feat: Add support for OpenAI

.env.example

@@ -1 +1,13 @@
+# LLM provider: "anthropic" (default) or "azure"
+# LLM_PROVIDER=anthropic
+
+# Required when LLM_PROVIDER = "anthropic" or unset
 ANTHROPIC_API_KEY=
+
+# Azure OpenAI (when LLM_PROVIDER = "azure")
+# Example: HMS endpoint https://azure-ai.hms.edu
+# If your server expects paths under /openai, use https://azure-ai.hms.edu/openai
+AZURE_OPENAI_ENDPOINT=
+AZURE_OPENAI_API_KEY=
+AZURE_OPENAI_API_VERSION=2024-05-01-preview
+AZURE_OPENAI_DEPLOYMENT=gpt-5

.gitignore

@@ -91,6 +91,7 @@ bower_components
 build/Release
 
 # Dependency directories
+.pnpm-store/
 node_modules/
 jspm_packages/
 

README.md

@@ -50,12 +50,16 @@ You can add your own graphs, biomedical or otherwise, without changing any code.
    cp .env.example .env
    ```
 
-   Open `.env` and add your API key. The CLI currently uses [Anthropic](https://www.anthropic.com/) as its LLM provider:
+   Open `.env` and add credentials for your chosen LLM provider. The CLI supports [Anthropic](https://www.anthropic.com/) (default) and [Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service).
+
+   **Anthropic (default):**
 
    ```env
    ANTHROPIC_API_KEY=your_key_here
    ```
 
+   **Azure OpenAI:** set `LLM_PROVIDER=azure` and your Azure credentials (see [OpenAI (Azure)](#openai-azure) below).
+
 4. **Start the CLI**:
 
    ```bash
@@ -83,6 +87,24 @@ Find the relationship between metformin and breast cancer.
 
 The agent will search the knowledge graph, traverse relationships, and synthesize an answer while citing the specific nodes and edges it used.
 
+### OpenAI (Azure)
+
+To use Azure OpenAI (e.g. an institutional endpoint like HMS), set in `.env`:
+
+```env
+LLM_PROVIDER=azure
+AZURE_OPENAI_ENDPOINT=https://azure-ai.hms.edu
+AZURE_OPENAI_API_KEY=your_azure_key
+AZURE_OPENAI_API_VERSION=2024-05-01-preview
+AZURE_OPENAI_DEPLOYMENT=gpt-4o-1120
+```
+
+If your server expects paths under `/openai`, use `https://azure-ai.hms.edu/openai` as the endpoint.
+
+### Switching models
+
+The `/models` command is listed in the CLI help. Model selection is currently via environment: set `LLM_PROVIDER` to `anthropic` or `azure` and configure the corresponding API keys, then restart the CLI for the change to take effect. In-app model switching (e.g. choosing a model from a list when you type `/models`) will be supported when the TUI library adds custom command registration and transport invalidation.
+
 ## Adding Your Own Knowledge Graph
 
 Adding a new graph takes four steps and requires **no code changes**.
@@ -182,7 +204,7 @@ Tool renderers provide rich visualization of tool outputs in the terminal. See `
 - **Runtime**: [Bun](https://bun.sh/)
 - **Language**: TypeScript
 - **UI**: [React 19](https://react.dev/) with [@ai-tui/core](https://www.npmjs.com/package/@ai-tui/core)
-- **LLM**: [Vercel AI SDK](https://sdk.vercel.ai/) (currently configured for Anthropic Claude)
+- **LLM**: [Vercel AI SDK](https://sdk.vercel.ai/) (Anthropic Claude or Azure OpenAI via `LLM_PROVIDER`)
 - **Data**: Local parquet files queried via [DuckDB](https://duckdb.org/)
 - **Validation**: [Zod](https://zod.dev/)
 

package.json

@@ -14,6 +14,7 @@
 	},
 	"dependencies": {
 		"@ai-sdk/anthropic": "^3.0.17",
+		"@ai-sdk/azure": "^3.0.0",
 		"@ai-sdk/react": "^3.0.41",
 		"@ai-tui/core": "^0.1.1",
 		"@duckdb/node-api": "1.4.4-r.1",

src/index.tsx

@@ -1,4 +1,5 @@
 import { createAnthropic } from "@ai-sdk/anthropic";
+import { createAzure } from "@ai-sdk/azure";
 import {
 	Agent,
 	type ConfigInput,
@@ -16,21 +17,77 @@ import { GraphLoader, makeParquetGraphTools } from "./parquet-tools/index.ts";
 import { graphAgentPrompt, regularPrompt } from "./prompts.ts";
 import { GetNodeDetailsTool } from "./tool-renderers/index.ts";
 
-export const env = createEnv({
+const providerSchema = z.enum(["anthropic", "azure"]);
+
+const rawEnv = createEnv({
 	server: {
-		ANTHROPIC_API_KEY: z.string().min(1),
+		LLM_PROVIDER: z
+			.string()
+			.optional()
+			.transform((v) => (v ? providerSchema.parse(v) : "anthropic")),
+		ANTHROPIC_API_KEY: z.string().optional(),
+		AZURE_OPENAI_ENDPOINT: z.string().url().optional(),
+		AZURE_OPENAI_API_KEY: z.string().optional(),
+		AZURE_OPENAI_API_VERSION: z
+			.string()
+			.optional()
+			.default("2024-05-01-preview"),
+		AZURE_OPENAI_DEPLOYMENT: z.string().optional().default("gpt-4o-1120"),
 	},
 	runtimeEnv: process.env,
 	emptyStringAsUndefined: true,
 });
 
+function validateEnv() {
+	const provider = rawEnv.LLM_PROVIDER;
+	if (provider === "anthropic") {
+		if (!rawEnv.ANTHROPIC_API_KEY?.trim()) {
+			throw new Error(
+				"ANTHROPIC_API_KEY is required when LLM_PROVIDER is anthropic (or unset). Set it in .env",
+			);
+		}
+		return {
+			...rawEnv,
+			ANTHROPIC_API_KEY: rawEnv.ANTHROPIC_API_KEY!,
+		};
+	}
+	// provider === "azure"
+	if (!rawEnv.AZURE_OPENAI_ENDPOINT?.trim() || !rawEnv.AZURE_OPENAI_API_KEY?.trim()) {
+		throw new Error(
+			"AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY are required when LLM_PROVIDER=azure. Set them in .env",
+		);
+	}
+	return {
+		...rawEnv,
+		AZURE_OPENAI_ENDPOINT: rawEnv.AZURE_OPENAI_ENDPOINT!,
+		AZURE_OPENAI_API_KEY: rawEnv.AZURE_OPENAI_API_KEY!,
+	};
+}
+
+export const env = validateEnv();
+
 // Discover graph metadata instantly; parquet data loads lazily per agent
 const DATA_DIR = join(import.meta.dir, "..", "data");
 const loader = new GraphLoader(DATA_DIR);
 
-const anthropic = createAnthropic({
-	apiKey: env.ANTHROPIC_API_KEY,
-});
+const anthropic =
+	env.LLM_PROVIDER === "anthropic"
+		? createAnthropic({ apiKey: env.ANTHROPIC_API_KEY })
+		: null;
+
+const azure =
+	env.LLM_PROVIDER === "azure"
+		? createAzure({
+				baseURL: env.AZURE_OPENAI_ENDPOINT,
+				apiKey: env.AZURE_OPENAI_API_KEY,
+				apiVersion: env.AZURE_OPENAI_API_VERSION,
+		  })
+		: null;
+
+const currentModelDisplay =
+	env.LLM_PROVIDER === "azure"
+		? { providerName: "OpenAI", name: `Azure (${env.AZURE_OPENAI_DEPLOYMENT})` }
+		: { providerName: "Anthropic", name: "Claude Opus 4.5" };
 
 /**
  * Custom tool component renderers for graph agent tools.
@@ -46,7 +103,7 @@ const configValue: ConfigInput = {
 			new Agent({
 				id: meta.slug,
 				name: meta.name,
-				model: { providerName: "Anthropic", name: "Claude Opus 4.5" },
+				model: currentModelDisplay,
 				color: meta.color as HexColor,
 				toolComponents: graphToolComponents,
 				createTransport: async ({ transportOptions }) => {
@@ -55,8 +112,13 @@ const configValue: ConfigInput = {
 						loader,
 					)) as ToolSet;
 
+					const model =
+						env.LLM_PROVIDER === "azure" && azure
+							? azure(env.AZURE_OPENAI_DEPLOYMENT)
+							: anthropic!("claude-opus-4-5");
+
 					const agent = new ToolLoopAgent({
-						model: anthropic("claude-opus-4-5"),
+						model,
 						tools: graphTools,
 						instructions: `${regularPrompt}\n\n${graphAgentPrompt}`,
 						stopWhen: stepCountIs(50),
@@ -69,6 +131,7 @@ const configValue: ConfigInput = {
 				},
 			}),
 	) as ConfigInput["agents"],
+	commands: [{ name: "/models", hint: "Switch LLM model (set LLM_PROVIDER and restart)" }],
 	appName: {
 		sections: [
 			{