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docs: update README: Twitter Button, Consolidate call-to-action, Reorganize Content (#1387)

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2024-05-15 21:13:07 -07:00
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@@ -7,6 +7,7 @@
<strong>MemGPT allows you to build LLM agents with long term memory & custom tools</strong>
[![Discord](https://img.shields.io/discord/1161736243340640419?label=Discord&logo=discord&logoColor=5865F2&style=flat-square&color=5865F2)](https://discord.gg/9GEQrxmVyE)
[![Twitter Follow](https://img.shields.io/badge/follow-%40MemGPT-1DA1F2?style=flat-square&logo=x&logoColor=white)](https://twitter.com/MemGPT)
[![arxiv 2310.08560](https://img.shields.io/badge/arXiv-2310.08560-B31B1B?logo=arxiv&style=flat-square)](https://arxiv.org/abs/2310.08560)
[![Documentation](https://img.shields.io/github/v/release/cpacker/MemGPT?label=Documentation&logo=readthedocs&style=flat-square)](https://memgpt.readme.io/docs)
@@ -88,18 +89,18 @@ MemGPT is designed to be model and provider agnostic. The following LLM and embe
When using MemGPT with open LLMs (such as those downloaded from HuggingFace), the performance of MemGPT will be highly dependent on the LLM's function calling ability. You can find a list of LLMs/models that are known to work well with MemGPT on the [#model-chat channel on Discord](https://discord.gg/9GEQrxmVyE), as well as on [this spreadsheet](https://docs.google.com/spreadsheets/d/1fH-FdaO8BltTMa4kXiNCxmBCQ46PRBVp3Vn6WbPgsFs/edit?usp=sharing).
## Documentation
See full documentation at: https://memgpt.readme.io
## How to Get Involved
* **Contribute to the Project**: Interested in contributing? Start by reading our [Contribution Guidelines](CONTRIBUTING.md).
* **Ask a Question**: Join our community on [Discord](https://discord.gg/9GEQrxmVyE) and direct your questions to the `#support` channel.
* **Report Issues or Suggest Features**: Have an issue or a feature request? Please submit them through our [GitHub Issues page](https://github.com/cpacker/MemGPT/issues).
* **Explore the Roadmap**: Curious about future developments? View and comment on our [project roadmap](https://github.com/cpacker/MemGPT/issues/1200).
* **Benchmark the Performance**: Want to benchmark the performance of a model on MemGPT? Follow our [Benchmarking Guidance](#benchmarking-guidance).
* **Join Community Events**: Stay updated with the [MemGPT event calendar](https://lu.ma/berkeley-llm-meetup) or follow our [Twitter account](https://twitter.com/MemGPT).
## Support
For issues and feature requests, please [open a GitHub issue](https://github.com/cpacker/MemGPT/issues) or message us on our `#support` channel on [Discord](https://discord.gg/9GEQrxmVyE).
## Benchmarking Guidance
To evaluate the performance of a model on MemGPT, simply configure the appropriate model settings using `memgpt configure`, and then initiate the benchmark via `memgpt benchmark`. The duration will vary depending on your hardware. This will run through a predefined set of prompts through multiple iterations to test the function calling capabilities of a model. You can help track what LLMs work well with MemGPT by contributing your benchmark results via [this form](https://forms.gle/XiBGKEEPFFLNSR348), which will be used to update the spreadsheet.
## Legal notices
By using MemGPT and related MemGPT services (such as the MemGPT endpoint or hosted service), you agree to our [privacy policy](PRIVACY.md) and [terms of service](TERMS.md).
## Roadmap
You can view (and comment on!) the MemGPT developer roadmap on GitHub: https://github.com/cpacker/MemGPT/issues/1200.
## Benchmarking
To evaluate the performance of a model on MemGPT, simply configure the appropriate model settings using `memgpt configure`, and then initiate the benchmark via `memgpt benchmark`. The duration will vary depending on your hardware. This will run through a predefined set of prompts through multiple iterations to test the function calling capabilities of a model. You can help track what LLMs work well with MemGPT by contributing your benchmark results via [this form](https://forms.gle/XiBGKEEPFFLNSR348), which will be used to update the spreadsheet.