Adds Azure OpenAI LLM to Mem0 TS SDK (#2536)

docs/changelog/overview.mdx

@@ -129,6 +129,7 @@ mode: "wide"
 
 <Update label="2025-04-11" description="v2.1.16">
 **New Features:**
+- **Azure OpenAI:** Added support for Azure OpenAI
 - **Mistral LLM:** Added Mistral LLM integration in OSS
 
 **Improvements:**

docs/components/llms/models/azure_openai.mdx

@@ -2,6 +2,8 @@
 title: Azure OpenAI
 ---
 
+<Note> Mem0 Now Supports Azure OpenAI Models in TypeScript SDK </Note>
+
 To use Azure OpenAI models, you have to set the `LLM_AZURE_OPENAI_API_KEY`, `LLM_AZURE_ENDPOINT`, `LLM_AZURE_DEPLOYMENT` and `LLM_AZURE_API_VERSION` environment variables. You can obtain the Azure API key from the [Azure](https://azure.microsoft.com/).
 
 > **Note**: The following are currently unsupported with reasoning models `Parallel tool calling`,`temperature`, `top_p`, `presence_penalty`, `frequency_penalty`, `logprobs`, `top_logprobs`, `logit_bias`, `max_tokens`
@@ -9,7 +11,8 @@ To use Azure OpenAI models, you have to set the `LLM_AZURE_OPENAI_API_KEY`, `LLM
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -48,7 +51,38 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
-We also support the new [OpenAI structured-outputs](https://platform.openai.com/docs/guides/structured-outputs/introduction) model.
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'azure_openai',
+    config: {
+      apiKey: process.env.AZURE_OPENAI_API_KEY || '',
+      modelProperties: {
+        endpoint: 'https://your-api-base-url',
+        deployment: 'your-deployment-name',
+        modelName: 'your-model-name',
+        apiVersion: 'version-to-use',
+        // Any other parameters you want to pass to the Azure OpenAI API
+      },
+    },
+  },
+};
+
+const memory = new Memory(config);
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+</CodeGroup>
+
+
+We also support the new [OpenAI structured-outputs](https://platform.openai.com/docs/guides/structured-outputs/introduction) model. Typescript SDK does not support the `azure_openai_structured` model yet.
 
 ```python
 import os

mem0-ts/src/client/telemetry.ts

@@ -1,7 +1,7 @@
 // @ts-nocheck
 import type { TelemetryClient, TelemetryOptions } from "./telemetry.types";
 
-let version = "2.1.12";
+let version = "2.1.16";
 
 // Safely check for process.env in different environments
 let MEM0_TELEMETRY = true;

mem0-ts/src/oss/src/config/manager.ts

@@ -42,6 +42,9 @@ export class ConfigManager {
           model:
             userConfig.llm?.config?.model ||
             DEFAULT_MEMORY_CONFIG.llm.config.model,
+          modelProperties:
+            userConfig.llm?.config?.modelProperties ||
+            DEFAULT_MEMORY_CONFIG.llm.config.modelProperties,
         },
       },
       historyDbPath:

mem0-ts/src/oss/src/llms/azure.ts

@@ -0,0 +1,82 @@
+import { AzureOpenAI } from "openai";
+import { LLM, LLMResponse } from "./base";
+import { LLMConfig, Message } from "../types";
+
+export class AzureOpenAILLM implements LLM {
+  private client: AzureOpenAI;
+  private model: string;
+
+  constructor(config: LLMConfig) {
+    if (!config.apiKey || !config.modelProperties?.endpoint) {
+      throw new Error("Azure OpenAI requires both API key and endpoint");
+    }
+
+    const { endpoint, ...rest } = config.modelProperties;
+
+    this.client = new AzureOpenAI({
+      apiKey: config.apiKey,
+      endpoint: endpoint as string,
+      ...rest,
+    });
+    this.model = config.model || "gpt-4";
+  }
+
+  async generateResponse(
+    messages: Message[],
+    responseFormat?: { type: string },
+    tools?: any[],
+  ): Promise<string | LLMResponse> {
+    const completion = await this.client.chat.completions.create({
+      messages: messages.map((msg) => {
+        const role = msg.role as "system" | "user" | "assistant";
+        return {
+          role,
+          content:
+            typeof msg.content === "string"
+              ? msg.content
+              : JSON.stringify(msg.content),
+        };
+      }),
+      model: this.model,
+      response_format: responseFormat as { type: "text" | "json_object" },
+      ...(tools && { tools, tool_choice: "auto" }),
+    });
+
+    const response = completion.choices[0].message;
+
+    if (response.tool_calls) {
+      return {
+        content: response.content || "",
+        role: response.role,
+        toolCalls: response.tool_calls.map((call) => ({
+          name: call.function.name,
+          arguments: call.function.arguments,
+        })),
+      };
+    }
+
+    return response.content || "";
+  }
+
+  async generateChat(messages: Message[]): Promise<LLMResponse> {
+    const completion = await this.client.chat.completions.create({
+      messages: messages.map((msg) => {
+        const role = msg.role as "system" | "user" | "assistant";
+        return {
+          role,
+          content:
+            typeof msg.content === "string"
+              ? msg.content
+              : JSON.stringify(msg.content),
+        };
+      }),
+      model: this.model,
+    });
+
+    const response = completion.choices[0].message;
+    return {
+      content: response.content || "",
+      role: response.role,
+    };
+  }
+}

mem0-ts/src/oss/src/types/index.ts

@@ -39,6 +39,7 @@ export interface LLMConfig {
   config?: Record<string, any>;
   apiKey?: string;
   model?: string;
+  modelProperties?: Record<string, any>;
 }
 
 export interface Neo4jConfig {
@@ -127,6 +128,7 @@ export const MemoryConfigSchema = z.object({
     config: z.object({
       apiKey: z.string(),
       model: z.string().optional(),
+      modelProperties: z.record(z.string(), z.any()).optional(),
     }),
   }),
   historyDbPath: z.string().optional(),

mem0-ts/src/oss/src/utils/factory.ts

@@ -25,6 +25,7 @@ import { SupabaseHistoryManager } from "../storage/SupabaseHistoryManager";
 import { HistoryManager } from "../storage/base";
 import { GoogleEmbedder } from "../embeddings/google";
 import { GoogleLLM } from "../llms/google";
+import { AzureOpenAILLM } from "../llms/azure";
 
 export class EmbedderFactory {
   static create(provider: string, config: EmbeddingConfig): Embedder {
@@ -56,6 +57,8 @@ export class LLMFactory {
         return new OllamaLLM(config);
       case "google":
         return new GoogleLLM(config);
+      case "azure_openai":
+        return new AzureOpenAILLM(config);
       case "mistral":
         return new MistralLLM(config);
       default:

mem0-ts/src/oss/src/utils/telemetry.ts

@@ -4,7 +4,7 @@ import type {
   TelemetryEventData,
 } from "./telemetry.types";
 
-let version = "2.1.15";
+let version = "2.1.16";
 
 // Safely check for process.env in different environments
 let MEM0_TELEMETRY = true;