---
title: Update Memory Settings
description: "Update Mudabbir's memory backend configuration: switch between file store and Mem0, configure LLM and embedder providers, set vector store options, and toggle auto-learning."
api: POST /api/memory/settings
baseUrl: http://localhost:8888
layout: '@/layouts/APIEndpointLayout.astro'
auth: bearer
section: API Reference
ogType: article
keywords: ["update memory settings", "memory configuration", "backend switch"]
tags: ["api", "memory"]
---

## Overview

Updates the memory backend configuration. Changes take effect immediately — the memory manager is reloaded with the new settings.

## Request Body

<ParamTable type="body">
  <Param name="memory_backend" type="string" enum={["file", "mem0"]}>
    Memory backend to use. `file` is the default file-based store, `mem0` enables semantic memory with auto-learn.
  </Param>
  <Param name="mem0_llm_provider" type="string" enum={["ollama", "openai", "anthropic"]}>
    LLM provider for mem0's extraction and summarization.
  </Param>
  <Param name="mem0_llm_model" type="string">
    LLM model name (e.g., `llama3.2`, `gpt-4o-mini`).
  </Param>
  <Param name="mem0_embedder_provider" type="string" enum={["ollama", "openai"]}>
    Embedding provider for semantic search.
  </Param>
  <Param name="mem0_embedder_model" type="string">
    Embedding model name (e.g., `nomic-embed-text`, `text-embedding-3-small`).
  </Param>
  <Param name="mem0_vector_store" type="string" enum={["qdrant"]}>
    Vector store backend.
  </Param>
  <Param name="mem0_ollama_base_url" type="string">
    Ollama server URL (default: `http://localhost:11434`).
  </Param>
  <Param name="mem0_auto_learn" type="boolean">
    Enable automatic memory extraction from conversations.
  </Param>
</ParamTable>

## Response

<ResponseField name="status" type="string">`"ok"` on success</ResponseField>

<RequestExample>
<Tabs items={["cURL", "JavaScript", "Python"]}>
  <Tab title="cURL">
    ```bash
    curl -X POST "http://localhost:8888/api/memory/settings" \
      -H "Authorization: Bearer <token>" \
      -H "Content-Type: application/json" \
      -d '{
        "memory_backend": "mem0",
        "mem0_llm_provider": "ollama",
        "mem0_llm_model": "llama3.2",
        "mem0_embedder_provider": "ollama",
        "mem0_embedder_model": "nomic-embed-text",
        "mem0_vector_store": "qdrant",
        "mem0_auto_learn": true
      }'
    ```
  </Tab>
  <Tab title="JavaScript">
    ```javascript
    const response = await fetch("http://localhost:8888/api/memory/settings", {
      method: "POST",
      headers: {
        "Authorization": "Bearer <token>",
        "Content-Type": "application/json"
      },
      body: JSON.stringify({
        memory_backend: "mem0",
        mem0_llm_provider: "ollama",
        mem0_llm_model: "llama3.2",
        mem0_embedder_provider: "ollama",
        mem0_embedder_model: "nomic-embed-text",
        mem0_vector_store: "qdrant",
        mem0_auto_learn: true
      })
    });
    const data = await response.json();
    console.log(data);
    ```
  </Tab>
  <Tab title="Python">
    ```python
    import requests

    response = requests.post(
        "http://localhost:8888/api/memory/settings",
        headers={"Authorization": "Bearer <token>"},
        json={
            "memory_backend": "mem0",
            "mem0_llm_provider": "ollama",
            "mem0_llm_model": "llama3.2",
            "mem0_embedder_provider": "ollama",
            "mem0_embedder_model": "nomic-embed-text",
            "mem0_vector_store": "qdrant",
            "mem0_auto_learn": True
        }
    )
    print(response.json())
    ```
  </Tab>
</Tabs>
</RequestExample>

<ResponseExample>
<Tabs items={["200"]}>
  <Tab title="200">
    ```json
    {
      "status": "ok"
    }
    ```
  </Tab>
</Tabs>
</ResponseExample>
