[docs] Essential cookbook added and overall revamp to the structure of cookbooks (#3668)
This commit is contained in:
Parth Sharma
@@ -1,7 +1,9 @@
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---
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title: Personalized AI Tutor
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title: AI Tutor with Mem0
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description: "Keep student progress and preferences persistent across tutoring sessions."
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---
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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.
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## Overview
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@@ -110,3 +112,14 @@ for m in memories['results']:
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## Conclusion
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As the conversation progresses, Mem0's memory automatically updates based on the interactions, providing a continuously improving personalized learning experience. This setup ensures that the AI Tutor can offer contextually relevant and accurate responses, enhancing the overall educational process.
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---
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<CardGroup cols={2}>
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<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
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Learn the foundations of memory-powered companions with production-ready patterns.
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</Card>
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<Card title="Travel Assistant with Mem0" icon="plane" href="/cookbooks/companions/travel-assistant">
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Build a travel companion that remembers preferences and past conversations.
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</Card>
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</CardGroup>
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-2
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---
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title: Mem0 with Ollama
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title: Local Companion with Mem0 and Ollama
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description: "Run Mem0 end-to-end on your machine using Ollama-powered LLMs and embedders."
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---
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## Running Mem0 Locally with Ollama
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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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@@ -70,3 +70,14 @@ memories = m.get_all(user_id="john")
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## Conclusion
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This local setup of Mem0 using Ollama provides a fully self-contained solution for memory management and AI interactions. It allows for greater control over your data and models while still leveraging the powerful capabilities of Mem0.
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---
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<CardGroup cols={2}>
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<Card title="Configure Open Source" icon="gear" href="/open-source/configuration">
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Explore advanced configuration options for vector stores, LLMs, and embedders.
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</Card>
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<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
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Learn core companion patterns that work with any LLM provider.
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</Card>
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</CardGroup>
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---
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title: AI Companion in Node.js
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title: Node.js Companion with Mem0
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description: "Build a JavaScript fitness coach that remembers user goals run after run."
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---
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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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## Overview
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@@ -124,3 +126,14 @@ export OPENAI_API_KEY=your_api_key
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This implementation demonstrates how to create an AI Companion that maintains context across conversations using Mem0's memory capabilities. The system automatically stores and retrieves relevant information, creating a more personalized and context-aware interaction experience.
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As users interact with the system, Mem0's memory system continuously learns and adapts, making future responses more relevant and personalized. This setup is ideal for creating long-term learning AI assistants that can maintain context and provide increasingly personalized responses over time.
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---
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<CardGroup cols={2}>
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<Card title="Build AI with Personality" icon="sparkles" href="/cookbooks/essentials/building-ai-with-personality">
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Separate user and agent memories to keep your companion's personality consistent.
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</Card>
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<Card title="Quickstart Demo with Mem0" icon="rocket" href="/cookbooks/companions/quickstart-demo">
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Run the full showcase app to see memory-powered companions in action.
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</Card>
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</CardGroup>
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---
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title: Mem0 Demo
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title: Quickstart Demo with Mem0
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description: "Spin up the showcase companion app to see Mem0 memories in action."
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---
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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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<video
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@@ -66,3 +68,13 @@ You can find the complete source code for this demo on GitHub:
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This setup demonstrates how to build an AI Companion that maintains memory across interactions using Mem0. The system continuously adapts to user interactions, making future responses more relevant and personalized. Experiment with the application and enhance it further to suit your use case!
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---
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<CardGroup cols={2}>
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<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
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Deep dive into production patterns for fitness coaches, tutors, and assistants.
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</Card>
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<Card title="Node.js Companion with Mem0" icon="code" href="/cookbooks/companions/nodejs-companion">
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Implement a command-line companion using the Node.js SDK.
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</Card>
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</CardGroup>
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---
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title: Personal AI Travel Assistant
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title: Travel Assistant with Mem0
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description: "Plan itineraries that remember traveler preferences across trips."
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---
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@@ -200,3 +201,14 @@ if __name__ == "__main__":
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## Conclusion
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This Personalized AI Travel Assistant leverages Mem0's memory capabilities to provide context-aware responses. As you interact with it, the assistant learns and improves, offering increasingly personalized travel advice and information.
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---
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<CardGroup cols={2}>
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<Card title="Tag and Organize Memories" icon="tag" href="/cookbooks/essentials/tagging-and-organizing-memories">
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Use categories to organize travel preferences, destinations, and user context.
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</Card>
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<Card title="AI Tutor with Mem0" icon="graduation-cap" href="/cookbooks/companions/ai-tutor">
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Build an educational companion that remembers learning progress and preferences.
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</Card>
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</CardGroup>
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---
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title: 'Mem0 with OpenAI Agents SDK for Voice'
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description: 'Integrate memory capabilities into your voice agents using Mem0 and OpenAI Agents SDK'
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title: Voice Companion with Mem0 and OpenAI
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description: "Pair the OpenAI Agents SDK with Mem0 to build a voice assistant that remembers."
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---
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## Building Voice Agents with Memory using Mem0 and OpenAI Agents SDK
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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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@@ -536,3 +535,14 @@ async def save_memories(
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# Rest of your function...
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```
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---
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<CardGroup cols={2}>
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<Card title="Multimodal Support" icon="image" href="/platform/features/multimodal-support">
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Learn how to add vision and audio memory alongside voice interactions.
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</Card>
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<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
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Master the core patterns for building memory-powered companions.
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</Card>
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</CardGroup>
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---
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title: YouTube Assistant Extension
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title: YouTube Research with Mem0
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description: "Layer personalized context over any video using the Mem0 YouTube assistant."
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---
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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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## Features
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@@ -53,3 +55,14 @@ This extension is not available on the Chrome Web Store yet. You can install it
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## Privacy and Data Security
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Your API keys are stored locally in your browser. Your messages are sent to the Mem0 API for extracting and retrieving memories. Mem0 is committed to ensuring your data's privacy and security.
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---
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<CardGroup cols={2}>
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<Card title="Tag and Organize Memories" icon="tag" href="/cookbooks/essentials/tagging-and-organizing-memories">
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Categorize video insights to build a searchable research knowledge base.
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</Card>
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<Card title="Deep Research with Mem0" icon="magnifying-glass" href="/cookbooks/operations/deep-research">
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Combine memory with search tools to conduct comprehensive research projects.
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</Card>
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</CardGroup>
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@@ -0,0 +1,525 @@
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---
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title: Build a Mem0 Companion
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description: "Spin up a fitness coach that remembers goals, adapts tone, and keeps sessions personal."
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---
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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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Problem: LLMs are stateless. GPT doesn't remember conversations. You could stuff everything inside the context window, but that becomes slow, expensive, and breaks at scale.
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The solution: Mem0. It extracts and stores what matters from conversations, then retrieves it when needed. Your companion remembers user preferences, past events, and history.
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In this cookbook we'll build a **fitness companion** that:
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- Remembers user goals across sessions
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- Recalls past workouts and progress
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- Adapts its personality based on user preferences
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- Handles both short-term context (today's chat) and long-term memory (months of history)
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By the end, you'll have a working fitness companion and know how to handle common production challenges.
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---
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## The Basic Loop with Memory
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Max wants to train for a marathon. He starts chatting with Ray, an AI running coach.
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```python
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from openai import OpenAI
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from mem0 import MemoryClient
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openai_client = OpenAI(api_key="your-openai-key")
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mem0_client = MemoryClient(api_key="your-mem0-key")
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def chat(user_input, user_id):
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# Retrieve relevant memories
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memories = mem0_client.search(user_input, user_id=user_id, limit=5)
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context = "\\n".join(m["memory"] for m in memories["results"])
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# Call LLM with memory context
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response = openai_client.chat.completions.create(
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model="gpt-4o-mini",
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messages=[
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{"role": "system", "content": f"You're Ray, a running coach. Memories:\\n{context}"},
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{"role": "user", "content": user_input}
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]
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).choices[0].message.content
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# Store the exchange
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mem0_client.add([
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{"role": "user", "content": user_input},
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{"role": "assistant", "content": response}
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], user_id=user_id)
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return response
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```
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**Session 1:**
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```python
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chat("I want to run a marathon in under 4 hours", user_id="max")
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# Output: "That's a solid goal. What's your current weekly mileage?"
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# Stored in Mem0: "Max wants to run sub-4 marathon"
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```
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**Session 2 (next day, app restarted):**
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```python
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chat("What should I focus on today?", user_id="max")
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# Output: "Based on your sub-4 marathon goal, let's work on building your aerobic base..."
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```
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<Info>
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Ray remembers Max's goal across sessions. The app restarted, but the memory persisted. This is the core pattern: retrieve memories, pass them as context, store new exchanges.
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</Info>
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Ray remembers. Restart the app, and the goal persists. From here on, we'll focus on just the Mem0 API calls.
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---
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## Organizing Memory by Type
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### Separating Temporary from Permanent
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Max mentions his knee hurts. That's different from his marathon goal - one is temporary, the other is long-term.
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**Categories vs Metadata:**
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- **Categories**: AI-assigned by Mem0 based on content (you can't force them)
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- **Metadata**: Manually set by you for forced tagging
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Define custom categories at the project level. Mem0 will automatically tag memories with relevant categories based on content:
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```python
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mem0_client.project.update(custom_categories=[
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{"goals": "Race targets and training objectives"},
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{"constraints": "Injuries, limitations, recovery needs"},
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{"preferences": "Training style, surfaces, schedules"}
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])
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```
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<Note>
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**Categories vs Metadata:** Categories are AI-assigned by Mem0 based on content semantics. You define the palette, Mem0 picks which ones apply. If you need guaranteed tagging, use `metadata` instead.
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</Note>
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Now when you add memories, Mem0 automatically assigns the appropriate categories:
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```python
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# Add goal - Mem0 automatically tags it as "goals"
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mem0_client.add(
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[{"role": "user", "content": "Sub-4 marathon is my A-race"}],
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user_id="max"
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)
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# Add constraint - Mem0 automatically tags it as "constraints"
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mem0_client.add(
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[{"role": "user", "content": "My right knee flares up on downhills"}],
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user_id="max"
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)
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```
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Mem0 reads the content and intelligently picks which categories apply. You define the palette, it handles the tagging.
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**Important:** You cannot force specific categories. Mem0's platform decides which categories are relevant based on content. If you need to force-tag something, use `metadata` instead:
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```python
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# Force tag using metadata (not categories)
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mem0_client.add(
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[{"role": "user", "content": "Some workout note"}],
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user_id="max",
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metadata={"workout_type": "speed", "forced_tag": "custom_label"}
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)
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```
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### Filtering by Category
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Retrieve just constraints for workout planning:
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```python
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constraints = mem0_client.search(
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"injury concerns",
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user_id="max",
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filters={"categories": {"in": ["constraints"]}}
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)
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print([m["memory"] for m in constraints["results"]])
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# Output: ["Max's right knee flares up on downhills"]
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```
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Ray can plan workouts that avoid aggravating Max's knee, without pulling in race goals or other unrelated memories.
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---
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## Filtering What Gets Stored
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### The Problem
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Run the basic loop for a week and check what's stored:
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```python
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memories = mem0_client.get_all(user_id="max")
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print([m["memory"] for m in memories["results"]])
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# Output: ["Max wants to run marathon under 4 hours", "hey", "lol ok", "cool thanks", "gtg bye"]
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```
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<Warning>
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Without filters, Mem0 stores everything—greetings, filler, and casual chat. This pollutes retrieval: instead of pulling "marathon goal," you get "lol ok." Set custom instructions to keep memory clean.
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</Warning>
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Noise. Greetings and filler clutter the memory.
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### Custom Instructions
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Tell Mem0 what matters:
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```python
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mem0_client.project.update(custom_instructions="""
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Extract from running coach conversations:
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- Training goals and race targets
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- Physical constraints or injuries
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- Training preferences (time of day, surfaces, weather)
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- Progress milestones
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Exclude:
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- Greetings and filler
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- Casual chatter
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- Hypotheticals unless planning related
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""")
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```
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Now chat again:
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```python
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chat("hey how's it going", user_id="max")
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chat("I prefer trail running over roads", user_id="max")
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memories = mem0_client.get_all(user_id="max")
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print([m["memory"] for m in memories["results"]])
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# Output: ["Max wants to run marathon under 4 hours", "Max prefers trail running over roads"]
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```
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<Info>
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**Expected output:** Only 2 memories stored—the marathon goal and trail preference. The greeting "hey how's it going" was filtered out automatically. Custom instructions are working.
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</Info>
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Only meaningful facts. Filler gets dropped automatically.
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---
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---
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## Agent Memory for Personality
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### Why Agents Need Memory Too
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Max prefers direct feedback, not motivational fluff. Ray needs to remember how to communicate - that's agent memory, separate from user memory.
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Store agent personality:
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```python
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mem0_client.add(
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[{"role": "system", "content": "Max wants direct, data-driven feedback. Skip motivational language."}],
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agent_id="ray_coach"
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)
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```
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Retrieve agent style alongside user memories:
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```python
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# Get coach personality
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agent_memories = mem0_client.search("coaching style", agent_id="ray_coach")
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# Output: ["Max wants direct, data-driven feedback. Skip motivational language."]
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# Store conversations with agent_id
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mem0_client.add([
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{"role": "user", "content": "How'd my run look today?"},
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{"role": "assistant", "content": "Pace was 8:15/mile. Heart rate 152, zone 2."}
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], user_id="max", agent_id="ray_coach")
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```
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<Info>
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**Expected behavior:** Ray's responses are now data-driven and direct. The agent memory stored the coaching style preference, so future responses adapt automatically without Max having to repeat his preference.
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</Info>
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No "Great job!" or "Keep it up!" - just data. Ray adapts to Max's preference.
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---
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## Managing Short-Term Context
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### When to Store in Mem0
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Don't send every single message to Mem0. Keep recent context in memory, let Mem0 handle the important long-term facts.
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```python
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# Store only meaningful exchanges in Mem0
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mem0_client.add([
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{"role": "user", "content": "I want to run a marathon"},
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{"role": "assistant", "content": "Let's build a training plan"}
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], user_id="max")
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# Skip storing filler
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# "hey" → don't store
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# "cool thanks" → don't store
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# Or rely on custom_instructions to filter automatically
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```
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Last 10 messages in your app's buffer. Important facts in Mem0. Faster, cheaper, still works.
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---
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## Time-Bound Memories
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### Auto-Expiring Facts
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Max tweaks his ankle. It'll heal in two weeks - the memory should expire too.
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```python
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from datetime import datetime, timedelta
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expiration = (datetime.now() + timedelta(days=14)).strftime("%Y-%m-%d")
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mem0_client.add(
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[{"role": "user", "content": "Rolled my left ankle, needs rest"}],
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user_id="max",
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expiration_date=expiration
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)
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```
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In 14 days, this memory disappears automatically. Ray stops asking about the ankle.
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||||
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||||
---
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## Putting It All Together
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||||
|
||||
Here's the Mem0 setup combining everything:
|
||||
|
||||
```python
|
||||
from mem0 import MemoryClient
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
mem0_client = MemoryClient(api_key="your-mem0-key")
|
||||
|
||||
# Configure memory filtering and categories
|
||||
mem0_client.project.update(
|
||||
custom_instructions="""
|
||||
Extract: goals, constraints, preferences, progress
|
||||
Exclude: greetings, filler, casual chat
|
||||
""",
|
||||
custom_categories=[
|
||||
{"name": "goals", "description": "Training targets"},
|
||||
{"name": "constraints", "description": "Injuries and limitations"},
|
||||
{"name": "preferences", "description": "Training style"}
|
||||
]
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
**Week 1 - Store goals and preferences:**
|
||||
|
||||
```python
|
||||
mem0_client.add([
|
||||
{"role": "user", "content": "I want to run a sub-4 marathon"},
|
||||
{"role": "assistant", "content": "Got it. Let's build a training plan."}
|
||||
], user_id="max", agent_id="ray", categories=["goals"])
|
||||
|
||||
mem0_client.add([
|
||||
{"role": "user", "content": "I prefer trail running over roads"}
|
||||
], user_id="max", categories=["preferences"])
|
||||
|
||||
```
|
||||
|
||||
**Week 3 - Temporary injury with expiration:**
|
||||
|
||||
```python
|
||||
expiration = (datetime.now() + timedelta(days=14)).strftime("%Y-%m-%d")
|
||||
mem0_client.add(
|
||||
[{"role": "user", "content": "Rolled ankle, need light workouts"}],
|
||||
user_id="max",
|
||||
categories=["constraints"],
|
||||
expiration_date=expiration
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
**Retrieve for context:**
|
||||
|
||||
```python
|
||||
memories = mem0_client.search("training plan", user_id="max", limit=5)
|
||||
# Gets: marathon goal, trail preference, ankle injury (if still valid)
|
||||
|
||||
```
|
||||
|
||||
Ray remembers goals, preferences, and personality. Handles temporary injuries. Works across sessions.
|
||||
|
||||
---
|
||||
|
||||
## Common Production Patterns
|
||||
|
||||
### Episodic Stories with run_id
|
||||
|
||||
Training for Boston is different from training for New York. Separate the memory threads:
|
||||
|
||||
```python
|
||||
mem0_client.add(messages, user_id="max", run_id="boston-2025")
|
||||
mem0_client.add(messages, user_id="max", run_id="nyc-2025")
|
||||
|
||||
# Retrieve only Boston memories
|
||||
boston_memories = mem0_client.search(
|
||||
"training plan",
|
||||
user_id="max",
|
||||
run_id="boston-2025"
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
Each race gets its own episodic boundary. No cross-contamination.
|
||||
|
||||
### Importing Historical Data
|
||||
|
||||
Max has 6 months of training logs to backfill:
|
||||
|
||||
```python
|
||||
old_logs = [
|
||||
[{"role": "user", "content": "Completed 20-mile long run"}],
|
||||
[{"role": "user", "content": "Hit 8:00 pace on tempo run"}],
|
||||
]
|
||||
|
||||
for log in old_logs:
|
||||
mem0_client.add(log, user_id="max")
|
||||
|
||||
```
|
||||
|
||||
### Handling Contradictions
|
||||
|
||||
Max changes his goal from sub-4 to sub-3:45:
|
||||
|
||||
```python
|
||||
# Find the old memory
|
||||
memories = mem0_client.get_all(user_id="max")
|
||||
goal_memory = [m for m in memories["results"] if "sub-4" in m["memory"]][0]
|
||||
|
||||
# Update it
|
||||
mem0_client.update(goal_memory["id"], "Max wants to run sub-3:45 marathon")
|
||||
|
||||
```
|
||||
|
||||
Update instead of creating duplicates.
|
||||
|
||||
### Multiple Agents
|
||||
|
||||
Max works with Ray for running and Jordan for strength training:
|
||||
|
||||
```python
|
||||
chat("easy run today", user_id="max", agent_id="ray")
|
||||
chat("leg day workout", user_id="max", agent_id="jordan")
|
||||
|
||||
```
|
||||
|
||||
Each coach maintains separate personality memory while sharing user context.
|
||||
|
||||
### Filtering by Date
|
||||
|
||||
Prioritize recent training over old data:
|
||||
|
||||
```python
|
||||
recent = mem0_client.search(
|
||||
"training progress",
|
||||
user_id="max",
|
||||
filters={"created_at": {"gte": "2025-10-01"}}
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
### Metadata Tagging
|
||||
|
||||
Tag workouts by type:
|
||||
|
||||
```python
|
||||
mem0_client.add(
|
||||
[{"role": "user", "content": "10x400m intervals"}],
|
||||
user_id="max",
|
||||
metadata={"workout_type": "speed", "intensity": "high"}
|
||||
)
|
||||
|
||||
# Later, find all speed workouts
|
||||
speed_sessions = mem0_client.search(
|
||||
"speed work",
|
||||
user_id="max",
|
||||
filters={"metadata": {"workout_type": "speed"}}
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
### Pruning Old Memories
|
||||
|
||||
Delete irrelevant memories:
|
||||
|
||||
```python
|
||||
mem0_client.delete(memory_id="mem_xyz")
|
||||
|
||||
# Or clear an entire run_id
|
||||
mem0_client.delete_all(user_id="max", run_id="old-training-cycle")
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## What You Built
|
||||
|
||||
A companion that:
|
||||
|
||||
- **Persists across sessions** - Mem0 storage
|
||||
- **Filters noise** - custom instructions
|
||||
- **Organizes by type** - categories
|
||||
- **Adapts personality** - **`agent_id`**
|
||||
- **Stays fast** - short-term buffer
|
||||
- **Handles temporal facts** - expiration
|
||||
- **Scales to production** - batching, metadata, pruning
|
||||
|
||||
This pattern works for any companion: fitness coaches, tutors, roleplay characters, therapy bots, creative writing partners.
|
||||
|
||||
---
|
||||
|
||||
<Tip>
|
||||
Start with 2-3 categories max (e.g., goals, constraints, preferences). More categories dilute tagging accuracy. You can always add more later after seeing what Mem0 extracts.
|
||||
</Tip>
|
||||
|
||||
---
|
||||
|
||||
## Production Checklist
|
||||
|
||||
Before launching:
|
||||
|
||||
- Set custom instructions for your domain
|
||||
- Define 2-3 categories (goals, constraints, preferences)
|
||||
- Add expiration strategy for time-bound facts
|
||||
- Implement error handling for API calls
|
||||
- Monitor memory quality in Mem0 dashboard
|
||||
- Clear test data from production project
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Build AI with Personality" icon="sparkles" href="/cookbooks/essentials/building-ai-with-personality">
|
||||
Separate user and agent memories so companions stay consistent across sessions.
|
||||
</Card>
|
||||
<Card title="Tag Support Memories" icon="tag" href="/cookbooks/essentials/tagging-and-organizing-memories">
|
||||
Organize customer context to keep assistants responsive at scale.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -0,0 +1,603 @@
|
||||
---
|
||||
title: Scope User vs Agent Memories
|
||||
description: "Use **user_id** and **agent_id** to balance personalization with consistent assistant behavior."
|
||||
---
|
||||
|
||||
# Build AI with Distinct Personalities
|
||||
|
||||
While building memory systems for AI apps, you need to juggle between agent and user memories. The user delivers information from their side, but there's often value inside the agent's responses as well.
|
||||
|
||||
Unlike other memory APIs, we built one that allows you the flexibility to store memory from all sources. User and agent memories in Mem0 are handled by:
|
||||
|
||||
- **user_id** - Memories about specific users
|
||||
- **agent_id** - Memories from the agent itself
|
||||
|
||||
In this guide, you'll see how these parameters work together, along with practical examples to help you build a fitness coach that remembers user workouts while maintaining a consistent coaching personality.
|
||||
|
||||
---
|
||||
|
||||
## User Memories
|
||||
|
||||
Let's start by tracking individual user workouts. We'll use **`user_id`** to keep each person's data separate.
|
||||
|
||||
```python
|
||||
from openai import OpenAI
|
||||
from mem0 import MemoryClient
|
||||
import os
|
||||
|
||||
openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
mem0_client = MemoryClient()
|
||||
|
||||
# Sarah logs her workout
|
||||
mem0_client.add(
|
||||
"Completed 5K run in 28 minutes - felt great!",
|
||||
user_id="sarah"
|
||||
)
|
||||
|
||||
# Mike logs his workout
|
||||
mem0_client.add(
|
||||
"Bench press: 185 lbs x 10 reps, 3 sets",
|
||||
user_id="mike"
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
Now when we coach Sarah, we retrieve only her workout history:
|
||||
|
||||
```python
|
||||
# Get Sarah's workout history
|
||||
sarah_history = mem0_client.search(
|
||||
"What exercises has Sarah done recently?",
|
||||
filters={"user_id": "sarah"}
|
||||
)
|
||||
|
||||
# Generate coaching advice
|
||||
response = openai_client.chat.completions.create(
|
||||
model="gpt-4",
|
||||
messages=[
|
||||
{"role": "system", "content": "You're a supportive fitness coach."},
|
||||
{"role": "user", "content": f"Based on this history: {sarah_history}, suggest the next workout."}
|
||||
]
|
||||
)
|
||||
|
||||
print(response.choices[0].message.content)
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Great job on that 5K! Your endurance is building nicely. Let's add some
|
||||
strength work to complement your running. Try 3 sets of bodyweight squats
|
||||
(15 reps each) to strengthen your legs and improve your running power.
|
||||
|
||||
```
|
||||
|
||||
<Info>
|
||||
**Expected output:** Sarah gets running advice based on her 5K, not Mike's bench press data. The **`user_id`** filter ensures memories are isolated—each user only sees their own workout history.
|
||||
</Info>
|
||||
|
||||
This works perfectly! Each user has private workout history, and Sarah never sees Mike's data.
|
||||
|
||||
---
|
||||
|
||||
## Adding Coach Personality
|
||||
|
||||
Our fitness coach needs a consistent personality - supportive, motivational, and celebrates small wins. Let's see what happens if we try storing this with **`user_id`**:
|
||||
|
||||
```python
|
||||
# Adding coach personality for Sarah
|
||||
mem0_client.add(
|
||||
"I'm a supportive fitness coach who celebrates every achievement and uses athlete-focused language",
|
||||
user_id="sarah"
|
||||
)
|
||||
|
||||
# Adding the same personality for Mike
|
||||
mem0_client.add(
|
||||
"I'm a supportive fitness coach who celebrates every achievement and uses athlete-focused language",
|
||||
user_id="mike"
|
||||
)
|
||||
|
||||
# For 1,000 users, we'd repeat this 1,000 times...
|
||||
|
||||
```
|
||||
|
||||
This approach has some limitations:
|
||||
|
||||
1. **Duplication**: We're storing the same coaching personality 1,000 times for 1,000 users
|
||||
2. **Hard to update**: Want to change the coaching style? Update 1,000 memories
|
||||
3. **Mixed with user data**: Coach personality and user workouts are stored together, making queries complex
|
||||
|
||||
<Warning>
|
||||
Storing agent personality with **`user_id`** doesn't scale. For 10,000 users, you'd duplicate the same personality 10,000 times. Updating the coaching style means updating 10,000 separate memories. This wastes storage and makes maintenance impossible.
|
||||
</Warning>
|
||||
|
||||
What if the coach could have a personality that's shared across all users?
|
||||
|
||||
---
|
||||
|
||||
## Agent Memories
|
||||
|
||||
Here's where **`agent_id`** comes in. We can store the coach's personality once and share it across all users:
|
||||
|
||||
```python
|
||||
# Store coach personality ONCE with agent_id
|
||||
mem0_client.add(
|
||||
"I'm FitCoach - a supportive fitness coach who celebrates every achievement. "
|
||||
"I use motivational language, focus on progress over perfection, and help users build sustainable habits.",
|
||||
agent_id="fitcoach_v1"
|
||||
)
|
||||
|
||||
# Sarah's workout (still private)
|
||||
mem0_client.add(
|
||||
"Completed 5K run in 28 minutes",
|
||||
user_id="sarah"
|
||||
)
|
||||
|
||||
# Mike's workout (still private)
|
||||
mem0_client.add(
|
||||
"Bench press: 185 lbs x 10 reps, 3 sets",
|
||||
user_id="mike"
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
Now when coaching Sarah, we retrieve both the agent's personality AND her workout history:
|
||||
|
||||
```python
|
||||
# Get both coach personality and Sarah's workouts
|
||||
coaching_context = mem0_client.search(
|
||||
"coaching context for Sarah",
|
||||
filters={
|
||||
"OR": [
|
||||
{"agent_id": "fitcoach_v1"}, # Coach personality
|
||||
{"user_id": "sarah"} # Sarah's workout history
|
||||
]
|
||||
}
|
||||
)
|
||||
|
||||
# Generate personalized coaching
|
||||
response = openai_client.chat.completions.create(
|
||||
model="gpt-4",
|
||||
messages=[
|
||||
{"role": "system", "content": str(coaching_context)},
|
||||
{"role": "user", "content": "What should I focus on in my next workout?"}
|
||||
]
|
||||
)
|
||||
|
||||
print(response.choices[0].message.content)
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Amazing work on that 5K! 🎉 You're crushing your running goals!
|
||||
|
||||
Now let's build some complementary strength. I recommend adding bodyweight
|
||||
squats to your routine - they'll make you an even stronger runner. Start
|
||||
with 3 sets of 15 reps, and remember: progress over perfection!
|
||||
|
||||
```
|
||||
|
||||
Notice how the response has the motivational tone (from **`agent_id`**) combined with personalized advice based on Sarah's 5K run (from **`user_id`**).
|
||||
|
||||
The coach personality is now:
|
||||
|
||||
- ✅ Stored once, shared by all users
|
||||
- ✅ Easy to update (change one memory, affects all users)
|
||||
- ✅ Cleanly separated from user workout data
|
||||
|
||||
<Info>
|
||||
**Expected behavior:** The response combines the motivational tone (from **`agent_id`**) with Sarah's specific 5K progress (from **`user_id`**). One agent personality, infinite users—update the agent once, and all users get the new coaching style.
|
||||
</Info>
|
||||
|
||||
---
|
||||
|
||||
## Combining Both: Relationship Memories
|
||||
|
||||
There's a third type of memory - one that captures the relationship between a specific user and the coach. To store these, use `metadata` to track which agent the relationship is with:
|
||||
|
||||
```python
|
||||
# Coach personality (shared across all users)
|
||||
mem0_client.add(
|
||||
"I'm FitCoach - supportive and motivational",
|
||||
agent_id="fitcoach_v1"
|
||||
)
|
||||
|
||||
# Sarah's workout data (private to Sarah)
|
||||
mem0_client.add(
|
||||
"Goal: Run a half marathon by June. Currently runs 5K comfortably.",
|
||||
user_id="sarah"
|
||||
)
|
||||
|
||||
# Sarah-Coach relationship (stored as user memory with agent context in metadata)
|
||||
mem0_client.add(
|
||||
"Sarah and I have an inside joke: 'No pain, no protein shake!' "
|
||||
"She responds best to gentle encouragement after tough workouts.",
|
||||
user_id="sarah",
|
||||
metadata={"agent_id": "fitcoach_v1", "type": "relationship"}
|
||||
)
|
||||
|
||||
# Mike-Coach relationship (different from Sarah's)
|
||||
mem0_client.add(
|
||||
"Mike prefers data-driven feedback with specific numbers and percentages. "
|
||||
"Less motivational talk, more concrete metrics.",
|
||||
user_id="mike",
|
||||
metadata={"agent_id": "fitcoach_v1", "type": "relationship"}
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
Now when coaching Sarah, retrieve her data including relationship with this specific coach:
|
||||
|
||||
```python
|
||||
# Get Sarah's memories including relationship with fitcoach_v1
|
||||
sarah_context = mem0_client.search(
|
||||
"How should I coach Sarah today?",
|
||||
user_id="sarah",
|
||||
filters={"metadata": {"agent_id": "fitcoach_v1"}}
|
||||
)
|
||||
|
||||
# Get coach personality
|
||||
coach_personality = mem0_client.search(
|
||||
"coaching personality",
|
||||
agent_id="fitcoach_v1"
|
||||
)
|
||||
|
||||
# Combine both contexts
|
||||
full_context = str(coach_personality) + "\\n" + str(sarah_context)
|
||||
|
||||
response = openai_client.chat.completions.create(
|
||||
model="gpt-4",
|
||||
messages=[
|
||||
{"role": "system", "content": full_context},
|
||||
{"role": "user", "content": "Just finished today's workout!"}
|
||||
]
|
||||
)
|
||||
|
||||
print(response.choices[0].message.content)
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Awesome work today! 💪 No pain, no protein shake, right? 😄
|
||||
|
||||
You're making real progress toward that half marathon goal. Keep this
|
||||
momentum going - your consistency is your superpower!
|
||||
|
||||
```
|
||||
|
||||
When coaching Mike with the same approach:
|
||||
|
||||
```python
|
||||
# Get Mike's memories including relationship with fitcoach_v1
|
||||
mike_context = mem0_client.search(
|
||||
"How should I coach Mike today?",
|
||||
user_id="mike",
|
||||
filters={"metadata": {"agent_id": "fitcoach_v1"}}
|
||||
)
|
||||
|
||||
coach_personality = mem0_client.search(
|
||||
"coaching personality",
|
||||
agent_id="fitcoach_v1"
|
||||
)
|
||||
|
||||
full_context = str(coach_personality) + "\\n" + str(mike_context)
|
||||
|
||||
# ... same coaching code ...
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Solid session. Your bench press shows 8% improvement over last week
|
||||
(171 lbs avg to 185 lbs). Target: 200 lbs by end of month.
|
||||
On track at current rate (+3.2% weekly).
|
||||
|
||||
```
|
||||
|
||||
Same coach, completely different experience based on each user's relationship preferences stored in metadata.
|
||||
|
||||
<Note>
|
||||
**Relationship memories** are stored with **`user_id`** (they're private to each user) but include `agent_id` in metadata to track which agent the relationship is with. This lets you retrieve the user's preferences for how this specific agent should interact with them.
|
||||
</Note>
|
||||
|
||||
---
|
||||
|
||||
## Putting It All Together
|
||||
|
||||
Here's a complete example showing all three memory layers working together:
|
||||
|
||||
```python
|
||||
from openai import OpenAI
|
||||
from mem0 import MemoryClient
|
||||
import os
|
||||
|
||||
# Initialize clients
|
||||
openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
mem0_client = MemoryClient()
|
||||
|
||||
# Layer 1: Agent personality (shared)
|
||||
mem0_client.add(
|
||||
"I'm FitCoach - supportive, motivational, celebrates small wins",
|
||||
agent_id="fitcoach_v1",
|
||||
metadata={"type": "personality"}
|
||||
)
|
||||
|
||||
# Layer 2: User profile (private)
|
||||
mem0_client.add(
|
||||
"Sarah's goal: Run half marathon by June. Currently comfortable at 5K distance.",
|
||||
user_id="sarah",
|
||||
metadata={"type": "profile"}
|
||||
)
|
||||
|
||||
# Layer 3: Relationship (stored as user memory with agent context in metadata)
|
||||
mem0_client.add(
|
||||
"Sarah responds best to encouragement. Inside joke: 'No pain, no protein shake!'",
|
||||
user_id="sarah",
|
||||
metadata={"agent_id": "fitcoach_v1", "type": "relationship"}
|
||||
)
|
||||
|
||||
# Log today's workout
|
||||
mem0_client.add(
|
||||
"Completed 8K run in 45 minutes - new personal record!",
|
||||
user_id="sarah",
|
||||
metadata={"type": "workout", "date": "2025-01-23"}
|
||||
)
|
||||
|
||||
# Generate coaching response
|
||||
# Get Sarah's context (includes all her memories)
|
||||
sarah_context = mem0_client.search(
|
||||
"Generate coaching advice for Sarah",
|
||||
user_id="sarah"
|
||||
)
|
||||
|
||||
# Get coach personality
|
||||
coach_personality = mem0_client.search(
|
||||
"coaching personality",
|
||||
agent_id="fitcoach_v1"
|
||||
)
|
||||
|
||||
# Combine contexts
|
||||
full_context = str(coach_personality) + "\\n" + str(sarah_context)
|
||||
|
||||
response = openai_client.chat.completions.create(
|
||||
model="gpt-4",
|
||||
messages=[
|
||||
{"role": "system", "content": full_context},
|
||||
{"role": "user", "content": "Just finished my run today!"}
|
||||
]
|
||||
)
|
||||
|
||||
print(response.choices[0].message.content)
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
🎉 YES! 8K is a HUGE milestone! You just crushed your previous distance!
|
||||
|
||||
Remember when you could barely do 5K? Look at you now! That half marathon
|
||||
in June is looking more achievable every single day. No pain, no protein
|
||||
shake - and today, you EARNED that shake! 💪
|
||||
|
||||
Next week, let's aim for 9K. You're ready for it!
|
||||
|
||||
```
|
||||
|
||||
The response combines:
|
||||
|
||||
- ✅ Motivational tone (agent personality)
|
||||
- ✅ Specific goal reference (Sarah's profile)
|
||||
- ✅ Inside joke (relationship memory)
|
||||
- ✅ Progress tracking (workout history)
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
**Three memory layers:**
|
||||
|
||||
1. **Agent memories** (blue) - Shared personality and capabilities
|
||||
2. **User memories** (red) - Private workout data and goals
|
||||
3. **Relationship memories** (purple) - User memories with agent context stored in metadata
|
||||
|
||||
---
|
||||
|
||||
## When to Use What
|
||||
|
||||
### Use **`user_id`** only (most apps)
|
||||
|
||||
**Best for:** Apps that just need to track user-specific data without AI personality
|
||||
|
||||
**Examples:**
|
||||
|
||||
- Todo lists
|
||||
- Note-taking apps
|
||||
- Personal finance trackers
|
||||
- Support ticket history
|
||||
|
||||
```python
|
||||
mem0_client.add(
|
||||
"Bought groceries for $127.43",
|
||||
user_id="sarah"
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Use **`agent_id`** only (rare)
|
||||
|
||||
**Best for:** AI tools that work the same for everyone, no user-specific data
|
||||
|
||||
**Examples:**
|
||||
|
||||
- Calculator bots
|
||||
- Language translators
|
||||
- Company policy assistants (same policies for all)
|
||||
|
||||
```python
|
||||
mem0_client.add(
|
||||
"I can calculate: arithmetic, algebra, basic calculus. I cannot solve differential equations.",
|
||||
agent_id="calculator_v1"
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Use both **`user_id`** and **`agent_id`** (AI with personality)
|
||||
|
||||
**Best for:** AI agents with persistent personalities that remember individual users
|
||||
|
||||
**Examples:**
|
||||
|
||||
- Fitness coaches (this guide!)
|
||||
- Educational tutors
|
||||
- AI companions
|
||||
- Therapy/mental health bots
|
||||
- Game NPCs with character development
|
||||
|
||||
```python
|
||||
# Agent personality (shared across all users)
|
||||
mem0_client.add(
|
||||
"Coaching personality and style",
|
||||
agent_id="agent_name"
|
||||
)
|
||||
|
||||
# User data (private)
|
||||
mem0_client.add(
|
||||
"User's goals and progress",
|
||||
user_id="user_name"
|
||||
)
|
||||
|
||||
# Relationship (user memory with agent context in metadata)
|
||||
mem0_client.add(
|
||||
"User's relationship with this specific agent",
|
||||
user_id="user_name",
|
||||
metadata={"agent_id": "agent_name", "type": "relationship"}
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
<Tip>
|
||||
**When to combine user_id + agent_id:** Use both when your AI has a consistent personality that should work the same for everyone (agent_id), while also tracking individual user data (user_id). Most personal AI assistants, coaches, and tutors fit this pattern.
|
||||
</Tip>
|
||||
|
||||
---
|
||||
|
||||
## Best Practices
|
||||
|
||||
### 1. Use clear naming conventions
|
||||
|
||||
```python
|
||||
# Good: Descriptive and versioned
|
||||
agent_id="fitcoach_v1"
|
||||
user_id="sarah_123"
|
||||
|
||||
# Bad: Generic and unclear
|
||||
agent_id="agent1"
|
||||
user_id="user_abc"
|
||||
|
||||
```
|
||||
|
||||
### 2. Tag memories with metadata
|
||||
|
||||
```python
|
||||
# For relationship memories, store agent context in metadata
|
||||
mem0_client.add(
|
||||
content,
|
||||
user_id="sarah",
|
||||
metadata={
|
||||
"agent_id": "fitcoach_v1",
|
||||
"type": "relationship",
|
||||
"version": "v1"
|
||||
}
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
### 3. Test data isolation
|
||||
|
||||
Ensure users never see each other's data:
|
||||
|
||||
```python
|
||||
# Get Mike's memories
|
||||
mike_memories = mem0_client.get_all(filters={"user_id": "mike"})
|
||||
|
||||
# Get Sarah's memories
|
||||
sarah_memories = mem0_client.get_all(filters={"user_id": "sarah"})
|
||||
|
||||
# Verify no overlap
|
||||
assert len(set(mike_memories) & set(sarah_memories)) == 0, "Data leak detected!"
|
||||
|
||||
```
|
||||
|
||||
### 4. Version your agents
|
||||
|
||||
Allows A/B testing different coaching styles:
|
||||
|
||||
```python
|
||||
# Version 1: Gentle and encouraging
|
||||
mem0_client.add(
|
||||
"I'm supportive and celebrate small wins",
|
||||
agent_id="fitcoach_v1"
|
||||
)
|
||||
|
||||
# Version 2: Data-driven and metric-focused
|
||||
mem0_client.add(
|
||||
"I provide concrete metrics and percentage improvements",
|
||||
agent_id="fitcoach_v2"
|
||||
)
|
||||
|
||||
# Assign users to different versions by storing version in metadata
|
||||
mem0_client.add(
|
||||
"Sarah's workout preferences and relationship with coach",
|
||||
user_id="sarah",
|
||||
metadata={"agent_id": "fitcoach_v1"}
|
||||
)
|
||||
mem0_client.add(
|
||||
"Mike's workout preferences and relationship with coach",
|
||||
user_id="mike",
|
||||
metadata={"agent_id": "fitcoach_v2"}
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## What You Built
|
||||
|
||||
A fitness coach with three-layer memory architecture:
|
||||
|
||||
- **Agent personality (agent_id)** - Shared coaching style across all users, updated once
|
||||
- **User profiles (user_id)** - Private workout history, goals, and progress for each person
|
||||
- **Relationship memories (user_id + metadata)** - Personalized interaction preferences per user-agent pair
|
||||
- **Data isolation** - Sarah never sees Mike's workouts, guaranteed by user_id filtering
|
||||
|
||||
This pattern scales from 10 to 10,000 users without duplicating agent personality.
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
With Mem0's **`user_id`** and **`agent_id`** parameters, you can build AI agents that maintain consistent personalities across all users while remembering individual user data privately. Store relationship preferences with **`user_id`** and track agent context in metadata.
|
||||
|
||||
Most apps only need **`user_id`**. Add **`agent_id`** when your AI needs a consistent personality that evolves independently from user data—fitness coaches, tutors, therapy bots, and game NPCs all fit this pattern.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Control What Gets Stored" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
|
||||
Filter low-signal conversations before they pollute long-term memory.
|
||||
</Card>
|
||||
<Card title="Organize Support Memories" icon="tag" href="/cookbooks/essentials/tagging-and-organizing-memories">
|
||||
Categorize customer context so teams can retrieve the right facts fast.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
BIN
Binary file not shown.
|
After Width: | Height: | Size: 69 KiB |
@@ -0,0 +1,361 @@
|
||||
---
|
||||
title: Choose Vector vs Graph Memory
|
||||
description: "Blend vector search with graph relationships to answer multi-hop questions."
|
||||
---
|
||||
|
||||
|
||||
Most AI agents use vector stores for RAG operations - they work great for semantic search and retrieving relevant context. But there's a gap when queries require understanding connections between entities.
|
||||
|
||||
Mem0 brings graph memory into the picture to fill this gap. In this cookbook, we'll create a company knowledge base with Mem0, using both vector and graph stores. You'll learn when each one helps along the way.
|
||||
|
||||
---
|
||||
|
||||
## Vector and Graph Stores
|
||||
|
||||
When you add a memory to Mem0, it goes into a **vector store** by default. Vector stores are excellent at semantic search - finding memories that match the meaning of your query.
|
||||
|
||||
**Graph stores** work differently. They extract **entities** (people, projects, teams) and **relationships between them** (works_with, reports_to, member_of). This lets you answer questions that need connecting information across multiple memories.
|
||||
|
||||
We will go through examples in this cookbook while building a company's knowledge base along the way.
|
||||
|
||||
---
|
||||
|
||||
## Starting Simple
|
||||
|
||||
Since we're building a company knowledge base, let's add some employee information:
|
||||
|
||||
```python
|
||||
from mem0 import MemoryClient
|
||||
|
||||
client = MemoryClient(api_key="your-api-key")
|
||||
# Add employee info
|
||||
client.add("Emma is a software engineer in Seattle", user_id="company_kb")
|
||||
client.add("David is a product manager in Austin", user_id="company_kb")
|
||||
|
||||
```
|
||||
|
||||
Now let's search for Emma's role:
|
||||
|
||||
```python
|
||||
results = client.search("What does Emma do?", filters={"user_id": "company_kb"})
|
||||
print(results['results'][0]['memory'])
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Emma is a software engineer in Seattle
|
||||
|
||||
```
|
||||
|
||||
<Info>
|
||||
**Expected output:** Vector search returned Emma's role instantly. When queries ask for facts directly stored in one memory, vector semantic search is perfect—fast and accurate.
|
||||
</Info>
|
||||
|
||||
This works perfectly. Vector search found the memory that semantically matches "What does Emma do?" and returned Emma's role.
|
||||
|
||||
---
|
||||
|
||||
## Adding Team Structure
|
||||
|
||||
Let's add some information about how the team works together:
|
||||
|
||||
```python
|
||||
client.add("Emma works with David on the mobile app redesign", user_id="company_kb")
|
||||
client.add("David reports to Rachel, who manages the design team", user_id="company_kb")
|
||||
|
||||
```
|
||||
|
||||
Now we have two pieces of information stored:
|
||||
|
||||
1. Emma works with David
|
||||
2. David reports to Rachel
|
||||
|
||||
Let's try asking something that needs both pieces:
|
||||
|
||||
```python
|
||||
results = client.search(
|
||||
"Who is Emma's teammate's manager?",
|
||||
filters={"user_id": "company_kb"}
|
||||
)
|
||||
|
||||
for r in results['results']:
|
||||
print(r['memory'])
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Emma works with David on the mobile app redesign
|
||||
David reports to Rachel, who manages the design team
|
||||
|
||||
```
|
||||
|
||||
Vector search returned both memories, but it didn't connect them. You'd need to manually figure out:
|
||||
|
||||
- Emma's teammate is David (from memory 1)
|
||||
- David's manager is Rachel (from memory 2)
|
||||
- So the answer is Rachel
|
||||
|
||||
<Warning>
|
||||
Vector search can't traverse relationships. It returns relevant memories, but you must connect the dots manually. For "Who is Emma's teammate's manager?", vector search gives you the pieces—not the answer. This breaks down as queries get more complex (3+ hops).
|
||||
</Warning>
|
||||
|
||||
---
|
||||
|
||||
## Enter Graph Memory
|
||||
|
||||
Let's add the same information with graph memory enabled:
|
||||
|
||||
```python
|
||||
client.add(
|
||||
"Emma works with David on the mobile app redesign",
|
||||
user_id="company_kb",
|
||||
enable_graph=True
|
||||
)
|
||||
|
||||
client.add(
|
||||
"David reports to Rachel, who manages the design team",
|
||||
user_id="company_kb",
|
||||
enable_graph=True
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
When you set `enable_graph=True`, Mem0 extracts entities and relationships:
|
||||
|
||||
- `emma --[works_with]--> david`
|
||||
- `david --[reports_to]--> rachel`
|
||||
- `rachel --[manages]--> design_team`
|
||||
|
||||
Now the same query works differently:
|
||||
|
||||
```python
|
||||
results = client.search(
|
||||
"Who is Emma's teammate's manager?",
|
||||
filters={"user_id": "company_kb"},
|
||||
enable_graph=True
|
||||
)
|
||||
|
||||
print(results['results'][0]['memory'])
|
||||
print("\\nRelationships found:")
|
||||
for rel in results.get('relations', []):
|
||||
print(f" {rel['source']}, {rel['target']} ({rel['relationship']})")
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
David reports to Rachel, who manages the design team
|
||||
|
||||
Relationships found:
|
||||
emma, david (works_with)
|
||||
david, rachel (reports_to)
|
||||
|
||||
```
|
||||
|
||||
<Info>
|
||||
**Expected behavior:** Graph memory returns the direct answer—"David reports to Rachel"—plus the relationship chain that got there. No manual connecting needed. The graph traversed: Emma → works_with → David → reports_to → Rachel.
|
||||
</Info>
|
||||
|
||||
Graph memory traversed the relationships automatically: Emma works with David, David reports to Rachel, so Rachel is the answer.
|
||||
|
||||
---
|
||||
|
||||
## How It Connects
|
||||
|
||||
Here's what the graph looks like behind the scenes:
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
Emma[Emma] -->|works_with| David[David]
|
||||
David -->|reports_to| Rachel[Rachel]
|
||||
Rachel -->|manages| DesignTeam[Design Team]
|
||||
David -->|works_on| MobileApp[Mobile App]
|
||||
Emma -->|works_on| MobileApp
|
||||
|
||||
```
|
||||
|
||||
Graph memory lets you discover relations and memories which are tricky to do with direct vector stores.
|
||||
|
||||
Vector search would need the exact words in your query to match. Graph memory follows the connections.
|
||||
|
||||
---
|
||||
|
||||
## When to Use Each
|
||||
|
||||
Use **vector store** (default) when:
|
||||
|
||||
- Searching documents by semantic similarity
|
||||
- Looking up facts that don't need relationships
|
||||
- Building FAQs or knowledge bases where each item stands alone
|
||||
|
||||
Use **graph memory** when:
|
||||
|
||||
- Tracking organizational hierarchies (who reports to whom)
|
||||
- Understanding project teams (who collaborates with whom)
|
||||
- Building CRMs (which contacts connect to which companies)
|
||||
- Product recommendations (what items are bought together)
|
||||
|
||||
For our company knowledge base, we'll use both:
|
||||
|
||||
- Vector for individual facts: "Emma specializes in React"
|
||||
- Graph for relationships: "Emma works with David"
|
||||
|
||||
---
|
||||
|
||||
## Putting It Together
|
||||
|
||||
Let's build a small company knowledge base with both approaches:
|
||||
|
||||
```python
|
||||
# Facts about individuals - vector store is fine
|
||||
client.add("Emma specializes in React and TypeScript", user_id="company_kb")
|
||||
client.add("David has 5 years of product management experience", user_id="company_kb")
|
||||
|
||||
# Relationships - use graph memory
|
||||
client.add(
|
||||
"Emma and David work together on the mobile app",
|
||||
user_id="company_kb",
|
||||
enable_graph=True
|
||||
)
|
||||
|
||||
client.add(
|
||||
"David reports to Rachel",
|
||||
user_id="company_kb",
|
||||
enable_graph=True
|
||||
)
|
||||
|
||||
client.add(
|
||||
"Rachel runs weekly team syncs every Tuesday",
|
||||
user_id="company_kb",
|
||||
enable_graph=True
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
Now we can ask different types of questions:
|
||||
|
||||
```python
|
||||
# Direct fact - vector search
|
||||
results = client.search("What are Emma's skills?", filters={"user_id": "company_kb"})
|
||||
print(results['results'][0]['memory'])
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Emma specializes in React and TypeScript
|
||||
|
||||
```
|
||||
|
||||
```python
|
||||
# Multi-hop relationship - graph search
|
||||
results = client.search(
|
||||
"What meetings does Emma's project manager's boss run?",
|
||||
filters={"user_id": "company_kb"},
|
||||
enable_graph=True
|
||||
)
|
||||
print(results['results'][0]['memory'])
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Rachel runs weekly team syncs every Tuesday
|
||||
|
||||
```
|
||||
|
||||
Graph memory connected: Emma works with David, David reports to Rachel, Rachel runs team syncs.
|
||||
|
||||
<Tip>
|
||||
Enable graph memory when your queries need multi-hop traversal: org charts (who reports to whom), project teams (who collaborates), CRMs (which contacts connect to companies). For single-fact lookups, stick with vector search—it's faster and cheaper.
|
||||
</Tip>
|
||||
|
||||
---
|
||||
|
||||
## The Tradeoff
|
||||
|
||||
Graph memory adds processing time and cost. When you call `client.add()` with `enable_graph=True`, Mem0 makes extra LLM calls to extract entities and relationships.
|
||||
|
||||
<Note>
|
||||
**Cost consideration:** Graph memory extraction adds ~2-3 extra LLM calls per `add()` operation to identify entities and relationships. Use it selectively—enable graph for organizational structure and long-term relationships, skip it for temporary notes and simple facts.
|
||||
</Note>
|
||||
|
||||
Use graph memory when the relationship traversal adds real value. For most use cases, vector search is sufficient and faster.
|
||||
|
||||
```python
|
||||
# Long-term organizational structure - worth using graph
|
||||
client.add(
|
||||
"Emma mentors two junior engineers on the frontend team",
|
||||
user_id="company_kb",
|
||||
enable_graph=True
|
||||
)
|
||||
|
||||
# Temporary notes - skip graph, not worth the cost
|
||||
client.add(
|
||||
"Emma is out sick today",
|
||||
user_id="company_kb",
|
||||
run_id="daily_notes"
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Enabling Graph Memory
|
||||
|
||||
You can enable graph memory in two ways:
|
||||
|
||||
**Per-call** (recommended to start):
|
||||
|
||||
```python
|
||||
client.add("Emma works with David", user_id="company_kb", enable_graph=True)
|
||||
client.search("team structure", filters={"user_id": "company_kb"}, enable_graph=True)
|
||||
|
||||
```
|
||||
|
||||
**Project-wide** (if most of your data has relationships):
|
||||
|
||||
```python
|
||||
client.project.update(enable_graph=True)
|
||||
|
||||
# Now every add uses graph automatically
|
||||
client.add("Emma mentors Jordan", user_id="company_kb")
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## What You Built
|
||||
|
||||
A hybrid company knowledge base that combines both architectures:
|
||||
|
||||
- **Vector search** - Fast semantic lookups for individual facts (Emma's skills, David's experience)
|
||||
- **Graph memory** - Multi-hop relationship traversal (Emma's teammate's manager, project hierarchies)
|
||||
- **Selective enablement** - Graph only for long-term organizational structure, vector for everything else
|
||||
- **Cost optimization** - Skip graph extraction for temporary notes and simple facts
|
||||
|
||||
This pattern scales from 10-person startups to enterprise org charts with thousands of employees.
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
Vector stores handle most memory operations efficiently—semantic search works great for finding relevant information. Add graph memory when your queries need to understand how entities connect across multiple hops.
|
||||
|
||||
The key is knowing which tool fits your query pattern: direct questions work with vectors, multi-hop relationship queries need graphs.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Build AI with Personality" icon="sparkles" href="/cookbooks/essentials/building-ai-with-personality">
|
||||
Scope memories across user and agent IDs to balance personalization and reuse.
|
||||
</Card>
|
||||
<Card title="Export Everything Safely" icon="download" href="/cookbooks/essentials/exporting-memories">
|
||||
Learn how to migrate or audit stored memories with structured exports.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -0,0 +1,508 @@
|
||||
---
|
||||
title: Control Memory Ingestion
|
||||
description: "Filter speculation, enforce formats, and gate low-confidence data before it persists."
|
||||
---
|
||||
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
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.
|
||||
|
||||
Mem0 provides **three tools to control** what gets stored:
|
||||
|
||||
1. **Custom instructions** define what to remember and what to ignore.
|
||||
2. **Confidence thresholds** ensure only verified facts persist.
|
||||
3. **Memory updates** let you change information without creating duplicates.
|
||||
|
||||
In this tutorial, we will:
|
||||
|
||||
- Filter speculative statements with custom instructions
|
||||
- Configure confidence thresholds for fact verification
|
||||
- Update stored information without duplication
|
||||
- Build a complete ingestion pipeline
|
||||
|
||||
---
|
||||
|
||||
## Setup
|
||||
|
||||
```python
|
||||
from mem0 import MemoryClient
|
||||
|
||||
client = MemoryClient(api_key="your-api-key")
|
||||
```
|
||||
|
||||
<Note>
|
||||
Replace `your-api-key` with your actual Mem0 API key from the [dashboard](https://app.mem0.ai). Without proper API authentication, memory operations will fail.
|
||||
</Note>
|
||||
|
||||
---
|
||||
|
||||
## The Problem
|
||||
|
||||
Uncontrolled ingestion stores everything, including speculation:
|
||||
|
||||
```python
|
||||
# Patient mentions speculation
|
||||
messages = [{"role": "user", "content": "I think I might be allergic to penicillin"}]
|
||||
client.add(messages, user_id="patient_123")
|
||||
|
||||
# Check what got stored
|
||||
results = client.search("patient allergies", filters={"user_id": "patient_123"})
|
||||
print(results['results'][0]['memory'])
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Patient is allergic to penicillin
|
||||
```
|
||||
|
||||
<Warning>
|
||||
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.
|
||||
</Warning>
|
||||
|
||||
The speculation became a confirmed fact. Let's add controls.
|
||||
|
||||
---
|
||||
|
||||
## Custom Instructions
|
||||
|
||||
Custom instructions tell Mem0 what to store and what to ignore.
|
||||
|
||||
```python
|
||||
instructions = """
|
||||
Only store CONFIRMED medical facts.
|
||||
|
||||
Store:
|
||||
- Confirmed diagnoses from doctors
|
||||
- Known allergies with documented reactions
|
||||
- Current medications being taken
|
||||
|
||||
Ignore:
|
||||
- Speculation (words like "might", "maybe", "I think")
|
||||
- Unverified symptoms
|
||||
- Casual mentions without confirmation
|
||||
"""
|
||||
|
||||
client.project.update(custom_instructions=instructions)
|
||||
|
||||
# Same speculative statement
|
||||
messages = [{"role": "user", "content": "I think I might be allergic to penicillin"}]
|
||||
client.add(messages, user_id="patient_123")
|
||||
|
||||
# Check what got stored
|
||||
results = client.get_all(filters={"user_id": "patient_123"})
|
||||
print(f"Memories stored: {len(results['results'])}")
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Memories stored: 0
|
||||
```
|
||||
|
||||
<Info>
|
||||
**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.
|
||||
</Info>
|
||||
|
||||
The speculation was filtered out.
|
||||
|
||||
---
|
||||
|
||||
## Designing Custom Instructions
|
||||
|
||||
When designing instructions, consider the trade-off between precision and recall:
|
||||
|
||||
**Too restrictive:** You'll miss important information (false negatives)
|
||||
|
||||
```python
|
||||
# Too strict - filters out useful context
|
||||
"""
|
||||
Only store information if explicitly stated by a doctor with full name,
|
||||
date, time, and medical license number.
|
||||
"""
|
||||
|
||||
```
|
||||
|
||||
**Too permissive:** You'll store unreliable data (false positives)
|
||||
|
||||
```python
|
||||
# Too loose - stores speculation as fact
|
||||
"""
|
||||
Store any health-related information mentioned.
|
||||
"""
|
||||
|
||||
```
|
||||
|
||||
**Balanced approach:**
|
||||
|
||||
```python
|
||||
# Clear categories with examples
|
||||
"""
|
||||
Store CONFIRMED facts:
|
||||
- Diagnoses: "Dr. Smith diagnosed hypertension on March 15th"
|
||||
- Allergies: "Patient had hives reaction to penicillin"
|
||||
- Medications: "Taking Lisinopril 10mg daily"
|
||||
|
||||
Ignore SPECULATION:
|
||||
- "I think I might have..."
|
||||
- "Maybe it's..."
|
||||
- "Could be related to..."
|
||||
"""
|
||||
|
||||
```
|
||||
|
||||
<Tip>
|
||||
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.
|
||||
</Tip>
|
||||
|
||||
Start with clear categories and iterate based on retrieval quality.
|
||||
|
||||
---
|
||||
|
||||
## Confidence Thresholds
|
||||
|
||||
Mem0 assigns confidence scores to extracted memories. Use these to filter low-quality data.
|
||||
|
||||
### Setting Thresholds
|
||||
|
||||
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
|
||||
- **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
|
||||
# Configure stricter instructions
|
||||
client.project.update(
|
||||
custom_instructions="""
|
||||
Only extract memories with HIGH confidence.
|
||||
Require specific details (dates, dosages, doctor names) for medical facts.
|
||||
Skip vague or uncertain statements.
|
||||
"""
|
||||
)
|
||||
|
||||
# Test with uncertain statement
|
||||
messages = [{"role": "user", "content": "The doctor mentioned something about my blood pressure"}]
|
||||
result1 = client.add(messages, user_id="patient_123")
|
||||
|
||||
# Test with confirmed fact
|
||||
messages = [{"role": "user", "content": "Dr. Smith diagnosed me with hypertension on March 15th"}]
|
||||
result2 = client.add(messages, user_id="patient_123")
|
||||
|
||||
print("Vague statement stored:", len(result1['results']) > 0)
|
||||
print("Confirmed fact stored:", len(result2['results']) > 0)
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Vague statement stored: False
|
||||
Confirmed fact stored: True
|
||||
```
|
||||
|
||||
<Info icon="check">
|
||||
**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>
|
||||
|
||||
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
|
||||
client.project.update(
|
||||
custom_instructions="""
|
||||
Medical memory rules:
|
||||
|
||||
STORE:
|
||||
- Confirmed diagnoses
|
||||
- Verified allergies
|
||||
- Current medications
|
||||
|
||||
NEVER STORE:
|
||||
- Social Security Numbers
|
||||
- Insurance policy numbers
|
||||
- Credit card information
|
||||
- Full addresses
|
||||
- Phone numbers
|
||||
|
||||
Replace identifiers with generic references if mentioned.
|
||||
"""
|
||||
)
|
||||
|
||||
# Test with PII
|
||||
messages = [
|
||||
{"role": "user", "content": "My SSN is 123-45-6789 and I'm allergic to penicillin"}
|
||||
]
|
||||
client.add(messages, user_id="patient_123")
|
||||
|
||||
# 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(
|
||||
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
|
||||
|
||||
**Maintains relationships:**
|
||||
|
||||
- If using graph memory, connections to other entities persist
|
||||
|
||||
---
|
||||
|
||||
## 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.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Expire Short-Term Data" icon="timer" href="/cookbooks/essentials/memory-expiration-short-and-long-term">
|
||||
Automatically clean up session context before it clutters retrieval.
|
||||
</Card>
|
||||
<Card title="Choose Your Memory Architecture" icon="sitemap" href="/cookbooks/essentials/choosing-memory-architecture-vector-vs-graph">
|
||||
Learn when to layer graph memory alongside vectors for multi-hop queries.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -0,0 +1,289 @@
|
||||
---
|
||||
title: Export Stored Memories
|
||||
description: "Retrieve, review, and migrate user memories with structured exports."
|
||||
---
|
||||
|
||||
|
||||
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.
|
||||
|
||||
This cookbook shows you how to retrieve and export your data for inspection, migration, or compliance.
|
||||
|
||||
---
|
||||
|
||||
## Setup
|
||||
|
||||
```python
|
||||
from mem0 import MemoryClient
|
||||
|
||||
client = MemoryClient(api_key="your-api-key")
|
||||
|
||||
```
|
||||
|
||||
<Note>
|
||||
Your API key needs export permissions to download memory data. Check your project settings on the [dashboard](https://app.mem0.ai) if export operations fail with authentication errors.
|
||||
</Note>
|
||||
|
||||
Let's add some sample memories to work with:
|
||||
|
||||
```python
|
||||
# Dev's work history
|
||||
client.add(
|
||||
"Dev works at TechCorp as a senior engineer",
|
||||
user_id="dev",
|
||||
metadata={"type": "professional"}
|
||||
)
|
||||
|
||||
# Arjun's preferences
|
||||
client.add(
|
||||
"Arjun prefers morning meetings and async communication",
|
||||
user_id="arjun",
|
||||
metadata={"type": "preference"}
|
||||
)
|
||||
|
||||
# Carl's project notes
|
||||
client.add(
|
||||
"Carl is leading the API redesign project, targeting Q2 launch",
|
||||
user_id="carl",
|
||||
metadata={"type": "project"}
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Getting All Memories
|
||||
|
||||
Use `get_all()` with filters to retrieve everything for a specific user:
|
||||
|
||||
```python
|
||||
dev_memories = client.get_all(
|
||||
filters={"user_id": "dev"},
|
||||
page_size=50
|
||||
)
|
||||
|
||||
print(f"Total memories: {dev_memories['count']}")
|
||||
print(f"First memory: {dev_memories['results'][0]['memory']}")
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Total memories: 1
|
||||
First memory: Dev works at TechCorp as a senior engineer
|
||||
|
||||
```
|
||||
|
||||
<Info>
|
||||
**Expected output:** `get_all()` retrieved Dev's complete memory record. This method returns everything matching your filters—no semantic search, no ranking, just raw retrieval. Perfect for exports and audits.
|
||||
</Info>
|
||||
|
||||
You can filter by metadata to get specific types:
|
||||
|
||||
```python
|
||||
carl_projects = client.get_all(
|
||||
filters={
|
||||
"AND": [
|
||||
{"user_id": "carl"},
|
||||
{"metadata": {"type": "project"}}
|
||||
]
|
||||
}
|
||||
)
|
||||
|
||||
for memory in carl_projects['results']:
|
||||
print(memory['memory'])
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Carl is leading the API redesign project, targeting Q2 launch
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Searching Memories
|
||||
|
||||
When you need semantic search instead of retrieving everything, use `search()`:
|
||||
|
||||
```python
|
||||
results = client.search(
|
||||
query="What does Dev do for work?",
|
||||
filters={"user_id": "dev"},
|
||||
top_k=5
|
||||
)
|
||||
|
||||
for result in results['results']:
|
||||
print(f"{result['memory']} (score: {result['score']:.2f})")
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Dev works at TechCorp as a senior engineer (score: 0.89)
|
||||
|
||||
```
|
||||
|
||||
Search works across all memory fields and ranks by relevance. Use it when you have a specific question, use `get_all()` when you need everything.
|
||||
|
||||
---
|
||||
|
||||
## Exporting to Structured Format
|
||||
|
||||
For migrations or compliance, you can export memories into a structured schema using Pydantic-style JSON schemas.
|
||||
|
||||
### Step 1: Define the schema
|
||||
|
||||
```python
|
||||
professional_profile_schema = {
|
||||
"properties": {
|
||||
"full_name": {
|
||||
"type": "string",
|
||||
"description": "The person's full name"
|
||||
},
|
||||
"current_role": {
|
||||
"type": "string",
|
||||
"description": "Current job title or role"
|
||||
},
|
||||
"company": {
|
||||
"type": "string",
|
||||
"description": "Current employer"
|
||||
}
|
||||
},
|
||||
"title": "ProfessionalProfile",
|
||||
"type": "object"
|
||||
}
|
||||
|
||||
```
|
||||
|
||||
### Step 2: Create export job
|
||||
|
||||
```python
|
||||
export_job = client.create_memory_export(
|
||||
schema=professional_profile_schema,
|
||||
filters={"user_id": "dev"}
|
||||
)
|
||||
|
||||
print(f"Export ID: {export_job['id']}")
|
||||
print(f"Status: {export_job['status']}")
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Export ID: exp_abc123
|
||||
Status: processing
|
||||
|
||||
```
|
||||
|
||||
<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.
|
||||
</Info>
|
||||
|
||||
### Step 3: Download the export
|
||||
|
||||
```python
|
||||
# Get by ID
|
||||
export_data = client.get_memory_export(
|
||||
memory_export_id=export_job['id']
|
||||
)
|
||||
|
||||
print(export_data['data'])
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```json
|
||||
{
|
||||
"full_name": "Dev",
|
||||
"current_role": "senior engineer",
|
||||
"company": "TechCorp"
|
||||
}
|
||||
|
||||
```
|
||||
|
||||
You can also retrieve exports by filters:
|
||||
|
||||
```python
|
||||
# Get latest export matching filters
|
||||
export_by_filters = client.get_memory_export(
|
||||
filters={"user_id": "dev"}
|
||||
)
|
||||
|
||||
print(export_by_filters['data'])
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Adding Export Instructions
|
||||
|
||||
Guide how Mem0 resolves conflicts or formats the export:
|
||||
|
||||
```python
|
||||
export_with_instructions = client.create_memory_export(
|
||||
schema=professional_profile_schema,
|
||||
filters={"user_id": "arjun"},
|
||||
export_instructions="""
|
||||
1. Use the most recent information if there are conflicts
|
||||
2. Only include confirmed facts, not speculation
|
||||
3. Return null for missing fields rather than guessing
|
||||
"""
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
<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.
|
||||
</Tip>
|
||||
|
||||
---
|
||||
|
||||
## Platform Export
|
||||
|
||||
You can also export memories directly from the Mem0 platform UI:
|
||||
|
||||
1. Navigate to **Memory Exports** in your project dashboard
|
||||
2. Click **Create Export**
|
||||
3. Select your filters and schema
|
||||
4. Download the completed export as JSON
|
||||
|
||||
This is useful for one-off exports or manual data reviews.
|
||||
|
||||
<Warning>
|
||||
Exported data expires after 7 days. Download and store exports locally if you need long-term archives. After expiration, you'll need to recreate the export job.
|
||||
</Warning>
|
||||
|
||||
---
|
||||
|
||||
## What You Built
|
||||
|
||||
A complete memory export system with multiple retrieval methods:
|
||||
|
||||
- **Bulk retrieval (get_all)** - Fetch all memories matching filters for comprehensive audits
|
||||
- **Semantic search** - Query-based lookups with relevance scoring
|
||||
- **Structured exports** - Pydantic-schema exports for migrations and compliance
|
||||
- **Export instructions** - Guide conflict resolution and data formatting
|
||||
- **Platform UI exports** - One-off manual downloads via dashboard
|
||||
|
||||
This covers data portability, GDPR compliance, system migrations, and manual reviews.
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
Use **`get_all()`** for bulk retrieval, **`search()`** for specific questions, and **`create_memory_export()`** for structured data exports with custom schemas. Remember exports expire after 7 days—download them locally for long-term archives.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Expire Short-Term Data" icon="timer" href="/cookbooks/essentials/memory-expiration-short-and-long-term">
|
||||
Keep exports lean by clearing session context before you archive it.
|
||||
</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>
|
||||
</CardGroup>
|
||||
@@ -0,0 +1,277 @@
|
||||
---
|
||||
title: Set Memory Expiration
|
||||
description: "Define short-term versus long-term retention so the store stays fresh."
|
||||
---
|
||||
|
||||
|
||||
While building memory systems, we realized their size grows fast. Session notes, temporary context, chat history - everything starts accumulating and bogging down the system. This pollutes search results and increase storage costs. Not every memory needs to persist forever.
|
||||
|
||||
In this cookbook, we'll go through how to use short-term vs long-term memories and see where it's best to use them.
|
||||
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
By default, Mem0 memories persist forever. This works for user preferences and core facts, but temporary data should expire automatically.
|
||||
|
||||
In this tutorial, we will:
|
||||
|
||||
- Understand default (permanent) memory behavior
|
||||
- Add expiration dates for temporary memories
|
||||
- Decide what should be temporary vs permanent
|
||||
|
||||
---
|
||||
|
||||
## Setup
|
||||
|
||||
```python
|
||||
from mem0 import MemoryClient
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
client = MemoryClient(api_key="your-api-key")
|
||||
```
|
||||
|
||||
<Note>
|
||||
Import `datetime` and `timedelta` to calculate expiration dates. Without these imports, you'll need to manually format ISO timestamps—error-prone and harder to read.
|
||||
</Note>
|
||||
|
||||
---
|
||||
|
||||
## Default Behavior: Everything Persists
|
||||
|
||||
By default, all memories persist forever:
|
||||
|
||||
```python
|
||||
# Store user preference
|
||||
client.add("User prefers dark mode", user_id="sarah")
|
||||
|
||||
# Store session context
|
||||
client.add("Currently browsing electronics category", user_id="sarah")
|
||||
|
||||
# 6 months later - both still exist
|
||||
results = client.get_all(filters={"user_id": "sarah"})
|
||||
print(f"Total memories: {len(results['results'])}")
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Total memories: 2
|
||||
|
||||
```
|
||||
|
||||
Both the preference and session context persist. The preference is useful, but the 6-month-old session context is not.
|
||||
|
||||
---
|
||||
|
||||
## The Problem: Memory Bloat
|
||||
|
||||
Without expiration, memories accumulate forever. Session notes from weeks ago mix with current preferences. Storage grows, search results get polluted with irrelevant old context, and retrieval quality degrades.
|
||||
|
||||
<Warning>
|
||||
Memory bloat degrades search quality. When "User prefers dark mode" competes with "Currently browsing electronics" from 6 months ago, semantic search returns stale session data instead of actual preferences. Old memories pollute retrieval.
|
||||
</Warning>
|
||||
|
||||
---
|
||||
|
||||
## Short-Term Memories: Adding Expiration
|
||||
|
||||
Set `expiration_date` to make memories temporary:
|
||||
|
||||
```python
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# Session context - expires in 7 days
|
||||
expires_at = (datetime.now() + timedelta(days=7)).isoformat()
|
||||
|
||||
client.add(
|
||||
"Currently browsing electronics category",
|
||||
user_id="sarah",
|
||||
expiration_date=expires_at
|
||||
)
|
||||
|
||||
# User preference - no expiration, persists forever
|
||||
client.add(
|
||||
"User prefers dark mode",
|
||||
user_id="sarah"
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
<Info icon="check">
|
||||
**Expected behavior:** After 7 days, the session context automatically disappears—no cron jobs, no manual cleanup. The preference persists forever. Mem0 handles expiration transparently.
|
||||
</Info>
|
||||
|
||||
Memories with `expiration_date` are automatically removed after expiring. No cleanup job needed - Mem0 handles it.
|
||||
|
||||
<Tip>
|
||||
Start conservative with short expiration windows (7 days), then extend them based on usage patterns. It's easier to increase retention than to clean up over-retained stale data. Monitor search quality to find the right balance.
|
||||
</Tip>
|
||||
|
||||
---
|
||||
|
||||
## When to Use Each
|
||||
|
||||
### Permanent Memories (no expiration_date):
|
||||
|
||||
**Use for:**
|
||||
|
||||
- User preferences and settings
|
||||
- Account information
|
||||
- Important facts and milestones
|
||||
- Historical data that matters long-term
|
||||
|
||||
```python
|
||||
client.add("User prefers email notifications", user_id="sarah")
|
||||
client.add("User's birthday is March 15th", user_id="sarah")
|
||||
client.add("User completed onboarding on Jan 5th", user_id="sarah")
|
||||
|
||||
```
|
||||
|
||||
### Temporary Memories (with expiration_date):
|
||||
|
||||
**Use for:**
|
||||
|
||||
- Session context (current page, browsing history)
|
||||
- Temporary reminders
|
||||
- Recent chat history
|
||||
- Cached data
|
||||
|
||||
```python
|
||||
expires_7d = (datetime.now() + timedelta(days=7)).isoformat()
|
||||
|
||||
client.add(
|
||||
"Currently viewing product ABC123",
|
||||
user_id="sarah",
|
||||
expiration_date=expires_7d
|
||||
)
|
||||
|
||||
client.add(
|
||||
"Asked about return policy",
|
||||
user_id="sarah",
|
||||
expiration_date=expires_7d
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Setting Different Expiration Periods
|
||||
|
||||
Different data needs different lifetimes:
|
||||
|
||||
```python
|
||||
# Session context - 7 days
|
||||
expires_7d = (datetime.now() + timedelta(days=7)).isoformat()
|
||||
client.add("Browsing electronics", user_id="sarah", expiration_date=expires_7d)
|
||||
|
||||
# Recent chat - 30 days
|
||||
expires_30d = (datetime.now() + timedelta(days=30)).isoformat()
|
||||
client.add("User asked about warranty", user_id="sarah", expiration_date=expires_30d)
|
||||
|
||||
# Important preference - no expiration
|
||||
client.add("User prefers dark mode", user_id="sarah")
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Using Metadata to Track Memory Types
|
||||
|
||||
Tag memories to make filtering easier:
|
||||
|
||||
```python
|
||||
expires_7d = (datetime.now() + timedelta(days=7)).isoformat()
|
||||
|
||||
# Tag session context
|
||||
client.add(
|
||||
"Browsing electronics",
|
||||
user_id="sarah",
|
||||
expiration_date=expires_7d,
|
||||
metadata={"type": "session"}
|
||||
)
|
||||
|
||||
# Tag preference
|
||||
client.add(
|
||||
"User prefers dark mode",
|
||||
user_id="sarah",
|
||||
metadata={"type": "preference"}
|
||||
)
|
||||
|
||||
# Query only preferences
|
||||
preferences = client.get_all(
|
||||
filters={
|
||||
"AND": [
|
||||
{"user_id": "sarah"},
|
||||
{"metadata": {"type": "preference"}}
|
||||
]
|
||||
}
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Checking Expiration Status
|
||||
|
||||
See which memories will expire and when:
|
||||
|
||||
```python
|
||||
results = client.get_all(filters={"user_id": "sarah"})
|
||||
|
||||
for memory in results['results']:
|
||||
exp_date = memory.get('expiration_date')
|
||||
|
||||
if exp_date:
|
||||
print(f"Temporary: {memory['memory']}")
|
||||
print(f" Expires: {exp_date}\\n")
|
||||
else:
|
||||
print(f"Permanent: {memory['memory']}\\n")
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Temporary: Browsing electronics
|
||||
Expires: 2025-11-01T10:30:00Z
|
||||
|
||||
Temporary: Viewed MacBook Pro and Dell XPS
|
||||
Expires: 2025-11-01T10:30:00Z
|
||||
|
||||
Permanent: User prefers dark mode
|
||||
|
||||
Permanent: User prefers email notifications
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## What You Built
|
||||
|
||||
A self-cleaning memory system with automatic retention policies:
|
||||
|
||||
- **Automatic expiration** - Memories self-destruct after defined periods, no cron jobs needed
|
||||
- **Tiered retention** - 7-day session context, 30-day chat history, permanent preferences
|
||||
- **Metadata tagging** - Classify memories by type (session, preference, chat) for filtered retrieval
|
||||
- **Expiration tracking** - Check which memories will expire and when using `get_all()`
|
||||
|
||||
This pattern keeps storage costs low and search quality high as your memory store scales.
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
Memory expiration keeps storage clean and search results relevant. Use **`expiration_date`** for temporary data (session context, recent chats), skip it for permanent facts (preferences, account info). Mem0 handles cleanup automatically—no background jobs required.
|
||||
|
||||
Start by identifying what's temporary versus permanent, then set conservative expiration windows and adjust based on retrieval quality.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
|
||||
Pair expirations with ingestion rules so only trusted context persists.
|
||||
</Card>
|
||||
<Card title="Export Memories Safely" icon="download" href="/cookbooks/essentials/exporting-memories">
|
||||
Build compliant archives once your retention windows are dialed in.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -0,0 +1,251 @@
|
||||
---
|
||||
title: Tag and Organize Memories
|
||||
description: "Let Mem0 auto-categorize support data so teams retrieve the right facts fast."
|
||||
---
|
||||
|
||||
|
||||
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?
|
||||
|
||||
Mem0 handles this for you by providing the flexibility to organize memories with custom categories. This cookbook shows you how to tag and organize memories for a customer support platform.
|
||||
|
||||
---
|
||||
|
||||
## Setup
|
||||
|
||||
```python
|
||||
from mem0 import MemoryClient
|
||||
|
||||
client = MemoryClient(api_key="your-api-key")
|
||||
```
|
||||
|
||||
<Note>
|
||||
Define custom categories at the **project level** with `client.project.update()` before adding memories. Categories apply to all future memories—Mem0 auto-assigns them based on content semantics.
|
||||
</Note>
|
||||
|
||||
---
|
||||
|
||||
## The Problem
|
||||
|
||||
Without categories, all memories sit in one undifferentiated bucket. Support agents waste time searching through everything to find billing issues, account details, or past tickets.
|
||||
|
||||
Let's see what happens without organization:
|
||||
|
||||
```python
|
||||
# Joseph (support agent) stores various customer interactions
|
||||
client.add(
|
||||
"Maria called about her account password reset",
|
||||
user_id="maria",
|
||||
)
|
||||
|
||||
client.add(
|
||||
"Maria was charged twice for last month's subscription",
|
||||
user_id="maria",
|
||||
)
|
||||
|
||||
client.add(
|
||||
"Maria wants to upgrade to the premium plan",
|
||||
user_id="maria",
|
||||
)
|
||||
|
||||
# Now try to find just billing issues
|
||||
all_memories = client.get_all(filters={"user_id": "maria"})
|
||||
print(f"Total memories: {len(all_memories['results'])}")
|
||||
|
||||
for memory in all_memories['results']:
|
||||
print(f"- {memory['memory']}")
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Total memories: 3
|
||||
- Maria called about her account password reset
|
||||
- Maria was charged twice for last month's subscription
|
||||
- Maria wants to upgrade to the premium plan
|
||||
|
||||
```
|
||||
|
||||
<Warning>
|
||||
Without categories, agents waste time reading through everything. For a customer with 100 memories, finding one billing issue means scanning all 100. Categories let you filter to exactly what you need—billing issues only, no password resets or feedback mixed in.
|
||||
</Warning>
|
||||
|
||||
Everything is mixed together. Support agents have to read through all memories to find what they need.
|
||||
|
||||
---
|
||||
|
||||
## Custom Categories
|
||||
|
||||
Define categories that match how your support team thinks about customer issues:
|
||||
|
||||
```python
|
||||
custom_categories = [
|
||||
{"support_tickets": "Customer issues and resolutions"},
|
||||
{"account_info": "Account details and preferences"},
|
||||
{"billing": "Payment history and billing questions"},
|
||||
{"product_feedback": "Feature requests and feedback"},
|
||||
]
|
||||
|
||||
client.project.update(custom_categories=custom_categories)
|
||||
|
||||
```
|
||||
|
||||
<Tip>
|
||||
Start with 3-5 clear categories that match how your team thinks. Too many categories dilute auto-tagging accuracy. Add more later if needed—it's easier to expand than to fix over-complicated classification.
|
||||
</Tip>
|
||||
|
||||
These categories are now available project-wide. Every memory can be tagged with one or more categories.
|
||||
|
||||
---
|
||||
|
||||
## Tagging Memories
|
||||
|
||||
Once categories are defined at the project level, Mem0 automatically assigns them based on content:
|
||||
|
||||
```python
|
||||
# Billing issue - automatically tagged as "billing"
|
||||
client.add(
|
||||
"Maria was charged twice for last month's subscription",
|
||||
user_id="maria",
|
||||
metadata={"priority": "high", "source": "phone_call"}
|
||||
)
|
||||
|
||||
# Account update - automatically tagged as "account_info"
|
||||
client.add(
|
||||
"Maria changed her email to maria.new@example.com",
|
||||
user_id="maria",
|
||||
metadata={"source": "web_portal"}
|
||||
)
|
||||
|
||||
# Product feedback - automatically tagged as "product_feedback"
|
||||
client.add(
|
||||
"Maria requested a dark mode feature for the dashboard",
|
||||
user_id="maria",
|
||||
metadata={"source": "chat"}
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
Mem0 reads the content and intelligently assigns the appropriate categories. You don't manually tag - the platform does it for you based on the category definitions.
|
||||
|
||||
---
|
||||
|
||||
## Retrieving by Category
|
||||
|
||||
Filter memories by category to find exactly what you need:
|
||||
|
||||
```python
|
||||
# Joseph needs to pull all billing issues for audit
|
||||
billing_issues = client.get_all(
|
||||
filters={
|
||||
"AND": [
|
||||
{"user_id": "maria"},
|
||||
{"categories": {"in": ["billing"]}}
|
||||
]
|
||||
}
|
||||
)
|
||||
|
||||
print("Billing issues:")
|
||||
for memory in billing_issues['results']:
|
||||
print(f"- {memory['memory']}")
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
Billing issues:
|
||||
- Maria was charged twice for last month's subscription
|
||||
|
||||
```
|
||||
|
||||
<Info icon="check">
|
||||
**Expected output:** Only the billing issue returned—no password reset, no upgrade request. Category filtering worked. Joseph can audit billing without reading through unrelated support tickets.
|
||||
</Info>
|
||||
|
||||
Only billing-related memories are returned. No need to filter through account updates or feedback.
|
||||
|
||||
You can also retrieve multiple categories:
|
||||
|
||||
```python
|
||||
# Get both account info and billing
|
||||
account_and_billing = client.get_all(
|
||||
filters={
|
||||
"AND": [
|
||||
{"user_id": "maria"},
|
||||
{"categories": {"in": ["account_info", "billing"]}}
|
||||
]
|
||||
}
|
||||
)
|
||||
|
||||
for memory in account_and_billing['results']:
|
||||
print(f"[{memory['categories'][0]}] {memory['memory']}")
|
||||
|
||||
```
|
||||
|
||||
**Output:**
|
||||
|
||||
```
|
||||
[account_info] Maria changed her email to maria.new@example.com
|
||||
[billing] Maria was charged twice for last month's subscription
|
||||
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Updating Categories
|
||||
|
||||
Categories are automatically assigned based on content. To trigger re-categorization, update the memory content:
|
||||
|
||||
```python
|
||||
# Find memories that need re-categorization
|
||||
needs_update = client.get_all(
|
||||
filters={
|
||||
"AND": [
|
||||
{"user_id": "maria"},
|
||||
{"categories": {"in": ["misc"]}}
|
||||
]
|
||||
}
|
||||
)
|
||||
|
||||
# Update memory content to trigger re-categorization
|
||||
for memory in needs_update['results']:
|
||||
client.update(
|
||||
memory_id=memory['id'],
|
||||
data=memory['memory'] # Re-process with current category definitions
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
When you update a memory, Mem0 re-analyzes it against your current category definitions. This is useful when you introduce new categories or refine category descriptions.
|
||||
|
||||
---
|
||||
|
||||
## What You Built
|
||||
|
||||
A customer support platform with intelligent memory organization:
|
||||
|
||||
- **Project-wide categories** - Support tickets, billing, account info, and product feedback auto-classified
|
||||
- **Automatic tagging** - Mem0 assigns categories based on content semantics, no manual tagging
|
||||
- **Filtered retrieval** - Pull only billing issues or only account updates using `categories: {in: [...]}`
|
||||
- **Re-categorization** - Update memory content to trigger re-analysis against new category definitions
|
||||
- **Multi-category support** - Memories can belong to multiple categories when appropriate
|
||||
|
||||
This pattern scales from 10 customers to 10,000 without degrading retrieval speed.
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
Categories make retrieval faster and compliance easier. Define 3-5 clear categories with `client.project.update()`, let Mem0 auto-assign them based on content, then filter with `categories: {in: [...]}` to pull exactly what you need.
|
||||
|
||||
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>
|
||||
<Card title="Export Tagged Memories" icon="download" href="/cookbooks/essentials/exporting-memories">
|
||||
Use categories to drive audits, migrations, and compliance reports.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
+16
-1
@@ -1,4 +1,8 @@
|
||||
# Mem0 Chrome Extension
|
||||
---
|
||||
title: Chrome Extension with Mem0
|
||||
description: "Add Mem0's universal memory layer to Chrome chat surfaces."
|
||||
---
|
||||
|
||||
|
||||
Enhance your AI interactions with Mem0, a Chrome extension that introduces a universal memory layer across platforms like ChatGPT, Claude, and Perplexity. Mem0 ensures seamless context sharing, making your AI experiences more personalized and efficient.
|
||||
|
||||
@@ -53,3 +57,14 @@ You can install the Mem0 Chrome Extension using one of the following methods:
|
||||
## Privacy and Data Security
|
||||
|
||||
Your messages are sent to the Mem0 API for extracting and retrieving memories. Mem0 is committed to ensuring your data's privacy and security.
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
|
||||
Learn the foundations of memory-powered assistants that work across platforms.
|
||||
</Card>
|
||||
<Card title="Multimodal Support" icon="image" href="/platform/features/multimodal-support">
|
||||
Extend your browser interactions with vision and audio memory.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -1,7 +1,9 @@
|
||||
---
|
||||
title: Eliza OS Character
|
||||
title: Eliza OS Character with Mem0
|
||||
description: "Bring persistent personality to Eliza OS agents using Mem0."
|
||||
---
|
||||
|
||||
|
||||
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.
|
||||
|
||||
## Overview
|
||||
@@ -70,3 +72,14 @@ pnpm start
|
||||
You have now created a personalized Eliza OS Character using Mem0. You can now start interacting with the character by running the project and talking to the character.
|
||||
|
||||
This is a simple example of how to use Mem0 to create a personalized AI agent. You can use this as a starting point to create your own AI agent.
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Build AI with Personality" icon="sparkles" href="/cookbooks/essentials/building-ai-with-personality">
|
||||
Separate agent and user memories to maintain consistent character personalities.
|
||||
</Card>
|
||||
<Card title="AI Tutor with Mem0" icon="graduation-cap" href="/cookbooks/companions/ai-tutor">
|
||||
Build another type of personalized companion with memory capabilities.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
+13
-2
@@ -1,7 +1,9 @@
|
||||
---
|
||||
title: LlamaIndex Multi-Agent Learning System
|
||||
title: LlamaIndex Multi-Agent with Mem0
|
||||
description: "Share a persistent memory layer across collaborating LlamaIndex agents."
|
||||
---
|
||||
|
||||
|
||||
<Snippet file="blank-notif.mdx" />
|
||||
|
||||
Build an intelligent multi-agent learning system that uses Mem0 to maintain persistent memory across multiple specialized agents. This example demonstrates how to create a tutoring system where different agents collaborate while sharing a unified memory layer.
|
||||
@@ -357,4 +359,13 @@ Based on our previous session, I remember we covered Vision Language Models and
|
||||
- [LlamaIndex Agent Workflows](https://docs.llamaindex.ai/en/stable/use_cases/agents/)
|
||||
- [Mem0 Platform](https://app.mem0.ai/)
|
||||
|
||||
<Snippet file="get-help.mdx" />
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="LlamaIndex ReAct with Mem0" icon="brain" href="/cookbooks/frameworks/llamaindex-react">
|
||||
Start with single-agent patterns before scaling to multi-agent systems.
|
||||
</Card>
|
||||
<Card title="Build AI with Personality" icon="sparkles" href="/cookbooks/essentials/building-ai-with-personality">
|
||||
Learn how to scope memories across multiple agents and users.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
+14
-1
@@ -1,7 +1,9 @@
|
||||
---
|
||||
title: LlamaIndex ReAct Agent
|
||||
title: LlamaIndex ReAct with Mem0
|
||||
description: "Teach a ReAct agent to store and recall context via Mem0."
|
||||
---
|
||||
|
||||
|
||||
Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store.
|
||||
|
||||
## Overview
|
||||
@@ -184,3 +186,14 @@ I've ordered a pizza for you, and the bill has been sent to your email. Enjoy yo
|
||||
```
|
||||
|
||||
<Note>The agent is able to remember the past preferences the user shared and use them to perform actions.</Note>
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="LlamaIndex Multiagent with Mem0" icon="users" href="/cookbooks/frameworks/llamaindex-multiagent">
|
||||
Scale to multi-agent workflows with shared memory coordination.
|
||||
</Card>
|
||||
<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
|
||||
Master the core patterns for memory-powered agents across frameworks.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
+13
-1
@@ -1,7 +1,9 @@
|
||||
---
|
||||
title: Multimodal Demo with Mem0
|
||||
title: Multimodal Retrieval with Mem0
|
||||
description: "Store and recall visual context alongside text conversations."
|
||||
---
|
||||
|
||||
|
||||
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.
|
||||
|
||||
> Experience the power of multimodal AI! Test out Mem0's image understanding capabilities at [multimodal-demo.mem0.ai](https://multimodal-demo.mem0.ai)
|
||||
@@ -29,3 +31,13 @@ Enhance your AI interactions with Mem0's multimodal capabilities. Mem0 now suppo
|
||||
|
||||
Visit [multimodal-demo.mem0.ai](https://multimodal-demo.mem0.ai) to experience Mem0's multimodal capabilities firsthand. Upload images and see how Mem0 understands and remembers visual context across your conversations.
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Multimodal Support" icon="image" href="/platform/features/multimodal-support">
|
||||
Learn how to store and retrieve vision and audio memories in your apps.
|
||||
</Card>
|
||||
<Card title="Voice Companion with OpenAI" icon="microphone" href="/cookbooks/companions/voice-companion-openai">
|
||||
Build voice-first companions that remember conversations.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
+13
-1
@@ -1,5 +1,6 @@
|
||||
---
|
||||
title: Mem0 as an Agentic Tool
|
||||
title: Agents SDK Tool with Mem0
|
||||
description: "Expose Mem0 memories as callable tools inside OpenAI agent workflows."
|
||||
---
|
||||
|
||||
|
||||
@@ -224,3 +225,14 @@ context = Mem0Context(user_id="user123")
|
||||
- [Mem0 Documentation](https://docs.mem0.ai)
|
||||
- [Mem0 Dashboard](https://app.mem0.ai/dashboard)
|
||||
- [API Reference](https://docs.mem0.ai/api-reference)
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="OpenAI Tool Calls with Mem0" icon="wrench" href="/cookbooks/integrations/openai-tool-calls">
|
||||
Extend OpenAI assistants with tool-based memory operations.
|
||||
</Card>
|
||||
<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
|
||||
Learn the core patterns for memory-powered agents with any SDK.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -1,7 +1,9 @@
|
||||
---
|
||||
title: "AWS Bedrock Example"
|
||||
title: AWS Bedrock with Mem0
|
||||
description: "Pair Mem0 with AWS Bedrock, OpenSearch, and Neptune for a managed stack."
|
||||
---
|
||||
|
||||
|
||||
This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock**, **OpenSearch Service (AOSS)**, and **AWS Neptune Analytics** for persistent memory capabilities in Python.
|
||||
|
||||
## Installation
|
||||
@@ -128,3 +130,14 @@ memory = m.get(memory_id)
|
||||
## Conclusion
|
||||
|
||||
With Mem0 and AWS services like Bedrock, OpenSearch, and Neptune Analytics, you can build intelligent AI companions that remember, adapt, and personalize their responses over time. This makes them ideal for long-term assistants, tutors, or support bots with persistent memory and natural conversation abilities.
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Neptune Analytics with Mem0" icon="database" href="/cookbooks/integrations/neptune-analytics">
|
||||
Explore graph-based memory storage with AWS Neptune Analytics.
|
||||
</Card>
|
||||
<Card title="Graph Memory Features" icon="sitemap" href="/platform/features/graph-memory">
|
||||
Learn how to leverage knowledge graphs for entity relationships.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
+13
-4
@@ -1,11 +1,9 @@
|
||||
---
|
||||
title: 'Healthcare Assistant with Mem0 and Google ADK'
|
||||
description: 'Build a personalized healthcare agent that remembers patient information across conversations using Mem0 and Google ADK'
|
||||
title: Healthcare Coach with Mem0 and Google ADK
|
||||
description: "Guide patients with an assistant that remembers history across ADK sessions."
|
||||
---
|
||||
|
||||
|
||||
## Healthcare Assistant with Memory
|
||||
|
||||
This example demonstrates how to build a healthcare assistant that remembers patient information across conversations using Google ADK and Mem0.
|
||||
|
||||
## Overview
|
||||
@@ -290,3 +288,14 @@ The `threshold` parameter in the search function ensures that only highly releva
|
||||
This example demonstrates how to build a healthcare assistant with persistent memory using Google ADK and Mem0. The integration allows for a more personalized patient experience by maintaining context across conversation turns, which is particularly valuable in healthcare scenarios where continuity of information is crucial.
|
||||
|
||||
By storing and retrieving patient information intelligently, the assistant provides more relevant responses without requiring the patient to repeat their medical history, symptoms, or preferences.
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Tag and Organize Memories" icon="tag" href="/cookbooks/essentials/tagging-and-organizing-memories">
|
||||
Categorize patient data by symptoms, history, and visit context.
|
||||
</Card>
|
||||
<Card title="Support Inbox with Mem0" icon="headset" href="/cookbooks/operations/support-inbox">
|
||||
Apply similar memory patterns to customer support workflows.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -1,7 +1,9 @@
|
||||
---
|
||||
title: Mem0 with Mastra
|
||||
title: Mastra Agent with Mem0
|
||||
description: "Extend Mastra agents with persistent memories powered by Mem0."
|
||||
---
|
||||
|
||||
|
||||
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).
|
||||
@@ -123,3 +125,14 @@ In the example above:
|
||||
- We import the `@mastra/mem0` integration
|
||||
- We define two tools that use the Mem0 API client to create new memories and recall previously saved memories
|
||||
- The tool accepts `question` as an input and returns the memory as a string
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Build AI with Personality" icon="sparkles" href="/cookbooks/essentials/building-ai-with-personality">
|
||||
Separate agent and user memories to maintain consistent personalities.
|
||||
</Card>
|
||||
<Card title="Agents SDK Tool with Mem0" icon="robot" href="/cookbooks/integrations/agents-sdk-tool">
|
||||
Explore tool-calling patterns with the OpenAI Agents SDK.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
+14
-1
@@ -1,7 +1,9 @@
|
||||
---
|
||||
title: "AWS Neptune Analytics"
|
||||
title: Neptune Analytics with Mem0
|
||||
description: "Combine Mem0 graph memory with AWS Neptune Analytics and Bedrock."
|
||||
---
|
||||
|
||||
|
||||
This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock** and **AWS Neptune Analytics** for persistent memory capabilities in Python.
|
||||
|
||||
## Installation
|
||||
@@ -118,3 +120,14 @@ memory = m.get(memory_id)
|
||||
## Conclusion
|
||||
|
||||
With Mem0 and AWS services like Bedrock and Neptune Analytics, you can build intelligent AI companions that remember, adapt, and personalize their responses over time. This makes them ideal for long-term assistants, tutors, or support bots with persistent memory and natural conversation abilities.
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="AWS Bedrock with Mem0" icon="aws" href="/cookbooks/integrations/aws-bedrock">
|
||||
Combine Neptune Analytics with AWS Bedrock for complete AWS stack.
|
||||
</Card>
|
||||
<Card title="Graph Memory Architecture" icon="sitemap" href="/cookbooks/essentials/choosing-memory-architecture-vector-vs-graph">
|
||||
Understand when to use graph vs vector memory for your use case.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
+14
-1
@@ -1,7 +1,9 @@
|
||||
---
|
||||
title: OpenAI Inbuilt Tools
|
||||
title: OpenAI Tool Calls with Mem0
|
||||
description: "Wire Mem0 memories into OpenAI's inbuilt function-calling flow."
|
||||
---
|
||||
|
||||
|
||||
Integrate Mem0’s memory capabilities with OpenAI’s Inbuilt Tools to create AI agents with persistent memory.
|
||||
|
||||
## Getting Started
|
||||
@@ -310,3 +312,14 @@ run().catch(console.error);
|
||||
- [Mem0 Dashboard](https://app.mem0.ai/dashboard)
|
||||
- [API Reference](https://docs.mem0.ai/api-reference)
|
||||
- [OpenAI Documentation](https://platform.openai.com/docs)
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<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>
|
||||
</CardGroup>
|
||||
+14
-1
@@ -1,7 +1,9 @@
|
||||
---
|
||||
title: 'Personalized Search with Tavily'
|
||||
title: Tavily Search with Mem0
|
||||
description: "Blend Tavily's realtime results with personal context stored in Mem0."
|
||||
---
|
||||
|
||||
|
||||
<Snippet file="security-compliance.mdx" />
|
||||
|
||||
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.
|
||||
@@ -191,3 +193,14 @@ With Mem0 and Tavily, you can build a search assistant that doesn't just fetch r
|
||||
Whether for shopping, travel, or daily life, this approach turns a generic search into a truly personalized experience.
|
||||
|
||||
Full Code: [Personalized Search GitHub](https://github.com/mem0ai/mem0/blob/main/examples/misc/personalized_search.py)
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Deep Research with Mem0" icon="magnifying-glass" href="/cookbooks/operations/deep-research">
|
||||
Build comprehensive research agents that remember findings across sessions.
|
||||
</Card>
|
||||
<Card title="Tag and Organize Memories" icon="tag" href="/cookbooks/essentials/tagging-and-organizing-memories">
|
||||
Categorize search results and user preferences for better personalization.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
+12
-5
@@ -1,7 +1,9 @@
|
||||
---
|
||||
title: Memory-Guided Content Writing
|
||||
title: Content Writing with Mem0
|
||||
description: "Store voice guidelines once and apply them across every draft."
|
||||
---
|
||||
|
||||
|
||||
This guide demonstrates how to leverage **Mem0** to streamline content writing by applying your unique writing style and preferences using persistent memory.
|
||||
|
||||
## Why Use Mem0?
|
||||
@@ -207,8 +209,13 @@ We believe this strategy will effectively increase our market share. To achieve
|
||||
|
||||
Mem0 enables a seamless, intelligent content-writing workflow, perfect for content creators, marketers, and technical writers looking to scale their personal tone and structure across work.
|
||||
|
||||
## Help & Resources
|
||||
---
|
||||
|
||||
- [Mem0 Platform](https://app.mem0.ai/)
|
||||
|
||||
<Snippet file="get-help.mdx" />
|
||||
<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>
|
||||
<Card title="Email Automation with Mem0" icon="envelope" href="/cookbooks/operations/email-automation">
|
||||
Automate email drafting with memory-powered context and tone matching.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
+14
-1
@@ -1,7 +1,9 @@
|
||||
---
|
||||
title: Personalized Deep Research
|
||||
title: Deep Research with Mem0
|
||||
description: "Run multi-session investigations that remember past findings and preferences."
|
||||
---
|
||||
|
||||
|
||||
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)
|
||||
@@ -64,3 +66,14 @@ Watch Deep Research in action:
|
||||
> 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}>
|
||||
<Card title="Search Memory Operations" icon="magnifying-glass" href="/core-concepts/memory-operations/search">
|
||||
Master semantic search to retrieve research findings across sessions.
|
||||
</Card>
|
||||
<Card title="YouTube Research with Mem0" icon="video" href="/cookbooks/companions/youtube-research">
|
||||
Build a video research assistant that remembers insights from content.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
+13
-1
@@ -1,7 +1,9 @@
|
||||
---
|
||||
title: Email Processing with Mem0
|
||||
title: Email Automation with Mem0
|
||||
description: "Capture, categorize, and recall inbox threads using persistent memories."
|
||||
---
|
||||
|
||||
|
||||
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.
|
||||
|
||||
## Overview
|
||||
@@ -194,3 +196,13 @@ print(f"Found {len(meeting_emails['results'])} relevant emails")
|
||||
|
||||
By combining Mem0's memory capabilities with email processing, you can create intelligent email management systems that help users organize, prioritize, and act on their inbox effectively. The advanced capabilities like automatic categorization, action item extraction, and priority management can significantly reduce the time spent on email management, allowing users to focus on more important tasks.
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Tag and Organize Memories" icon="tag" href="/cookbooks/essentials/tagging-and-organizing-memories">
|
||||
Categorize email threads by sender, topic, and priority for faster retrieval.
|
||||
</Card>
|
||||
<Card title="Support Inbox with Mem0" icon="headset" href="/cookbooks/operations/support-inbox">
|
||||
Build customer support agents that remember context across tickets.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
+13
-1
@@ -1,5 +1,6 @@
|
||||
---
|
||||
title: Customer Support AI Agent
|
||||
title: Support Inbox with Mem0
|
||||
description: "Build a support assistant that keeps past tickets and resolutions at its fingertips."
|
||||
---
|
||||
|
||||
|
||||
@@ -109,3 +110,14 @@ for m in memories['results']:
|
||||
### Conclusion
|
||||
|
||||
As the conversation progresses, Mem0's memory automatically updates based on the interactions, providing a continuously improving personalized support experience.
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
|
||||
Master the foundational patterns for building memory-powered assistants.
|
||||
</Card>
|
||||
<Card title="Email Automation with Mem0" icon="envelope" href="/cookbooks/operations/email-automation">
|
||||
Extend support capabilities with intelligent email processing and routing.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
+14
-1
@@ -1,7 +1,9 @@
|
||||
---
|
||||
title: Multi-User Collaboration with Mem0
|
||||
title: Team Task Agent with Mem0
|
||||
description: "Coordinate multi-user projects with shared memories and roles."
|
||||
---
|
||||
|
||||
|
||||
## Overview
|
||||
|
||||
Build a multi-user collaborative chat or task management system with Mem0. Each message is attributed to its author, and all messages are stored in a shared project space. Mem0 makes it easy to track contributions, sort and group messages, and collaborate in real time.
|
||||
@@ -121,3 +123,14 @@ agent.print_grouped_by_actor()
|
||||
## Conclusion
|
||||
|
||||
Mem0 enables fast, transparent collaboration for teams and agents, with full attribution, flexible memory search, and easy message organization.
|
||||
|
||||
---
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Build AI with Personality" icon="sparkles" href="/cookbooks/essentials/building-ai-with-personality">
|
||||
Learn how to scope memories across users and agents for team workflows.
|
||||
</Card>
|
||||
<Card title="Support Inbox with Mem0" icon="headset" href="/cookbooks/operations/support-inbox">
|
||||
Apply collaborative memory patterns to customer support scenarios.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -0,0 +1,140 @@
|
||||
---
|
||||
title: Overview
|
||||
description: How to use mem0 in your existing applications?
|
||||
---
|
||||
|
||||
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:
|
||||
|
||||
- More personalized
|
||||
- More reliable
|
||||
- Cost-effective by reducing the number of LLM interactions
|
||||
- More engaging
|
||||
- Enables long-term memory
|
||||
|
||||
Here are some examples of how Mem0 can be integrated into various applications:
|
||||
|
||||
## Essentials
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
|
||||
Learn the core memory lifecycle before diving into codebases.
|
||||
</Card>
|
||||
<Card title="Scope User vs Agent Memories" icon="sparkles" href="/cookbooks/essentials/building-ai-with-personality">
|
||||
Balance personalization with consistent assistant behavior.
|
||||
</Card>
|
||||
<Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
|
||||
Filter speculation, enforce formats, and gate low-confidence data.
|
||||
</Card>
|
||||
<Card title="Set Memory Expiration" icon="timer" href="/cookbooks/essentials/memory-expiration-short-and-long-term">
|
||||
Define short-term versus long-term retention strategies.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Companion Playbooks
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Quickstart Demo with Mem0" icon="rocket" href="/cookbooks/companions/quickstart-demo">
|
||||
Spin up the showcase app to see Mem0 memories in action.
|
||||
</Card>
|
||||
<Card title="Node.js Companion with Mem0" icon="code" href="/cookbooks/companions/nodejs-companion">
|
||||
Build a JavaScript coach that remembers user goals.
|
||||
</Card>
|
||||
<Card title="AI Tutor with Mem0" icon="graduation-cap" href="/cookbooks/companions/ai-tutor">
|
||||
Deliver lessons that adapt to student progress and gaps.
|
||||
</Card>
|
||||
<Card title="Travel Assistant with Mem0" icon="plane" href="/cookbooks/companions/travel-assistant">
|
||||
Plan itineraries that remember traveler preferences across trips.
|
||||
</Card>
|
||||
<Card title="YouTube Research with Mem0" icon="video" href="/cookbooks/companions/youtube-research">
|
||||
Layer personalized context over any video in the browser.
|
||||
</Card>
|
||||
<Card title="Voice Companion with Mem0 and OpenAI" icon="microphone" href="/cookbooks/companions/voice-companion-openai">
|
||||
Pair the OpenAI Agents SDK with Mem0 for voice-first experiences.
|
||||
</Card>
|
||||
<Card title="Local Companion with Mem0 and Ollama" icon="server" href="/cookbooks/companions/local-companion-ollama">
|
||||
Run Mem0 end-to-end on your machine with Ollama models.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Ops & Automations
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Support Inbox with Mem0" icon="headset" href="/cookbooks/operations/support-inbox">
|
||||
Keep past tickets and resolutions at an agent's fingertips.
|
||||
</Card>
|
||||
<Card title="Email Automation with Mem0" icon="envelope" href="/cookbooks/operations/email-automation">
|
||||
Capture, categorize, and recall inbox threads via memory.
|
||||
</Card>
|
||||
<Card title="Content Writing with Mem0" icon="pencil" href="/cookbooks/operations/content-writing">
|
||||
Store tone and style guidelines once—apply them everywhere.
|
||||
</Card>
|
||||
<Card title="Deep Research with Mem0" icon="magnifying-glass" href="/cookbooks/operations/deep-research">
|
||||
Run multi-session investigations without repeating yourself.
|
||||
</Card>
|
||||
<Card title="Team Task Agent with Mem0" icon="users" href="/cookbooks/operations/team-task-agent">
|
||||
Coordinate projects with shared memories across contributors.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Integrations & Platforms
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Agents SDK Tool with Mem0" icon="robot" href="/cookbooks/integrations/agents-sdk-tool">
|
||||
Expose Mem0 memories as callable tools inside agent workflows.
|
||||
</Card>
|
||||
<Card title="OpenAI Tool Calls with Mem0" icon="wrench" href="/cookbooks/integrations/openai-tool-calls">
|
||||
Drop memories into OpenAI's inbuilt function-calling flows.
|
||||
</Card>
|
||||
<Card title="Mastra Agent with Mem0" icon="code" href="/cookbooks/integrations/mastra-agent">
|
||||
Extend Mastra agents with persistent memory state.
|
||||
</Card>
|
||||
<Card title="Healthcare Coach with Mem0 and Google ADK" icon="heart-pulse" href="/cookbooks/integrations/healthcare-google-adk">
|
||||
Remember patient history across ADK sessions.
|
||||
</Card>
|
||||
<Card title="AWS Bedrock with Mem0" icon="aws" href="/cookbooks/integrations/aws-bedrock">
|
||||
Pair Mem0 with Bedrock, OpenSearch, and Neptune Analytics.
|
||||
</Card>
|
||||
<Card title="Neptune Analytics with Mem0" icon="database" href="/cookbooks/integrations/neptune-analytics">
|
||||
Build a hybrid vector + graph memory store on AWS.
|
||||
</Card>
|
||||
<Card title="Tavily Search with Mem0" icon="search" href="/cookbooks/integrations/tavily-search">
|
||||
Blend realtime search with personal context.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Frameworks & Multimodal
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="LlamaIndex ReAct with Mem0" icon="brain" href="/cookbooks/frameworks/llamaindex-react">
|
||||
Teach a ReAct agent to store and recall context via Mem0.
|
||||
</Card>
|
||||
<Card title="LlamaIndex Multi-Agent with Mem0" icon="users" href="/cookbooks/frameworks/llamaindex-multiagent">
|
||||
Share a persistent memory layer across collaborating agents.
|
||||
</Card>
|
||||
<Card title="Multimodal Retrieval with Mem0" icon="image" href="/cookbooks/frameworks/multimodal-retrieval">
|
||||
Store and recall visual context alongside text conversations.
|
||||
</Card>
|
||||
<Card title="Eliza OS Character with Mem0" icon="robot" href="/cookbooks/frameworks/eliza-os-character">
|
||||
Bring persistent personality to Eliza OS agents.
|
||||
</Card>
|
||||
<Card title="Chrome Extension with Mem0" icon="globe" href="/cookbooks/frameworks/chrome-extension">
|
||||
Add Mem0's universal memory layer to Chrome chat surfaces.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
---
|
||||
|
||||
## Contribute a Cookbook
|
||||
|
||||
Have a unique Mem0 use case or integration? We'd love to feature your cookbook!
|
||||
|
||||
All cookbooks follow a standardized template to ensure consistency and quality. Check out our template to see the structure and best practices.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Cookbook Template" icon="book-open" href="/templates/cookbook_template">
|
||||
Follow this structure for narrative, end-to-end Mem0 workflows.
|
||||
</Card>
|
||||
<Card title="Contribution Guide" icon="github" href="https://github.com/mem0ai/mem0/blob/main/CONTRIBUTING.md">
|
||||
Learn how to submit your cookbook to the Mem0 repository.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -174,8 +174,8 @@ For full list of supported fields, required formats, and advanced options, see t
|
||||
|
||||
## See it live
|
||||
|
||||
- <Link href="/examples/customer-support-agent">Customer Support Agent</Link> shows add + search powering a support flow.
|
||||
- <Link href="/examples/personal-ai-tutor">Personal AI Tutor</Link> uses add to personalize lesson plans.
|
||||
- <Link href="/cookbooks/operations/support-inbox">Support Inbox with Mem0</Link> shows add + search powering a support flow.
|
||||
- <Link href="/cookbooks/companions/ai-tutor">AI Tutor with Mem0</Link> uses add to personalize lesson plans.
|
||||
|
||||
{/* DEBUG: verify CTA targets */}
|
||||
|
||||
@@ -190,6 +190,6 @@ For full list of supported fields, required formats, and advanced options, see t
|
||||
title="Build a Support Agent"
|
||||
description="Follow the cookbook to apply add/search/update in production."
|
||||
icon="rocket"
|
||||
href="/examples/customer-support-agent"
|
||||
href="/cookbooks/operations/support-inbox"
|
||||
/>
|
||||
</CardGroup>
|
||||
|
||||
@@ -169,7 +169,7 @@ memory.delete_all(user_id="alice")
|
||||
|
||||
## See it live
|
||||
|
||||
- <Link href="/examples/customer-support-agent">Customer Support Agent</Link> demonstrates compliance-driven deletes.
|
||||
- <Link href="/cookbooks/operations/support-inbox">Support Inbox with Mem0</Link> demonstrates compliance-driven deletes.
|
||||
- <Link href="/platform/features/direct-import">Data Management tooling</Link> shows how deletes fit into broader lifecycle flows.
|
||||
|
||||
{/* DEBUG: verify CTA targets */}
|
||||
|
||||
@@ -193,8 +193,8 @@ For the full list of filter logic, comparison operators, and optional search par
|
||||
|
||||
## See it live
|
||||
|
||||
- <Link href="/examples/customer-support-agent">Customer Support Agent</Link> demonstrates scoped search with rerankers.
|
||||
- <Link href="/examples/personalized-search-tavily-mem0">Personalized Search with Tavily</Link> shows hybrid search in action.
|
||||
- <Link href="/cookbooks/operations/support-inbox">Support Inbox with Mem0</Link> demonstrates scoped search with rerankers.
|
||||
- <Link href="/cookbooks/integrations/tavily-search">Tavily Search with Mem0</Link> shows hybrid search in action.
|
||||
|
||||
{/* DEBUG: verify CTA targets */}
|
||||
|
||||
@@ -209,6 +209,6 @@ For the full list of filter logic, comparison operators, and optional search par
|
||||
title="Build Hybrid Search"
|
||||
description="Follow the cookbook to combine Mem0 with external search."
|
||||
icon="rocket"
|
||||
href="/examples/personalized-search-tavily-mem0"
|
||||
href="/cookbooks/integrations/tavily-search"
|
||||
/>
|
||||
</CardGroup>
|
||||
|
||||
@@ -150,8 +150,8 @@ memory.update(
|
||||
|
||||
## See it live
|
||||
|
||||
- <Link href="/examples/customer-support-agent">Customer Support Agent</Link> uses updates to refine customer profiles.
|
||||
- <Link href="/examples/personal-ai-tutor">Personal AI Tutor</Link> demonstrates user preference corrections mid-course.
|
||||
- <Link href="/cookbooks/operations/support-inbox">Support Inbox with Mem0</Link> uses updates to refine customer profiles.
|
||||
- <Link href="/cookbooks/companions/ai-tutor">AI Tutor with Mem0</Link> demonstrates user preference corrections mid-course.
|
||||
|
||||
{/* DEBUG: verify CTA targets */}
|
||||
|
||||
|
||||
@@ -108,8 +108,8 @@ results = memory.search(
|
||||
|
||||
## See it live
|
||||
|
||||
- <Link href="/examples/personal-ai-tutor">Personal AI Tutor</Link> shows session vs user memories in action.
|
||||
- <Link href="/examples/customer-support-agent">Customer Support Agent</Link> demonstrates shared org memory.
|
||||
- <Link href="/cookbooks/companions/ai-tutor">AI Tutor with Mem0</Link> shows session vs user memories in action.
|
||||
- <Link href="/cookbooks/operations/support-inbox">Support Inbox with Mem0</Link> demonstrates shared org memory.
|
||||
|
||||
{/* DEBUG: verify CTA targets */}
|
||||
|
||||
@@ -124,6 +124,6 @@ results = memory.search(
|
||||
title="See a Cookbook"
|
||||
description="Apply layered memories inside a customer support agent."
|
||||
icon="rocket"
|
||||
href="/examples/customer-support-agent"
|
||||
href="/cookbooks/operations/support-inbox"
|
||||
/>
|
||||
</CardGroup>
|
||||
|
||||
+46
-45
@@ -304,70 +304,71 @@
|
||||
"tab": "Cookbooks",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Overview",
|
||||
"group": "Getting Started",
|
||||
"icon": "lightbulb",
|
||||
"pages": [
|
||||
"examples"
|
||||
"cookbooks/overview"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Getting Started Examples",
|
||||
"icon": "play",
|
||||
"group": "Essentials",
|
||||
"icon": "flag",
|
||||
"pages": [
|
||||
"examples/mem0-demo",
|
||||
"examples/ai_companion_js",
|
||||
"examples/mem0-with-ollama",
|
||||
"examples/personal-ai-tutor"
|
||||
"cookbooks/essentials/building-ai-companion",
|
||||
"cookbooks/essentials/building-ai-with-personality",
|
||||
"cookbooks/essentials/controlling-memory-ingestion",
|
||||
"cookbooks/essentials/memory-expiration-short-and-long-term",
|
||||
"cookbooks/essentials/tagging-and-organizing-memories",
|
||||
"cookbooks/essentials/exporting-memories",
|
||||
"cookbooks/essentials/choosing-memory-architecture-vector-vs-graph"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Production Use Cases",
|
||||
"group": "Companion Playbooks",
|
||||
"icon": "users",
|
||||
"pages": [
|
||||
"cookbooks/companions/quickstart-demo",
|
||||
"cookbooks/companions/nodejs-companion",
|
||||
"cookbooks/companions/ai-tutor",
|
||||
"cookbooks/companions/travel-assistant",
|
||||
"cookbooks/companions/youtube-research",
|
||||
"cookbooks/companions/voice-companion-openai",
|
||||
"cookbooks/companions/local-companion-ollama"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Ops & Automations",
|
||||
"icon": "briefcase",
|
||||
"pages": [
|
||||
"examples/customer-support-agent",
|
||||
"examples/personal-travel-assistant",
|
||||
"examples/email_processing",
|
||||
"examples/memory-guided-content-writing",
|
||||
"examples/personalized-deep-research"
|
||||
"cookbooks/operations/support-inbox",
|
||||
"cookbooks/operations/email-automation",
|
||||
"cookbooks/operations/content-writing",
|
||||
"cookbooks/operations/deep-research",
|
||||
"cookbooks/operations/team-task-agent"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Framework Integrations",
|
||||
"icon": "puzzle-piece",
|
||||
"group": "Integrations & Platforms",
|
||||
"icon": "plug",
|
||||
"pages": [
|
||||
"examples/llama-index-mem0",
|
||||
"examples/llamaindex-multiagent-learning-system",
|
||||
"examples/personalized-search-tavily-mem0",
|
||||
"examples/mem0-agentic-tool",
|
||||
"examples/mem0-mastra",
|
||||
"examples/eliza_os"
|
||||
"cookbooks/integrations/agents-sdk-tool",
|
||||
"cookbooks/integrations/openai-tool-calls",
|
||||
"cookbooks/integrations/mastra-agent",
|
||||
"cookbooks/integrations/healthcare-google-adk",
|
||||
"cookbooks/integrations/aws-bedrock",
|
||||
"cookbooks/integrations/neptune-analytics",
|
||||
"cookbooks/integrations/tavily-search"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Specialized Features",
|
||||
"icon": "wand-magic-sparkles",
|
||||
"group": "Frameworks & Multimodal",
|
||||
"icon": "layers",
|
||||
"pages": [
|
||||
"examples/multimodal-demo",
|
||||
"examples/mem0-openai-voice-demo",
|
||||
"examples/mem0-google-adk-healthcare-assistant",
|
||||
"examples/collaborative-task-agent"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Extensions & Tools",
|
||||
"icon": "wrench",
|
||||
"pages": [
|
||||
"examples/chrome-extension",
|
||||
"examples/youtube-assistant",
|
||||
"examples/openai-inbuilt-tools"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Cloud & Infrastructure",
|
||||
"icon": "cloud",
|
||||
"pages": [
|
||||
"examples/aws_example",
|
||||
"examples/aws_neptune_analytics_hybrid_store"
|
||||
"cookbooks/frameworks/llamaindex-react",
|
||||
"cookbooks/frameworks/llamaindex-multiagent",
|
||||
"cookbooks/frameworks/multimodal-retrieval",
|
||||
"cookbooks/frameworks/eliza-os-character",
|
||||
"cookbooks/frameworks/chrome-extension"
|
||||
]
|
||||
}
|
||||
]
|
||||
|
||||
@@ -1,116 +0,0 @@
|
||||
---
|
||||
title: Overview
|
||||
description: How to use mem0 in your existing applications?
|
||||
---
|
||||
|
||||
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:
|
||||
|
||||
- More personalized
|
||||
- More reliable
|
||||
- Cost-effective by reducing the number of LLM interactions
|
||||
- More engaging
|
||||
- Enables long-term memory
|
||||
|
||||
Here are some examples of how Mem0 can be integrated into various applications:
|
||||
|
||||
## Examples
|
||||
|
||||
Explore how **Mem0** can power real-world applications and bring personalized, intelligent experiences to life:
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Mem0 Demo" icon="rocket" href="/examples/mem0-demo">
|
||||
Get started with **Mem0** with this simple demo showcasing basic memory operations.
|
||||
</Card>
|
||||
|
||||
<Card title="AI Companion in Node.js" icon="node" href="/examples/ai_companion_js">
|
||||
Build a personalized AI Companion in **Node.js** that remembers conversations and adapts over time.
|
||||
</Card>
|
||||
|
||||
<Card title="Mem0 with Ollama" icon="server" href="/examples/mem0-with-ollama">
|
||||
Run **Mem0 locally** with **Ollama** to create private, stateful AI experiences without cloud APIs.
|
||||
</Card>
|
||||
|
||||
<Card title="Personal AI Tutor" icon="graduation-cap" href="/examples/personal-ai-tutor">
|
||||
Create an **AI Tutor** that adapts to student progress, learning style, and history.
|
||||
</Card>
|
||||
|
||||
<Card title="Customer Support Agent" icon="headset" href="/examples/customer-support-agent">
|
||||
Build a **Customer Support AI** that recalls user preferences and past chats.
|
||||
</Card>
|
||||
|
||||
<Card title="Personal Travel Assistant" icon="plane" href="/examples/personal-travel-assistant">
|
||||
Develop a **Personal Travel Assistant** that remembers your preferences and past trips.
|
||||
</Card>
|
||||
|
||||
<Card title="Chrome Extension" icon="puzzle-piece" href="/examples/chrome-extension">
|
||||
Add **long-term memory** to ChatGPT, Claude, or Perplexity via the **Mem0 Chrome Extension**.
|
||||
</Card>
|
||||
|
||||
<Card title="YouTube Assistant" icon="video" href="/examples/youtube-assistant">
|
||||
Integrate **Mem0** into **YouTube's** native UI with personalized responses.
|
||||
</Card>
|
||||
|
||||
<Card title="Memory-Guided Content Writing" icon="pen" href="/examples/memory-guided-content-writing">
|
||||
Create a **Writing Assistant** that understands and adapts to your unique style.
|
||||
</Card>
|
||||
|
||||
<Card title="Multimodal AI Demo" icon="image" href="/examples/multimodal-demo">
|
||||
Supercharge AI with **Mem0's multimodal memory** — blend text, images, and more.
|
||||
</Card>
|
||||
|
||||
<Card title="Email Processing" icon="envelope" href="/examples/email_processing">
|
||||
Use Mem0's memory capabilities to process emails with persistent memory.
|
||||
</Card>
|
||||
|
||||
<Card title="Personalized Research Agent" icon="magnifying-glass" href="/examples/personalized-deep-research">
|
||||
Build a **Deep Research AI** that remembers your research goals.
|
||||
</Card>
|
||||
|
||||
<Card title="Multi-User Collaboration" icon="users" href="/examples/collaborative-task-agent">
|
||||
Build collaborative agents with shared memory across multiple users.
|
||||
</Card>
|
||||
|
||||
<Card title="LlamaIndex ReAct Agent" icon="book-open" href="/examples/llama-index-mem0">
|
||||
Combine **LlamaIndex** and Mem0 to create a **ReAct Agent** with persistent memory.
|
||||
</Card>
|
||||
|
||||
<Card title="LlamaIndex Multi-Agent System" icon="book-open" href="/examples/llamaindex-multiagent-learning-system">
|
||||
Multi-agent learning system powered by memory.
|
||||
</Card>
|
||||
|
||||
<Card title="Personalized Search with Tavily" icon="search" href="/examples/personalized-search-tavily-mem0">
|
||||
Build a personalized search experience using Mem0 and Tavily.
|
||||
</Card>
|
||||
|
||||
<Card title="Mem0 as an Agentic Tool" icon="robot" href="/examples/mem0-agentic-tool">
|
||||
Integrate Mem0's memory capabilities with OpenAI's Agents SDK.
|
||||
</Card>
|
||||
|
||||
<Card title="OpenAI Inbuilt Tools" icon="wrench" href="/examples/openai-inbuilt-tools">
|
||||
Use Mem0 with OpenAI's Inbuilt Tools to create AI agents with persistent memory.
|
||||
</Card>
|
||||
|
||||
<Card title="OpenAI Voice Demo" icon="microphone" href="/examples/mem0-openai-voice-demo">
|
||||
Voice-enabled AI agents with persistent memory using OpenAI.
|
||||
</Card>
|
||||
|
||||
<Card title="Healthcare Assistant with Google ADK" icon="heart-pulse" href="/examples/mem0-google-adk-healthcare-assistant">
|
||||
Build a personalized healthcare assistant with persistent memory using Google ADK.
|
||||
</Card>
|
||||
|
||||
<Card title="Mem0 with Mastra" icon="wand-magic-sparkles" href="/examples/mem0-mastra">
|
||||
Integrate Mem0 with Mastra for powerful agentic workflows.
|
||||
</Card>
|
||||
|
||||
<Card title="Eliza OS Character" icon="comment" href="/examples/eliza_os">
|
||||
Build conversational AI characters with persistent memory using Eliza OS.
|
||||
</Card>
|
||||
|
||||
<Card title="AWS Bedrock Example" icon="aws" href="/examples/aws_example">
|
||||
Use Mem0 with **AWS Bedrock**, **OpenSearch**, and **Neptune Analytics**.
|
||||
</Card>
|
||||
|
||||
<Card title="AWS Neptune Analytics" icon="aws" href="/examples/aws_neptune_analytics_hybrid_store">
|
||||
Hybrid memory store with **AWS Neptune Analytics** and Bedrock.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -100,7 +100,7 @@ mode: "custom"
|
||||
|
||||
<div className="grid gap-6 sm:grid-cols-2 lg:grid-cols-3">
|
||||
<a
|
||||
href="/examples"
|
||||
href="/cookbooks/overview"
|
||||
className="group flex h-full flex-col overflow-hidden rounded-2xl border border-zinc-800/40 bg-zinc-900/40 transition hover:border-primary/60 hover:bg-zinc-900"
|
||||
>
|
||||
<img
|
||||
|
||||
+5
-5
@@ -121,11 +121,11 @@ Key differentiators:
|
||||
|
||||
## Examples and Use Cases
|
||||
|
||||
- [Personal AI Tutor](https://docs.mem0.ai/examples/personal-ai-tutor): Build an AI tutor that remembers learning progress and adapts teaching methods
|
||||
- [Customer Support Agent](https://docs.mem0.ai/examples/customer-support-agent): Create support agents that remember customer history and preferences
|
||||
- [Personalized Travel Assistant](https://docs.mem0.ai/examples/personal-travel-assistant): Develop travel agents that learn from past trips and preferences
|
||||
- [Memory-Guided Content Writing](https://docs.mem0.ai/examples/memory-guided-content-writing): Build content generators that remember writing style and topic preferences
|
||||
- [Collaborative Task Agent](https://docs.mem0.ai/examples/collaborative-task-agent): Multi-agent systems with shared memory for team coordination
|
||||
- [AI Tutor with Mem0](https://docs.mem0.ai/cookbooks/companions/ai-tutor): Build an AI tutor that remembers learning progress and adapts teaching methods
|
||||
- [Support Inbox with Mem0](https://docs.mem0.ai/cookbooks/operations/support-inbox): Create support agents that remember customer history and preferences
|
||||
- [Travel Assistant with Mem0](https://docs.mem0.ai/cookbooks/companions/travel-assistant): Develop travel agents that learn from past trips and preferences
|
||||
- [Content Writing with Mem0](https://docs.mem0.ai/cookbooks/operations/content-writing): Build content generators that remember writing style and topic preferences
|
||||
- [Team Task Agent with Mem0](https://docs.mem0.ai/cookbooks/operations/team-task-agent): Multi-agent systems with shared memory for team coordination
|
||||
|
||||
## API Reference
|
||||
|
||||
|
||||
@@ -168,6 +168,6 @@ memory = Memory.from_config_file("config.yaml")
|
||||
title="Deploy with Docker Compose"
|
||||
description="Follow the end-to-end OSS deployment walkthrough."
|
||||
icon="server"
|
||||
href="/examples/mem0-with-ollama"
|
||||
href="/cookbooks/companions/local-companion-ollama"
|
||||
/>
|
||||
</CardGroup>
|
||||
|
||||
@@ -47,7 +47,7 @@ Mem0 Open Source delivers the same adaptive memory engine as the platform, but p
|
||||
</CardGroup>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Deploy with Docker Compose" icon="server" href="/examples/mem0-with-ollama">
|
||||
<Card title="Deploy with Docker Compose" icon="server" href="/cookbooks/companions/local-companion-ollama">
|
||||
Follow the reference deployment to persist memories and expose REST endpoints.
|
||||
</Card>
|
||||
<Card title="Use the REST API" icon="code" href="/open-source/features/rest-api">
|
||||
|
||||
@@ -92,4 +92,4 @@ Explore async support, graph memory, and multi-agent memory organization
|
||||
|
||||
- **[OpenAI Compatibility](/open-source/features/openai_compatibility)** - Use Mem0 with OpenAI-compatible chat completions
|
||||
- **[Contributing Guide](/contributing/development)** - Learn how to contribute to Mem0
|
||||
- **[Examples](/examples/mem0-with-ollama)** - See Mem0 in action with Ollama and other integrations
|
||||
- **[Examples](/cookbooks/companions/local-companion-ollama)** - See Mem0 in action with Ollama and other integrations
|
||||
|
||||
@@ -147,4 +147,4 @@ See complete API documentation and integration examples
|
||||
|
||||
- **[Platform vs OSS](/platform/platform-vs-oss)** - Understand the differences between Platform and Open Source
|
||||
- **[Troubleshooting](/platform/faqs)** - Common issues and solutions
|
||||
- **[Integration Examples](/examples/mem0-demo)** - See Mem0 in action
|
||||
- **[Integration Examples](/cookbooks/companions/quickstart-demo)** - See Mem0 in action
|
||||
|
||||
Reference in New Issue
Block a user