@deuz-sdk/core

Pure, web-first, multi-provider TypeScript AI SDK — 28 chat provider ids across four wires, plus embeddings, images, speech, transcription and video — with zero runtime dependencies and a canonical streaming protocol of its own.

npm i @deuz-sdk/core

import { streamChat } from '@deuz-sdk/core';
import { createAnthropic } from '@deuz-sdk/core/anthropic';

const model = createAnthropic({ apiKey: process.env.ANTHROPIC_API_KEY! })('claude-opus-4-8');
const result = streamChat({ model, messages: [{ role: 'user', content: 'Merhaba!' }] });
for await (const text of result.textStream) process.stdout.write(text);

streamChat returns synchronously and never throws: failures arrive as typed parts on the stream.

What is in the box

• Edge-safe core — Web APIs only; everything stateful or non-deterministic (clock, randomness, fetch, keys, logging, tracing) is injected through one Dependencies seam.
• Canonical stream — every provider's SSE is normalized to one typed delta stream before anything else touches it. Retries, timeouts, tool loops, fail-over and the UI wire all build on that one language.
• Agentic loop — parallel tool execution, self-healing tool errors, runaway guards, budget/stop conditions, durable checkpoints, HMAC-signed human approvals, sub-agents and agent handoffs.
• Guardrails — onInput / onToolCall / onOutput hooks, each returning pass / block / rewrite, reported on the stream.
• Context management — layered compaction with a rolling summary, a manual compactMessages(), and automatic recovery from a provider's context-overflow rejection.
• Persistence you can point at a database — SQLite, Redis and Postgres packs behind the MemoryStore / ChatStore / SessionStore / RunStore seams.
• MCP — zero-config servers in the loop (mcp: [{ url }]), OAuth 2.0, sampling, roots, reconnect, and a cross-call connection pool.
• Batteries — memory (mem0-style), RAG with hybrid retrieval, skills, structured output, middleware, pricing, observability and an OpenTelemetry bridge.
• Modalities — text, images, speech, transcription and video.
• React bindings — @deuz-sdk/react (the @deuz-sdk/core/react subpath remains for compatibility).

Providers

28 chat provider ids, four wires, one call shape. A provider id is the string a descriptor carries and the key that resolves an API key and a base URL; a factory is the function that mints descriptors for it. The two do not count the same — createOpenAI and createOpenAIResponses are two factories over the one id openai, and createKimi is an alias of createMoonshot. A model descriptor is a plain { provider, modelId, surface } value — factory settings ride a non-enumerable symbol, so keys never leak through Object.keys or JSON.stringify.

| Group | Provider ids | Subpath | | --- | --- | --- | | Dedicated (9 ids) | anthropic, openai, xai, google, vertex-anthropic, vertex-google, azure, bedrock, yunwu | /anthropic, /openai, /xai, /google, /vertex, /azure, /bedrock, /yunwu | | OpenAI-compat cloud hosts (17 ids) | groq, mistral, deepseek, together, openrouter, cerebras, fireworks, moonshot (a.k.a. Kimi), qwen, glm, minimax, perplexity, cohere, deepinfra, nvidia, sambanova, hyperbolic | /providers | | Keyless local hosts (2 ids) | ollama, lmstudio | /providers |

voyage is deliberately absent: it is an embeddings provider, so it speaks none of the four chat wires and cannot be handed to streamChat.

Which of the four chat wires each one speaks — the exhaustive ModelSurface → adapter switch:

| Wire (surface) | Covers | | --- | --- | | Anthropic Messages (anthropic) | Anthropic, Claude on Vertex | | OpenAI Responses (responses) | OpenAI (GPT-5.x reasoning + tools) | | Gemini native (native, generateContent) | Google Gemini, Gemini on Vertex — reasoning, thought signatures, caching, native PDF | | OpenAI Chat Completions (chat_completions) | everything else, including Gemini-compat and Azure / Bedrock |

Embeddings, images, speech, transcription and video are separate model kinds with their own surfaces and their own adapters — not chat wires, and none of them can be handed to streamChat by accident. Embeddings ship for OpenAI, Google, Voyage and Yunwu; speech adds openai / elevenlabs, transcription adds openai / deepgram, video any OpenAI-Videos-shaped relay.

Ollama and LM Studio need no API key — they dial localhost and set the keyless escape for you. Any other OpenAI-shaped host gets a real provider id through createOpenAICompatible({ id, baseURL }), and createProviderRegistry resolves 'groq:llama-4-maverick' strings.

Unknown model slugs never throw: they fall back to a conservative capability row and report an unknown-model warning, so a model released this morning works without an SDK release. Correct the row per factory or per call with capabilities. This path is verified against live APIs, not only in theory — grok-4.5 and gemini-3.6-flash both postdate the pinned rows and both run end to end, tool loop included.

Where it fits

Deuz is not a framework and it is not a claim about ASI. It is a small runtime you can hold in your head, for the part of the problem that does not get easier as models improve: remembering across sessions, using tools safely, surviving a crash, asking a human before something risky, and staying observable while it does. Zero runtime dependencies, no ambient state, no vendor for durability, nothing phoning home.

Node ≥ 22, or any runtime with fetch. Optional peers only when you use them: zod (or any Standard Schema library), @modelcontextprotocol/sdk, react, unpdf / mammoth / xlsx, playwright, @opentelemetry/api, redis, pg.

Full documentation, architecture tour and the honest limitations list: github.com/Deuz-AI/Deuz-SDK.