Imagine an AI agent, not just capable of generating text, but also adept at using a vast array of tools to solve complex problems. That's the promise of Tulip Agent, a novel architecture designed to empower Large Language Models (LLMs) with efficient and dynamic tool utilization. Current LLM-based agents face limitations when dealing with extensive toolsets. Listing every tool in the model's prompt gobbles up precious context window space and inflates costs. Similarly, embedding the entire prompt for tool retrieval becomes computationally expensive. Tulip Agent sidesteps these issues with a clever trick: a searchable tool library. Implemented as a vector store, this library allows the agent to recursively search for the right tool based on the task at hand, dramatically reducing inference costs. Instead of being limited by a pre-defined toolset, Tulip Agent can adapt and extend its capabilities, much like a human learning new skills. This dynamic approach makes it ideal for open-ended scenarios, such as robotics. In tests involving mathematical problem-solving, Tulip Agent consistently outperformed traditional methods, demonstrating its ability to break down complex tasks, identify appropriate tools, and execute them efficiently. The research also explored variations in LLM and embedding model performance. Interestingly, while more advanced agent functionalities required a more powerful LLM (like GPT-4), the choice of embedding model had a minimal impact. But the real magic happens when Tulip Agent is given the power to manage its own tools. Through Create, Read, Update, and Delete (CRUD) operations, the agent can modify its tool library on the fly. Imagine an agent encountering a task it can't solve. Instead of giving up, it generates the code for a new tool, tests its validity, and adds it to its repertoire. This self-learning capability opens doors to a future where AI agents can autonomously adapt to new challenges and complex, ever-changing environments. While this research focuses on Python functions, the architecture can be extended to encompass other tools and programming languages. The implications are far-reaching, from building more efficient software agents to creating truly adaptable robots that can learn and evolve in the real world. Tulip Agent isn't just a step forward in AI research, it's a glimpse into a future where AI agents become more capable, adaptable, and cost-effective problem-solvers.