cheanus

cheanus / SRSA

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The Spaced Repetition Systems for Agents

agent ai ai-agent long-term-memory skills
17
1
100% credibility
Found Mar 25, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

SRSA is a spaced repetition tool that turns AI agent memories into flashcards for ongoing review and self-correction to combat memory drift.

How It Works

1
📖 Discover SRSA

You learn about a helpful tool that trains AI assistants to remember important facts better over time, like flashcards for smart helpers.

2
🛠️ Set up the memory trainer

You grab the simple memory trainer and prepare it with a quick settings file for how often to review.

3
➕ Create your first card

You make a flashcard by adding a question, like 'What news does the user like?', and the correct answer.

4
🌟 Start daily reviews

You pull up the next due question, check the answer, and rate how well it stuck in memory.

5
📊 Track your progress

You see stats on how many cards are due, your review history, and how much better recall is getting.

🎉 Smarter AI memory

Your AI assistant now remembers details accurately and improves with every review session.

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AI-Generated Review

What is SRSA?

SRSA brings spaced repetition systems for AI agents to life in Python, turning raw agent memories into reviewable flashcards scheduled via the FSRS spaced repetition algorithm on GitHub. It tackles memory drift—where agents forget context, mix up facts, or degrade over time—by enabling self-reviews that detect gaps, correct errors, and prune junk. Developers get a CLI tool to create cards, run sessions (like `review get-question`, rate with "again/hard/good/easy"), and track stats like retrievability and due intervals.

Why is it gaining traction?

Unlike basic agent memory stores, SRSA layers on top of any system for dynamic self-improvement, using FSRS for token-efficient spacing that cuts unnecessary reviews. Users notice analytics on recall accuracy, easy overrides for bad cards, and progress tracking across days—perfect for spaced repetition learning on GitHub. The decoupled design and agent "skill" integration hook devs experimenting with persistent AI brains.

Who should use this?

AI agent builders refining long-term recall in chatbots or RAG pipelines. Teams using spaced repetition Obsidian plugins or apps who want agent-side memory tuning. Python devs prototyping spaced repetition methode for multi-session agents, especially with German spaced repetition deutsch resources like lernplan intervalle.

Verdict

Early alpha with 17 stars and 1.0% credibility score—solid README and CLI, but lacks tests and broad adoption signals maturity risks. Worth a spin for agent memory hacks if you're okay prototyping; skip for production until more battle-tested.

(198 words)

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