Memovee is an agentic movie database that lets you explore movies using natural language instead of filters or rigid queries.

You can ask things like:

- “Movies that take place in someone’s mind”

- “Top mystery films on Netflix released in the last 5 years”

- “Slow-burn sci-fi movies with strong world-building”

Under the hood, this isn’t just an LLM wrapper. Memovee uses a structured movie database and an agent layer that translates natural-language intent into deterministic queries and aggregations, then reflects on the results before responding.

The agent implementation used by Memovee is open source: https://github.com/upmaru/memovee-tama

This repository shows how intent parsing, query planning, execution, and result refinement are handled step-by-step, rather than relying on opaque prompt chains.

The core engine itself is not open-sourced yet. Memovee is one concrete application built on top of a more general engine whose goal is to make it possible to build systems like this for any enterprise domain — not just movies.

Engine project: https://kritama.com

This is still early and focused purely on movies (not TV yet). Coverage and regional availability vary, and there’s a lot left to improve — especially around reasoning depth, evals, and edge cases.

Happy to answer questions about:

- The agent architecture

- How natural-language intent is mapped to structured data

- Agentic vs deterministic tradeoffs

- The path toward a reusable enterprise engine

Looking forward to feedback — especially critical ones.