Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Procedural memory for AI coding agents: transforms scattered session history into persistent, cross-agent memory so every agent learns from every other
| Date | Stars |
|---|---|
| 2026-07-31 | 406 |
| 2026-08-06 | 408 |
Today
+2 stars today
This week
— stars this week
This month
— stars this month
Momentum
43.0
growth rate 0.00%/day
# cass-memory <div align="center"> <img src="cm_illustration.webp" alt="cass-memory - Procedural memory for AI coding agents"> </div>     **Procedural memory for AI coding agents.** Transforms scattered agent sessions into persistent, cross-agent memory—so every agent learns from every other agent's experience. <div align="center"> **One-liner install (Linux/macOS):** ```bash curl -fsSL "https://raw.githubusercontent.com/Dicklesworthstone/cass_memory_system/main/install.sh?$(date +%s)" \ | bash -s -- --easy-mode --verify ``` **Or via package managers:** ```bash # macOS/Linux (Homebrew) brew install dicklesworthstone/tap/cm # Windows (Scoop) scoop bucket add dicklesworthstone https://github.com/Dicklesworthstone/scoop-bucket scoop install dicklesworthstone/cm ``` </div> --- ## 🤖 Agent Quickstart (JSON) **Always use `--json` in agent contexts.** stdout = data, stderr = diagnostics, exit 0 = success. ```bash # 1) Get task-specific memory before you start cm context "implement auth rate limiting" --json # 2) See the minimum viable workflow cm quickstart --json # 3) Build the playbook (memory onboarding) cm onboard status --json cm onboard sample --fill-gaps --json cm onboard read /path/to/session.jsonl --template --json cm onboard mark-done /path/to/session.jsonl ``` ## Table of Contents - [Why This Exists](#-why-this-exists) - [How It Works](#-how-it-works) - [Key Features](#-key-features) - [For AI Agents](#-for-ai-agents-the-most-important-section) - [Installation](#-installation) - [CLI Reference](#-cli-reference) - [The ACE Pipeline](#-the-ace-pipeline) - [Data Models](#-data-models) - [Scoring Algorithm](#-scoring-algorithm) - [Configuration](#-configuration) - [MCP Server](#-mcp-server) - [Architecture & Engineering](#-architecture--engineering) - [Deep Dive: Core Algorithms](#-deep-dive-core-algorithms) - [Privacy & Security](#-privacy--security) - [Trauma Guard: Safety System](#-trauma-guard-safety-system) - [Performance Characteristics](#-performance-characteristics) - [Starter Playbooks](#-starter-playbooks) - [Extensibility](#-extensibility-adding-new-components) - [Troubleshooting](#-troubleshooting) - [Design Philosophy](#-design-philosophy) - [Comparison with Alternatives](#-comparison-with-alternatives) - [Roadmap](#-roadmap) --- ## 💡 Why This Exists ### The Problem AI coding agents accumulate valuable knowledge through sessions: debugging strategies, code patterns, user preferences, project-specific insights. But this knowledge is: 1. **Trapped in sessions** — Each session ends, context is lost forever 2. **Agent-specific** — Claude Code doesn't know what Cursor learned yesterday 3. **Unstructured** — Raw conversation logs aren't actionable as guidance 4. **Subject to collapse** — Naive summarization loses critical nuances and details You've solved authentication bugs three times this month across different agents. Each time, you started from scratch because the knowledge from previous sessions was inaccessible. ### The Solution `cass-memory` implements a **three-layer cognitive architecture** that transforms raw session logs into actionable, confidence-tracked rules: | Layer | Role | Implementation | |-------|------|----------------| | **Episodic Memory** | Raw ground truth from all agents | `cass` search engine | | **Working Memory** | Structured session summaries | Diary entries | | **Procedural Memory** | Distilled rules with tracking | Playbook bullets | This mirrors how human expertise develops: raw experiences (episodic) are consolidated into structured memories (working), which eventually become automatic knowledge (procedural). ### Who Benefits - **AI Agents**: Get relevant rules and historic
Excerpt of 88,918 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:801ede2b577880fb, llm:Repository topics: ai-agents, memory; description: 'Procedural memory for AI coding agents: transforms scattered session history into persistent, cross-agent memory so every agent learns from every other'
matched fp:801ede2b577880fb, llm:Repository topics: ai-agents, memory; description: 'Procedural memory for AI coding agents: transforms scattered session history into persistent, cross-agent memory so every agent learns from every other'