers. You help developers add caching, fallbacks, load balancing, request retries, guardrails, semantic caching, budget limits, and observability to LLM calls — using a single unified API that works with 200+ models from OpenAI, Anthropic, Google, and open-source providers.

Core Capabilities

typescript
import Portkey from "portkey-ai";

const portkey = new Portkey({
  apiKey: process.env.PORTKEY_API_KEY,
  config: {
    strategy: { mode: "fallback" },        // Auto-fallback on errors
    targets: [
      {
        provider: "openai", api_key: process.env.OPENAI_KEY,
        override_params: { model: "gpt-4o" },
        weight: 0.7,
      },
      {
        provider: "anthropic", api_key: process.env.ANTHROPIC_KEY,
        override_params: { model: "claude-sonnet-4-20250514" },
        weight: 0.3,
      },
    ],
    cache: { mode: "semantic", max_age: 3600 },  // Semantic caching
    retry: { attempts: 3, on_status_codes: [429, 500, 503] },
  },
});

// Use like OpenAI SDK — Portkey handles routing, caching, fallbacks
const response = await portkey.chat.completions.create({
  messages: [{ role: "user", content: "Explain microservices" }],
  max_tokens: 1024,
});

// Guardrails
const guarded = new Portkey({
  apiKey: process.env.PORTKEY_API_KEY,
  config: {
    before_request_hooks: [{ type: "guardrail", id: "no-pii" }],
    after_request_hooks: [{ type: "guardrail", id: "no-hallucination" }],
  },
});

// Budget limits
// Set in Portkey dashboard: max $100/day per API key

Installation

bash
npm install portkey-ai
# or
pip install portkey-ai

Best Practices

  1. OpenAI SDK compatible — Drop-in replacement; change import and add config; existing code works
  2. Fallbacks — Route to backup provider when primary fails; 99.99% effective uptime
  3. Semantic caching — Cache similar (not just identical) queries; 40-60% cache hit rate typical
  4. Load balancing — Split traffic across providers by weight; optimize cost vs quality
  5. Retry with backoff — Auto-retry on 429/500/503; configurable attempts and status codes
  6. Guardrails — PII detection, content moderation, hallucination checks; pre and post request
  7. Budget limits — Set per-key spending caps; prevent runaway costs from bugs or abuse
  8. Observability — Dashboard shows latency, cost, tokens, errors per provider; no additional SDK