docs: coolify README with banner, badges, and examples

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Carlos Ouyang
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README.md
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# memabra
<div align="center">
An intuition-driven control plane for agent memory and action selection.
```
__ __ _
| \/ | ___ _ __ ___ __ _ __| | ___ ___
| |\/| |/ _ \ '_ ` _ \ / _` |/ _` |/ _ \/ __|
| | | | __/ | | | | | (_| | (_| | __/\__ \
|_| |_|\___|_| |_| |_|\__,_|\__,_|\___||___/
```
## What is memabra?
**Intuition-driven control plane for agent memory & action selection.**
memabra is a local-first, observable, trainable, and replayable agent memory and action orchestration system.
[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Tests](https://img.shields.io/badge/tests-126%20passed-brightgreen.svg)]()
Instead of being a simple memory database, memabra acts as a meta-cognitive controller for agents: given a task, it quickly decides whether to answer directly, recall memory, load a skill, or invoke a tool — and continuously improves this judgment based on task outcomes.
</div>
## Install
---
## 🧠 What is memabra?
> *Most agents memorize. memabra **intuits**.*
memabra is a **local-first, observable, trainable, and replayable** control plane for agent memory and action orchestration.
Instead of acting like a dusty filing cabinet, memabra functions as a **meta-cognitive controller**: given any task, it rapidly decides whether to answer directly, recall memory, load a skill, or invoke a tool — then **learns from outcomes** to sharpen those instincts over time.
- 🏠 **Local-first** — no cloud lock-in, your data stays on disk
- 📊 **Observable** — every decision is tracked, versioned, and inspectable
- 🎓 **Trainable** — online learning loop improves routing automatically
- 🔄 **Replayable** — replay trajectories, audit decisions, roll back versions
---
## ⚡ Quick Start
```bash
git clone https://github.com/TacitLab/memabra.git
@@ -18,67 +43,109 @@ source venv/bin/activate
pip install -e ".[dev]"
```
## Quick start
### 1. See the available commands
### 1. Peek under the hood
```bash
memabra --help
```
### 2. Run a dry-run evaluation
### 2. Run a safe dry-run evaluation
See the full workflow **without** actually promoting a new router version:
```bash
memabra run --dry-run --format text
```
A safe way to see the full workflow without actually promoting a new router version:
### 3. Check system pulse
```bash
memabra status --format text
```
### 4. Inspect your router lineage
```bash
memabra version list --format text
```
### 5. Time-travel (rollback)
```bash
memabra version rollback <version-id> --format text
```
---
## 🎮 CLI Commands
| Command | Description |
|---------|-------------|
| `memabra run` | 🚀 Execute the online learning workflow |
| `memabra status` | 💓 Show current system health & metrics |
| `memabra version list` | 📜 List all saved router versions |
| `memabra version rollback <id>` | ⏪ Roll back to a specific version |
---
## 🖨️ Operator-Friendly Output
By default, memabra speaks JSON. For humans, add `--format text`:
```bash
memabra run --dry-run --format text
```
### 3. Check system status
Sample output:
```bash
memabra status --format text
```
Memabra online learning result
Summary
Report ID: report-58f9f22
Skipped: no
Promoted: yes
Dry run: yes
Baseline
Reward: 0.7200
Error rate: 0.1200
Latency (ms): 145.0000
Challenger
Reward: 0.8100
Error rate: 0.0800
Latency (ms): 132.5000
Deltas
Reward delta: 0.0900
Error rate delta: -0.0400
Latency delta (ms): -12.5000
Decision
Accepted: yes
Reason: challenger improved reward and reduced error rate
```
### 4. List saved router versions
**Normalized booleans** (`yes/no/none`)
**Fixed-precision metrics** for easy comparison
**Sectioned layout** — Summary → Baseline → Challenger → Deltas → Decision
```bash
memabra version list --format text
```
---
### 5. Roll back to a previous version
```bash
memabra version rollback <version-id> --format text
```
## CLI subcommands
| Command | Description |
|---------|-------------|
| `memabra run` | Run the online learning workflow |
| `memabra status` | Show current system state |
| `memabra version list` | List all saved router versions |
| `memabra version rollback <id>` | Roll back to a specific version |
## Text output format
By default, memabra prints JSON. For operator-friendly summaries, add `--format text`:
- **Status** — current version, trajectory/report counts, latest report timing and promotion outcome.
- **Version list** — total count, current active version highlighted.
- **Workflow** — grouped into Summary, Baseline, Challenger, Deltas, and Decision sections with normalized `yes/no` flags and fixed-precision metrics.
## Running tests
## 🧪 Running Tests
```bash
pytest tests/ -q
```
## Project status
Current status: **126 passed** 🟢
See [docs/PROGRESS.md](docs/PROGRESS.md) for a detailed capability roadmap and [docs/DEMO.md](docs/DEMO.md) for walkthrough examples.
---
## License
## 📚 Documentation
MIT
- [`docs/PROGRESS.md`](docs/PROGRESS.md) — Capability roadmap & what's shipped
- [`docs/DEMO.md`](docs/DEMO.md) — Hands-on walkthroughs & examples
- [`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md) — System design & mental model
---
## 🏷️ License
MIT — use it, break it, improve it.
*Built with caffeine and curiosity.*