Update Docs (#3520)
This commit is contained in:
Deshraj Yadav
@@ -163,11 +163,10 @@ Organize your monitoring with structured sessions:
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4. **Tagging**: Use tags to organize different types of memory operations
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5. **Environment Separation**: Use different projects or tags for dev/staging/prod
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## Help & Resources
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## Help
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- [AgentOps Documentation](https://docs.agentops.ai/)
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- [AgentOps Dashboard](https://app.agentops.ai/)
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- [Mem0 Platform](https://app.mem0.ai/)
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<Snippet file="get-help.mdx" />
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<Snippet file="get-help.mdx" />
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@@ -2,7 +2,7 @@
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title: Agno
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---
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This integration of [**Mem0**](https://github.com/mem0ai/mem0) with [Agno](https://github.com/agno-agi/agno, enables persistent, multimodal memory for Agno-based agents - improving personalization, context awareness, and continuity across conversations.
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This integration of [**Mem0**](https://github.com/mem0ai/mem0) with [Agno](https://github.com/agno-agi/agno) enables persistent, multimodal memory for Agno-based agents - improving personalization, context awareness, and continuity across conversations.
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## Overview
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@@ -195,7 +195,7 @@ Customize the integration to your needs:
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- **Memory Search**: Configure search relevance and result count
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- **Memory Formatting**: Support for various OpenAI message formats
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## Help & Resources
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## Help
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- [Agno Documentation](https://docs.agno.com/introduction)
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- [Mem0 Platform](https://app.mem0.ai/)
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@@ -6,8 +6,7 @@ Build conversational AI agents with memory capabilities. This integration combin
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## Overview
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In this guide, we'll explore an example of creating a conversational AI system with memory:
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- A customer service bot that can recall previous interactions and provide personalized responses.
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This guide demonstrates creating a conversational AI system with memory. We'll build a customer service bot that can recall previous interactions and provide personalized responses.
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## Setup and Configuration
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@@ -133,6 +132,4 @@ This integration enables the creation of more intelligent and personalized AI ag
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## Help
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In case of any questions, please feel free to reach out to us using one of the following methods:
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<Snippet file="get-help.mdx" />
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@@ -29,9 +29,9 @@ Install required packages:
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pip install mem0ai boto3 opensearch-py
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```
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Set environment variables:
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Set environment variables.
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Be sure to configure your AWS credentials using environment variables, IAM roles, or the AWS CLI.
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Configure your AWS credentials using environment variables, IAM roles, or the AWS CLI.
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```python
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import os
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@@ -60,9 +60,9 @@ messages = [
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{"role": "user", "content": "I am more of a beach person than a mountain person."},
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{
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"role": "assistant",
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"content": "That's interesting. Do you like hotels or airbnb?",
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"content": "That's interesting. Do you like hotels or Airbnb?",
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},
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{"role": "user", "content": "I like airbnb more."},
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{"role": "user", "content": "I like Airbnb more."},
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]
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store_user_preferences("crew_user_1", messages)
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@@ -154,7 +154,7 @@ if __name__ == "__main__":
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## Benefits
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1. **Persistent Context & Memory**: Maintains user preferences and interaction history across sessions
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2. **Flexible & Scalable Design**: Easily extendable with new agents, tasks and capabilities
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2. **Flexible & Scalable Design**: Easily extendable with new agents, tasks, and capabilities
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## Conclusion
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@@ -447,8 +447,7 @@ By integrating ElevenLabs Conversational AI with Mem0, you can create voice agen
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## Help
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- For more details on ElevenLabs, visit the [ElevenLabs Conversational AI Documentation](https://elevenlabs.io/docs/api-reference/conversational-ai)
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- For Mem0 documentation, refer to the [Mem0 Platform](https://app.mem0.ai/)
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- If you need further assistance, please feel free to reach out to us through the following methods:
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- [ElevenLabs Conversational AI Documentation](https://elevenlabs.io/docs/api-reference/conversational-ai)
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- [Mem0 Platform](https://app.mem0.ai/)
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<Snippet file="get-help.mdx" />
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@@ -6,11 +6,11 @@ The [**Mem0 Memory**](https://github.com/mem0ai/mem0) integration with [Flowise]
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## Overview
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1. 🧠 Provides persistent memory storage for Flowise chatflows
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2. 🔄 Seamless integration with existing Flowise templates
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3. 🚀 Compatible with various LLM nodes in Flowise
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4. 📝 Supports custom memory configurations
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5. ⚡ Easy to set up and manage
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1. Provides persistent memory storage for Flowise chatflows
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2. Seamless integration with existing Flowise templates
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3. Compatible with various LLM nodes in Flowise
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4. Supports custom memory configurations
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5. Easy to set up and manage
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## Prerequisites
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@@ -115,12 +115,11 @@ Additional settings available in [Mem0 Project Settings](https://app.mem0.ai/das
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2. **Memory Organization**: Utilize projects and organizations for better memory management
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3. **Regular Maintenance**: Monitor and clean up unused memories periodically
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## Help & Resources
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## Help
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- [Flowise Documentation](https://flowiseai.com/docs)
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- [Flowise GitHub Repository](https://github.com/FlowiseAI/Flowise)
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- [Flowise Website](https://flowiseai.com/)
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- [Mem0 Platform](https://app.mem0.ai/)
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- Need assistance? Reach out through:
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<Snippet file="get-help.mdx" />
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@@ -9,7 +9,7 @@ Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [Google Agent Developm
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1. Store and retrieve memories from Mem0 within Google ADK agents
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2. Multi-agent workflows with shared memory across hierarchies
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3. Retrieve relevant memories from past conversations
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4. Personalized responses
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4. Personalized responses based on user history
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## Prerequisites
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@@ -130,9 +130,9 @@ from google.adk.tools.agent_tool import AgentTool
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travel_agent = Agent(
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name="travel_specialist",
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model="gemini-2.0-flash",
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instruction="""You are a travel planning specialist. Use get_user_context to
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instruction="""You are a travel planning specialist. Use search_memory to
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understand the user's travel preferences and history before making recommendations.
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After providing advice, use store_interaction to save travel-related information.""",
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After providing advice, use save_memory to save travel-related information.""",
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description="Specialist in travel planning and recommendations",
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tools=[search_memory, save_memory]
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)
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@@ -141,9 +141,9 @@ travel_agent = Agent(
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health_agent = Agent(
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name="health_advisor",
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model="gemini-2.0-flash",
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instruction="""You are a health and wellness advisor. Use get_user_context to
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instruction="""You are a health and wellness advisor. Use search_memory to
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understand the user's health goals and dietary preferences.
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After providing advice, use store_interaction to save health-related information.""",
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After providing advice, use save_memory to save health-related information.""",
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description="Specialist in health and wellness advice",
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tools=[search_memory, save_memory]
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)
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@@ -155,7 +155,7 @@ coordinator_agent = Agent(
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instruction="""You are a coordinator that delegates requests to specialist agents.
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For travel-related questions (trips, hotels, flights, destinations), delegate to the travel specialist.
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For health-related questions (fitness, diet, wellness, exercise), delegate to the health advisor.
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Use get_user_context to understand the user before delegation.""",
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Use search_memory to understand the user before delegation.""",
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description="Coordinates requests between specialist agents",
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tools=[
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AgentTool(agent=travel_agent, skip_summarization=False),
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@@ -282,6 +282,5 @@ os.environ["GOOGLE_CLOUD_LOCATION"] = "us-central1"
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- [Google ADK Documentation](https://google.github.io/adk-docs/)
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- [Mem0 Platform](https://app.mem0.ai/)
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- If you need further assistance, please feel free to reach out to us through the following methods:
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<Snippet file="get-help.mdx" />
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@@ -94,7 +94,7 @@ client = OpenAI(
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# Sample conversation messages
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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
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{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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@@ -133,7 +133,6 @@ Integrating Mem0 with Keywords AI provides a powerful combination for building A
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## Help
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For more information, refer to:
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- [Keywords AI Documentation](https://docs.keywordsai.co)
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- [Mem0 Platform](https://app.mem0.ai/)
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@@ -331,6 +331,4 @@ Each tool provides structured input validation through Pydantic models and retur
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## Help
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In case of any questions, please feel free to reach out to us using one of the following methods:
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<Snippet file="get-help.mdx" />
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@@ -163,9 +163,8 @@ By integrating LangChain with Mem0, you can build a personalized Travel Agent AI
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## Help
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- For more details on LangChain, visit the [LangChain documentation](https://python.langchain.com/).
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- [Mem0 Platform](https://app.mem0.ai/).
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- If you need further assistance, please feel free to reach out to us through the following methods:
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- [LangChain Documentation](https://python.langchain.com/)
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- [Mem0 Platform](https://app.mem0.ai/)
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<Snippet file="get-help.mdx" />
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@@ -165,8 +165,7 @@ By integrating LangGraph with Mem0, you can build a personalized Customer Suppor
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## Help
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- For more details on LangGraph, visit the [LangChain documentation](https://python.langchain.com/docs/langgraph).
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- [Mem0 Platform](https://app.mem0.ai/).
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- If you need further assistance, please feel free to reach out to us through following methods:
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- [LangGraph Documentation](https://python.langchain.com/docs/langgraph)
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- [Mem0 Platform](https://app.mem0.ai/)
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<Snippet file="get-help.mdx" />
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@@ -33,7 +33,7 @@ MEM0_API_KEY=your_mem0_api_key
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OPENAI_API_KEY=your_openai_api_key
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```
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> **Note**: Make sure to have a Livekit and Deepgram account. You can find these variables `LIVEKIT_URL` , `LIVEKIT_API_KEY` and `LIVEKIT_API_SECRET` from [LiveKit Cloud Console](https://cloud.livekit.io/) and for more information you can refer this website [LiveKit Documentation](https://docs.livekit.io/home/cloud/keys-and-tokens/). For `DEEPGRAM_API_KEY` you can get from [Deepgram Console](https://console.deepgram.com/) refer this website [Deepgram Documentation](https://developers.deepgram.com/docs/create-additional-api-keys) for more details.
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> **Note**: Make sure to have a Livekit and Deepgram account. You can find these variables `LIVEKIT_URL`, `LIVEKIT_API_KEY`, and `LIVEKIT_API_SECRET` from the [LiveKit Cloud Console](https://cloud.livekit.io/). For more information, refer to the [LiveKit Documentation](https://docs.livekit.io/home/cloud/keys-and-tokens/). For `DEEPGRAM_API_KEY`, you can get it from the [Deepgram Console](https://console.deepgram.com/). Refer to the [Deepgram Documentation](https://developers.deepgram.com/docs/create-additional-api-keys) for more details.
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## Code Breakdown
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@@ -196,7 +196,7 @@ or to start your agent in console mode to run inside your terminal:
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```sh
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python mem0-livekit-voice-agent.py console
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```
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5. After the script starts, you can interact with the voice agent using [Livekit's Agent Platform](https://agents-playground.livekit.io/) and connect to the agent inorder to start conversations.
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5. After the script starts, you can interact with the voice agent using [LiveKit's Agent Platform](https://agents-playground.livekit.io/) and connect to the agent to start conversations.
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## Best Practices for Voice Agents with Memory
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@@ -229,10 +229,9 @@ logger = logging.getLogger("memory_voice_agent")
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- Ensure your `.env` file is correctly configured and loaded.
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## Help & Resources
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## Help
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- [LiveKit Documentation](https://docs.livekit.io/)
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- [Mem0 Platform](https://app.mem0.ai/)
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- Need assistance? Reach out through:
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<Snippet file="get-help.mdx" />
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@@ -5,7 +5,7 @@ title: LlamaIndex
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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.
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<Note type="info">
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🎉 Exciting news! [**Mem0Memory**](https://docs.llamaindex.ai/en/stable/examples/memory/Mem0Memory/) now supports **ReAct** and **FunctionCalling** agents.
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[**Mem0Memory**](https://docs.llamaindex.ai/en/stable/examples/memory/Mem0Memory/) now supports **ReAct** and **FunctionCalling** agents.
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</Note>
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### Installation
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@@ -207,9 +207,8 @@ By integrating LlamaIndex with Mem0, you can build a personalized agent that can
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## Help
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- For more details on LlamaIndex, visit the [LlamaIndex documentation](https://llamahub.ai/l/memory/llama-index-memory-mem0).
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- [Mem0 Platform](https://app.mem0.ai/).
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- If you need further assistance, please feel free to reach out to us through following methods:
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- [LlamaIndex Documentation](https://llamahub.ai/l/memory/llama-index-memory-mem0)
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- [Mem0 Platform](https://app.mem0.ai/)
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<Snippet file="get-help.mdx" />
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@@ -127,8 +127,7 @@ By integrating Mastra with Mem0, you can build intelligent agents that learn and
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## Help
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- For more details on Mastra, visit the [Mastra documentation](https://docs.mastra.ai/).
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- [Mem0 Platform](https://app.mem0.ai/).
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- If you need further assistance, please feel free to reach out to us through the following methods:
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- [Mastra Documentation](https://docs.mastra.ai/)
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- [Mem0 Platform](https://app.mem0.ai/)
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<Snippet file="get-help.mdx" />
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@@ -130,9 +130,9 @@ health_agent = Agent(
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triage_agent = Agent(
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name="Personal Assistant",
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instructions="""You are a helpful personal assistant that routes requests to specialists.
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For travel-related questions (trips, hotels, flights, destinations), hand off to Travel Planner.
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For health-related questions (fitness, diet, wellness, exercise), hand off to Health Advisor.
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For general questions, you can handle them directly using available tools.""",
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For travel-related questions (trips, hotels, flights, destinations), hand off to the Travel Planner.
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For health-related questions (fitness, diet, wellness, exercise), hand off to the Health Advisor.
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For general questions, handle them directly using available tools.""",
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handoffs=[travel_agent, health_agent],
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model="gpt-4o"
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)
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@@ -201,7 +201,7 @@ if __name__ == "__main__":
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- **Seamless Handoffs**: Agents maintain context across handoffs
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### 3. Flexible Memory Operations
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- **Retrieve Capabilities**: Retrieve relevant memories from previous conversation
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- **Retrieve Capabilities**: Retrieve relevant memories from previous conversations
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- **User Segmentation**: Organize memories by user ID
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- **Memory Management**: Built-in tools for saving and retrieving information
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@@ -229,6 +229,5 @@ mem0.add(
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- [OpenAI Agents SDK Documentation](https://openai.github.io/openai-agents-python/)
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- [Mem0 Platform](https://app.mem0.ai/)
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- If you need further assistance, please feel free to reach out to us through the following methods:
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<Snippet file="get-help.mdx" />
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@@ -24,21 +24,21 @@ d. Enter this key in the extension preferences
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- Manage persistent user preferences
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- Search through stored memories
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## ✨ Features
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## Features
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**Remember Everything**: Never lose important information - store notes, preferences, and conversations that your AI can recall later
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**Remember Everything**: Never lose important information. Store notes, preferences, and conversations that your AI can recall later.
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**Smart Connections**: Automatically links related topics, just like your brain does - helping you discover useful connections
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**Smart Connections**: Automatically links related topics, helping you discover useful connections.
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**Cost Saver**: Spend less on AI usage by efficiently retrieving relevant information instead of regenerating responses
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**Cost Saver**: Spend less on AI usage by efficiently retrieving relevant information instead of regenerating responses.
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## 🔑 How This Helps You
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## How This Helps You
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**More Personal Experience**: Your AI remembers your preferences and past conversations, making interactions feel more natural
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**More Personal Experience**: Your AI remembers your preferences and past conversations, making interactions feel more natural.
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**Learn Your Style**: Adapts to how you work and what you like, becoming more helpful over time
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**Learn Your Style**: Adapts to how you work and what you like, becoming more helpful over time.
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**No More Repetition**: Stop explaining the same things over and over - your AI remembers your context and preferences
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**No More Repetition**: Stop explaining the same things repeatedly. Your AI remembers your context and preferences.
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---
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@@ -5,16 +5,16 @@ title: Vercel AI SDK
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The [**Mem0 AI SDK Provider**](https://www.npmjs.com/package/@mem0/vercel-ai-provider) is a library developed by **Mem0** to integrate with the Vercel AI SDK. This library brings enhanced AI interaction capabilities to your applications by introducing persistent memory functionality.
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<Note type="info">
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🎉 Exciting news! Mem0 AI SDK now supports <strong>Vercel AI SDK V5</strong>.
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Mem0 AI SDK now supports <strong>Vercel AI SDK V5</strong>.
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</Note>
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## Overview
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1. 🧠 Offers persistent memory storage for conversational AI
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2. 🔄 Enables smooth integration with the Vercel AI SDK
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3. 🚀 Ensures compatibility with multiple LLM providers
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4. 📝 Supports structured message formats for clarity
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5. ⚡ Facilitates streaming response capabilities
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1. Offers persistent memory storage for conversational AI
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2. Enables smooth integration with the Vercel AI SDK
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3. Ensures compatibility with multiple LLM providers
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4. Supports structured message formats for clarity
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5. Facilitates streaming response capabilities
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## Setup and Configuration
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@@ -318,8 +318,7 @@ Mem0’s Vercel AI SDK enables the creation of intelligent, context-aware applic
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## Help
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- For more details on Vercel AI SDK, visit the [Vercel AI SDK documentation](https://sdk.vercel.ai/docs/introduction)
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- [Vercel AI SDK Documentation](https://sdk.vercel.ai/docs/introduction)
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- [Mem0 Platform](https://app.mem0.ai/)
|
||||
- If you need further assistance, please feel free to reach out to us through following methods:
|
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|
||||
<Snippet file="get-help.mdx" />
|
||||
Reference in New Issue
Block a user