Marvin
If you’ve ever tried building an AI-powered app and found yourself tangled in state management, prompt engineering, and figuring out how to keep your agents from forgetting everything between tasks, Marvin might be the kind of tool that makes the whole process feel less like a puzzle and more like a conversation. It’s a Python framework designed to help developers work with large language models in a way that’s structured, flexible, and surprisingly intuitive. You’re not just sending prompts to an API – you’re building agents that remember, collaborate, and respond with context.
I first tried Marvin while experimenting with a writing assistant that could switch between different tones. I wanted one agent to write poetic descriptions and another to explain technical concepts clearly. With Marvin, I was able to define both agents with just a few lines of code. I gave each one a name and a short set of instructions, and suddenly I had a poet and a scientist who could work together. I asked the scientist to explain entropy, passed that explanation to the poet, and got a haiku that actually made sense. It felt like I was orchestrating a little team of personalities, each with their own role.
The way Marvin handles memory is one of its most useful features. You can create memory modules that persist across conversations, so your agents don’t start from scratch every time. I used this to store user preferences – things like tone, formatting style, and favorite examples. Once the agent learned those preferences, it started responding in a way that felt tailored. It remembered what I liked and adjusted its answers accordingly. That kind of continuity makes a big difference when you’re building something meant to feel personal or adaptive.
Marvin also makes it easy to get structured output. If you want your results in a specific format – like a list of objects with defined fields – you can use Pydantic models to shape the response. I tried this with a character generator for a mystery story. I defined a model with fields like name, role, and aura, and Marvin filled it out with characters that matched the structure. It wasn’t just generating text – it was producing usable data that I could plug into other parts of my app.
The interface is clean and doesn’t ask much from you. You start with a simple import, define your agents or tasks, and run them with a single function. There’s no need to manage conversation history manually or build your own orchestration layer. Marvin handles that behind the scenes, which frees you up to focus on what your agents should do, not how they should do it.
If you’re curious about building AI applications that go beyond one-off prompts, Marvin is worth exploring. It’s designed for developers who want to create agents with memory, personality, and structure – without having to reinvent the wheel. You can check it out at askmarvin.ai and see how it fits into your own projects. Whether you’re building a chatbot, a writing assistant, or something more experimental, Marvin gives you the tools to make it feel thoughtful and alive.