MemGPT is a framework that allows you to build large language model agents infused with memory and custom tools. It enables anyone to build and deploy stateful large language model agents with long-term memory, which can connect to external data sources like PDF files. MemGPT also provides the ability to define and call custom tools, such as integrating Google search, and the option to incorporate other plugins.

The recent updates to MemGPT include the introduction of MemGPT Services, which support the deployment of multi-agent and multi-user services, making it more suitable for various applications. MemGPT is an open-source framework, and this article will guide you through the process of installing and using the MemGPT Dev Portal.

To get started with MemGPT, you can install it using the provided pip command. If you have previously installed MemGPT, you can update it by using the -u flag. Once installed, you can run MemGPT using the provided command, which will allow you to start chatting with the agent within your preferred IDE or command-line interface.

For a more comprehensive setup, you can install MemGPT with the Dev Portal using Docker. This will provide you with a user interface to create, monitor, and chat with your large language model agents. The process involves cloning the MemGPT repository, modifying the provided .env file, and running the Docker Compose command.

The MemGPT Dev Portal offers various features, including the ability to create agents, configure data sources, and define custom tools. You can also select from pre-built personas and user templates to customize your agent's behavior and interactions.

By leveraging MemGPT's capabilities, you can build powerful AI agents with long-term memory, enhanced contextual understanding, and the ability to integrate external data sources and custom tools. This framework simplifies the development and deployment of stateful large language model agents, making it a valuable tool for a wide range of applications.

MemGPT is a powerful framework that simplifies the development and deployment of stateful large language model agents. Its key features include:

1. Long-Term Memory: MemGPT allows the creation of agents that can retain and access information over extended periods, reducing the cost of having different large language models utilize less tokens as they refer to previous context, enhancing the agent's contextual understanding.

2. External Data Sources: MemGPT enables large language model agents to connect to external data sources through the Retrieval Augmented Generation (RAG) setup. This facilitates tasks such as document analysis, allowing the agent to access and utilize relevant information from external sources.

3. Custom Tools: MemGPT provides the ability to define and utilize custom functions, such as integrating Google Search, to extend the capabilities of the large language model agent. This allows for the creation of more versatile and powerful AI agents tailored to specific use cases.

The MemGPT framework is open-source and continuously evolving, with recent updates introducing support for the deployment of multi-agent and multi-user services through the MemGPT Services. This makes the platform more suitable for a wide range of applications, empowering users to build and deploy stateful large language model agents with long-term memory, external data integration, and custom tool integration.

To set up the MemGPT development portal using Docker, follow these steps:

1. Ensure you have Docker installed on your system. If not, install it for your operating system (Mac, Windows, or Linux).

2. Make sure you have at least 8GB of VRAM available, as this is a requirement for running MemGPT.

3. Go to the MemGPT GitHub repository and copy the repository link.

4. Open your command prompt or terminal and run the following command to clone the repository:

   git clone <repository_link>
   

5. Navigate to the cloned mgpt directory:

   cd mgpt
   

6. Open the .env file in your preferred code editor and make the following changes:

   • Set a password for accessing the MemGPT development portal.
   • Add your OpenAI API key.

7. Save the changes to the .env file.

8. In the terminal, run the following command to start the Docker containers:

   docker-compose up
   

   This will start the MemGPT development portal.

9. Once the Docker containers are running, open a web browser and navigate to http://localhost to access the MemGPT development portal.

10. You can now start creating and managing your MemGPT agents, configure data sources, and utilize the various tools provided by the framework.

That's it! You have successfully set up the MemGPT development portal using Docker. Enjoy exploring the capabilities of this powerful framework for building large language model agents with long-term memory and custom tools.

To create your first MemGPT agent, follow these steps:

1. Select the Agent Model: In the MemGPT UI, choose the model you want to use for your agent. Currently, GPT-4 is the only option available.

2. Configure the Persona: MemGPT provides pre-built personas that you can use, or you can create your own custom persona. Select the persona that best fits your use case.

3. Select the User: Choose the user profile that will interact with your agent. You can use the default "Basic User" or create a custom user profile.

4. Enable Tools: MemGPT offers various tools that you can enable for your agent, such as Archival Memory, Conversation Search, and more. Select the tools you want to use.

5. Create the Agent: Once you've configured the agent's settings, click the "Create Agent" button to generate your first MemGPT agent.

6. Start Chatting: After creating the agent, you can click on the conversation icon to start chatting with your new AI assistant. Use the chat interface to interact with your agent and see its capabilities in action.

Remember, MemGPT is a powerful framework that allows you to build large language model agents with long-term memory and the ability to connect to external data sources. By configuring the personas, users, and tools, you can create a customized AI agent tailored to your specific needs.

Once you have created your MemGPT agent, you can start interacting with it through the conversational interface. This interface allows you to engage in natural language conversations with your agent, taking advantage of its long-term memory and contextual understanding capabilities.

To begin, navigate to the "Agents" tab in the MemGPT UI. Here, you can select your agent and click on the conversation icon to open the chat window. This is where you can start conversing with your agent.

The chat window provides several features to enhance your interaction:

1. Prompt Templates: You can use pre-defined prompt templates to structure your conversations and guide the agent's responses.
2. Core Memory: The agent's core memory stores key information that can be referenced throughout the conversation, ensuring continuity and context-awareness.
3. Data Sources: If you have connected external data sources, such as documents or databases, the agent can access and utilize this information to provide more informed and relevant responses.
4. Tools: MemGPT allows you to integrate custom tools, such as search engines or APIs, to extend the agent's capabilities and enable it to perform a wider range of tasks.
5. Chat Settings: You can customize the chat settings, such as the agent's personality, tone, and response length, to tailor the interaction to your preferences.

As you converse with your agent, you'll notice its ability to maintain context, reference previous information, and provide coherent and contextually appropriate responses. This is the power of MemGPT's long-term memory and state management capabilities.

Remember, the more you interact with your agent and provide it with relevant data sources, the more knowledgeable and capable it will become over time. Leverage the conversational interface to explore the full potential of your MemGPT agent and unlock new possibilities for your applications and workflows.

The MGPT framework is a powerful tool that simplifies the development and deployment of stateful large language model agents. It offers various functionalities such as long-term memory, state management, and the ability to connect to external data sources. The recent updates to MGPT, including the introduction of the MGPT Services and the new user interface, have made it even more accessible and versatile.

The new UI allows users to easily create, monitor, and chat with their large language model agents. It provides features like agent creation, data source integration, and custom tool definition, making it easier to build and deploy complex AI agents. The ability to utilize different language models, including GPT-4, further enhances the capabilities of these agents.

Overall, the MGPT framework is a valuable resource for anyone interested in building and deploying advanced AI agents. Its open-source nature and the continuous updates from the development team make it a promising tool for the future of large language model applications.