feat(ai): Add OpenAI Completions and Responses API providers

- Implement OpenAICompletionsLLM for Chat Completions API with streaming
- Implement OpenAIResponsesLLM for Responses API with reasoning support
- Update types to use LLM/Context instead of AI/Request
- Add support for reasoning tokens, tool calls, and streaming
- Create test examples for both OpenAI providers
- Update Anthropic provider to match new interface

packages/ai/test/examples/anthropic.ts

@@ -1,10 +1,9 @@
-import Anthropic from "@anthropic-ai/sdk";
-import { MessageCreateParamsBase } from "@anthropic-ai/sdk/resources/messages.mjs";
 import chalk from "chalk";
-import { AnthropicAI } from "../../src/providers/anthropic";
-import { Request, Message, Tool } from "../../src/types";
-
-const anthropic = new Anthropic();
+import { readFileSync } from "fs";
+import { fileURLToPath } from "url";
+import { dirname, join } from "path";
+import { AnthropicLLM, AnthropicLLMOptions } from "../../src/providers/anthropic";
+import { Context, Tool } from "../../src/types";
 
 // Define a simple calculator tool
 const tools: Tool[] = [
@@ -24,23 +23,27 @@ const tools: Tool[] = [
     }
 ];
 
-const ai = new AnthropicAI("claude-sonnet-4-0");
-const context: Request = {
+const options: AnthropicLLMOptions = {
+    onText: (t) => process.stdout.write(t),
+    onThinking: (t) => process.stdout.write(chalk.dim(t)),
+    thinking: { enabled: true }
+};
+const ai = new AnthropicLLM("claude-sonnet-4-0", process.env.ANTHROPIC_OAUTH_TOKEN ?? process.env.ANTHROPIC_API_KEY);
+const context: Context = {
+        systemPrompt: "You are a helpful assistant that can use tools to answer questions.",
         messages: [
         {
             role: "user",
             content: "Think about birds briefly. Then give me a list of 10 birds. Finally, calculate 42 * 17 + 123 and 453 + 434 in parallel using the calculator tool.",
         }
     ],
-    tools,
-    onText: (t) => process.stdout.write(t),
-    onThinking: (t) => process.stdout.write(chalk.dim(t))
+    tools
 }
 
-const options = {thinking: { enabled: true }};
 let msg = await ai.complete(context, options)
 context.messages.push(msg);
-console.log(JSON.stringify(msg, null, 2));
+console.log();
+console.log(chalk.yellow(JSON.stringify(msg, null, 2)));
 
 for (const toolCall of msg.toolCalls || []) {
     if (toolCall.name === "calculate") {
@@ -56,7 +59,8 @@ for (const toolCall of msg.toolCalls || []) {
 }
 
 msg = await ai.complete(context, options);
-console.log(JSON.stringify(msg, null, 2));
+console.log();
+console.log(chalk.yellow(JSON.stringify(msg, null, 2)));
 
 
 

packages/ai/test/examples/openai-completions.ts

@@ -0,0 +1,65 @@
+import chalk from "chalk";
+import { Context, Tool } from "../../src/types";
+import { OpenAICompletionsLLM, OpenAICompletionsLLMOptions } from "../../src/providers/openai-completions";
+
+// Define a simple calculator tool
+const tools: Tool[] = [
+    {
+        name: "calculate",
+        description: "Perform a mathematical calculation",
+        parameters: {
+            type: "object" as const,
+            properties: {
+                expression: {
+                    type: "string",
+                    description: "The mathematical expression to evaluate"
+                }
+            },
+            required: ["expression"]
+        }
+    }
+];
+
+const options: OpenAICompletionsLLMOptions = {
+    onText: (t) => process.stdout.write(t),
+    onThinking: (t) => process.stdout.write(chalk.dim(t)),
+    reasoningEffort: "medium",
+    toolChoice: "auto"
+};
+const ai = new OpenAICompletionsLLM("gpt-5-mini");
+const context: Context = {
+        systemPrompt: "You are a helpful assistant that can use tools to answer questions.",
+        messages: [
+        {
+            role: "user",
+            content: "Think about birds briefly. Then give me a list of 10 birds. Finally, calculate 42 * 17 + 123 and 453 + 434 in parallel using the calculator tool.",
+        }
+    ],
+    tools
+}
+
+let msg = await ai.complete(context, options)
+context.messages.push(msg);
+console.log();
+console.log(chalk.yellow(JSON.stringify(msg, null, 2)));
+
+for (const toolCall of msg.toolCalls || []) {
+    if (toolCall.name === "calculate") {
+        const expression = toolCall.arguments.expression;
+        const result = eval(expression);
+        context.messages.push({
+            role: "toolResult",
+            content: `The result of ${expression} is ${result}.`,
+            toolCallId: toolCall.id,
+            isError: false
+        });
+    }
+}
+
+msg = await ai.complete(context, options);
+console.log();
+console.log(chalk.yellow(JSON.stringify(msg, null, 2)));
+
+
+
+

packages/ai/test/examples/openai-responses.ts

@@ -0,0 +1,60 @@
+import chalk from "chalk";
+import { OpenAIResponsesLLMOptions, OpenAIResponsesLLM } from "../../src/providers/openai-responses.js";
+import type { Context, Tool } from "../../src/types.js";
+
+// Define a simple calculator tool
+const tools: Tool[] = [
+    {
+        name: "calculate",
+        description: "Perform a mathematical calculation",
+        parameters: {
+            type: "object" as const,
+            properties: {
+                expression: {
+                    type: "string",
+                    description: "The mathematical expression to evaluate"
+                }
+            },
+            required: ["expression"]
+        }
+    }
+];
+
+const ai = new OpenAIResponsesLLM("gpt-5");
+const context: Context = {
+    messages: [
+        {
+            role: "user",
+            content: "Think about birds briefly. Then give me a list of 10 birds. Finally, calculate 42 * 17 + 123 and 453 + 434 in parallel using the calculator tool.",
+        }
+    ],
+    tools,
+}
+
+const options: OpenAIResponsesLLMOptions = {
+    onText: (t) => process.stdout.write(t),
+    onThinking: (t) => process.stdout.write(chalk.dim(t)),
+    reasoningEffort: "low",
+    reasoningSummary: "auto"
+};
+let msg = await ai.complete(context, options)
+context.messages.push(msg);
+console.log();
+console.log(chalk.yellow(JSON.stringify(msg, null, 2)));
+
+for (const toolCall of msg.toolCalls || []) {
+    if (toolCall.name === "calculate") {
+        const expression = toolCall.arguments.expression;
+        const result = eval(expression);
+        context.messages.push({
+            role: "toolResult",
+            content: `The result of ${expression} is ${result}.`,
+            toolCallId: toolCall.id,
+            isError: false
+        });
+    }
+}
+
+msg = await ai.complete(context, options);
+console.log();
+console.log(chalk.yellow(JSON.stringify(msg, null, 2)));