@llm-ports/core

The foundation of llm-ports. SDK-independent interfaces, multimodal content blocks, registry with cost-and-budget gating, validation strategies. Zero dependencies on any LLM SDK.

Installation

pnpm add @llm-ports/core
# Plus at least one adapter:
pnpm add @llm-ports/adapter-anthropic

What you get

| Export | Purpose | |--------|---------| | LLMPort | The interface adapters implement: generateText, generateStructured, streamText, streamStructured, runAgent | | EmbeddingsPort | Sibling interface for embeddings (most chat adapters do not implement this) | | ContentBlock, MessageContent | Multimodal message content (text, image, audio, tool_use, tool_result) | | createRegistryFromEnv() | Builds a routing registry from LLM_PROVIDER_* and LLM_TASK_ROUTE_* env vars | | declareTasks<T>() | Type-safe task name helper with autocomplete | | InMemoryBudget, InMemoryCost | Default backends; replace with Redis-backed for multi-process | | ValidationStrategy | Pluggable strategies for handling failed structured-output validation | | BudgetExceededError, NoProvidersAvailableError, ValidationError, ... | Typed error classes |

Minimal example

import { createRegistryFromEnv } from "@llm-ports/core";
import { createAnthropicAdapter } from "@llm-ports/adapter-anthropic";

const registry = createRegistryFromEnv({
  adapters: {
    anthropic: createAnthropicAdapter({
      apiKey: process.env.ANTHROPIC_API_KEY!,
    }),
  },
});

export const llm = registry.getPort();

// Then anywhere in your codebase:
const result = await llm.generateText({
  taskType: "triage",
  prompt: "Classify this email: ...",
});

.env:

LLM_PROVIDER_FAST=anthropic|claude-haiku-4-5|cost:5/day
LLM_PROVIDER_SMART=anthropic|claude-sonnet-4-6|cost:50/day
LLM_TASK_ROUTE_TRIAGE=fast,smart

License

MIT