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: 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.