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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.
