As someone with over five years of experience in Python serialization and the Django framework, I've been heavily involved in the development and maintenance of Tiger, a function store that provides isolated storage, auto-documentation, usage tracking, and version control.
Tiger was born out of the need to solve the difficulties associated with library creation and storage in Python. It serves as a GitHub repository and On-Premises service, storing functions as tools for various large language model (LLM) agent frameworks such as crewAI, LangChain, Autogen, and OpenInterpreter. These AI agents, when integrated with Tiger, become capable of executing codes, searching through stored data, reading Telegram messages, and much more.
Tiger is a two-part system. The first part is a Docker container that provides a dashboard and storage. The second part is a Python client library that I've designed to serialize the functions. This client sends the serialized functions to the Docker container for storage, triggers the auto-documentation process, and keeps usage records.
In the development of Tiger, I've utilized various technologies including Flask, Django, Dill, and Cloudpickle. Notably, I've used the Upsonic Serializer for serializing some functions.
Our goal is to establish a strong community and use case that offers tools for AI agents. This allows all AI agents to easily access modern applications and skills without additional effort. Additionally, companies can easily use Tiger to perform their unique functions. You can provide up-to-date tools for any type of AI agent. Imagine your research agents recommending documents to your team members for reading. This is the high level of usability and standardization we promise.
Tiger can enhance any agent with well-designed tools. Therefore, you can use it to impart search capabilities, the ability to run various programs, and communication skills to all your agents.
Tiger was born out of the need to solve the difficulties associated with library creation and storage in Python. It serves as a GitHub repository and On-Premises service, storing functions as tools for various large language model (LLM) agent frameworks such as crewAI, LangChain, Autogen, and OpenInterpreter. These AI agents, when integrated with Tiger, become capable of executing codes, searching through stored data, reading Telegram messages, and much more.
Tiger is a two-part system. The first part is a Docker container that provides a dashboard and storage. The second part is a Python client library that I've designed to serialize the functions. This client sends the serialized functions to the Docker container for storage, triggers the auto-documentation process, and keeps usage records.
In the development of Tiger, I've utilized various technologies including Flask, Django, Dill, and Cloudpickle. Notably, I've used the Upsonic Serializer for serializing some functions.
Our goal is to establish a strong community and use case that offers tools for AI agents. This allows all AI agents to easily access modern applications and skills without additional effort. Additionally, companies can easily use Tiger to perform their unique functions. You can provide up-to-date tools for any type of AI agent. Imagine your research agents recommending documents to your team members for reading. This is the high level of usability and standardization we promise.
Tiger can enhance any agent with well-designed tools. Therefore, you can use it to impart search capabilities, the ability to run various programs, and communication skills to all your agents.