@agentskit/adapters

Connect to any LLM provider — and swap between them — without touching your app code.

Tags: ai · agents · llm · agentskit · openai · anthropic · claude · gemini · chatgpt · ollama · embeddings · providers

Why adapters

• Vendor independence — switch from OpenAI to Anthropic to a local Ollama model by changing one line; your hooks, runtime, and tools stay untouched
• 20+ providers included — Anthropic, OpenAI, Gemini, Ollama, DeepSeek, Grok, Kimi, Mistral, Cohere, Together, Groq, Fireworks, OpenRouter, Hugging Face, LM Studio, vLLM, llama.cpp, LangChain, Vercel AI SDK, and any raw ReadableStream
• Embedder functions built in — the same adapter pattern covers text embeddings, so you can reuse provider config for both chat and RAG
• One-line local AI — ollama({ model: 'llama3.1' }) for fully offline agents with no API key required

Install

npm install @agentskit/adapters

Quick example

import { anthropic, openai, ollama } from '@agentskit/adapters'
import { createRuntime } from '@agentskit/runtime'

// Switch provider by swapping one import
const adapter = anthropic({ apiKey: process.env.ANTHROPIC_API_KEY, model: 'claude-sonnet-4-6' })
// const adapter = openai({ apiKey: process.env.OPENAI_API_KEY, model: 'gpt-4o' })
// const adapter = ollama({ model: 'llama3.1' })

const runtime = createRuntime({ adapter })
const result = await runtime.run('Summarize the latest AI news')
console.log(result.content)

Embeddings (for RAG)

Use the same package for vector embeddings — wire openaiEmbedder, geminiEmbedder, or ollamaEmbedder into @agentskit/rag:

import { openaiEmbedder } from '@agentskit/adapters'
import { createRAG } from '@agentskit/rag'
import { fileVectorMemory } from '@agentskit/memory'

const rag = createRAG({
  embed: openaiEmbedder({ apiKey: process.env.OPENAI_API_KEY! }),
  store: fileVectorMemory({ path: './vectors' }),
})

Features

• Providers: Anthropic, OpenAI, Gemini, Ollama, DeepSeek, Grok, Kimi, Mistral, Cohere, Together, Groq, Fireworks, OpenRouter, Hugging Face, LM Studio, vLLM, llama.cpp, LangChain, LangGraph, Vercel AI SDK, generic ReadableStream
• Embedders: openaiEmbedder, geminiEmbedder, ollamaEmbedder, deepseekEmbedder, grokEmbedder, kimiEmbedder, createOpenAICompatibleEmbedder
• All adapters satisfy Adapter contract v1 (ADR 0001) — substitutable anywhere in the ecosystem
• Custom adapter authoring via createAdapter()
• Higher-order adapters: createRouter (cost/latency/classifier), createEnsembleAdapter (fan-out + merge), createFallbackAdapter (ordered try-next)

Higher-order adapters

import { createRouter, anthropic, openai } from '@agentskit/adapters'

// Auto-pick cheapest capable candidate per request.
const router = createRouter({
  candidates: [
    { id: 'haiku', adapter: anthropic({ model: 'claude-haiku-4-5' }), cost: 0.25 },
    { id: 'sonnet', adapter: anthropic({ model: 'claude-sonnet-4-6' }), cost: 3 },
    { id: 'gpt-mini', adapter: openai({ model: 'gpt-4o-mini' }), cost: 0.15 },
  ],
})

See Adapter router, Ensemble, and Fallback chain.

Ecosystem

| Package | Role | |---------|------| | @agentskit/core | Adapter, EmbedFn, types | | @agentskit/runtime | Headless createRuntime | | @agentskit/rag | createRAG + embedders | | @agentskit/memory | Vector + chat memory backends |

Testing Adapters

Three built-in utilities let you test agents without hitting a real LLM.

mockAdapter — deterministic responses

import { mockAdapter } from '@agentskit/adapters'

const adapter = mockAdapter({
  response: [
    { type: 'text', content: 'Hello!' },
    { type: 'done' },
  ],
})

Pass a function to make responses request-aware, or pass an array of arrays to return different chunks on each call (sequenced mode). Use the optional history array to capture every request for assertions.

recordingAdapter + inMemorySink — capture real calls

import { recordingAdapter, inMemorySink, anthropic } from '@agentskit/adapters'

const sink = inMemorySink()
const adapter = recordingAdapter(
  anthropic({ apiKey: process.env.ANTHROPIC_API_KEY!, model: 'claude-sonnet-4-6' }),
  sink,
)
// Runs the real LLM and captures every chunk to sink.fixture

replayAdapter — replay captured fixtures

import { replayAdapter } from '@agentskit/adapters'
import fixture from './fixture.json'

const adapter = replayAdapter(fixture) // no network calls

Typical workflow: record once in dev → commit JSON fixture → replay in CI.

Contributors

License

MIT — see LICENSE.

Docs

Full documentation · GitHub