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HumphreySun98/Smart-Study-Agent — The AI study agent that learns how you learn — RL picks the action, FSRS picks the timing, the LLM o…

Description and use

Here's a brief summary of the open-source project:

**Project Summary:** HumphreySun98/Smart-Study-Agent is an AI-powered study agent that learns individual learning habits by combining Reinforcement Learning (RL), Finite State Response System (FSRS), and a Large Language Model (LLM). This agent can pick the best study action, timing, and generate quizzes to aid in learning.

**Value Proposition:** The value of this project lies in its ability to turn isolated prompts and tools into repeatable agent workflows. This enables users to standardize agent memory, add tool-use pipelines, and coordinate multi-agent workflows, making it an ideal solution for those who want to streamline their learning process.

**Practical Adoption Path:** To adopt this project, users can start by installing the Chrome extension and exploring its features. They can then integrate the agent with their existing tools and workflows, and fine-tune its behavior using the exposed API/SDK/CLI. The project's documentation and community support can help users navigate the adoption process.

**Production Readiness:** With a score of 74/100, this project is considered medium-readiness for production. While it has shown promise, users should perform dependency and maintenance checks before deploying it in a production environment. Additionally, a final review of the license,

Problem

HumphreySun98/Smart-Study-Agent helps turn isolated prompts and tools into repeatable agent workflows.

Outcome

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.

Workflow

  • coordinate multi-agent workflows
  • add tool-use pipelines
  • standardize agent memory

Evidence

The AI study agent that learns how you learn — RL picks the action, FSRS picks the timing, the LLM only writes the quizzes. Chrome extension · Anki export · MCP server · runs on Ollama

Source

  • Record date: 2026-09-11T02:28:22.057Z