Get started

Install the provider with Core:

pnpm add @anvia/core @anvia/anthropic

Create the client on the server and pass its completion model into an agent:

import { AgentBuilder } from '@anvia/core'
import { AnthropicClient } from '@anvia/anthropic'

const anthropic = new AnthropicClient({
  apiKey: process.env.ANTHROPIC_API_KEY,
})

const agent = new AgentBuilder(
  'analyst',
  anthropic.completionModel('claude-sonnet-4-20250514'),
)
  .instructions('Analyze the evidence before answering.')
  .build()

const result = await agent.prompt('Summarize the incident report.').send()
console.log(result.output)

The returned AnthropicCompletionModel works with direct completion, streaming, tools, agents, extractors, and pipeline stages.

List Anthropic models

const models = await anthropic.listModels()

Use listing for inventory and diagnostics. Keep a separate production allowlist rather than enabling every returned model automatically.

Use Vertex AI instead

import { AnthropicVertexClient } from '@anvia/anthropic'

const vertex = new AnthropicVertexClient({
  projectId: 'my-gcp-project',
  region: 'global',
})

const model = vertex.completionModel('claude-sonnet-5')

Vertex uses the same normalized completion adapter but a different official SDK and authentication path. It does not expose listModels().

Before production

• Keep Anthropic and Google credentials out of browser code.
• Set explicit model IDs and output limits.
• Authorize every tool inside the application, not only through its schema.
• Bound agent turns and cancellation.
• Test image input and tool behavior against the selected model.
• Review Vertex AI or compatible endpoints when not using Anthropic’s standard API.