@jawcode-dev/ai
Advanced tools
Unified LLM API with automatic model discovery, provider configuration, token and cost tracking, and simple context persistence and hand-off to other models mid-session.
Note: This library only includes models that support tool calling (function calling), as this is essential for agentic workflows.
MOONSHOT_API_KEY)QIANFAN_API_KEY)NVIDIA_API_KEY)NANO_GPT_API_KEY)VENICE_API_KEY)/login kilo or KILO_API_KEY)LITELLM_API_KEY)ZAI_API_KEY)MINIMAX_CODE_API_KEY or MINIMAX_CODE_CN_API_KEY)XIAOMI_API_KEY)ZENMUX_API_KEY)QWEN_OAUTH_TOKEN or QWEN_PORTAL_API_KEY)CLOUDFLARE_AI_GATEWAY_API_KEY and provider-specific gateway base URL)OLLAMA_API_KEY)OLLAMA_CLOUD_API_KEY)VLLM_API_KEY for secured deployments)npm install @jawcode-dev/ai
import { z, getModel, stream, complete, Context, Tool } from "@jawcode-dev/ai";
// Fully typed with auto-complete support for both providers and models
const model = getModel("openai", "gpt-4o-mini");
// Define tools with Zod schemas for type safety and validation
const tools: Tool[] = [
{
name: "get_time",
description: "Get the current time",
parameters: z.object({
timezone: z
.string()
.optional()
.describe("Optional timezone (e.g., America/New_York)"),
}),
},
];
// Build a conversation context (easily serializable and transferable between models)
const context: Context = {
systemPrompt: ["You are a helpful assistant."],
messages: [{ role: "user", content: "What time is it?" }],
tools,
};
// Option 1: Streaming with all event types
const s = stream(model, context);
for await (const event of s) {
switch (event.type) {
case "start":
console.log(`Starting with ${event.partial.model}`);
break;
case "text_start":
console.log("\n[Text started]");
break;
case "text_delta":
process.stdout.write(event.delta);
break;
case "text_end":
console.log("\n[Text ended]");
break;
case "thinking_start":
console.log("[Model is thinking...]");
break;
case "thinking_delta":
process.stdout.write(event.delta);
break;
case "thinking_end":
console.log("[Thinking complete]");
break;
case "toolcall_start":
console.log(`\n[Tool call started: index ${event.contentIndex}]`);
break;
case "toolcall_delta":
// Partial tool arguments are being streamed
const partialCall = event.partial.content[event.contentIndex];
if (partialCall.type === "toolCall") {
console.log(`[Streaming args for ${partialCall.name}]`);
}
break;
case "toolcall_end":
console.log(`\nTool called: ${event.toolCall.name}`);
console.log(`Arguments: ${JSON.stringify(event.toolCall.arguments)}`);
break;
case "done":
console.log(`\nFinished: ${event.reason}`);
break;
case "error":
console.error(`Error: ${event.error}`);
break;
}
}
// Get the final message after streaming, add it to the context
const finalMessage = await s.result();
context.messages.push(finalMessage);
// Handle tool calls if any
const toolCalls = finalMessage.content.filter((b) => b.type === "toolCall");
for (const call of toolCalls) {
// Execute the tool
const result =
call.name === "get_time"
? new Date().toLocaleString("en-US", {
timeZone: call.arguments.timezone || "UTC",
dateStyle: "full",
timeStyle: "long",
})
: "Unknown tool";
// Add tool result to context (supports text and images)
context.messages.push({
role: "toolResult",
toolCallId: call.id,
toolName: call.name,
content: [{ type: "text", text: result }],
isError: false,
timestamp: Date.now(),
});
}
// Continue if there were tool calls
if (toolCalls.length > 0) {
const continuation = await complete(model, context);
context.messages.push(continuation);
console.log("After tool execution:", continuation.content);
}
console.log(`Total tokens: ${finalMessage.usage.input} in, ${finalMessage.usage.output} out`);
console.log(`Cost: $${finalMessage.usage.cost.total.toFixed(4)}`);
// Option 2: Get complete response without streaming
const response = await complete(model, context);
for (const block of response.content) {
if (block.type === "text") {
console.log(block.text);
} else if (block.type === "toolCall") {
console.log(`Tool: ${block.name}(${JSON.stringify(block.arguments)})`);
}
}
Tools enable LLMs to interact with external systems. This library uses Zod schemas for type-safe tool definitions with automatic validation. Schemas are converted to JSON Schema for providers as needed.
import { z, Tool } from "@jawcode-dev/ai";
// Define tool parameters with Zod
const weatherTool: Tool = {
name: "get_weather",
description: "Get current weather for a location",
parameters: z.object({
location: z.string().describe("City name or coordinates"),
units: z.enum(["celsius", "fahrenheit"]).default("celsius"),
}),
};
const bookMeetingTool: Tool = {
name: "book_meeting",
description: "Schedule a meeting",
parameters: z.object({
title: z.string().min(1),
startTime: z.string().describe("ISO 8601 date-time"),
endTime: z.string().describe("ISO 8601 date-time"),
attendees: z.array(z.email()).min(1),
}),
};
Tool results use content blocks and can include both text and images:
import * as fs from "node:fs";
const context: Context = {
messages: [{ role: "user", content: "What is the weather in London?" }],
tools: [weatherTool],
};
const response = await complete(model, context);
// Check for tool calls in the response
for (const block of response.content) {
if (block.type === "toolCall") {
// Execute your tool with the arguments
// See "Validating Tool Arguments" section for validation
const result = await executeWeatherApi(block.arguments);
// Add tool result with text content
context.messages.push({
role: "toolResult",
toolCallId: block.id,
toolName: block.name,
content: [{ type: "text", text: JSON.stringify(result) }],
isError: false,
timestamp: Date.now(),
});
}
}
// Tool results can also include images (for vision-capable models)
const imageBuffer = fs.readFileSync("chart.png");
context.messages.push({
role: "toolResult",
toolCallId: "tool_xyz",
toolName: "generate_chart",
content: [
{ type: "text", text: "Generated chart showing temperature trends" },
{ type: "image", data: imageBuffer.toBase64(), mimeType: "image/png" },
],
isError: false,
timestamp: Date.now(),
});
During streaming, tool call arguments are progressively parsed as they arrive. This enables real-time UI updates before the complete arguments are available:
const s = stream(model, context);
for await (const event of s) {
if (event.type === "toolcall_delta") {
const toolCall = event.partial.content[event.contentIndex];
// toolCall.arguments contains partially parsed JSON during streaming
// This allows for progressive UI updates
if (toolCall.type === "toolCall" && toolCall.arguments) {
// BE DEFENSIVE: arguments may be incomplete
// Example: Show file path being written even before content is complete
if (toolCall.name === "write_file" && toolCall.arguments.path) {
console.log(`Writing to: ${toolCall.arguments.path}`);
// Content might be partial or missing
if (toolCall.arguments.content) {
console.log(`Content preview: ${toolCall.arguments.content.substring(0, 100)}...`);
}
}
}
}
if (event.type === "toolcall_end") {
// Here toolCall.arguments is complete (but not yet validated)
const toolCall = event.toolCall;
console.log(`Tool completed: ${toolCall.name}`, toolCall.arguments);
}
}
Important notes about partial tool arguments:
toolcall_delta events, arguments contains the best-effort parse of partial JSONarguments will be an empty object {}, never undefinedtoolcall_delta event with the full arguments.When using agentLoop, tool arguments are automatically validated against your Zod parameter schemas before execution. If validation fails, the error is returned to the model as a tool result, allowing it to retry.
When implementing your own tool execution loop with stream() or complete(), use validateToolCall to validate arguments before passing them to your tools:
import { stream, validateToolCall, Tool } from "@jawcode-dev/ai";
const tools: Tool[] = [weatherTool, calculatorTool];
const s = stream(model, { messages, tools });
for await (const event of s) {
if (event.type === "toolcall_end") {
const toolCall = event.toolCall;
try {
// Validate arguments against the tool's schema (throws on invalid args)
const validatedArgs = validateToolCall(tools, toolCall);
const result = await executeMyTool(toolCall.name, validatedArgs);
// ... add tool result to context
} catch (error) {
// Validation failed - return error as tool result so model can retry
context.messages.push({
role: "toolResult",
toolCallId: toolCall.id,
toolName: toolCall.name,
content: [{ type: "text", text: error.message }],
isError: true,
timestamp: Date.now(),
});
}
}
}
All streaming events emitted during assistant message generation:
| Event Type | Description | Key Properties |
|---|---|---|
start | Stream begins | partial: Initial assistant message structure |
text_start | Text block starts | contentIndex: Position in content array |
text_delta | Text chunk received | delta: New text, contentIndex: Position |
text_end | Text block complete | content: Full text, contentIndex: Position |
thinking_start | Thinking block starts | contentIndex: Position in content array |
thinking_delta | Thinking chunk received | delta: New text, contentIndex: Position |
thinking_end | Thinking block complete | content: Full thinking, contentIndex: Position |
toolcall_start | Tool call begins | contentIndex: Position in content array |
toolcall_delta | Tool arguments streaming | delta: JSON chunk, partial.content[contentIndex].arguments: Partial parsed args |
toolcall_end | Tool call complete | toolCall: Complete validated tool call with id, name, arguments |
done | Stream complete | reason: Stop reason ("stop", "length", "toolUse"), message: Final assistant message |
error | Error occurred | reason: Error type ("error" or "aborted"), error: AssistantMessage with partial content |
Models with vision capabilities can process images. You can check if a model supports images via the input property. If you pass images to a non-vision model, they are silently ignored.
import * as fs from "node:fs";
import { getModel, complete } from "@jawcode-dev/ai";
const model = getModel("openai", "gpt-4o-mini");
// Check if model supports images
if (model.input.includes("image")) {
console.log("Model supports vision");
}
const imageBuffer = fs.readFileSync("image.png");
const base64Image = imageBuffer.toBase64();
const response = await complete(model, {
messages: [
{
role: "user",
content: [
{ type: "text", text: "What is in this image?" },
{ type: "image", data: base64Image, mimeType: "image/png" },
],
},
],
});
// Access the response
for (const block of response.content) {
if (block.type === "text") {
console.log(block.text);
}
}
Many models support thinking/reasoning capabilities where they can show their internal thought process. You can check if a model supports reasoning via the reasoning property. If you pass reasoning options to a non-reasoning model, they are silently ignored.
import { getModel, streamSimple, completeSimple } from "@jawcode-dev/ai";
// Many models across providers support thinking/reasoning
const model = getModel("anthropic", "anthropic-model-sonnet-4-20250514");
// or getModel('openai', 'gpt-5-mini');
// or getModel('google', 'gemini-2.5-flash');
// or getModel('xai', 'grok-code-fast-1');
// or getModel('groq', 'openai/gpt-oss-20b');
// or getModel('cerebras', 'gpt-oss-120b');
// or getModel('openrouter', 'z-ai/glm-4.5v');
// Check if model supports reasoning
if (model.reasoning) {
console.log("Model supports reasoning/thinking");
}
// Use the simplified reasoning option
const response = await completeSimple(
model,
{
messages: [{ role: "user", content: "Solve: 2x + 5 = 13" }],
},
{
reasoning: "medium", // 'minimal' | 'low' | 'medium' | 'high' | 'xhigh' (xhigh maps to high on non-OpenAI providers)
}
);
// Access thinking and text blocks
for (const block of response.content) {
if (block.type === "thinking") {
console.log("Thinking:", block.thinking);
} else if (block.type === "text") {
console.log("Response:", block.text);
}
}
For fine-grained control, use the provider-specific options:
import { getModel, complete } from "@jawcode-dev/ai";
// OpenAI Reasoning (o1, o3, gpt-5)
const openaiModel = getModel("openai", "gpt-5-mini");
await complete(openaiModel, context, {
reasoningEffort: "medium",
reasoningSummary: "detailed", // OpenAI Responses API only
});
// Anthropic Thinking (Anthropic model Sonnet 4)
const anthropicModel = getModel("anthropic", "anthropic-model-sonnet-4-20250514");
await complete(anthropicModel, context, {
thinkingEnabled: true,
thinkingBudgetTokens: 8192, // Optional token limit
});
// Google Gemini Thinking
const googleModel = getModel("google", "gemini-2.5-flash");
await complete(googleModel, context, {
thinking: {
enabled: true,
budgetTokens: 8192, // -1 for dynamic, 0 to disable
},
});
When streaming, thinking content is delivered through specific events:
const s = streamSimple(model, context, { reasoning: "high" });
for await (const event of s) {
switch (event.type) {
case "thinking_start":
console.log("[Model started thinking]");
break;
case "thinking_delta":
process.stdout.write(event.delta); // Stream thinking content
break;
case "thinking_end":
console.log("\n[Thinking complete]");
break;
}
}
Every AssistantMessage includes a stopReason field that indicates how the generation ended:
"stop" - Normal completion, the model finished its response"length" - Output hit the maximum token limit"toolUse" - Model is calling tools and expects tool results"error" - An error occurred during generation"aborted" - Request was cancelled via abort signalWhen a request ends with an error (including aborts and tool call validation errors), the streaming API emits an error event:
// In streaming
for await (const event of stream) {
if (event.type === "error") {
// event.reason is either "error" or "aborted"
// event.error is the AssistantMessage with partial content
console.error(`Error (${event.reason}):`, event.error.errorMessage);
console.log("Partial content:", event.error.content);
}
}
// The final message will have the error details
const message = await stream.result();
if (message.stopReason === "error" || message.stopReason === "aborted") {
console.error("Request failed:", message.errorMessage);
// message.content contains any partial content received before the error
// message.usage contains partial token counts and costs
}
The abort signal allows you to cancel in-progress requests. Aborted requests have stopReason === 'aborted':
import { getModel, stream } from "@jawcode-dev/ai";
const model = getModel("openai", "gpt-4o-mini");
// Abort after 2 seconds
const signal = AbortSignal.timeout(2000);
const s = stream(
model,
{
messages: [{ role: "user", content: "Write a long story" }],
},
{
signal,
}
);
for await (const event of s) {
if (event.type === "text_delta") {
process.stdout.write(event.delta);
} else if (event.type === "error") {
// event.reason tells you if it was "error" or "aborted"
console.log(`${event.reason === "aborted" ? "Aborted" : "Error"}:`, event.error.errorMessage);
}
}
// Get results (may be partial if aborted)
const response = await s.result();
if (response.stopReason === "aborted") {
console.log("Request was aborted:", response.errorMessage);
console.log("Partial content received:", response.content);
console.log("Tokens used:", response.usage);
}
Aborted messages can be added to the conversation context and continued in subsequent requests:
const context = {
messages: [{ role: "user", content: "Explain quantum computing in detail" }],
};
// First request gets aborted after 2 seconds
const controller1 = new AbortController();
setTimeout(() => controller1.abort(), 2000);
const partial = await complete(model, context, { signal: controller1.signal });
// Add the partial response to context
context.messages.push(partial);
context.messages.push({ role: "user", content: "Please continue" });
// Continue the conversation
const continuation = await complete(model, context);
All providers accept the base StreamOptions (in addition to provider-specific options):
apiKey: Override the provider API keyheaders: Extra request headers merged on top of model-defined headerssessionId: Provider-specific session identifier (prompt caching/routing)signal: Abort in-flight requestsonPayload: Callback invoked with the provider request payload just before sendingExample:
const response = await complete(model, context, {
apiKey: "sk-live",
headers: { "X-Debug-Trace": "true" },
onPayload: (payload) => {
console.log("request payload", payload);
},
});
The library implements 4 API interfaces, each with its own streaming function and options:
anthropic-messages: Anthropic's Messages API (streamAnthropic, AnthropicOptions)google-generative-ai: Google's Generative AI API (streamGoogle, GoogleOptions)openai-completions: OpenAI's Chat Completions API (streamOpenAICompletions, OpenAICompletionsOptions)openai-responses: OpenAI's Responses API (streamOpenAIResponses, OpenAIResponsesOptions)A provider offers models through a specific API. For example:
anthropic-messages APIgoogle-generative-ai APIopenai-responses APIopenai-completions API (OpenAI-compatible)import { getProviders, getModels, getModel } from "@jawcode-dev/ai";
// Get all available providers
const providers = getProviders();
console.log(providers); // ['openai', 'anthropic', 'google', 'xai', 'groq', ...]
// Get all models from a provider (fully typed)
const anthropicModels = getModels("anthropic");
for (const model of anthropicModels) {
console.log(`${model.id}: ${model.name}`);
console.log(` API: ${model.api}`); // 'anthropic-messages'
console.log(` Context: ${model.contextWindow} tokens`);
console.log(` Vision: ${model.input.includes("image")}`);
console.log(` Reasoning: ${model.reasoning}`);
}
// Get a specific model (both provider and model ID are auto-completed in IDEs)
const model = getModel("openai", "gpt-4o-mini");
console.log(`Using ${model.name} via ${model.api} API`);
You can create custom models for local inference servers or custom endpoints.
For local Ollama, OLLAMA_API_KEY is optional and mainly needed for authenticated/self-hosted gateways. ollama remains the local OpenAI-compatible runtime integration.
import { Model, stream } from "@jawcode-dev/ai";
// Example: local Ollama using the OpenAI-compatible API
const ollamaModel: Model<"openai-completions"> = {
id: "llama-3.1-8b",
name: "Llama 3.1 8B (Ollama)",
api: "openai-completions",
provider: "ollama",
baseUrl: "http://localhost:11434/v1",
reasoning: false,
input: ["text"],
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
contextWindow: 128000,
maxTokens: 32000,
};
const localResponse = await stream(ollamaModel, context, {
apiKey: process.env.OLLAMA_API_KEY, // Optional; local Ollama usually runs without auth
});
// Example: Ollama Cloud using the native /api/chat transport
const ollamaCloudModel: Model<"ollama-chat"> = {
id: "gpt-oss:120b",
name: "GPT OSS 120B (Ollama Cloud)",
api: "ollama-chat",
provider: "ollama-cloud",
baseUrl: "https://ollama.com",
reasoning: true,
input: ["text", "image"],
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
contextWindow: 262144,
maxTokens: 8192,
};
const cloudResponse = await stream(ollamaCloudModel, context, {
apiKey: process.env.OLLAMA_CLOUD_API_KEY,
});
// Example: LiteLLM proxy with explicit compat settings
const litellmModel: Model<"openai-completions"> = {
id: "gpt-4o",
name: "GPT-4o (via LiteLLM)",
api: "openai-completions",
provider: "litellm",
baseUrl: "http://localhost:4000/v1",
reasoning: false,
input: ["text", "image"],
cost: { input: 2.5, output: 10, cacheRead: 0, cacheWrite: 0 },
contextWindow: 128000,
maxTokens: 16384,
compat: {
supportsStore: false, // LiteLLM doesn't support the store field
},
};
// Example: Custom endpoint with headers (bypassing Cloudflare bot detection)
const proxyModel: Model<"anthropic-messages"> = {
id: "anthropic-model-sonnet-4",
name: "Anthropic model Sonnet 4 (Proxied)",
api: "anthropic-messages",
provider: "custom-proxy",
baseUrl: "https://proxy.example.com/v1",
reasoning: true,
input: ["text", "image"],
cost: { input: 3, output: 15, cacheRead: 0.3, cacheWrite: 3.75 },
contextWindow: 200000,
maxTokens: 8192,
headers: {
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36",
"X-Custom-Auth": "bearer-token-here",
},
};
The openai-completions API is implemented by many providers with minor differences. By default, the library auto-detects compatibility settings based on baseUrl for known providers (Cerebras, xAI, Mistral, Chutes, etc.). For custom proxies or unknown endpoints, you can override these settings via the compat field:
interface OpenAICompat {
supportsStore?: boolean; // Whether provider supports the `store` field (default: true)
supportsDeveloperRole?: boolean; // Whether provider supports `developer` role vs `system` (default: true)
supportsReasoningEffort?: boolean; // Whether provider supports `reasoning_effort` (default: true)
maxTokensField?: "max_completion_tokens" | "max_tokens"; // Which field name to use (default: max_completion_tokens)
extraBody?: Record<string, unknown>; // Extra request-body fields for custom proxy routing or provider-specific options
}
If compat is not set, the library falls back to URL-based detection. If compat is partially set, unspecified fields use the detected defaults. This is useful for:
store fieldModels are typed by their API, ensuring type-safe options:
// TypeScript knows this is an Anthropic model
const anthropic-model = getModel("anthropic", "anthropic-model-sonnet-4-20250514");
// So these options are type-checked for AnthropicOptions
await stream(anthropic-model, context, {
thinkingEnabled: true, // ✓ Valid for anthropic-messages
thinkingBudgetTokens: 2048, // ✓ Valid for anthropic-messages
// reasoningEffort: 'high' // ✗ TypeScript error: not valid for anthropic-messages
});
The library supports seamless handoffs between different LLM providers within the same conversation. This allows you to switch models mid-conversation while preserving context, including thinking blocks, tool calls, and tool results.
When messages from one provider are sent to a different provider, the library automatically transforms them for compatibility:
<thinking> tagsimport { getModel, complete, Context } from "@jawcode-dev/ai";
// Start with Anthropic model
const anthropic-model = getModel("anthropic", "anthropic-model-sonnet-4-20250514");
const context: Context = {
messages: [],
};
context.messages.push({ role: "user", content: "What is 25 * 18?" });
const anthropic-modelResponse = await complete(anthropic-model, context, {
thinkingEnabled: true,
});
context.messages.push(anthropic-modelResponse);
// Switch to GPT-5 - it will see Anthropic model's thinking as <thinking> tagged text
const gpt5 = getModel("openai", "gpt-5-mini");
context.messages.push({ role: "user", content: "Is that calculation correct?" });
const gptResponse = await complete(gpt5, context);
context.messages.push(gptResponse);
// Switch to Gemini
const gemini = getModel("google", "gemini-2.5-flash");
context.messages.push({ role: "user", content: "What was the original question?" });
const geminiResponse = await complete(gemini, context);
All providers can handle messages from other providers, including:
This enables flexible workflows where you can:
The Context object can be easily serialized and deserialized using standard JSON methods, making it simple to persist conversations, implement chat history, or transfer contexts between services:
import { Context, getModel, complete } from "@jawcode-dev/ai";
// Create and use a context
const context: Context = {
systemPrompt: ["You are a helpful assistant."],
messages: [{ role: "user", content: "What is TypeScript?" }],
};
const model = getModel("openai", "gpt-4o-mini");
const response = await complete(model, context);
context.messages.push(response);
// Serialize the entire context
const serialized = JSON.stringify(context);
console.log("Serialized context size:", serialized.length, "bytes");
// Save to database, localStorage, file, etc.
localStorage.setItem("conversation", serialized);
// Later: deserialize and continue the conversation
const restored: Context = JSON.parse(localStorage.getItem("conversation")!);
restored.messages.push({ role: "user", content: "Tell me more about its type system" });
// Continue with any model
const newModel = getModel("anthropic", "anthropic-model-haiku-4-5-20251001");
const continuation = await complete(newModel, restored);
Note: If the context contains images (encoded as base64 as shown in the Image Input section), those will also be serialized.
The library supports browser environments. You must pass the API key explicitly since environment variables are not available in browsers:
import { getModel, complete } from "@jawcode-dev/ai";
// API key must be passed explicitly in browser
const model = getModel("anthropic", "anthropic-model-haiku-4-5-20251001");
const response = await complete(
model,
{
messages: [{ role: "user", content: "Hello!" }],
},
{
apiKey: "your-api-key",
}
);
Security Warning: Exposing API keys in frontend code is dangerous. Anyone can extract and abuse your keys. Only use this approach for internal tools or demos. For production applications, use a backend proxy that keeps your API keys secure.
In Node.js environments, you can set environment variables to avoid passing API keys:
| Provider | Environment Variable(s) |
|---|---|
| OpenAI | OPENAI_API_KEY |
| Anthropic | ANTHROPIC_API_KEY or ANTHROPIC_OAUTH_TOKEN (or ANTHROPIC_FOUNDRY_API_KEY when ANTHROPIC_MODEL_CODE_USE_FOUNDRY=true) |
GEMINI_API_KEY | |
| Vertex AI | GOOGLE_CLOUD_PROJECT (or GCLOUD_PROJECT) + GOOGLE_CLOUD_LOCATION + ADC |
| Mistral | MISTRAL_API_KEY |
| Groq | GROQ_API_KEY |
| Cerebras | CEREBRAS_API_KEY |
| Together | TOGETHER_API_KEY |
| Qianfan | QIANFAN_API_KEY |
| Hugging Face | HUGGINGFACE_HUB_TOKEN or HF_TOKEN |
| Synthetic | SYNTHETIC_API_KEY |
| NVIDIA | NVIDIA_API_KEY |
| NanoGPT | NANO_GPT_API_KEY |
| Venice | VENICE_API_KEY |
| Moonshot | MOONSHOT_API_KEY |
| xAI | XAI_API_KEY |
| OpenRouter | OPENROUTER_API_KEY |
| LiteLLM | LITELLM_API_KEY |
| Ollama | OLLAMA_API_KEY (optional for local deployments) |
| Ollama Cloud | OLLAMA_CLOUD_API_KEY |
| Qwen Portal | QWEN_OAUTH_TOKEN or QWEN_PORTAL_API_KEY |
| zAI | ZAI_API_KEY |
| MiniMax Code | MINIMAX_CODE_API_KEY (international) or MINIMAX_CODE_CN_API_KEY (China) |
| Xiaomi MiMo | XIAOMI_API_KEY |
| ZenMux | ZENMUX_API_KEY |
| vLLM | VLLM_API_KEY |
| Cloudflare AI Gateway | CLOUDFLARE_AI_GATEWAY_API_KEY |
| GitHub Copilot | COPILOT_GITHUB_TOKEN or GH_TOKEN or GITHUB_TOKEN |
For Cloudflare AI Gateway models, use provider base URL format
https://gateway.ai.cloudflare.com/v1/<account>/<gateway>/anthropic.
For Anthropic Foundry routing, set ANTHROPIC_MODEL_CODE_USE_FOUNDRY=true plus:
FOUNDRY_BASE_URL, ANTHROPIC_FOUNDRY_API_KEY, optional ANTHROPIC_CUSTOM_HEADERS,
and optional mTLS material (ANTHROPIC_MODEL_CODE_CLIENT_CERT, ANTHROPIC_MODEL_CODE_CLIENT_KEY, NODE_EXTRA_CA_CERTS).
Provider endpoint defaults for the current OpenAI-compatible integrations:
https://api.together.xyz/v1https://api.moonshot.ai/v1https://qianfan.baidubce.com/v2https://integrate.api.nvidia.com/v1https://nano-gpt.com/api/v1https://router.huggingface.co/v1https://api.venice.ai/api/v1https://api.xiaomimimo.com/anthropichttps://zenmux.ai/api/v1https://zenmux.ai/api/anthropichttp://127.0.0.1:8000/v1http://127.0.0.1:11434/v1)https://ollama.com/api, configured here as base URL https://ollama.com)http://localhost:4000/v1https://gateway.ai.cloudflare.com/v1/<account>/<gateway>/anthropichttps://portal.qwen.ai/v1
When set, the library automatically uses these keys:// Uses OPENAI_API_KEY from environment
const model = getModel("openai", "gpt-4o-mini");
const response = await complete(model, context);
// Or override with explicit key
const response = await complete(model, context, {
apiKey: "sk-different-key",
});
import { getEnvApiKey } from "@jawcode-dev/ai";
// Check if an API key is set in environment variables
const key = getEnvApiKey("openai"); // checks OPENAI_API_KEY
Several providers support OAuth authentication (some also support static API keys):
For paid Cloud Code Assist subscriptions, set GOOGLE_CLOUD_PROJECT or GOOGLE_CLOUD_PROJECT_ID to your project ID.
Vertex AI models use Application Default Credentials (ADC):
gcloud auth application-default loginGOOGLE_APPLICATION_CREDENTIALS to point to a service account JSON key fileAlso set GOOGLE_CLOUD_PROJECT (or GCLOUD_PROJECT) and GOOGLE_CLOUD_LOCATION. You can also pass project/location in the call options.
Example:
# Local (uses your user credentials)
gcloud auth application-default login
export GOOGLE_CLOUD_PROJECT="my-project"
export GOOGLE_CLOUD_LOCATION="us-central1"
# CI/Production (service account key file)
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account.json"
import { getModel, complete } from "@jawcode-dev/ai";
(async () => {
const model = getModel("google-vertex", "gemini-2.5-flash");
const response = await complete(model, {
messages: [{ role: "user", content: "Hello from Vertex AI" }],
});
for (const block of response.content) {
if (block.type === "text") console.log(block.text);
}
})().catch(console.error);
Official docs: Application Default Credentials
The quickest way to authenticate:
bunx @jawcode-dev/ai login # interactive provider selection
bunx @jawcode-dev/ai login anthropic # login to specific provider
bunx @jawcode-dev/ai login vllm # store vLLM API key (or placeholder for local no-auth)
bunx @jawcode-dev/ai login xai # sign in with xAI/Grok OAuth
bunx @jawcode-dev/ai list # list available providers
Credentials are saved to agent.db in the agent directory. /login qianfan opens the Qianfan console and stores the pasted API key; /login xai opens xAI/Grok OAuth login and stores refreshable OAuth credentials.
login supports OAuth providers (Anthropic, OpenAI code provider, GitHub Copilot, Gemini CLI, Antigravity, xAI) and API-key onboarding flows.
For the current API-key onboarding flows, the library covers Together, Moonshot, Qianfan, NVIDIA, NanoGPT, Hugging Face, Venice, Xiaomi, vLLM, LiteLLM, Cloudflare AI Gateway, Qwen Portal, and Ollama Cloud. Ollama remains the local runtime integration; set OLLAMA_API_KEY only when your local or self-hosted deployment enforces bearer auth.
The library provides login and token refresh functions. Credential storage is the caller's responsibility.
import {
// Login functions (return credentials, do not store)
loginAnthropic,
loginOpenAIOpenAI code,
loginGitHubCopilot,
loginGeminiCli,
loginAntigravity,
loginCloudflareAiGateway,
loginHuggingface,
loginLiteLLM,
loginMoonshot,
loginNvidia,
loginNanoGPT,
loginQianfan,
loginQwenPortal,
loginTogether,
loginVenice,
loginVllm,
loginXiaomi,
// Token management
refreshOAuthToken, // (provider, credentials) => new credentials
getOAuthApiKey, // (provider, credentialsMap) => { newCredentials, apiKey } | null
// Types
type OAuthProvider, // includes 'anthropic', 'openai-code', 'github-copilot', 'google-gemini-cli', 'google-antigravity', 'together', 'moonshot', 'qianfan', 'nvidia', 'nanogpt', 'huggingface', 'venice', 'xiaomi', 'vllm', 'litellm', 'cloudflare-ai-gateway', 'qwen-portal', ...
type OAuthCredentials,
} from "@jawcode-dev/ai";
loginOpenAIOpenAI code accepts an optional originator value used in the OAuth flow:
await loginOpenAIOpenAI code({
onAuth: ({ url }) => console.log(url),
originator: "my-cli",
});
import { loginGitHubCopilot } from "@jawcode-dev/ai";
import * as fs from "node:fs";
const credentials = await loginGitHubCopilot({
onAuth: (url, instructions) => {
console.log(`Open: ${url}`);
if (instructions) console.log(instructions);
},
onPrompt: async (prompt) => {
return await getUserInput(prompt.message);
},
onProgress: (message) => console.log(message),
});
// Store credentials yourself
const auth = { "github-copilot": { type: "oauth", ...credentials } };
fs.writeFileSync("credentials.json", JSON.stringify(auth, null, 2));
Use getOAuthApiKey() to get an API key, automatically refreshing if expired:
import { getModel, complete, getOAuthApiKey } from "@jawcode-dev/ai";
import * as fs from "node:fs";
// Load your stored credentials
const auth = JSON.parse(fs.readFileSync("credentials.json", "utf-8"));
// Get API key (refreshes if expired)
const result = await getOAuthApiKey("github-copilot", auth);
if (!result) throw new Error("Not logged in");
// Save refreshed credentials
auth["github-copilot"] = { type: "oauth", ...result.newCredentials };
fs.writeFileSync("credentials.json", JSON.stringify(auth, null, 2));
// Use the API key
const model = getModel("github-copilot", "gpt-4o");
const response = await complete(
model,
{
messages: [{ role: "user", content: "Hello!" }],
},
{ apiKey: result.apiKey }
);
OpenAI code provider: Requires a ChatGPT Plus or Pro subscription. Provides access to GPT-5.x OpenAI code models with extended context windows and reasoning capabilities. The library automatically handles session-based prompt caching when sessionId is provided in stream options.
GitHub Copilot: If you get "The requested model is not supported" error, enable the model manually in VS Code: open Copilot Chat, click the model selector, select the model (warning icon), and click "Enable".
Google Gemini CLI / Antigravity: These use Google Cloud OAuth. The apiKey returned by getOAuthApiKey() is a JSON string containing both the token and project ID, which the library handles automatically.
MIT
FAQs
Unified LLM API with automatic model discovery and provider configuration
The npm package @jawcode-dev/ai receives a total of 10 weekly downloads. As such, @jawcode-dev/ai popularity was classified as not popular.
We found that @jawcode-dev/ai demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Research
/Security News
Socket uncovered two malicious VS Code themes in a GlassWorm-linked cluster with thousands of installs across VS Code Marketplace and Open VSX.
Security News
/Company News
Capital One is partnering with Socket to proactively secure its open source supply chain.
Security News
Socket CTO Ahmad Nassri discusses how to keep AI agents from bypassing package blocks, limit credential access, and monitor their actions.