Show HN: VernLLM – The AI resilience layer for TypeScript

VernLLM, a lightweight resilience layer for TypeScript LLM chat completions, has been released as an open-source npm package. It provides built-in retries with exponential backoff, per-attempt timeouts, circuit breaking, caching, and structured output validation via Zod, supporting over 30 LLM providers including OpenAI, Anthropic, and Gemini. The library aims to eliminate the need for developers to write repetitive defensive code for LLM API calls.

Reliable LLM calls, by default. A lightweight resilience layer for LLM chat completions. Retries, timeouts, caching, and circuit breaking, dependency-light and typed from the start. $ npm install vern-llm What this call actually does. maxRetries: 3 → 3 attempts with exponential backoff timeoutMs: 10 000 → 10s hard timeout per attempt circuitBreaker: true → Trips after repeated failures onUsage → Token usage reported per call cachedLLMCall → Identical calls skip the network schema → Response validated and typed via Zod maxRetries: 3 Retry with backoff Retries a failed call up to N times with exponential backoff and jitter between attempts. timeoutMs: 10 000 Per-attempt timeout Each attempt is raced against a hard timeout, so no single request can hang the call. circuitBreaker: true Circuit breaker Trips after repeated failures and rejects immediately while open. Call getCircuitState to inspect it. nonRetryableStatus: 400, 401, 403, 404 Fail-fast status codes Status codes you mark as non-retryable skip the retry loop entirely. llm.cachedLLMCall { cacheKey, ttl, call } Caching Wraps call with a pluggable cache adapter. Identical calls return without hitting the network. schema: HiringSummarySchema Structured output Pass a Zod schema inside call params and receive validated, typed JSON. onUsage: { totalTokens } = {} Usage tracking Reports prompt, completion, and total tokens whenever the provider returns usage data. logger: myLogger Pluggable logger Bring your own Logger implementation or fall back to the built-in console logger. OpenAI /docs/adapters Anthropic /docs/adapters/anthropic Gemini /docs/adapters/gemini Groq /docs/adapters/openai-compatible Mistral /docs/adapters/openai-compatible DeepSeek /docs/adapters/openai-compatible Cerebras /docs/adapters/openai-compatible Together AI /docs/adapters/openai-compatible Fireworks AI /docs/adapters/openai-compatible Ollama /docs/adapters/openai-compatible OpenRouter /docs/adapters/openai-compatible Perplexity /docs/adapters/openai-compatible DeepInfra /docs/adapters/openai-compatible Novita /docs/adapters/openai-compatible Hyperbolic /docs/adapters/openai-compatible Moonshot Kimi /docs/adapters/openai-compatible Zhipu GLM /docs/adapters/openai-compatible LM Studio /docs/adapters/openai-compatible vLLM /docs/adapters/openai-compatible xAI Grok /docs/adapters/openai-compatible NVIDIA NIM /docs/adapters/openai-compatible Vercel AI Gateway /docs/adapters/openai-compatible Cloudflare Workers AI /docs/adapters/openai-compatible GitHub Models /docs/adapters/openai-compatible Nebius AI Studio /docs/adapters/openai-compatible SambaNova Cloud /docs/adapters/openai-compatible Baseten /docs/adapters/openai-compatible Featherless AI /docs/adapters/openai-compatible Friendli AI /docs/adapters/openai-compatible SiliconFlow /docs/adapters/openai-compatible Parasail /docs/adapters/openai-compatible StepFun /docs/adapters/openai-compatible MiniMax /docs/adapters/openai-compatible Lambda Labs /docs/adapters/openai-compatible Snowflake Cortex /docs/adapters/openai-compatible Anyscale /docs/adapters/openai-compatible Lepton AI /docs/adapters/openai-compatible kluster.ai /docs/adapters/openai-compatible Inference.net /docs/adapters/openai-compatible Infermatic /docs/adapters/openai-compatible AtlasCloud /docs/adapters/openai-compatible 01.AI Yi /docs/adapters/openai-compatible AWS Bedrock /docs/adapters/bedrock Custom HTTPS API /docs/adapters/custom-fetch Every project calling an LLM API ends up writing the same defensive code: retry logic, timeouts, a circuit breaker, a cache layer, usually copied between projects and slightly wrong each time. VernLLM gives you those primitives with sensible defaults out of the box, so you keep your existing client and just wrap it. No. VernLLM wraps the client you already have, OpenAI, Anthropic, Gemini, Bedrock, or anything OpenAI-compatible, so you keep your existing setup and just route calls through it. Yes. cachedLLMCall works with any adapter implementing get/set/delete, so you can plug in Redis, a database, or your own store instead of the built-in in-memory cache. Yes, written in TypeScript from the ground up. Structured output schemas, call params, and errors are all typed, so mistakes surface at compile time instead of at runtime. Zero bundled dependencies. Zod and provider SDKs are peer dependencies, and VernLLM only relies on their shapes structurally, so it stays dependency-light and typed from the start. 12.1 kB minified, 4.4 kB minified and gzipped. Small enough to drop into a project without thinking twice about it. Yes, VernLLM is MIT licensed and open source. Use it in personal or commercial projects, fork it, or contribute back on GitHub.