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docs/cookbooks/integrations/agents-sdk-tool.mdx
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@@ -3,6 +3,9 @@ title: Memory-Powered Agent SDK
description: "Expose Mem0 memory as callable tools inside the OpenAI Agents SDK so Python agents can persist facts and recall them across future conversations."
---
<Info icon="cloud">
**Works with:** Mem0 Platform (`MemoryClient`)
</Info>
Integrate Mem0's memory capabilities with OpenAI's Agents SDK to create AI agents with persistent memory. You can create agents that remember past conversations and use that context to provide better responses.
@@ -223,7 +226,7 @@ context = Mem0Context(user_id="user123")
## Resources
- [Mem0 Documentation](https://docs.mem0.ai/introduction)
- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-agents-sdk-tool" rel="nofollow">Mem0 Dashboard</a>
- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-agents-sdk-tool">Mem0 Dashboard</a>
- [API Reference](https://docs.mem0.ai/api-reference)
---
docs/cookbooks/integrations/aws-bedrock.mdx
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@@ -3,6 +3,9 @@ title: Bedrock with Persistent Memory
description: "Configure the mem0ai Python SDK with AWS Bedrock LLMs and OpenSearch Serverless to run a fully managed, persistent agent memory stack on AWS."
---
<Info icon="server">
**Works with:** Mem0 OSS (`Memory`)
</Info>
This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock** and **OpenSearch Service (AOSS)** for persistent memory capabilities in Python.
@@ -35,8 +38,8 @@ print(os.environ['AWS_SECRET_ACCESS_KEY'])
## Configuration and Usage
This sets up Mem0 with:
- [AWS Bedrock for LLM](https://docs.mem0.ai/components/llms/models/aws_bedrock)
- [AWS Bedrock for embeddings](https://docs.mem0.ai/components/embedders/models/aws_bedrock#aws-bedrock)
- [AWS Bedrock for LLM](https://docs.mem0.ai/components/llms/models/aws-bedrock)
- [AWS Bedrock for embeddings](https://docs.mem0.ai/components/embedders/models/aws-bedrock#aws-bedrock)
- [OpenSearch as the vector store](https://docs.mem0.ai/components/vectordbs/dbs/opensearch)
```python
docs/cookbooks/integrations/healthcare-google-adk.mdx
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@@ -3,6 +3,9 @@ title: Healthcare Coach with ADK
description: "Build a Google ADK healthcare coach with Mem0 that remembers symptoms, medical history, and reminders across every patient chat and follow-up session."
---
<Info icon="cloud">
**Works with:** Mem0 Platform (`MemoryClient`)
</Info>
This example demonstrates how to build a healthcare assistant that remembers patient information across conversations using Google ADK and Mem0.
@@ -123,7 +126,7 @@ Now we'll create our main agent with all the tools:
# Create the agent
healthcare_agent = Agent(
name="healthcare_assistant",
model="gemini-1.5-flash", # Using Gemini for healthcare assistant
model="gemini-2.0-flash", # Using Gemini for healthcare assistant
description="Healthcare assistant that helps patients with health information and appointment scheduling.",
instruction="""You are a helpful Healthcare Assistant with memory capabilities.
docs/cookbooks/integrations/mastra-agent.mdx
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@@ -3,10 +3,13 @@ title: Persistent Mastra Agents
description: "Add long-term Mem0 memory to Mastra agents through tool-use so your TypeScript agents can save and retrieve user facts across every workflow run."
---
<Info icon="cloud">
**Works with:** Mem0 Platform (`@mastra/mem0`)
</Info>
In this example you'll learn how to use Mem0 to add long-term memory capabilities to [Mastra's agent](https://mastra.ai/) via tool-use. This memory integration can work alongside Mastra's [agent memory features](https://mastra.ai/docs/agents/01-agent-memory).
You can find the complete example code in the [Mastra repository](https://github.com/mastra-ai/mastra/tree/main/examples/memory-with-mem0).
The complete example code, from installing the integration to wiring it into a Mastra agent, is shown below. Mem0's integration is published on npm as [`@mastra/mem0`](https://www.npmjs.com/package/@mastra/mem0).
## Overview
docs/cookbooks/integrations/openai-tool-calls.mdx
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@@ -3,6 +3,9 @@ title: Memory as OpenAI Tool
description: "Wire Mem0 into OpenAI function-calling so JavaScript agents automatically save and search memories through the native OpenAI tools API using Zod schemas."
---
<Info icon="cloud">
**Works with:** Mem0 Platform (`MemoryClient`)
</Info>
Integrate Mem0’s memory capabilities with OpenAI’s Inbuilt Tools to create AI agents with persistent memory.
@@ -23,7 +26,7 @@ MEM0_API_KEY=your_mem0_api_key
OPENAI_API_KEY=your_openai_api_key
```
Get your Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-openai-tool-calls" rel="nofollow">Mem0 Dashboard</a>.
Get your Mem0 API key from the <a href="https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=cookbook-openai-tool-calls">Mem0 Dashboard</a>.
### Configuration
@@ -280,7 +283,7 @@ run().catch(console.error);
## Resources
- [Mem0 Documentation](https://docs.mem0.ai/introduction)
- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-openai-tool-calls" rel="nofollow">Mem0 Dashboard</a>
- <a href="https://app.mem0.ai/dashboard?utm_source=oss&utm_medium=cookbook-openai-tool-calls">Mem0 Dashboard</a>
- [API Reference](https://docs.mem0.ai/api-reference)
- [OpenAI Documentation](https://platform.openai.com/docs)
@@ -290,8 +293,8 @@ run().catch(console.error);
<Card title="Agents SDK Tool with Mem0" icon="robot" href="/cookbooks/integrations/agents-sdk-tool">
Extend the OpenAI Agents SDK with Mem0 integration capabilities.
</Card>
<Card title="Control Memory Ingestion" icon="filter" href="/cookbooks/essentials/controlling-memory-ingestion">
Fine-tune what memories get stored during tool calls.
<Card title="Custom Instructions" icon="filter" href="/platform/features/custom-instructions">
Steer what memories get stored during tool calls.
</Card>
</CardGroup>
docs/cookbooks/integrations/tavily-search.mdx
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@@ -3,12 +3,14 @@ title: Search with Personal Context
description: "Blend Tavily realtime web search with Mem0 stored preferences to build a Python search assistant that personalizes results based on past chats."
---
<Info icon="cloud">
**Works with:** Mem0 Platform (`MemoryClient`)
</Info>
Imagine asking a search assistant for "coffee shops nearby" and instead of generic results, it shows remote-work-friendly cafes with great WiFi in your city because it remembers you mentioned working remotely before. Or when you search for "lunchbox ideas for kids" it knows you have a 7-year-old daughter and recommends peanut-free options that align with her allergy.
That's what we are going to build today, a Personalized Search Assistant powered by Mem0 for memory and [Tavily](https://tavily.com) for real-time search.
## Why Personalized Search
Most assistants treat every query like they've never seen you before. That means repeating yourself about your location, diet, or preferences, and getting results that feel generic.