---
title: "TypeScript LLM Adapters"
description: "The TypeScript core package provides a unified LLM abstraction layer for extracting structured facts, processing memories, and executing chat completions across diverse language model backends. Bui..."
last_updated: "2026-10-11T17:37:10.25946+00:00"
canonical_url: "https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/technical/llm-providers/typescript-llm-adapters"
---

<details>
<summary>Relevant source files</summary>

The following files were used as context for generating this wiki page:

- [mem0-ts/src/oss/src/utils/factory.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/utils/factory.ts)
- [integrations/openclaw/cli/oss-wizard.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/openclaw/cli/oss-wizard.ts)
- [integrations/openclaw/providers.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/openclaw/providers.ts)
- [mem0-ts/src/oss/src/memory/index.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/memory/index.ts)
- [integrations/vercel-ai-sdk/src/mem0-provider-selector.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/vercel-ai-sdk/src/mem0-provider-selector.ts)
- [mem0-ts/src/oss/src/llms/aws_bedrock.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/aws_bedrock.ts)
- [integrations/deepseek-plugin/src/index.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/deepseek-plugin/src/index.ts)
- [integrations/vercel-ai-sdk/src/provider-response-provider.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/vercel-ai-sdk/src/provider-response-provider.ts)
- [mem0-ts/src/oss/src/llms/deepseek.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/deepseek.ts)
- [mem0/utils/factory.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/utils/factory.py)
- [mem0-ts/src/oss/src/llms/mistral.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/mistral.ts)
- [mem0-ts/src/oss/src/llms/vllm.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/vllm.ts)
- [mem0-ts/src/oss/src/llms/groq.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/groq.ts)
- [mem0-ts/src/oss/src/llms/ollama.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/ollama.ts)
- [mem0-ts/src/oss/src/llms/xai.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/xai.ts)
- [integrations/vercel-ai-sdk/src/mem0-provider.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/vercel-ai-sdk/src/mem0-provider.ts)
- [mem0-ts/src/oss/src/index.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/index.ts)
- [integrations/vercel-ai-sdk/src/mem0-generic-language-model.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/vercel-ai-sdk/src/mem0-generic-language-model.ts)
- [mem0-ts/src/oss/src/llms/lmstudio.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/lmstudio.ts)
- [mem0-ts/src/oss/src/llms/anthropic.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/anthropic.ts)
- [mem0-ts/src/oss/src/llms/google.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/google.ts)
- [mem0-ts/src/oss/src/llms/sarvam.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/sarvam.ts)
- [mem0/llms/deepseek.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/llms/deepseek.py)
- [mem0-ts/src/oss/src/llms/openai.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/openai.ts)
- [integrations/vercel-ai-sdk/src/mem0-facade.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/vercel-ai-sdk/src/mem0-facade.ts)
- [mem0/llms/aws_bedrock.py](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0/llms/aws_bedrock.py)
- [mem0-ts/src/oss/src/llms/azure.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/azure.ts)
- [mem0-ts/src/oss/src/llms/langchain.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/langchain.ts)
- [integrations/vercel-ai-sdk/config/test-config.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/integrations/vercel-ai-sdk/config/test-config.ts)
- [mem0-ts/src/oss/src/llms/base.ts](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/base.ts)
</details>

## Overview

The [TypeScript core](https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/technical/typescript-core/typescript-core) package provides a unified LLM abstraction layer for extracting structured facts, processing memories, and executing chat completions across diverse language model backends. Built around a core `LLM` interface and standardized `LLMResponse` structures, the architecture enables modular provider integration and flexible configuration handling via the `LLMFactory`.

Sources: [mem0-ts/src/oss/src/llms/base.ts:1-18](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/base.ts#L1-L18), [mem0-ts/src/oss/src/utils/factory.ts:108-152](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/utils/factory.ts#L108-L152)

## LLM Abstraction and Types

### Core Interface and Response Structures

The TypeScript core establishes a standard contract for all language model integrations via the `LLM` interface. Implementations must provide two primary asynchronous methods: `generateResponse`, which accepts an array of message objects along with optional response formatting and tools, and `generateChat`, which takes typed `Message` arrays and returns a standardized `LLMResponse`.

```typescript
export interface LLMResponse {
  content: string;
  role: string;
  toolCalls?: Array<{
    name: string;
    arguments: string;
  }>;
}

export interface LLM {
  generateResponse(
    messages: Array<{ role: string; content: string }>,
    response_format?: { type: string },
    tools?: any[],
  ): Promise<any>;
  generateChat(messages: Message[]): Promise<LLMResponse>;
}
```
Sources: [mem0-ts/src/oss/src/llms/base.ts:3-19](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/base.ts#L3-L19)

## OpenAI and Compatible Subclasses

### Base OpenAI Client and Provider Configurations

The `OpenAILLM` class serves as the foundational base adapter utilizing the official `openai` package to execute chat completions and tool calls [mem0-ts/src/oss/src/llms/openai.ts:1-75](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/openai.ts#L1-L75). It maps input messages to valid roles (`system`, `user`, or `assistant`), serializes complex content blocks, and handles response parsing for both direct string outputs and structured tool invocations [mem0-ts/src/oss/src/llms/openai.ts:23-53](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/openai.ts#L23-L53). Several subclassed providers extend `OpenAILLM` by supplying customized default environment variables, base URLs, and model identifiers [mem0-ts/src/oss/src/llms/deepseek.ts:1-43](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/deepseek.ts#L1-L43), [mem0-ts/src/oss/src/llms/vllm.ts:1-49](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/vllm.ts#L1-L49), [mem0-ts/src/oss/src/llms/xai.ts:1-50](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/xai.ts#L1-L50), [mem0-ts/src/oss/src/llms/lmstudio.ts:1-41](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/lmstudio.ts#L1-L41), [mem0-ts/src/oss/src/llms/sarvam.ts:1-52](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/sarvam.ts#L1-L52).

| Provider Class | Default Model | Default Base URL | Key Resolution Order |
| :--- | :--- | :--- | :--- |
| `OpenAILLM` | `gpt-5-mini` | `config.baseURL` | `config.apiKey` |
| `DeepSeekLLM` | `deepseek-chat` | `config.baseURL` \|\| `DEEPSEEK_API_BASE` \|\| `https://api.deepseek.com` | `config.apiKey` \|\| `DEEPSEEK_API_KEY` |
| `VllmLLM` | `Qwen/Qwen2.5-32B-Instruct` | `config.baseURL` \|\| `config.vllmBaseURL` \|\| `config.vllm_base_url` \|\| `config.url` \|\| `VLLM_BASE_URL` \|\| `http://localhost:8000/v1` | `config.apiKey` \|\| `VLLM_API_KEY` \|\| `vllm-api-key` |
| `XAILLM` | `grok-4.3` | `config.baseURL` \|\| `XAI_API_BASE` \|\| `https://api.x.ai/v1` | `config.apiKey` \|\| `XAI_API_KEY` |
| `LMStudioLLM` | `lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF/Meta-Llama-3.1-70B-Instruct-IQ2_M.gguf` | `config.baseURL` ?? `http://localhost:1234/v1` | `config.apiKey` \|\| `lm-studio` |
| `SarvamLLM` | `sarvam-m` | `config.baseURL` \|\| `SARVAM_API_BASE` \|\| `https://api.sarvam.ai/v1` | `config.apiKey` \|\| `SARVAM_API_KEY` |

Sources: [mem0-ts/src/oss/src/llms/deepseek.ts:5-20](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/deepseek.ts#L5-L20), [mem0-ts/src/oss/src/llms/vllm.ts:5-26](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/vllm.ts#L5-L26), [mem0-ts/src/oss/src/llms/xai.ts:14-27](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/xai.ts#L14-L27), [mem0-ts/src/oss/src/llms/lmstudio.ts:5-18](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/lmstudio.ts#L5-L18), [mem0-ts/src/oss/src/llms/sarvam.ts:14-29](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/sarvam.ts#L14-L29), [mem0-ts/src/oss/src/llms/openai.ts:5-16](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/openai.ts#L5-L16)

> [!WARNING]
> `DeepSeekLLM`, `XAILLM`, and `SarvamLLM` throw an explicit synchronous `Error` during initialization if an API key cannot be resolved from configuration parameters or their respective environment variables (`DEEPSEEK_API_KEY`, `XAI_API_KEY`, `SARVAM_API_KEY`).
> Sources: [mem0-ts/src/oss/src/llms/deepseek.ts:7-10](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/deepseek.ts#L7-L10), [mem0-ts/src/oss/src/llms/xai.ts:16-19](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/xai.ts#L16-L19), [mem0-ts/src/oss/src/llms/sarvam.ts:16-19](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/sarvam.ts#L16-L19)

### Error Handling and Subclass Wrappers

Each subclass wraps its execution methods (`generateResponse` and `generateChat`) in `try...catch` blocks to intercept runtime exceptions from the underlying OpenAI client and rethrow them with provider-prefixed context messages [mem0-ts/src/oss/src/llms/deepseek.ts:22-42](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/deepseek.ts#L22-L42), [mem0-ts/src/oss/src/llms/vllm.ts:28-48](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/vllm.ts#L28-L48), [mem0-ts/src/oss/src/llms/xai.ts:29-49](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/xai.ts#L29-L49), [mem0-ts/src/oss/src/llms/lmstudio.ts:20-40](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/lmstudio.ts#L20-L40), [mem0-ts/src/oss/src/llms/sarvam.ts:31-51](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/sarvam.ts#L31-L51). For example, `LMStudioLLM` catches any error during generation and formats it into an `LM Studio LLM failed: ${message}` exception [mem0-ts/src/oss/src/llms/lmstudio.ts:27-30](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/lmstudio.ts#L27-L30).

Sources: [mem0-ts/src/oss/src/llms/deepseek.ts:22-42](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/deepseek.ts#L22-L42), [mem0-ts/src/oss/src/llms/vllm.ts:28-48](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/vllm.ts#L28-L48), [mem0-ts/src/oss/src/llms/xai.ts:29-49](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/xai.ts#L29-L49), [mem0-ts/src/oss/src/llms/lmstudio.ts:20-40](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/lmstudio.ts#L20-L40), [mem0-ts/src/oss/src/llms/sarvam.ts:31-51](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/sarvam.ts#L31-L51)

## Proprietary SDK Providers

### Anthropic, Google, Mistral, and Groq Integration

The TypeScript engine implements direct SDK client wrappers for proprietary language model providers that do not rely on the OpenAI-compatible base class. Each provider manages its own lazy client initialization using `loadPeer` to dynamically import underlying SDK packages such as `@anthropic-ai/sdk`, `@google/genai`, `@mistralai/mistralai`, and `groq-sdk` upon first invocation [mem0-ts/src/oss/src/llms/anthropic.ts:34-42](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/anthropic.ts#L34-L42), [mem0-ts/src/oss/src/llms/google.ts:16-24](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/google.ts#L16-L24), [mem0-ts/src/oss/src/llms/mistral.ts:19-27](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/mistral.ts#L19-L27), [mem0-ts/src/oss/src/llms/groq.ts:20-28](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/groq.ts#L20-L28).

| Provider Class | Default Model | Configuration Keys | SDK Peer Package |
| :--- | :--- | :--- | :--- |
| `AnthropicLLM` | `claude-sonnet-4-6` | `config.apiKey` \|\| `ANTHROPIC_API_KEY`, `config.baseURL`, `config.maxTokens`, `config.temperature`, `config.topP` | `@anthropic-ai/sdk` |
| `GoogleLLM` | `gemini-2.0-flash` | `config.apiKey`, `config.model` | `@google/genai` |
| `MistralLLM` | `mistral-tiny-latest` | `config.apiKey`, `config.model` | `@mistralai/mistralai` |
| `GroqLLM` | `llama3-70b-8192` | `config.apiKey` \|\| `GROQ_API_KEY`, `config.model` | `groq-sdk` |

Sources: [mem0-ts/src/oss/src/llms/anthropic.ts:14-32](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/anthropic.ts#L14-L32), [mem0-ts/src/oss/src/llms/google.ts:11-14](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/google.ts#L11-L14), [mem0-ts/src/oss/src/llms/mistral.ts:11-17](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/mistral.ts#L11-L17), [mem0-ts/src/oss/src/llms/groq.ts:11-18](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/groq.ts#L11-L18)

> [!WARNING]
> `AnthropicLLM`, `MistralLLM`, and `GroqLLM` throw an explicit synchronous `Error` during instantiation if the required API key is missing from configuration parameters and environment variables. `GoogleLLM` permits an undefined API key during construction to support ambient credentials.
> Sources: [mem0-ts/src/oss/src/llms/anthropic.ts:15-18](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/anthropic.ts#L15-L18), [mem0-ts/src/oss/src/llms/mistral.ts:11-14](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/mistral.ts#L11-L14), [mem0-ts/src/oss/src/llms/groq.ts:11-15](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/groq.ts#L11-L15), [mem0-ts/src/oss/src/llms/google.ts:11-14](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/google.ts#L11-L14)

### Message Formatting and Response Parsing

Each adapter adapts internal generic `Message` structures into provider-specific schemas. `AnthropicLLM` extracts system messages into a dedicated `system` property, forwards remaining messages with mapped roles, and iterates through response content blocks to handle text and `tool_use` blocks [mem0-ts/src/oss/src/llms/anthropic.ts:50-101](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/anthropic.ts#L50-L101). `GoogleLLM` formats message contents into part arrays and maps system roles to `model` roles while setting `responseMimeType` to `application/json` when `responseFormat.type` equals `json_object` [mem0-ts/src/oss/src/llms/google.ts:26-38](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/google.ts#L26-L38), [mem0-ts/src/oss/src/llms/google.ts:67-69](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/google.ts#L67-L69).

> [!TIP]
> `GoogleLLM` automatically strips leading markdown JSON fences (`^```json\n` and `\n```$`) from text completions when structured JSON output is returned without native mime-type enforcement.
> Sources: [mem0-ts/src/oss/src/llms/google.ts:89-91](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/google.ts#L89-L91)

`MistralLLM` includes a `contentToString` helper that processes text chunks and content arrays into strings, and maps tool calls when present in completion choices [mem0-ts/src/oss/src/llms/mistral.ts:30-47](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/mistral.ts#L30-L47), [mem0-ts/src/oss/src/llms/mistral.ts:78-90](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/mistral.ts#L78-L90). `GroqLLM` maps chat messages to standard completion inputs and returns message content strings or chat response structures directly [mem0-ts/src/oss/src/llms/groq.ts:30-68](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/groq.ts#L30-L68).

Sources: [mem0-ts/src/oss/src/llms/mistral.ts:30-93](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/mistral.ts#L30-L93), [mem0-ts/src/oss/src/llms/groq.ts:30-68](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/groq.ts#L30-L68)

## AWS Bedrock Converse Integration

### Message Formatting and Provider Extraction

The `AWSBedrockLLM` class implements the LLM interface using the AWS Bedrock **Converse API** (`ConverseCommand`). This provides a unified message and tool interface across multiple model families, including Anthropic, Amazon (Nova), Meta, Mistral, and Cohere. The optional `@aws-sdk/client-bedrock-runtime` package is loaded dynamically on first use via an `import()` statement to ensure the SDK remains optional. Credentials resolve via the standard AWS chain or explicit configuration properties (`awsRegion`, `awsAccessKeyId`, `awsSecretAccessKey`, `awsSessionToken`, or an injected `client`).

```typescript
const PROVIDERS = [
  "ai21",
  "amazon",
  "anthropic",
  "cohere",
  "meta",
  "mistral",
  "stability",
  "writer",
  "deepseek",
  "gpt-oss",
  "perplexity",
  "snowflake",
  "titan",
  "command",
  "j2",
  "llama",
  "minimax",
];
```
Sources: [mem0-ts/src/oss/src/llms/aws_bedrock.ts:8-26](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/aws_bedrock.ts#L8-L26)

> [!WARNING]
> Dynamic SDK loading must use a dynamic `import()`, never `require()`. Bundlers like tsup and esbuild rewrite `require()` into a `__require` shim that throws an error when evaluating published ESM bundles (`dist/oss/index.mjs`), causing the provider to fail even when the AWS SDK is installed.
> Sources: [mem0-ts/src/oss/src/llms/aws_bedrock.ts:119-125](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/aws_bedrock.ts#L119-L125)

Model family identifiers are validated and extracted against a recognized provider list. If `providerOverride` is specified, it must match one of the recognized provider strings; otherwise, the adapter searches the model string using regular expressions.

```typescript
export function extractProvider(
  model: string,
  providerOverride?: string,
): string {
  if (providerOverride) {
    if (!PROVIDERS.includes(providerOverride)) {
      throw new Error(
        `Unknown providerOverride '${providerOverride}'. Valid providers: ${PROVIDERS.join(", ")}`,
      );
    }
    return providerOverride;
  }
  for (const provider of PROVIDERS) {
    const re = new RegExp(
      `\\b${provider.replace(/[.*+?^${}()|[\]\\]/g, "\\$&")}\\b`,
    );
    if (re.test(model)) return provider;
  }
  throw new Error(`Unknown provider in model: ${model}`);
}
```
Sources: [mem0-ts/src/oss/src/llms/aws_bedrock.ts:32-51](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/aws_bedrock.ts#L32-L51)

Messages are split into a top-level `system` block and role-tagged content blocks for the Converse API. System roles are accumulated into system parts, while assistant and user roles are mapped to `converseMessages`. If no messages are provided, a default empty user message is added.

```typescript
/**
 * Split messages into a top-level `system` block (Converse passes system
 * prompts separately) and role-tagged content blocks for everything else.
 */
private formatMessages(messages: Message[]): {
  system?: { text: string }[];
  converseMessages: { role: string; content: { text: string }[] }[];
} {
  const systemParts: string[] = [];
  const converseMessages: { role: string; content: { text: string }[] }[] =
    [];

  for (const msg of messages) {
    const role = msg.role;
    const content =
      typeof msg.content === "string"
        ? msg.content
        : JSON.stringify(msg.content);
    if (role === "system") {
      systemParts.push(content);
    } else {
      converseMessages.push({
        role: role === "assistant" ? "assistant" : "user",
        content: [{ text: content }],
      });
    }
  }

  if (converseMessages.length === 0) {
    converseMessages.push({ role: "user", content: [{ text: "" }] });
  }

  return {
    system: systemParts.length
      ? [{ text: systemParts.join("\n") }]
      : undefined,
    converseMessages,
  };
}
```
Sources: [mem0-ts/src/oss/src/llms/aws_bedrock.ts:156-194](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/aws_bedrock.ts#L156-L194)

## Local Host and Framework Connectors

### Ollama Client Initialization and Model Validation

The `OllamaLLM` class connects to local Ollama servers to manage local model execution. Host URLs default to `http://localhost:11434` if neither `config.url` nor `config.baseURL` is supplied, and model selection defaults to `llama3.1:8b`. The peer dependency for Ollama is dynamically loaded via `loadPeer`.

```typescript
export class OllamaLLM implements LLM {
  private ollama!: Ollama;
  private model: string;
  private readonly host: string;
  // Using this variable to avoid calling the Ollama server multiple times
  private initialized: boolean = false;

  constructor(config: LLMConfig) {
    this.host = config.url || config.baseURL || "http://localhost:11434";
    this.model = config.model || "llama3.1:8b";
    this.ensureModelExists().catch((err) => {
      logger.error(`Error ensuring model exists: ${err}`);
    });
  }

  private async ensureClient(): Promise<void> {
    if (this.ollama) return;
    const sdk = await loadPeer("ollama", "Ollama LLM", () => import("ollama"));
    this.ollama = new sdk.Ollama({ host: this.host });
  }
```
Sources: [mem0-ts/src/oss/src/llms/ollama.ts:7-26](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/ollama.ts#L7-L26)

Before executing generations or chats, `ensureModelExists` checks whether the target model is present in the local server list using `this.ollama.list()`. If the model is absent, `this.ollama.pull` downloads it automatically while setting an internal flag to prevent redundant API checks across subsequent calls.

```typescript
private async ensureModelExists(): Promise<boolean> {
  if (this.initialized) {
    return true;
  }
  await this.ensureClient();
  const local_models = await this.ollama.list();
  if (!local_models.models.find((m: any) => m.name === this.model)) {
    logger.info(`Pulling model ${this.model}...`);
    await this.ollama.pull({ model: this.model });
  }
  this.initialized = true;
  return true;
}
```
Sources: [mem0-ts/src/oss/src/llms/ollama.ts:100-112](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/ollama.ts#L100-L112)

> [!NOTE]
> The `initialized` boolean flag on `OllamaLLM` ensures that `ensureModelExists` only queries and pulls models once during the instance lifecycle, avoiding redundant network roundtrips to the local Ollama daemon.
> Sources: [mem0-ts/src/oss/src/llms/ollama.ts:11-12](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/ollama.ts#L11-L12), [mem0-ts/src/oss/src/llms/ollama.ts:100-103](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/ollama.ts#L100-L103)

---

### LangChain Message Bridging and Schema Selection

The `LangchainLLM` adapter wraps existing LangChain language model instances (`BaseLanguageModel`). The constructor verifies that `config.model` is an object containing an `invoke` method. Model names are resolved from `modelId`, `model`, or defaulted to `langchain-model`.

```typescript
export class LangchainLLM implements LLM {
  private llmInstance: BaseLanguageModel;
  private modelName: string;

  constructor(config: LLMConfig) {
    if (!config.model || typeof config.model !== "object") {
      throw new Error(
        "Langchain provider requires an initialized Langchain instance passed via the 'model' field in the LLM config.",
      );
    }
    if (typeof (config.model as any).invoke !== "function") {
      throw new Error(
        "Provided Langchain 'instance' in the 'model' field does not appear to be a valid Langchain language model (missing invoke method).",
      );
    }
    this.llmInstance = config.model as BaseLanguageModel;
    this.modelName =
      (this.llmInstance as any).modelId ||
      (this.llmInstance as any).model ||
      "langchain-model";
  }
```
Sources: [mem0-ts/src/oss/src/llms/langchain.ts:37-57](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/langchain.ts#L37-L57)

The helper function `convertToLangchainMessages` translates Mem0 message roles into LangChain message classes. Unrecognized roles log a warning and fall back to `HumanMessage`.

| Mem0 Role | LangChain Message Class |
| :--- | :--- |
| `system` | `SystemMessage` |
| `user`, `human` | `HumanMessage` |
| `assistant`, `ai` | `AIMessage` |
| *unknown* | `HumanMessage` (with console warning) |

```typescript
const convertToLangchainMessages = async (
  messages: Message[],
): Promise<BaseMessage[]> => {
  const { AIMessage, HumanMessage, SystemMessage } =
    await import("@langchain/core/messages");
  return messages.map((msg) => {
    const content =
      typeof msg.content === "string"
        ? msg.content
        : JSON.stringify(msg.content);
    switch (msg.role?.toLowerCase()) {
      case "system":
        return new SystemMessage(content);
      case "user":
      case "human":
        return new HumanMessage(content);
      case "assistant":
      case "ai":
        return new AIMessage(content);
      default:
        console.warn(
          `Unsupported message role '${msg.role}' for Langchain. Treating as 'human'.`,
        );
        return new HumanMessage(content);
    }
  });
};
```
Sources: [mem0-ts/src/oss/src/llms/langchain.ts:9-35](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/langchain.ts#L9-L35)

During response generation, `LangchainLLM` inspects system and user prompts to select schemas for structured extraction:
- If the system prompt contains `"Personal Information Organizer"` and `"extract relevant pieces of information"`, `FactRetrievalSchema` is selected.
- If the user prompt contains `"smart memory manager"` and `"Compare newly retrieved facts"`, `MemoryUpdateSchema` is selected.

If a schema is matched and `withStructuredOutput` is available on the underlying instance, the adapter wraps the runnable using the schema. Tool bindings are applied via `bindTools` when available.

```typescript
// --- Internal Schema Selection Logic (runs regardless of response_format) ---
const systemPromptContent =
  (messages.find((m) => m.role === "system")?.content as string) || "";
const userPromptContent =
  (messages.find((m) => m.role === "user")?.content as string) || "";
// Check for memory prompts
if (
  systemPromptContent.includes("Personal Information Organizer") &&
  systemPromptContent.includes("extract relevant pieces of information")
) {
  selectedSchema = FactRetrievalSchema;
} else if (
  userPromptContent.includes("smart memory manager") &&
  userPromptContent.includes("Compare newly retrieved facts")
) {
  selectedSchema = MemoryUpdateSchema;
}

// --- Apply Structured Output if Schema Selected ---
if (
  selectedSchema &&
  typeof (this.llmInstance as any).withStructuredOutput === "function"
) {
  try {
    runnable = (this.llmInstance as any).withStructuredOutput(
      selectedSchema,
      { name: tools?.[0]?.function.name },
    );
    isStructuredOutput = true;
  } catch (e) {
    isStructuredOutput = false; // Ensure flag is false on error
    // No fallback to response_format here unless explicitly passed
    if (response_format?.type === "json_object") {
      invokeOptions.response_format = { type: "json_object" };
    }
  }
} else if (selectedSchema && response_format?.type === "json_object") {
  // Schema selected, but no .withStructuredOutput. Try basic response_format only if explicitly requested.
  if (
    (this.llmInstance as any)._identifyingParams?.response_format ||
    (this.llmInstance as any).response_format
  ) {
    invokeOptions.response_format = { type: "json_object" };
  }
} else if (!selectedSchema && response_format?.type === "json_object") {
  // Explicit JSON request, but no schema inferred. Try basic response_format.
  if (
    (this.llmInstance as any)._identifyingParams?.response_format ||
    (this.llmInstance as any).response_format
  ) {
    invokeOptions.response_format = { type: "json_object" };
  }
}

// --- Handle tool binding ---
if (tools && tools.length > 0) {
  if (typeof (runnable as any).bindTools === "function") {
    try {
      runnable = (runnable as any).bindTools(tools);
    } catch (e) {}
  } else {
  }
}
```
Sources: [mem0-ts/src/oss/src/llms/langchain.ts:70-132](https://github.com/blade47/mem0/blob/b7ad69afda6b6ed030347c66d48a13e4de9dec08/mem0-ts/src/oss/src/llms/langchain.ts#L70-L132)

## Related

- [Python LLM Adapters](https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/technical/llm-providers/python-llm-adapters)
- [Typescript Core](https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/technical/typescript-core/typescript-core)


## Sitemap

See the full [sitemap](https://www.doc0.dev/docs/faa36707-7c28-4f69-a18f-700ff61c704e/llms.txt) for all pages in this wiki.