docs(cookbooks): repair dead references and label OSS vs Platform support (#6909)

2026-08-14 16:50:46 +05:30
parent c50a2bfb8f
commit a0329f047b
31 changed files with 148 additions and 38 deletions
@@ -3,6 +3,9 @@ title: Personalized AI Tutor
 description: "Keep student progress and preferences persistent across tutoring sessions."
 ---

+<Info icon="server">
+**Works with:** Mem0 OSS (`Memory`)
+</Info>

 You can create a personalized AI Tutor using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.

@@ -3,6 +3,9 @@ title: Self-Hosted AI Companion
 description: "Run Mem0 end-to-end on your machine using Ollama-powered LLMs and embedders."
 ---

+<Info icon="server">
+**Works with:** Mem0 OSS (`Memory`)
+</Info>

 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.

@@ -3,6 +3,9 @@ title: Build a Node.js Companion
 description: "Build a JavaScript fitness coach that remembers user goals run after run."
 ---

+<Info icon="server">
+**Works with:** Mem0 OSS (`Memory`)
+</Info>

 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.

@@ -3,6 +3,9 @@ title: Interactive Memory Demo
 description: "Spin up the showcase companion app to see Mem0 memories in action."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform
+</Info>

 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.

@@ -3,6 +3,9 @@ title: Smart Travel Assistant
 description: "Plan itineraries that remember traveler preferences across trips."
 ---

+<Info icon="server">
+**Works with:** Mem0 OSS (`Memory`)
+</Info>

 Create a personalized AI Travel Assistant using Mem0. This guide provides step-by-step instructions and the complete code to get you started.

@@ -3,6 +3,9 @@ title: Voice-First AI Companion
 description: "Pair the OpenAI Agents SDK with Mem0 to build a voice assistant that remembers."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 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.

@@ -3,6 +3,9 @@ title: Research Assistant for YouTube
 description: "Layer personalized context over any video using the Mem0 YouTube assistant."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform
+</Info>

 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.

@@ -3,6 +3,9 @@ title: Build a Companion with Mem0
 description: "Spin up a fitness coach that remembers goals, adapts tone, and keeps sessions personal."
 ---

+<Info icon="layer-group">
+**Works with:** Mem0 OSS (`Memory`) and Mem0 Platform (`MemoryClient`)
+</Info>

 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.

@@ -3,6 +3,9 @@ title: Control Memory Ingestion
 description: "Filter speculation, enforce formats, and gate low-confidence data before it persists."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 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.

@@ -3,6 +3,10 @@ title: Partition Memories by Entity
 description: Keep memories separate by tagging each write and query with user, agent, app, and session identifiers.
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>
+
 Nora runs a travel service. When she stored all memories in one bucket, a recruiter's nut allergy accidentally appeared in a traveler's dinner reservation. Let's fix this by properly separating memories for different users, agents, and applications.

 <Info icon="clock">
@@ -3,6 +3,9 @@ title: Export Stored Memories
 description: "Retrieve, review, and migrate user memories with structured exports."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 Mem0 is a dynamic memory store that gives you full control over your data. Along with storing memories, it gives you the ability to retrieve, export, and migrate your data whenever you need.

@@ -169,20 +172,20 @@ export_job = client.create_memory_export(
 )

 print(f"Export ID: {export_job['id']}")
-print(f"Status: {export_job['status']}")
+print(f"Message: {export_job['message']}")

 ```

 **Output:**

 ```
-Export ID: exp_abc123
-Status: processing
+Export ID: 550e8400-e29b-41d4-a716-446655440000
+Message: Memory export request received. The export will be ready in a few seconds.

 ```

 <Info>
-**Export initiated:** Status is "processing". Large exports may take a few seconds. Poll with `get_memory_export()` until status changes to "completed" before downloading data.
+**Export initiated:** The export runs asynchronously and is usually ready within a few seconds. Retry `get_memory_export()` with the returned ID until it stops returning a "no export found" error.
 </Info>

 ### Step 3: Download the export
@@ -193,7 +196,7 @@ export_data = client.get_memory_export(
    memory_export_id=export_job['id']
 )

-print(export_data['data'])
+print(export_data)

 ```

@@ -216,7 +219,7 @@ export_by_filters = client.get_memory_export(
    filters={"user_id": "dev"}
 )

-print(export_by_filters['data'])
+print(export_by_filters)

 ```

@@ -240,7 +243,7 @@ export_with_instructions = client.create_memory_export(
 ```

 <Tip>
-Always check export status before downloading. Call `get_memory_export()` in a loop with a short delay until `status == "completed"`. Attempting to download while still processing returns incomplete data.
+If the export is still processing, `get_memory_export()` returns a 404 with `{"error": "No memory export request found"}`. Retry after a short delay until the call succeeds instead of polling a status field.
 </Tip>

 ---
@@ -3,6 +3,9 @@ title: Tag and Organize Memories
 description: "Let Mem0 auto-categorize support data so teams retrieve the right facts fast."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 When you have large volumes of memory data, sorting it during post-processing becomes difficult. What if your memory store understood the importance of creating tags and buckets without a lot of effort?

@@ -3,6 +3,9 @@ title: Persistent Eliza Characters
 description: "Bring persistent personality to Eliza OS agents using Mem0."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform
+</Info>

 You can create a personalized Eliza OS Character using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.

@@ -3,6 +3,10 @@ title: "Gemini 3 with Mem0 MCP"
 description: "Create snappy, smart, memory-aware agents by pairing Gemini 3 with Mem0 MCP server."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (MCP server)
+</Info>
+
 Gemini 3, when paired with Mem0's cloud MCP server, works in synergy to create snappy, smart, memory-aware agents.

 <Callout type="info" icon="sparkles" color="#8B5CF6">
@@ -3,6 +3,9 @@ title: Multi-Agent Collaboration
 description: "Share a persistent memory layer across collaborating LlamaIndex agents."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`Mem0Memory.from_client`)
+</Info>

 <Snippet file="blank-notif.mdx" />

@@ -3,6 +3,9 @@ title: ReAct Agents with Memory
 description: "Teach a ReAct agent to store and recall context via Mem0."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`Mem0Memory.from_client`)
+</Info>

 Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store.

@@ -78,7 +81,7 @@ from llama_index.core.agent import FunctionCallingAgent
 agent = FunctionCallingAgent.from_tools(
    [call_tool, email_tool, order_food_tool],
    llm=llm,
-    memory=memory_from_client,  # or memory_from_config
+    memory=memory_from_client,
    verbose=True,
 )
 ```
@@ -161,7 +164,7 @@ agent = FunctionCallingAgent.from_tools(
    [call_tool, email_tool, order_food_tool],
    llm=llm,
    # memory is provided
-    memory=memory_from_client,  # or memory_from_config
+    memory=memory_from_client,
    verbose=True,
 )
 response = agent.chat("I am feeling hungry, order me something and send me the bill")
@@ -3,6 +3,9 @@ title: Visual Memory Retrieval
 description: "Store and recall visual context alongside text conversations."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform
+</Info>

 Enhance your AI interactions with Mem0's multimodal capabilities. Mem0 now supports image understanding, allowing for richer context and more natural interactions across supported AI platforms.

@@ -3,6 +3,9 @@ title: Memory-Powered Agent SDK
 description: "Expose Mem0 memories as callable tools inside OpenAI agent workflows."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 Integrate Mem0's memory capabilities with OpenAI's Agents SDK to create AI agents with persistent memory. You can create agents that remember past conversations and use that context to provide better responses.

@@ -3,6 +3,9 @@ title: Bedrock with Persistent Memory
 description: "Pair Mem0 with AWS Bedrock and OpenSearch for a managed stack."
 ---

+<Info icon="server">
+**Works with:** Mem0 OSS (`Memory`)
+</Info>

 This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock** and **OpenSearch Service (AOSS)** for persistent memory capabilities in Python.

@@ -3,6 +3,9 @@ title: Healthcare Coach with ADK
 description: "Guide patients with an assistant that remembers history across ADK sessions."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 This example demonstrates how to build a healthcare assistant that remembers patient information across conversations using Google ADK and Mem0.

@@ -123,7 +126,7 @@ Now we'll create our main agent with all the tools:
 # Create the agent
 healthcare_agent = Agent(
    name="healthcare_assistant",
-    model="gemini-1.5-flash",  # Using Gemini for healthcare assistant
+    model="gemini-2.0-flash",  # Using Gemini for healthcare assistant
    description="Healthcare assistant that helps patients with health information and appointment scheduling.",
    instruction="""You are a helpful Healthcare Assistant with memory capabilities.

@@ -3,10 +3,13 @@ title: Persistent Mastra Agents
 description: "Extend Mastra agents with persistent memories powered by Mem0."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`@mastra/mem0`)
+</Info>

 In this example you'll learn how to use Mem0 to add long-term memory capabilities to [Mastra's agent](https://mastra.ai/) via tool-use. This memory integration can work alongside Mastra's [agent memory features](https://mastra.ai/docs/agents/01-agent-memory).

-You can find the complete example code in the [Mastra repository](https://github.com/mastra-ai/mastra/tree/main/examples/memory-with-mem0).
+The complete example code, from installing the integration to wiring it into a Mastra agent, is shown below. Mem0's integration is published on npm as [`@mastra/mem0`](https://www.npmjs.com/package/@mastra/mem0).

 ## Overview

@@ -3,6 +3,9 @@ title: Memory as OpenAI Tool
 description: "Wire Mem0 memories into OpenAI's inbuilt function-calling flow."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 Integrate Mem0’s memory capabilities with OpenAI’s Inbuilt Tools to create AI agents with persistent memory.

@@ -3,12 +3,14 @@ title: Search with Personal Context
 description: "Blend Tavily's realtime results with personal context stored in Mem0."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform (`MemoryClient`)
+</Info>

 Imagine asking a search assistant for "coffee shops nearby" and instead of generic results, it shows remote-work-friendly cafes with great WiFi in your city because it remembers you mentioned working remotely before. Or when you search for "lunchbox ideas for kids" it knows you have a 7-year-old daughter and recommends peanut-free options that align with her allergy.

 That's what we are going to build today, a Personalized Search Assistant powered by Mem0 for memory and [Tavily](https://tavily.com) for real-time search.

-
 ## Why Personalized Search

 Most assistants treat every query like they've never seen you before. That means repeating yourself about your location, diet, or preferences, and getting results that feel generic.
@@ -3,6 +3,9 @@ title: Content Creation Workflow
 description: "Store voice guidelines once and apply them across every draft."
 ---

+<Info icon="layer-group">
+**Works with:** Mem0 OSS (`Memory`) and Mem0 Platform (`MemoryClient`)
+</Info>

 This guide demonstrates how to leverage **Mem0** to streamline content writing by applying your unique writing style and preferences using persistent memory.

@@ -3,11 +3,12 @@ title: Multi-Session Research Agent
 description: "Run multi-session investigations that remember past findings and preferences."
 ---

+<Info icon="cloud">
+**Works with:** Mem0 Platform
+</Info>

 Deep Research is an intelligent agent that synthesizes large amounts of online data and completes complex research tasks, customized to your unique preferences and insights. Built on Mem0's technology, it enhances AI-driven online exploration with personalized memories.

-You can check out the GitHub repository here: [Personalized Deep Research](https://github.com/mem0ai/personalized-deep-research/tree/mem0)
-
 ## Overview

 Deep Research leverages Mem0's memory capabilities to:
@@ -61,12 +62,6 @@ Watch Deep Research in action:
 - **Technical Research**: Technology evaluation, solution comparison
 - **Business Research**: Strategic planning, opportunity analysis

-## Try It Out
-
-> To try it yourself, clone the repository and follow the instructions in the README to run it locally or deploy it.
-
- [Personalized Deep Research GitHub](https://github.com/mem0ai/personalized-deep-research/tree/mem0)
-
 ---

 <CardGroup cols={2}>
@@ -3,6 +3,9 @@ title: Automated Email Intelligence
 description: "Capture, categorize, and recall inbox threads using persistent memories."
 ---

+<Info icon="layer-group">
+**Works with:** Mem0 OSS (`Memory`) and Mem0 Platform (`MemoryClient`)
+</Info>

 This guide demonstrates how to build an intelligent email processing system using Mem0's memory capabilities. You'll learn how to store, categorize, retrieve, and analyze emails to create a smart email management solution.

@@ -3,6 +3,9 @@ title: Memory-Powered Support Agent
 description: "Build a support assistant that keeps past tickets and resolutions at its fingertips."
 ---

+<Info icon="server">
+**Works with:** Mem0 OSS (`Memory`)
+</Info>

 You can create a personalized Customer Support AI Agent using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.

@@ -3,6 +3,9 @@ title: Collaborative Task Assistant
 description: "Coordinate multi-user projects with shared memories and roles."
 ---

+<Info icon="server">
+**Works with:** Mem0 OSS (`Memory`)
+</Info>

 ## Overview

@@ -13,6 +13,56 @@ With Mem0, you can create stateful LLM-based applications such as chatbots, virt

 Here are some examples of how Mem0 can be integrated into various applications:

+## Pick by compatibility
+
+Every cookbook opens with a **Works with** badge naming the SDK surface it uses. Pick your setup below to see only the cookbooks that run on it. Three cookbooks work on both and appear under either tab.
+
+<Tabs>
+  <Tab title="Self-hosted OSS">
+    Ten cookbooks run on the open-source `Memory` class, with no Mem0 Platform account.
+
+    | Cookbook | Category | Works with |
+    | --- | --- | --- |
+    | [Personalized AI Tutor](/cookbooks/companions/ai-tutor) | Companions | OSS only |
+    | [Build a Node.js Companion](/cookbooks/companions/nodejs-companion) | Companions | OSS only |
+    | [Self-Hosted AI Companion](/cookbooks/companions/local-companion-ollama) | Companions | OSS only |
+    | [Smart Travel Assistant](/cookbooks/companions/travel-assistant) | Companions | OSS only |
+    | [Build a Companion with Mem0](/cookbooks/essentials/building-ai-companion) | Essentials | OSS and Platform |
+    | [Bedrock with Persistent Memory](/cookbooks/integrations/aws-bedrock) | Integrations | OSS only |
+    | [Automated Email Intelligence](/cookbooks/operations/email-automation) | Operations | OSS and Platform |
+    | [Collaborative Task Assistant](/cookbooks/operations/team-task-agent) | Operations | OSS only |
+    | [Content Creation Workflow](/cookbooks/operations/content-writing) | Operations | OSS and Platform |
+    | [Memory-Powered Support Agent](/cookbooks/operations/support-inbox) | Operations | OSS only |
+  </Tab>
+  <Tab title="Hosted Platform">
+    Twenty-one cookbooks run on the hosted Platform, using `MemoryClient` or the Mem0 MCP server with a `MEM0_API_KEY`.
+
+    | Cookbook | Category | Works with |
+    | --- | --- | --- |
+    | [Interactive Memory Demo](/cookbooks/companions/quickstart-demo) | Companions | Platform only |
+    | [Research Assistant for YouTube](/cookbooks/companions/youtube-research) | Companions | Platform only |
+    | [Voice-First AI Companion](/cookbooks/companions/voice-companion-openai) | Companions | Platform only |
+    | [Build a Companion with Mem0](/cookbooks/essentials/building-ai-companion) | Essentials | OSS and Platform |
+    | [Control Memory Ingestion](/cookbooks/essentials/controlling-memory-ingestion) | Essentials | Platform only |
+    | [Export Stored Memories](/cookbooks/essentials/exporting-memories) | Essentials | Platform only |
+    | [Partition Memories by Entity](/cookbooks/essentials/entity-partitioning-playbook) | Essentials | Platform only |
+    | [Tag and Organize Memories](/cookbooks/essentials/tagging-and-organizing-memories) | Essentials | Platform only |
+    | [Gemini 3 with Mem0 MCP](/cookbooks/frameworks/gemini-3-with-mem0-mcp) | Frameworks | Platform only |
+    | [Multi-Agent Collaboration](/cookbooks/frameworks/llamaindex-multiagent) | Frameworks | Platform only |
+    | [Persistent Eliza Characters](/cookbooks/frameworks/eliza-os-character) | Frameworks | Platform only |
+    | [ReAct Agents with Memory](/cookbooks/frameworks/llamaindex-react) | Frameworks | Platform only |
+    | [Visual Memory Retrieval](/cookbooks/frameworks/multimodal-retrieval) | Frameworks | Platform only |
+    | [Healthcare Coach with ADK](/cookbooks/integrations/healthcare-google-adk) | Integrations | Platform only |
+    | [Memory as OpenAI Tool](/cookbooks/integrations/openai-tool-calls) | Integrations | Platform only |
+    | [Memory-Powered Agent SDK](/cookbooks/integrations/agents-sdk-tool) | Integrations | Platform only |
+    | [Persistent Mastra Agents](/cookbooks/integrations/mastra-agent) | Integrations | Platform only |
+    | [Search with Personal Context](/cookbooks/integrations/tavily-search) | Integrations | Platform only |
+    | [Automated Email Intelligence](/cookbooks/operations/email-automation) | Operations | OSS and Platform |
+    | [Content Creation Workflow](/cookbooks/operations/content-writing) | Operations | OSS and Platform |
+    | [Multi-Session Research Agent](/cookbooks/operations/deep-research) | Operations | Platform only |
+  </Tab>
+</Tabs>
+
 ## Start here

 The most popular cookbooks to get going fast:
@@ -34,15 +34,11 @@ npx flowise start
 2. In this example, we use the **Conversation Chain** template.
 3. Replace the default **Buffer Memory** with **Mem0 Memory**.

-![Flowise Memory Integration](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/flowise-flow.png)
-
 ### 2. Obtain Your Mem0 API Key

 1. Navigate to the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=integration-flowise" rel="nofollow">Mem0 API Key dashboard</a>.
 2. Generate or copy your existing Mem0 API Key.

-![Mem0 API Key](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/api-key.png)
-
 ### 3. Configure Mem0 Credentials

 1. Enter the **Mem0 API Key** in the Mem0 Credentials section.
@@ -57,11 +53,6 @@ npx flowise start
 }
 ```

-<figure>
-  <img src="https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/creds.png" alt="Mem0 Credentials" />
-  <figcaption>Configure API Credentials</figcaption>
-</figure>
-
 ## Memory Features

 ### 1. Basic Memory Storage
@@ -72,8 +63,6 @@ Test your memory configuration:
 2. Run a test chat and store some information
 3. Verify the stored memories in the <a href="https://app.mem0.ai/dashboard/requests?utm_source=oss&utm_medium=integration-flowise" rel="nofollow">Mem0 Dashboard</a>

-![Flowise Test Chat](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/flowise-chat-1.png)
-
 ### 2. Memory Retention

 Validate memory persistence:
@@ -82,14 +71,10 @@ Validate memory persistence:
 2. Ask a question about previously stored information
 3. Confirm that the AI remembers the context

-![Testing Memory Retention](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/flowise-chat-2.png)
-
 ## Advanced Configuration

 ### Memory Settings

-![Mem0 Settings](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/settings.png)
-
 Available settings include:

 1. **Search Only Mode**: Enable memory retrieval without creating new memories
@@ -108,8 +93,6 @@ Additional settings available in <a href="https://app.mem0.ai/dashboard/project-
 1. **Custom Instructions**: Define memory extraction rules
 2. **Expiration Date**: Set automatic memory cleanup periods

-![Mem0 Project Settings](https://raw.githubusercontent.com/FlowiseAI/FlowiseDocs/main/en/.gitbook/assets/mem0/mem0-settings.png)
-
 ## Best Practices

 1. **User Identification**: Use consistent `user_id` values for reliable memory retrieval
@@ -6,7 +6,7 @@ description: "Use Mem0 as a memory store in LlamaIndex with support for ReAct an
 LlamaIndex supports Mem0 as a [memory store](https://llamahub.ai/l/memory/llama-index-memory-mem0). In this guide, we'll show you how to use it.

 <Note type="info">
-  [**Mem0Memory**](https://docs.llamaindex.ai/en/stable/examples/memory/Mem0Memory/) now supports **ReAct** and **FunctionCalling** agents.
+  [**Mem0Memory**](https://developers.llamaindex.ai/python/examples/memory/mem0memory/) now supports **ReAct** and **FunctionCalling** agents.
 </Note>

 ### Installation