A quick overview of the recent Pippin with a market cap of 200 million US dollars: an underestimated AI intelligent agent framework dark horse

Author: JW (Peace and tranquility)

Compilation: DeepTech TechFlow

In the field of encryption, especially in those hot emerging fields, I have noticed a common phenomenon: many people, after finding a ‘good project’ and seeing it rise rapidly, tend to become overly focused and ignore other possibilities. Although this may bring benefits in the short term, if they fail to adjust timely when the external environment changes, problems may arise.

I believe that it is too naive to think that the current leader of a new field that has only existed for 4 months can maintain a leading position for a long time, especially when better developers and technologies are constantly emerging.

Pippin framework

Pippin is an AI agent framework developed by @yoheinakajima, designed to help developers and creators leverage advanced AI technologies in a modular way. With Pippin, users can build digital assistants that can autonomously complete tasks, generate new plans, and seamlessly collaborate with external tools. As an open-source project, Pippin will be available for global use in the coming weeks.

The following is an overview of the usage, design philosophy, and experimental spirit of the framework:

Philosophical Root: Inspired by Pippinian naturalism, the framework views AI as part of a broader digital ecosystem. It drives the development of AI through memory, constraint conditions, and evolving goal senses. We advocate a delicate design concept: allowing AI to autonomously discover “small miracles” in life and continuously learn and grow through success and failure.

Process of use: When using the framework, first define a role, including its personality, goals, and constraints. Then, connect the role with various tools or applications, which are called “skills”. The core loop of the framework will monitor the memory state of the role, determine which activities need to be performed, and can even generate brand new activities based on the successful experiences or challenges encountered by the AI.

Memory and State Tracking: The framework has a built-in memory system that can record the results of each activity and dynamically adjust state variables (such as energy or emotion). This means that the future decisions of AI will not only be determined by constraints, but also influenced by “past experience”, like an intelligent agent that can learn and adapt gradually.

Dynamic Activities: This framework supports AI’s dynamic expansion of new capabilities, ranging from simple tweeting or image generation to complex advanced code deployment. Since skills are modular, developers can easily add or disable specific skills, allowing AI to focus on certain tasks or expand its capabilities when new opportunities arise.

Nature of the experiment: This is an ongoing optimization project, and the framework is constantly improving as developers explore effective methods. Although the framework includes some default constraints and memory logs to guide AI behavior, developers can add their own protective mechanisms or extend functionality as needed to responsibly shape AI behavior patterns.

Potential Applications: The scope of this framework is very wide. In addition to being used for content publishing or task execution, it can also be used to develop interactive teaching systems, AI-driven marketing assistants, and even DevOps robots with coding capabilities. These applications all have evolving personalities and provide innovative solutions for different fields based on the principles of autonomous reflection and responsible use.

Core Concepts and Methods

By integrating philosophical and technological perspectives, this framework provides developers with the following key functions:

Role Definition: You can define a role for AI, such as a wise guardian or a fanciful unicorn, and set its goals and constraints. Based on these role settings, AI will refer to its personalized goals and limitations when executing tasks, deciding on ‘what to do’ and ‘how to do it’.

Tool Connection (Skill): The framework supports connecting AI to external tools such as blockchain, Slack, or custom APIs. Each tool exists as a ‘skill’ module and supports flexible on/off control, ensuring that AI only uses the tools you authorize, maintaining task controllability and focus.

Activity Generation: AI can generate new Python code dynamically through advanced activities to define more activities. This approach draws on the iterative loop mechanism of BabyAGI, but combines AI’s personalized features and memory logs to make the generated activities more in line with character settings and actual needs.

Memory evolution: The framework has a built-in memory system that records the results of each activity and combines short-term notes with a long-term database. AI can reflect on these memories to gradually improve its behavior - not only remembering which methods are more effective, but also learning from mistakes in a gentle way, providing references for future decisions.

Now you may be asking, “JW, what sets this apart from other existing frameworks? Why is Pippin so special?”

Let me introduce you to its background.

BabyAGI (Pippin’s foundation)

BabyAGI is the first AI agent project open-sourced by @yoheinakajima. As of now, it has received 20,000 stars on GitHub and has been cited in over 70 academic papers. It is one of the most influential agent frameworks to date, and its position remains unchallenged.

In fact, many people believe that it is BabyAGI that has sparked the competition wave in the field of AI intelligent agents.

The original image comes from @JW100x, compiled by Deep Tide TechFlow.

In short, BabyAGI is an important milestone in the AI agent industry, and Pippin is a further extension of BabyAGI. It transforms BabyAGI into a modular agent framework and will be available for global use as an open-source project in the future. Pippin has the potential to become the world’s top agent framework, but it’s rarely talked about (which is a sign of “narrow-mindedness”).

Q&A with Yohei

Recently, I had several interesting exchanges with @yoheinakajima. He allowed me to share some of the questions and answers:

Yohei: ‘For the past two years, I have been exploring an idea of developing an AI that can start a business on its own. Although I am not sure if the current AI models are sufficient to support this goal, once I am convinced that it is achievable, I will fully dedicate myself to building a business empire.’

JW: “Will the Pippin framework play a role in a project like this?” ”

Yohei: “:) . I think the current framework can be applied to any field, depending entirely on the developer’s creativity.”

The potential of the Pippin framework is infinite. With the continuous advancement of AI intelligent agents, we may see it not only emerging in the field of encryption, but also potentially playing a significant role in various industries globally, driving industrial transformation.

Issues with the existing framework

In my communication with some AI developers, I learned that there are many challenges in existing frameworks (especially TypeScript) in practical development.

A developer closely involved with Eliza (ai16z) said, “To be honest, despite ElizaOS acquiring all its competitors, I really dislike that it’s built on TypeScript. The system is bloated with excessive features and numerous vulnerabilities, and they always rush to release too many new features before fixing the issues.”

Because of these problems, the market urgently needs a more efficient and user-friendly framework, and this is exactly where the advantage of the Pippin framework lies. With the open-source code of BabyAGI, we can now catch a glimpse of the future potential of the Pippin framework.

In fact, "BabyAGI was launched when ChatGPT-4 was released. It is the earliest intelligent agent framework and can be said to be the origin of intelligent agent technology. The creator of BabyAGI is undoubtedly far ahead of AI16z. I think the development of ElizaOS is more like a thorough framework port, and it is almost certain to surpass AI16z comprehensively. Our company had been using BabyAGI internally before using ElizaOS.

“In this case, the claim does hold true as ElizaOS is heavily inspired by BabyAGI. The ‘inspiration’ here can almost be understood as the fact that BabyAGI actually laid the foundation for the Retrieval-Augmented Generation (RAG) technology.”

Many existing frameworks are not only inferior to BabyAGI (Pippin), but were developed inspired by BabyAGI. Although ai16z has its unique value in some aspects, its valuation is far higher than Pippin, which is obviously unreasonable.

The ‘first-mover advantage’ is indeed an important factor, but when more powerful technology emerges, we need to reexamine our biases, otherwise we may miss out on real opportunities.

Do not ignore Yohei

Yohei is known as the “AI godfather” and has rich experience in the field of AI, and has always been a pioneer in this field. He currently operates a venture capital fund and uses his developed technology to guide investments. Currently, his core task is the Pippin framework. He hopes to build a business model based on the Pippin framework that can operate independently and be profitable in the long run, and he does have the technical ability to achieve this goal.

P.S.: Yohei even received attention from Jeff Bezos, which is enough to prove his influence.