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 | [Langchain4j](./langchain4j/)         | Java framework for building context-aware, reasoning applications using LLMs.                        |
 | [LlamaIndex](./llama-index/)          | A data framework for building LLM applications with modular integrations.                            |
 | [Make](./make/)                       | Cloud platform to build low-code workflows by integrating various software applications.             |
+| [MemGPT](./memgpt/)                   | System to build LLM agents with long term memory & custom tools                                      |
 | [MindsDB](./mindsdb/)                 | Platform to deploy, serve, and fine-tune models with numerous data source integrations.              |
 | [N8N](./n8n/)                         | Platform for node-based, low-code workflow automation.                                               |
-| [OpenLIT](./openlit/)                 | OpenTelemetry-native Observability & Evals for LLMs and Vector Databases.                            |
+| [OpenLIT](./openlit/)                 | Platform for OpenTelemetry-native Observability & Evals for LLMs and Vector Databases.               |
 | [OpenLLMetry](./openllmetry/)         | Set of OpenTelemetry extensions to add Observability for your LLM application.                       |
 | [Pandas-AI](./pandas-ai/)             | Python library to query/visualize your data (CSV, XLSX, PostgreSQL, etc.) in natural language        |
 | [Pipedream](./pipedream/)             | Platform for connecting apps and developing event-driven automations.                                |
diff --git a/qdrant-landing/content/documentation/frameworks/memgpt.md b/qdrant-landing/content/documentation/frameworks/memgpt.md
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+---
+title: MemGPT
+weight: 3200
+---
+
+# MemGPT
+
+[MemGPT](https://memgpt.ai/) is a system that enables LLMs to manage their own memory and overcome limited context windows to
+
+- Create perpetual chatbots that learn about you and change their personalities over time.
+- Create perpetual chatbots that can interface with large data stores.
+
+Qdrant is available as a storage backend in MemGPT for storing and semantically retrieving data.
+
+## Usage
+
+#### Installation
+
+To install the required dependencies, install `pymemgpt` with the `qdrant` extra.
+
+```sh
+pip install 'pymemgpt[qdrant]'
+```
+
+You can configure MemGPT to use either a Qdrant server or an in-memory instance with the `memgpt configure` command.
+
+#### Configuring the Qdrant server
+
+When you run `memgpt configure`, go through the prompts as described in the [MemGPT configuration documentation](https://memgpt.readme.io/docs/config).
+After you address several `memgpt` questions, you come to the following `memgpt` prompts:
+
+```console
+? Select storage backend for archival data: qdrant
+? Select Qdrant backend: server
+? Enter the Qdrant instance URI (Default: localhost:6333): https://xyz-example.eu-central.aws.cloud.qdrant.io
+```
+
+You can set an API key for authentication using the `QDRANT_API_KEY` environment variable.
+
+#### Configuring an in-memory instance
+
+```console
+? Select storage backend for archival data: qdrant
+? Select Qdrant backend: local
+```
+
+The data is persisted at the default MemGPT storage directory.
+
+## Further Reading
+
+- [MemGPT Examples][https://github.com/cpacker/MemGPT/tree/main/examples]
+- [MemGPT Documentation](https://memgpt.readme.io/docs/index).
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