> ## Documentation Index
> Fetch the complete documentation index at: https://pavan-c90e8846.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Memory Agents: AI with Context Retention

> Explore 12 memory agent projects that retain context across sessions, from personalized blog writing to brand reputation monitoring and customer support.

The Memory Agents category includes 12 projects that demonstrate how to build agents with advanced memory capabilities. These agents retain context across sessions, track preferences, and personalize responses over time, using memory systems like Memori, LangGraph, and Agno.

## Memory Agent Projects

| Project | Memory System | Description |
| - | - | - |
| Agno Memory Agent | Agno | Persistent memory integration with Agno-based agents |
| arXiv Researcher with Memori | Memori | OpenAI Agents-based research assistant using GibsonAI Memori |
| AWS Strands with Memori | Memori | AWS Strands agent enhanced with Memori memory fabric |
| Blog Writing Agent | Memori | Personalized blog writing agent that remembers your writing style |
| Social Media Agent | Memori | Social media automation agent with memory for brand voice |
| Job Search Agent | Memori | Job search agent with preference tracking over time |
| Brand Reputation Monitor | Memori | Brand reputation monitoring with news analysis and sentiment tracking |
| Product Launch Agent | Memori | Competitive intelligence tool for analyzing competitor launches |
| AI Consultant Agent | Memori v3, ExaAI | Consulting agent using Memori v3 for long-term memory and ExaAI for research |
| Customer Support Voice Agent | Memori v3, Firecrawl | Voice-enabled support assistant with Memori v3 and knowledge base management |
| YouTube Trend Agent | Memori, Agno, Exa | YouTube channel analysis agent for trend analysis and video ideas |
| Study Coach Agent | Memori v3, LangGraph | AI study coach with Memori v3 and LangGraph for multi-step verification |

## Getting Started with Memory Agents

<Steps>
  <Step title="Choose a memory project">
    ```bash theme={null}
    cd memory_agents/<project_name>
    ```
  </Step>

  <Step title="Set up your API keys">
    ```bash theme={null}
    cp .env.example .env
    ```

    Many memory agents require a Memori API key and an LLM provider key (e.g., OpenAI, Nebius).
  </Step>

  <Step title="Install and run">
    ```bash theme={null}
    pip install -r requirements.txt
    python main.py
    ```
  </Step>
</Steps>

<Note>
  Memory agents often write data to a persistent store. Check the README for information on where memory data is stored and how you can inspect or reset it.
</Note>


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