chore: merge origin/main into mintlify/af82bda4 and resolve conflicts
This commit is contained in:
mintlify[bot]
@@ -3,6 +3,9 @@ title: Personalized AI Tutor
|
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description: "Build a Python AI tutor with Mem0 and OpenAI GPT-4 that remembers student progress, learning preferences, and lesson history across every session."
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---
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<Info icon="server">
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||||
**Works with:** Mem0 OSS (`Memory`)
|
||||
</Info>
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||||
|
||||
You can create a personalized AI Tutor using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
|
||||
|
||||
|
||||
@@ -3,6 +3,9 @@ title: Self-Hosted AI Companion
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description: "Run a fully local Mem0 companion with Ollama for both the LLM and embedder, keeping user data on your machine with no cloud API calls required."
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||||
---
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||||
|
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<Info icon="server">
|
||||
**Works with:** Mem0 OSS (`Memory`)
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</Info>
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Mem0 can be utilized entirely locally by leveraging Ollama for both the embedding model and the language model (LLM). This guide will walk you through the necessary steps and provide the complete code to get you started.
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||||
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@@ -3,6 +3,9 @@ title: Build a Node.js Companion
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description: "Ship a Node.js fitness coach powered by Mem0 and OpenAI that remembers user goals, workout history, and preferences run after run in JavaScript."
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---
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|
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<Info icon="server">
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**Works with:** Mem0 OSS (`Memory`)
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</Info>
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||||
|
||||
You can create a personalized AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
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@@ -3,6 +3,9 @@ title: Interactive Memory Demo
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description: "Clone the Mem0 showcase companion app to see memory writes, retrievals, and personalized chat responses in an interactive Next.js demo."
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---
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<Info icon="cloud">
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**Works with:** Mem0 Platform
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</Info>
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You can create a personalized AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete setup instructions to get you started.
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@@ -45,7 +48,7 @@ Before you begin, follow these steps to set up the demo application:
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OPENAI_API_KEY=your_openai_api_key
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MEM0_API_KEY=your_mem0_api_key
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```
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You can obtain your `MEM0_API_KEY` by signing up at <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-companions-quickstart" rel="nofollow">Mem0 API Dashboard</a>.
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You can obtain your `MEM0_API_KEY` by signing up at <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-companions-quickstart">Mem0 API Dashboard</a>.
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5. Start the development server:
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```bash
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@@ -3,6 +3,9 @@ title: Smart Travel Assistant
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description: "Design a Python travel assistant with Mem0 and GPT-4 that recalls destinations, budgets, and traveler preferences to plan smarter itineraries."
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---
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<Info icon="server">
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**Works with:** Mem0 OSS (`Memory`)
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</Info>
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Create a personalized AI Travel Assistant using Mem0. This guide provides step-by-step instructions and the complete code to get you started.
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@@ -3,6 +3,9 @@ title: Voice-First AI Companion
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description: "Combine the OpenAI Agents SDK voice pipeline with Mem0 to build a Python voice assistant that remembers user preferences across spoken sessions."
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---
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<Info icon="cloud">
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**Works with:** Mem0 Platform (`MemoryClient`)
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</Info>
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This guide demonstrates how to combine OpenAI's Agents SDK for voice applications with Mem0's memory capabilities to create a voice assistant that remembers user preferences and past interactions.
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@@ -3,6 +3,9 @@ title: Research Assistant for YouTube
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description: "Install the Mem0 YouTube Chrome extension to chat about any video with personal context, layering AI answers over the native YouTube player."
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---
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<Info icon="cloud">
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**Works with:** Mem0 Platform
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</Info>
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Enhance your YouTube experience with Mem0's YouTube Assistant, a Chrome extension that brings AI-powered chat directly to your YouTube videos. Get instant, personalized answers about video content while leveraging your own knowledge and memories, all without leaving the page.
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@@ -3,6 +3,9 @@ title: Build a Companion with Mem0
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description: "Learn the full Mem0 memory lifecycle by building a stateful fitness companion that stores goals, adapts tone, and personalizes every chat reply."
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---
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<Info icon="layer-group">
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**Works with:** Mem0 OSS (`Memory`) and Mem0 Platform (`MemoryClient`)
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</Info>
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Essentially, creating a companion out of LLMs is as simple as a loop. But these loops work great for one type of character without personalization and fall short as soon as you restart the chat.
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@@ -1,516 +0,0 @@
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---
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title: Control Memory Ingestion
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description: "Use Mem0 filters, format rules, and confidence gates to keep speculation and low-quality data out of your memory store in a medical assistant demo."
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---
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AI assistants plugged with memory systems face a problem - they often store everything. Not every conversation needs to be remembered, and not every detail should go to the memory store. Without proper controls, memory systems accumulate unreliable data.
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Mem0 lets you control your memory ingestion pipeline. In this cookbook, we'll demonstrate these controls using a medical assistant example - showing how to filter unwanted data, enforce data formats, and implement confidence-based storage.
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---
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## Overview
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Without controls, everything gets stored - speculation, low-confidence data, and information that shouldn't persist. This uncontrolled ingestion leads to cluttered memory and retrieval failures.
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Mem0 provides **three tools to control** what gets stored:
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1. **Custom instructions** define what to remember and what to ignore.
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2. **Confidence thresholds** ensure only verified facts persist.
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3. **Memory updates** let you change information without creating duplicates.
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In this tutorial, we will:
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- Filter speculative statements with custom instructions
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- Configure confidence thresholds for fact verification
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- Update stored information without duplication
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- Build a complete ingestion pipeline
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---
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## Setup
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```python
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from mem0 import MemoryClient
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client = MemoryClient(api_key="your-api-key")
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```
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<Note>
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Replace `your-api-key` with your actual Mem0 API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-memory-ingestion" rel="nofollow">dashboard</a>. Without proper API authentication, memory operations will fail.
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</Note>
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---
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## The Problem
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Uncontrolled ingestion stores everything, including speculation:
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```python
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# Patient mentions speculation
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messages = [{"role": "user", "content": "I think I might be allergic to penicillin"}]
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client.add(messages, user_id="patient_123")
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# Check what got stored
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results = client.search("patient allergies", filters={"user_id": "patient_123"})
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print(results['results'][0]['memory'])
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```
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**Output:**
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```
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Patient is allergic to penicillin
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```
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<Warning>
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Without custom instructions, AI assistants treat speculation as confirmed facts. "I think I might be allergic" becomes "Patient is allergic": a dangerous transformation in sensitive domains like healthcare, legal, or financial services.
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</Warning>
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The speculation became a confirmed fact. Let's add controls.
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---
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## Custom Instructions
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Custom instructions tell Mem0 what to store and what to ignore.
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```python
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instructions = """
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Only store CONFIRMED medical facts.
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Store:
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- Confirmed diagnoses from doctors
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- Known allergies with documented reactions
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- Current medications being taken
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Ignore:
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- Speculation (words like "might", "maybe", "I think")
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- Unverified symptoms
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- Casual mentions without confirmation
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"""
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client.project.update(custom_instructions=instructions)
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# Same speculative statement
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messages = [{"role": "user", "content": "I think I might be allergic to penicillin"}]
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client.add(messages, user_id="patient_123")
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# Check what got stored
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results = client.get_all(filters={"user_id": "patient_123"})
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print(f"Memories stored: {len(results['results'])}")
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```
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**Output:**
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```
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Memories stored: 0
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```
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<Info>
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**Expected output:** Zero memories stored. The speculative statement "I think I might be allergic" was filtered out before reaching storage. Custom instructions are actively blocking unreliable data.
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</Info>
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The speculation was filtered out.
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---
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## Designing Custom Instructions
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When designing instructions, consider the trade-off between precision and recall:
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**Too restrictive:** You'll miss important information (false negatives)
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```python
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# Too strict - filters out useful context
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"""
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Only store information if explicitly stated by a doctor with full name,
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date, time, and medical license number.
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"""
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```
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**Too permissive:** You'll store unreliable data (false positives)
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```python
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# Too loose - stores speculation as fact
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"""
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Store any health-related information mentioned.
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"""
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```
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**Balanced approach:**
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```python
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# Clear categories with examples
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"""
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Store CONFIRMED facts:
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- Diagnoses: "Dr. Smith diagnosed hypertension on March 15th"
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- Allergies: "Patient had hives reaction to penicillin"
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- Medications: "Taking Lisinopril 10mg daily"
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Ignore SPECULATION:
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- "I think I might have..."
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- "Maybe it's..."
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- "Could be related to..."
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"""
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```
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<Tip>
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Start with strict instructions (only store confirmed facts), then relax them based on your use case. It's easier to allow more data than to clean up polluted memory. Test with sample conversations before deploying to production.
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</Tip>
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Start with clear categories and iterate based on retrieval quality.
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---
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## Confidence Thresholds
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Mem0 assigns confidence scores to extracted memories. Use these to filter low-quality data.
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|
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### Setting Thresholds
|
||||
|
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Setting the right confidence threshold depends on your application:
|
||||
|
||||
- **High-stakes domains** (medical, legal): Require 0.8+ confidence
|
||||
- **General assistants**: 0.6+ confidence is often sufficient
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||||
- **Exploratory systems**: Lower thresholds (0.4+) capture more data
|
||||
|
||||
Test your pipeline with multiple input examples and threshold combinations to find what works for your use case.
|
||||
|
||||
```python
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# Configure stricter instructions
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client.project.update(
|
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custom_instructions="""
|
||||
Only extract memories with HIGH confidence.
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Require specific details (dates, dosages, doctor names) for medical facts.
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Skip vague or uncertain statements.
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"""
|
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)
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|
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# Test with uncertain statement
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messages = [{"role": "user", "content": "The doctor mentioned something about my blood pressure"}]
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result1 = client.add(messages, user_id="patient_123")
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|
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# Test with confirmed fact
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messages = [{"role": "user", "content": "Dr. Smith diagnosed me with hypertension on March 15th"}]
|
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result2 = client.add(messages, user_id="patient_123")
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|
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print("Vague statement stored:", len(result1['results']) > 0)
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print("Confirmed fact stored:", len(result2['results']) > 0)
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|
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```
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**Output:**
|
||||
|
||||
```
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Vague statement stored: False
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Confirmed fact stored: True
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```
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|
||||
<Info icon="check">
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||||
**Expected behavior:** Low-confidence extractions are now filtered out automatically. Only verified facts with specific details (names, dates, dosages) persist in memory. The confidence threshold is working.
|
||||
</Info>
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||||
|
||||
The vague statement was filtered for low confidence. The confirmed fact with specific details was stored.
|
||||
|
||||
---
|
||||
|
||||
## Filtering Sensitive Information
|
||||
|
||||
Custom instructions can prevent storing personal identifiers:
|
||||
|
||||
```python
|
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client.project.update(
|
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custom_instructions="""
|
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Medical memory rules:
|
||||
|
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STORE:
|
||||
- Confirmed diagnoses
|
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- Verified allergies
|
||||
- Current medications
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||||
|
||||
NEVER STORE:
|
||||
- Social Security Numbers
|
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- Insurance policy numbers
|
||||
- Credit card information
|
||||
- Full addresses
|
||||
- Phone numbers
|
||||
|
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Replace identifiers with generic references if mentioned.
|
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"""
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)
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|
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# Test with PII
|
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messages = [
|
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{"role": "user", "content": "My SSN is 123-45-6789 and I'm allergic to penicillin"}
|
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]
|
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client.add(messages, user_id="patient_123")
|
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|
||||
# Check what was stored
|
||||
results = client.get_all(filters={"user_id": "patient_123"})
|
||||
for result in results['results']:
|
||||
print(result['memory'])
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Patient is allergic to penicillin
|
||||
```
|
||||
|
||||
The SSN was filtered out, but the allergy was stored.
|
||||
|
||||
---
|
||||
|
||||
## Updating Memories
|
||||
|
||||
When information changes, update existing memories instead of creating duplicates.
|
||||
|
||||
```python
|
||||
# Initial allergy stored
|
||||
result = client.add(
|
||||
[{"role": "user", "content": "Patient confirmed allergy to penicillin with documented hives reaction"}],
|
||||
user_id="patient_123"
|
||||
)
|
||||
|
||||
memory_id = result['results'][0]['id']
|
||||
print(f"Stored memory: {memory_id}")
|
||||
|
||||
# Later, patient gets retested - allergy was false positive
|
||||
client.update(
|
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memory_id=memory_id,
|
||||
text="Patient tested negative for penicillin allergy on April 2nd, 2025. Previous allergy was false positive.",
|
||||
metadata={"verified": True, "updated_date": "2025-04-02"}
|
||||
)
|
||||
|
||||
# Retrieve the updated memory
|
||||
updated = client.get(memory_id)
|
||||
print(f"\\nUpdated memory: {updated['memory']}")
|
||||
print(f"Metadata: {updated['metadata']}")
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Stored memory: mem_abc123
|
||||
|
||||
Updated memory: Patient tested negative for penicillin allergy on April 2nd, 2025. Previous allergy was false positive.
|
||||
Metadata: {'verified': True, 'updated_date': '2025-04-02'}
|
||||
|
||||
```
|
||||
|
||||
### Benefits of Updating
|
||||
|
||||
**Preserves history:**
|
||||
|
||||
- `created_at` shows when the memory was first stored
|
||||
- `updated_at` shows when it was modified
|
||||
- Audit trail for compliance
|
||||
|
||||
**Avoids conflicts:**
|
||||
|
||||
- No duplicate or contradicting memories
|
||||
- Single source of truth for each fact
|
||||
|
||||
<Warning>
|
||||
That “no duplicates” promise comes from the inference pipeline. Keep `infer=True` when you rely on automatic updates. Raw imports (`infer=False`) skip conflict checks, so mixing the two modes for the same fact will create duplicates.
|
||||
</Warning>
|
||||
|
||||
### Pick the right inference mode
|
||||
|
||||
| Mode | What it does | Best for | Watch out for |
|
||||
| --- | --- | --- | --- |
|
||||
| `infer=True` *(default)* | Runs the LLM pipeline so Mem0 extracts structured facts and resolves conflicts automatically. | Daily conversations, preference tracking, anything you want deduped. | Slightly slower because inference runs on every write. |
|
||||
| `infer=False` | Stores your payload exactly as-is: no inference, no dedupe. | Bulk imports, compliance snapshots, curated facts you already trust. | Later `infer=True` calls for the same fact will create duplicates you must clean manually. |
|
||||
|
||||
<Tip>
|
||||
Stay consistent per data source. If you need both behaviors, keep them in separate scopes (e.g., different `app_id` or `run_id`) so you always know which memories are inferred vs direct imports.
|
||||
</Tip>
|
||||
|
||||
---
|
||||
|
||||
## Update vs Delete
|
||||
|
||||
When should you update vs delete?
|
||||
|
||||
### Update when:
|
||||
|
||||
- Information changes but remains relevant
|
||||
- You need audit history
|
||||
- The memory has relationships to other data
|
||||
|
||||
```python
|
||||
# Medication dosage changed
|
||||
client.update(
|
||||
memory_id=med_id,
|
||||
text="Taking Lisinopril 20mg daily (increased from 10mg on March 1st)"
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
### Delete when:
|
||||
|
||||
- Information was completely wrong
|
||||
- Memory is no longer relevant
|
||||
- Duplicate entry
|
||||
|
||||
```python
|
||||
# Duplicate entry
|
||||
client.delete(memory_id)
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Putting It Together
|
||||
|
||||
Here's a complete ingestion pipeline with all controls:
|
||||
|
||||
```python
|
||||
from mem0 import MemoryClient
|
||||
import os
|
||||
|
||||
# Initialize client
|
||||
client = MemoryClient(api_key=os.getenv("MEM0_API_KEY"))
|
||||
|
||||
# Configure custom instructions
|
||||
client.project.update(
|
||||
custom_instructions="""
|
||||
Medical memory assistant rules:
|
||||
|
||||
STORE:
|
||||
- Confirmed diagnoses (with doctor name and date)
|
||||
- Verified allergies (with reaction details)
|
||||
- Current medications (with dosage)
|
||||
|
||||
IGNORE:
|
||||
- Speculation (might, maybe, possibly)
|
||||
- Unverified symptoms
|
||||
- Personal identifiers (SSN, insurance numbers)
|
||||
|
||||
CONFIDENCE:
|
||||
Require high confidence. Reject vague or uncertain statements.
|
||||
Require specific details: names, dates, dosages.
|
||||
"""
|
||||
)
|
||||
|
||||
# Helper function for safe ingestion
|
||||
def add_medical_memory(content, user_id, metadata=None):
|
||||
"""Add memory with automatic filtering."""
|
||||
result = client.add(
|
||||
[{"role": "user", "content": content}],
|
||||
user_id=user_id,
|
||||
metadata=metadata or {}
|
||||
)
|
||||
|
||||
if result['results']:
|
||||
print(f"✓ Stored: {result['results'][0]['memory']}")
|
||||
else:
|
||||
print(f"✗ Filtered: {content}")
|
||||
|
||||
return result
|
||||
|
||||
# Test cases
|
||||
print("Testing ingestion pipeline:\\n")
|
||||
|
||||
test_cases = [
|
||||
"I think I might be allergic to penicillin",
|
||||
"Dr. Johnson confirmed penicillin allergy on Jan 15th with hives reaction",
|
||||
"Patient SSN is 123-45-6789",
|
||||
"Currently taking Lisinopril 10mg daily for hypertension",
|
||||
"Feeling tired lately",
|
||||
"Dr. Martinez diagnosed Type 2 diabetes on February 3rd, 2025"
|
||||
]
|
||||
|
||||
for content in test_cases:
|
||||
add_medical_memory(content, user_id="patient_123")
|
||||
print()
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Testing ingestion pipeline:
|
||||
|
||||
✗ Filtered: I think I might be allergic to penicillin
|
||||
|
||||
✓ Stored: Patient has confirmed penicillin allergy diagnosed by Dr. Johnson on January 15th with hives reaction
|
||||
|
||||
✗ Filtered: Patient SSN is 123-45-6789
|
||||
|
||||
✓ Stored: Patient is currently taking Lisinopril 10mg daily for hypertension
|
||||
|
||||
✗ Filtered: Feeling tired lately
|
||||
|
||||
✓ Stored: Patient diagnosed with Type 2 diabetes by Dr. Martinez on February 3rd, 2025
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Per-Call Instructions
|
||||
|
||||
You can override project-level instructions for specific conversations:
|
||||
|
||||
First define custom instructions
|
||||
|
||||
```python
|
||||
custom_instructions="""Emergency intake mode:Store ALL symptoms and observations immediately.
|
||||
Flag for later review and verification."""
|
||||
|
||||
```
|
||||
|
||||
```python
|
||||
# Emergency intake - store everything temporarily
|
||||
emergency_messages = [
|
||||
{"role": "user", "content": "Patient arrived with chest pain and shortness of breath"}
|
||||
]
|
||||
|
||||
client.add(
|
||||
emergency_messages,
|
||||
user_id="patient_456",
|
||||
custom_instructions=custom_instructions,
|
||||
metadata={"type": "emergency", "review_required": True}
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
This is useful for:
|
||||
|
||||
- Different conversation types (emergency vs routine)
|
||||
- Channel-specific rules (phone vs in-person)
|
||||
- Temporary data collection that needs review
|
||||
|
||||
---
|
||||
|
||||
## What You Built
|
||||
|
||||
You now have a medical assistant with production-grade memory controls:
|
||||
|
||||
- **Custom instructions** - Filter speculation and enforce confirmed facts only
|
||||
- **Confidence thresholds** - Gate extractions below 0.7 confidence score
|
||||
- **Memory updates** - Modify stored information without creating duplicates
|
||||
- **Per-call instructions** - Apply temporary rules for specific conversations
|
||||
- **PII filtering** - Block sensitive data (SSNs, insurance numbers) automatically
|
||||
|
||||
These controls prevent retrieval failures and ensure your AI assistant works with reliable, verified information.
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
Start with conservative filters (only store confirmed facts) and iterate based on your application's needs. Combine custom instructions with confidence thresholds for the most reliable memory ingestion pipeline.
|
||||
|
||||
<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
|
||||
Learn core memory patterns including temporary vs permanent data handling.
|
||||
</Card>
|
||||
|
||||
<Snippet file="star-on-github.mdx" />
|
||||
@@ -3,6 +3,10 @@ title: Partition Memories by Entity
|
||||
description: Scope Mem0 memories per user, agent, app, or session so a travel service never leaks one traveler's allergies into another customer's booking.
|
||||
---
|
||||
|
||||
<Info icon="cloud">
|
||||
**Works with:** Mem0 Platform (`MemoryClient`)
|
||||
</Info>
|
||||
|
||||
Nora runs a travel service. When she stored all memories in one bucket, a recruiter's nut allergy accidentally appeared in a traveler's dinner reservation. Let's fix this by properly separating memories for different users, agents, and applications.
|
||||
|
||||
<Info icon="clock">
|
||||
@@ -17,7 +21,7 @@ from mem0 import MemoryClient
|
||||
client = MemoryClient(api_key="m0-...")
|
||||
```
|
||||
|
||||
Grab an API key from the <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=cookbook-entity-partitioning" rel="nofollow">Mem0 dashboard</a> to get started.
|
||||
Grab an API key from the <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=cookbook-entity-partitioning">Mem0 dashboard</a> to get started.
|
||||
|
||||
## Store and Retrieve Scoped Memories
|
||||
|
||||
@@ -328,10 +332,10 @@ You learned how to:
|
||||
href="/platform/features/v2-memory-filters"
|
||||
/>
|
||||
<Card
|
||||
title="Control Memory Ingestion"
|
||||
description="Pair scoped storage with rules that block low-quality facts."
|
||||
title="Custom Instructions"
|
||||
description="Pair scoped storage with instructions that steer what Mem0 extracts and stores."
|
||||
icon="shield-check"
|
||||
href="/cookbooks/essentials/controlling-memory-ingestion"
|
||||
href="/platform/features/custom-instructions"
|
||||
/>
|
||||
</CardGroup>
|
||||
|
||||
|
||||
@@ -3,6 +3,9 @@ title: Export Stored Memories
|
||||
description: "Export Mem0 user memories as structured data with the Python client for review, compliance audits, migrations, and downstream analytics workflows."
|
||||
---
|
||||
|
||||
<Info icon="cloud">
|
||||
**Works with:** Mem0 Platform (`MemoryClient`)
|
||||
</Info>
|
||||
|
||||
Mem0 is a dynamic memory store that gives you full control over your data. Along with storing memories, it gives you the ability to retrieve, export, and migrate your data whenever you need.
|
||||
|
||||
@@ -20,7 +23,7 @@ client = MemoryClient(api_key="your-api-key")
|
||||
```
|
||||
|
||||
<Note>
|
||||
Your API key needs export permissions to download memory data. Check your project settings on the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-exporting-memories" rel="nofollow">dashboard</a> if export operations fail with authentication errors.
|
||||
Your API key needs export permissions to download memory data. Check your project settings on the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-exporting-memories">dashboard</a> if export operations fail with authentication errors.
|
||||
</Note>
|
||||
|
||||
Let's add some sample memories to work with:
|
||||
@@ -169,20 +172,20 @@ export_job = client.create_memory_export(
|
||||
)
|
||||
|
||||
print(f"Export ID: {export_job['id']}")
|
||||
print(f"Status: {export_job['status']}")
|
||||
print(f"Message: {export_job['message']}")
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Export ID: exp_abc123
|
||||
Status: processing
|
||||
Export ID: 550e8400-e29b-41d4-a716-446655440000
|
||||
Message: Memory export request received. The export will be ready in a few seconds.
|
||||
|
||||
```
|
||||
|
||||
<Info>
|
||||
**Export initiated:** Status is "processing". Large exports may take a few seconds. Poll with `get_memory_export()` until status changes to "completed" before downloading data.
|
||||
**Export initiated:** The export runs asynchronously and is usually ready within a few seconds. Retry `get_memory_export()` with the returned ID until it stops returning a "no export found" error.
|
||||
</Info>
|
||||
|
||||
### Step 3: Download the export
|
||||
@@ -193,7 +196,7 @@ export_data = client.get_memory_export(
|
||||
memory_export_id=export_job['id']
|
||||
)
|
||||
|
||||
print(export_data['data'])
|
||||
print(export_data)
|
||||
|
||||
```
|
||||
|
||||
@@ -216,7 +219,7 @@ export_by_filters = client.get_memory_export(
|
||||
filters={"user_id": "dev"}
|
||||
)
|
||||
|
||||
print(export_by_filters['data'])
|
||||
print(export_by_filters)
|
||||
|
||||
```
|
||||
|
||||
@@ -240,7 +243,7 @@ export_with_instructions = client.create_memory_export(
|
||||
```
|
||||
|
||||
<Tip>
|
||||
Always check export status before downloading. Call `get_memory_export()` in a loop with a short delay until `status == "completed"`. Attempting to download while still processing returns incomplete data.
|
||||
If the export is still processing, `get_memory_export()` returns a 404 with `{"error": "No memory export request found"}`. Retry after a short delay until the call succeeds instead of polling a status field.
|
||||
</Tip>
|
||||
|
||||
---
|
||||
@@ -284,8 +287,8 @@ Use **`get_all()`** for bulk retrieval, **`search()`** for specific questions, a
|
||||
<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
|
||||
Learn core memory patterns including temporary vs permanent data handling.
|
||||
</Card>
|
||||
<Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
|
||||
Ensure only verified insights make it into your export pipeline.
|
||||
<Card title="Custom Instructions" icon="filter" href="/platform/features/custom-instructions">
|
||||
Steer what Mem0 extracts so only verified insights make it into your export pipeline.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
|
||||
@@ -3,6 +3,9 @@ title: Tag and Organize Memories
|
||||
description: "Auto-categorize Mem0 memories with custom project categories so support teams retrieve tickets, resolutions, and account facts in one Python call."
|
||||
---
|
||||
|
||||
<Info icon="cloud">
|
||||
**Works with:** Mem0 Platform (`MemoryClient`)
|
||||
</Info>
|
||||
|
||||
When you have large volumes of memory data, sorting it during post-processing becomes difficult. What if your memory store understood the importance of creating tags and buckets without a lot of effort?
|
||||
|
||||
@@ -246,8 +249,8 @@ Categories make retrieval faster and compliance easier. Define 3-5 clear categor
|
||||
Instead of searching through everything, agents jump directly to the information type they need: billing issues, account details, or support tickets.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
|
||||
Keep categories meaningful by filtering noise before it lands in storage.
|
||||
<Card title="Custom Instructions" icon="filter" href="/platform/features/custom-instructions">
|
||||
Keep categories meaningful by steering what Mem0 extracts before it lands in storage.
|
||||
</Card>
|
||||
<Card title="Export Tagged Memories" icon="download" href="/cookbooks/essentials/exporting-memories">
|
||||
Use categories to drive audits, migrations, and compliance reports.
|
||||
|
||||
@@ -3,6 +3,9 @@ title: Persistent Eliza Characters
|
||||
description: "Give ElizaOS characters long-term memory with Mem0 so your agent keeps personality, backstory, and user context across every conversation and restart."
|
||||
---
|
||||
|
||||
<Info icon="cloud">
|
||||
**Works with:** Mem0 Platform
|
||||
</Info>
|
||||
|
||||
You can create a personalized Eliza OS Character using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
|
||||
|
||||
|
||||
@@ -3,6 +3,10 @@ title: "Gemini 3 with Mem0 MCP"
|
||||
description: "Connect Google Gemini 3 to the Mem0 cloud MCP server to add memory tools like add_memory and search_memories to any MCP-compatible AI client."
|
||||
---
|
||||
|
||||
<Info icon="cloud">
|
||||
**Works with:** Mem0 Platform (MCP server)
|
||||
</Info>
|
||||
|
||||
Gemini 3, when paired with Mem0's cloud MCP server, works in synergy to create snappy, smart, memory-aware agents.
|
||||
|
||||
<Callout type="info" icon="sparkles" color="#8B5CF6">
|
||||
@@ -55,7 +59,7 @@ GEMINI_API_KEY=your-gemini-api-key-here
|
||||
```
|
||||
|
||||
<Note>
|
||||
Ensure you have your Mem0 API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-gemini-3" rel="nofollow">Mem0 Dashboard</a> and your Gemini API key from the [Google AI Studio](https://ai.studio/app/api-keys).
|
||||
Ensure you have your Mem0 API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-gemini-3">Mem0 Dashboard</a> and your Gemini API key from the [Google AI Studio](https://ai.studio/app/api-keys).
|
||||
</Note>
|
||||
|
||||
## Gemini Memory Agent
|
||||
|
||||
@@ -3,6 +3,9 @@ title: Multi-Agent Collaboration
|
||||
description: "Coordinate LlamaIndex AgentWorkflow tutors and reviewers over one shared Mem0 memory layer so specialized agents collaborate with the same context."
|
||||
---
|
||||
|
||||
<Info icon="cloud">
|
||||
**Works with:** Mem0 Platform (`Mem0Memory.from_client`)
|
||||
</Info>
|
||||
|
||||
<Snippet file="blank-notif.mdx" />
|
||||
|
||||
@@ -41,7 +44,7 @@ Set up your environment variables:
|
||||
- `MEM0_API_KEY`: Your Mem0 Platform API key
|
||||
- `OPENAI_API_KEY`: Your OpenAI API key
|
||||
|
||||
You can obtain your Mem0 Platform API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-llamaindex-multiagent" rel="nofollow">Mem0 Platform</a>.
|
||||
You can obtain your Mem0 Platform API key from the <a href="https://app.mem0.ai?utm_source=oss&utm_medium=cookbook-llamaindex-multiagent">Mem0 Platform</a>.
|
||||
|
||||
## Complete Implementation
|
||||
|
||||
@@ -357,7 +360,7 @@ Based on our previous session, I remember we covered Vision Language Models and
|
||||
## Help & Resources
|
||||
|
||||
- [LlamaIndex Agent Workflows](https://docs.llamaindex.ai/en/stable/use_cases/agents/)
|
||||
- <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=cookbook-llamaindex-multiagent" rel="nofollow">Mem0 Platform</a>
|
||||
- <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=cookbook-llamaindex-multiagent">Mem0 Platform</a>
|
||||
|
||||
---
|
||||
|
||||
|
||||
@@ -3,6 +3,9 @@ title: ReAct Agents with Memory
|
||||
description: "Add persistent Mem0 memory to a LlamaIndex ReAct agent so it can reason, call tools, and recall prior interactions across every conversation turn."
|
||||
---
|
||||
|
||||
<Info icon="cloud">
|
||||
**Works with:** Mem0 Platform (`Mem0Memory.from_client`)
|
||||
</Info>
|
||||
|
||||
Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store.
|
||||
|
||||
@@ -25,7 +28,7 @@ os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
|
||||
llm = OpenAI(model="gpt-5-mini")
|
||||
```
|
||||
|
||||
Initialize the Mem0 client. You can find your API key <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-llamaindex-react" rel="nofollow">here</a>. Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/overview).
|
||||
Initialize the Mem0 client. You can find your API key <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-llamaindex-react">here</a>. Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/overview).
|
||||
```python
|
||||
os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"
|
||||
|
||||
@@ -78,7 +81,7 @@ from llama_index.core.agent import FunctionCallingAgent
|
||||
agent = FunctionCallingAgent.from_tools(
|
||||
[call_tool, email_tool, order_food_tool],
|
||||
llm=llm,
|
||||
memory=memory_from_client, # or memory_from_config
|
||||
memory=memory_from_client,
|
||||
verbose=True,
|
||||
)
|
||||
```
|
||||
@@ -161,7 +164,7 @@ agent = FunctionCallingAgent.from_tools(
|
||||
[call_tool, email_tool, order_food_tool],
|
||||
llm=llm,
|
||||
# memory is provided
|
||||
memory=memory_from_client, # or memory_from_config
|
||||
memory=memory_from_client,
|
||||
verbose=True,
|
||||
)
|
||||
response = agent.chat("I am feeling hungry, order me something and send me the bill")
|
||||
|
||||
@@ -3,6 +3,9 @@ title: Visual Memory Retrieval
|
||||
description: "Store, link, and recall images alongside text with Mem0 multimodal memory so agents keep visual context across cross-session conversations."
|
||||
---
|
||||
|
||||
<Info icon="cloud">
|
||||
**Works with:** Mem0 Platform
|
||||
</Info>
|
||||
|
||||
Enhance your AI interactions with Mem0's multimodal capabilities. Mem0 now supports image understanding, allowing for richer context and more natural interactions across supported AI platforms.
|
||||
|
||||
|
||||
@@ -3,6 +3,9 @@ title: Memory-Powered Agent SDK
|
||||
description: "Expose Mem0 memory as callable tools inside the OpenAI Agents SDK so Python agents can persist facts and recall them across future conversations."
|
||||
---
|
||||
|
||||
<Info icon="cloud">
|
||||
**Works with:** Mem0 Platform (`MemoryClient`)
|
||||
</Info>
|
||||
|
||||
Integrate Mem0's memory capabilities with OpenAI's Agents SDK to create AI agents with persistent memory. You can create agents that remember past conversations and use that context to provide better responses.
|
||||
|
||||
@@ -223,7 +226,7 @@ context = Mem0Context(user_id="user123")
|
||||
## Resources
|
||||
|
||||
- [Mem0 Documentation](https://docs.mem0.ai/introduction)
|
||||
- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-agents-sdk-tool" rel="nofollow">Mem0 Dashboard</a>
|
||||
- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-agents-sdk-tool">Mem0 Dashboard</a>
|
||||
- [API Reference](https://docs.mem0.ai/api-reference)
|
||||
|
||||
---
|
||||
|
||||
@@ -3,6 +3,9 @@ title: Bedrock with Persistent Memory
|
||||
description: "Configure the mem0ai Python SDK with AWS Bedrock LLMs and OpenSearch Serverless to run a fully managed, persistent agent memory stack on AWS."
|
||||
---
|
||||
|
||||
<Info icon="server">
|
||||
**Works with:** Mem0 OSS (`Memory`)
|
||||
</Info>
|
||||
|
||||
This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock** and **OpenSearch Service (AOSS)** for persistent memory capabilities in Python.
|
||||
|
||||
@@ -35,8 +38,8 @@ print(os.environ['AWS_SECRET_ACCESS_KEY'])
|
||||
## Configuration and Usage
|
||||
|
||||
This sets up Mem0 with:
|
||||
- [AWS Bedrock for LLM](https://docs.mem0.ai/components/llms/models/aws_bedrock)
|
||||
- [AWS Bedrock for embeddings](https://docs.mem0.ai/components/embedders/models/aws_bedrock#aws-bedrock)
|
||||
- [AWS Bedrock for LLM](https://docs.mem0.ai/components/llms/models/aws-bedrock)
|
||||
- [AWS Bedrock for embeddings](https://docs.mem0.ai/components/embedders/models/aws-bedrock#aws-bedrock)
|
||||
- [OpenSearch as the vector store](https://docs.mem0.ai/components/vectordbs/dbs/opensearch)
|
||||
|
||||
```python
|
||||
|
||||
@@ -3,6 +3,9 @@ title: Healthcare Coach with ADK
|
||||
description: "Build a Google ADK healthcare coach with Mem0 that remembers symptoms, medical history, and reminders across every patient chat and follow-up session."
|
||||
---
|
||||
|
||||
<Info icon="cloud">
|
||||
**Works with:** Mem0 Platform (`MemoryClient`)
|
||||
</Info>
|
||||
|
||||
This example demonstrates how to build a healthcare assistant that remembers patient information across conversations using Google ADK and Mem0.
|
||||
|
||||
@@ -123,7 +126,7 @@ Now we'll create our main agent with all the tools:
|
||||
# Create the agent
|
||||
healthcare_agent = Agent(
|
||||
name="healthcare_assistant",
|
||||
model="gemini-1.5-flash", # Using Gemini for healthcare assistant
|
||||
model="gemini-2.0-flash", # Using Gemini for healthcare assistant
|
||||
description="Healthcare assistant that helps patients with health information and appointment scheduling.",
|
||||
instruction="""You are a helpful Healthcare Assistant with memory capabilities.
|
||||
|
||||
|
||||
@@ -3,10 +3,13 @@ title: Persistent Mastra Agents
|
||||
description: "Add long-term Mem0 memory to Mastra agents through tool-use so your TypeScript agents can save and retrieve user facts across every workflow run."
|
||||
---
|
||||
|
||||
<Info icon="cloud">
|
||||
**Works with:** Mem0 Platform (`@mastra/mem0`)
|
||||
</Info>
|
||||
|
||||
In this example you'll learn how to use Mem0 to add long-term memory capabilities to [Mastra's agent](https://mastra.ai/) via tool-use. This memory integration can work alongside Mastra's [agent memory features](https://mastra.ai/docs/agents/01-agent-memory).
|
||||
|
||||
You can find the complete example code in the [Mastra repository](https://github.com/mastra-ai/mastra/tree/main/examples/memory-with-mem0).
|
||||
The complete example code, from installing the integration to wiring it into a Mastra agent, is shown below. Mem0's integration is published on npm as [`@mastra/mem0`](https://www.npmjs.com/package/@mastra/mem0).
|
||||
|
||||
## Overview
|
||||
|
||||
|
||||
@@ -3,6 +3,9 @@ title: Memory as OpenAI Tool
|
||||
description: "Wire Mem0 into OpenAI function-calling so JavaScript agents automatically save and search memories through the native OpenAI tools API using Zod schemas."
|
||||
---
|
||||
|
||||
<Info icon="cloud">
|
||||
**Works with:** Mem0 Platform (`MemoryClient`)
|
||||
</Info>
|
||||
|
||||
Integrate Mem0’s memory capabilities with OpenAI’s Inbuilt Tools to create AI agents with persistent memory.
|
||||
|
||||
@@ -23,7 +26,7 @@ MEM0_API_KEY=your_mem0_api_key
|
||||
OPENAI_API_KEY=your_openai_api_key
|
||||
```
|
||||
|
||||
Get your Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-openai-tool-calls" rel="nofollow">Mem0 Dashboard</a>.
|
||||
Get your Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-openai-tool-calls">Mem0 Dashboard</a>.
|
||||
|
||||
### Configuration
|
||||
|
||||
@@ -280,7 +283,7 @@ run().catch(console.error);
|
||||
## Resources
|
||||
|
||||
- [Mem0 Documentation](https://docs.mem0.ai/introduction)
|
||||
- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-openai-tool-calls" rel="nofollow">Mem0 Dashboard</a>
|
||||
- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-openai-tool-calls">Mem0 Dashboard</a>
|
||||
- [API Reference](https://docs.mem0.ai/api-reference)
|
||||
- [OpenAI Documentation](https://platform.openai.com/docs)
|
||||
|
||||
@@ -290,8 +293,8 @@ run().catch(console.error);
|
||||
<Card title="Agents SDK Tool with Mem0" icon="robot" href="/cookbooks/integrations/agents-sdk-tool">
|
||||
Extend the OpenAI Agents SDK with Mem0 integration capabilities.
|
||||
</Card>
|
||||
<Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
|
||||
Fine-tune what memories get stored during tool calls.
|
||||
<Card title="Custom Instructions" icon="filter" href="/platform/features/custom-instructions">
|
||||
Steer what memories get stored during tool calls.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
|
||||
@@ -3,12 +3,14 @@ title: Search with Personal Context
|
||||
description: "Blend Tavily realtime web search with Mem0 stored preferences to build a Python search assistant that personalizes results based on past chats."
|
||||
---
|
||||
|
||||
<Info icon="cloud">
|
||||
**Works with:** Mem0 Platform (`MemoryClient`)
|
||||
</Info>
|
||||
|
||||
Imagine asking a search assistant for "coffee shops nearby" and instead of generic results, it shows remote-work-friendly cafes with great WiFi in your city because it remembers you mentioned working remotely before. Or when you search for "lunchbox ideas for kids" it knows you have a 7-year-old daughter and recommends peanut-free options that align with her allergy.
|
||||
|
||||
That's what we are going to build today, a Personalized Search Assistant powered by Mem0 for memory and [Tavily](https://tavily.com) for real-time search.
|
||||
|
||||
|
||||
## Why Personalized Search
|
||||
|
||||
Most assistants treat every query like they've never seen you before. That means repeating yourself about your location, diet, or preferences, and getting results that feel generic.
|
||||
|
||||
@@ -3,6 +3,9 @@ title: Content Creation Workflow
|
||||
description: "Store tone rules and style guides once in Mem0, then have OpenAI apply them across drafts so every article matches your writing voice consistently."
|
||||
---
|
||||
|
||||
<Info icon="layer-group">
|
||||
**Works with:** Mem0 OSS (`Memory`) and Mem0 Platform (`MemoryClient`)
|
||||
</Info>
|
||||
|
||||
This guide demonstrates how to leverage **Mem0** to streamline content writing by applying your unique writing style and preferences using persistent memory.
|
||||
|
||||
@@ -357,8 +360,8 @@ Mem0 enables a seamless, intelligent content-writing workflow, perfect for conte
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
|
||||
Filter and curate content examples to maintain consistent writing style.
|
||||
<Card title="Custom Instructions" icon="filter" href="/platform/features/custom-instructions">
|
||||
Steer what Mem0 extracts and stores to maintain a consistent writing style.
|
||||
</Card>
|
||||
<Card title="Email Automation with Mem0" icon="envelope" href="/cookbooks/operations/email-automation">
|
||||
Automate email drafting with memory-powered context and tone matching.
|
||||
|
||||
@@ -3,11 +3,12 @@ title: Multi-Session Research Agent
|
||||
description: "Build a personalized deep research agent that reuses Mem0 memories to keep findings, sources, and preferences aligned across multi-session investigations."
|
||||
---
|
||||
|
||||
<Info icon="cloud">
|
||||
**Works with:** Mem0 Platform
|
||||
</Info>
|
||||
|
||||
Deep Research is an intelligent agent that synthesizes large amounts of online data and completes complex research tasks, customized to your unique preferences and insights. Built on Mem0's technology, it enhances AI-driven online exploration with personalized memories.
|
||||
|
||||
You can check out the GitHub repository here: [Personalized Deep Research](https://github.com/mem0ai/personalized-deep-research/tree/mem0)
|
||||
|
||||
## Overview
|
||||
|
||||
Deep Research leverages Mem0's memory capabilities to:
|
||||
@@ -61,12 +62,6 @@ Watch Deep Research in action:
|
||||
- **Technical Research**: Technology evaluation, solution comparison
|
||||
- **Business Research**: Strategic planning, opportunity analysis
|
||||
|
||||
## Try It Out
|
||||
|
||||
> To try it yourself, clone the repository and follow the instructions in the README to run it locally or deploy it.
|
||||
|
||||
- [Personalized Deep Research GitHub](https://github.com/mem0ai/personalized-deep-research/tree/mem0)
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
|
||||
@@ -3,6 +3,9 @@ title: Automated Email Intelligence
|
||||
description: "Turn your inbox into searchable Mem0 memories that categorize threads, surface past conversations, and generate summaries with an OpenAI Python agent."
|
||||
---
|
||||
|
||||
<Info icon="layer-group">
|
||||
**Works with:** Mem0 OSS (`Memory`) and Mem0 Platform (`MemoryClient`)
|
||||
</Info>
|
||||
|
||||
This guide demonstrates how to build an intelligent email processing system using Mem0's memory capabilities. You'll learn how to store, categorize, retrieve, and analyze emails to create a smart email management solution.
|
||||
|
||||
|
||||
@@ -3,6 +3,9 @@ title: Memory-Powered Support Agent
|
||||
description: "Build a Python customer support agent with Mem0 and OpenAI that recalls past tickets, resolutions, and user context to deliver faster ticket replies."
|
||||
---
|
||||
|
||||
<Info icon="server">
|
||||
**Works with:** Mem0 OSS (`Memory`)
|
||||
</Info>
|
||||
|
||||
You can create a personalized Customer Support AI Agent using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
|
||||
|
||||
|
||||
@@ -3,6 +3,9 @@ title: Collaborative Task Assistant
|
||||
description: "Coordinate a multi-user task assistant with Mem0 shared project memories, tracking authored messages, roles, and updates across teammates in Python."
|
||||
---
|
||||
|
||||
<Info icon="server">
|
||||
**Works with:** Mem0 OSS (`Memory`)
|
||||
</Info>
|
||||
|
||||
## Overview
|
||||
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: Overview
|
||||
description: "Browse Mem0 cookbook tutorials for building stateful AI companions, RAG chatbots, and memory-powered agents with OpenAI, LlamaIndex, and more frameworks."
|
||||
title: "Cookbooks and Tutorials"
|
||||
sidebarTitle: "Overview"
|
||||
description: "Browse cookbook examples and tutorials for building AI applications with Mem0, from companion chatbots to AI agents."
|
||||
---
|
||||
|
||||
With Mem0, you can create stateful LLM-based applications such as chatbots, virtual assistants, or AI agents. Mem0 enhances your applications by providing a memory layer that makes responses:
|
||||
@@ -13,6 +14,55 @@ With Mem0, you can create stateful LLM-based applications such as chatbots, virt
|
||||
|
||||
Here are some examples of how Mem0 can be integrated into various applications:
|
||||
|
||||
## Pick by compatibility
|
||||
|
||||
Every cookbook opens with a **Works with** badge naming the SDK surface it uses. Pick your setup below to see only the cookbooks that run on it. Three cookbooks work on both and appear under either tab.
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Self-hosted OSS">
|
||||
Ten cookbooks run on the open-source `Memory` class, with no Mem0 Platform account.
|
||||
|
||||
| Cookbook | Category | Works with |
|
||||
| --- | --- | --- |
|
||||
| [Personalized AI Tutor](/cookbooks/companions/ai-tutor) | Companions | OSS only |
|
||||
| [Build a Node.js Companion](/cookbooks/companions/nodejs-companion) | Companions | OSS only |
|
||||
| [Self-Hosted AI Companion](/cookbooks/companions/local-companion-ollama) | Companions | OSS only |
|
||||
| [Smart Travel Assistant](/cookbooks/companions/travel-assistant) | Companions | OSS only |
|
||||
| [Build a Companion with Mem0](/cookbooks/essentials/building-ai-companion) | Essentials | OSS and Platform |
|
||||
| [Bedrock with Persistent Memory](/cookbooks/integrations/aws-bedrock) | Integrations | OSS only |
|
||||
| [Automated Email Intelligence](/cookbooks/operations/email-automation) | Operations | OSS and Platform |
|
||||
| [Collaborative Task Assistant](/cookbooks/operations/team-task-agent) | Operations | OSS only |
|
||||
| [Content Creation Workflow](/cookbooks/operations/content-writing) | Operations | OSS and Platform |
|
||||
| [Memory-Powered Support Agent](/cookbooks/operations/support-inbox) | Operations | OSS only |
|
||||
</Tab>
|
||||
<Tab title="Hosted Platform">
|
||||
Twenty cookbooks run on the hosted Platform, using `MemoryClient` or the Mem0 MCP server with a `MEM0_API_KEY`.
|
||||
|
||||
| Cookbook | Category | Works with |
|
||||
| --- | --- | --- |
|
||||
| [Interactive Memory Demo](/cookbooks/companions/quickstart-demo) | Companions | Platform only |
|
||||
| [Research Assistant for YouTube](/cookbooks/companions/youtube-research) | Companions | Platform only |
|
||||
| [Voice-First AI Companion](/cookbooks/companions/voice-companion-openai) | Companions | Platform only |
|
||||
| [Build a Companion with Mem0](/cookbooks/essentials/building-ai-companion) | Essentials | OSS and Platform |
|
||||
| [Export Stored Memories](/cookbooks/essentials/exporting-memories) | Essentials | Platform only |
|
||||
| [Partition Memories by Entity](/cookbooks/essentials/entity-partitioning-playbook) | Essentials | Platform only |
|
||||
| [Tag and Organize Memories](/cookbooks/essentials/tagging-and-organizing-memories) | Essentials | Platform only |
|
||||
| [Gemini 3 with Mem0 MCP](/cookbooks/frameworks/gemini-3-with-mem0-mcp) | Frameworks | Platform only |
|
||||
| [Multi-Agent Collaboration](/cookbooks/frameworks/llamaindex-multiagent) | Frameworks | Platform only |
|
||||
| [Persistent Eliza Characters](/cookbooks/frameworks/eliza-os-character) | Frameworks | Platform only |
|
||||
| [ReAct Agents with Memory](/cookbooks/frameworks/llamaindex-react) | Frameworks | Platform only |
|
||||
| [Visual Memory Retrieval](/cookbooks/frameworks/multimodal-retrieval) | Frameworks | Platform only |
|
||||
| [Healthcare Coach with ADK](/cookbooks/integrations/healthcare-google-adk) | Integrations | Platform only |
|
||||
| [Memory as OpenAI Tool](/cookbooks/integrations/openai-tool-calls) | Integrations | Platform only |
|
||||
| [Memory-Powered Agent SDK](/cookbooks/integrations/agents-sdk-tool) | Integrations | Platform only |
|
||||
| [Persistent Mastra Agents](/cookbooks/integrations/mastra-agent) | Integrations | Platform only |
|
||||
| [Search with Personal Context](/cookbooks/integrations/tavily-search) | Integrations | Platform only |
|
||||
| [Automated Email Intelligence](/cookbooks/operations/email-automation) | Operations | OSS and Platform |
|
||||
| [Content Creation Workflow](/cookbooks/operations/content-writing) | Operations | OSS and Platform |
|
||||
| [Multi-Session Research Agent](/cookbooks/operations/deep-research) | Operations | Platform only |
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
## Start here
|
||||
|
||||
The most popular cookbooks to get going fast:
|
||||
@@ -47,11 +97,18 @@ The most popular cookbooks to get going fast:
|
||||
Balance personalization with consistent behavior across users, agents, and apps.
|
||||
</Card>
|
||||
<Card
|
||||
title="Control Memory Ingestion"
|
||||
icon="filter"
|
||||
href="/cookbooks/essentials/controlling-memory-ingestion"
|
||||
title="Tag and Organize Memories"
|
||||
icon="tags"
|
||||
href="/cookbooks/essentials/tagging-and-organizing-memories"
|
||||
>
|
||||
Filter speculation and low-confidence data.
|
||||
Use categories to keep retrieval fast and audits simple.
|
||||
</Card>
|
||||
<Card
|
||||
title="Export Memories"
|
||||
icon="download"
|
||||
href="/cookbooks/essentials/exporting-memories"
|
||||
>
|
||||
Back up, migrate, and audit stored memory data.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
|
||||
Reference in New Issue
Block a user