opennexus 0.1.1
Intelligent, token-optimized memory stack for AI agents
Requires Python: >=3.9

# OpenNexus: The Intelligent Memory Stack for AI Agents

> **Beyond Vector RAG.** OpenNexus is a token-optimized cognitive layer for autonomous agents, implementing hierarchical abstraction, Bayesian surprise-driven forgetting, and multi-hop reasoning.

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## 🧠 The Theory: Why OpenNexus?

Most AI agents suffer from the **"Memory Wall"**: their context windows fill up with redundant information, leading to high latency and "forgetfulness." OpenNexus solves this through three pillars:

### 1. Bayesian Surprise-Driven Forgetting
Instead of a simple "Last In, First Out" (LRU) buffer, OpenNexus calculates the **informational novelty** of every interaction. 
- **High Surprise**: Stored in episodic memory and promoted to the semantic knowledge graph.
- **Low Surprise**: Summarized or pruned to save tokens.

### 2. Hierarchical Semantic Abstraction (H-MEM)
- **Episodic (SQLite/LanceDB)**: Fast, temporal log of recent events.
- **Semantic (Knowledge Graph)**: Crystallized facts and relationships (GraphRAG) for multi-hop reasoning (e.g., *"How does delay X impact goal Y?"*).
- **Archival (Git)**: "Time-travel" versioning of your agent's entire world-view.

### 3. Faithful Prompt Compression (LLMLingua-2)
Using bidirectional BERT-based extractive compression, OpenNexus reduces prompt size by **20-40%** without losing semantic coherence, effectively doubling your agent's virtual context window.

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## 📈 Performance Benchmarks

| Metric | Vector RAG (Standard) | OpenNexus (Optimized) |
| :--- | :--- | :--- |
| **Token Density** | 1.0x (Raw) | **1.4x - 2.0x** (Compressed) |
| **Reasoning Depth** | Single-hop only | **Multi-hop (Graph Traversal)** |
| **Search Speed** | Fast (Vector) | **Ultra-Fast (LanceDB Zero-Copy)** |
| **Versioning** | None | **Git-backed (Commits/Diffs)** |

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## 🚀 Installation & Setup

### 📦 Simple One-Line Install (Recommended)
Once released on PyPI, simply run:
```bash
pip install opennexus
```

Then setup your favorite agent instantly:
```bash
opennexus instruct --gemini    # Setup Gemini
opennexus instruct --claude    # Setup Claude Desktop
opennexus instruct --openclaw  # Setup OpenClaw
```

### 🛠 For Local Development (From Source)
If you want to contribute or modify the codebase:
1. **Clone & Setup**:
   ```bash
   git clone https://github.com/your-username/opennexus.git
   cd opennexus
   python -m venv venv && source venv/bin/activate
   pip install -e .
   ```
2. **Run Dev Server**:
   ```bash
   chmod +x start.sh && ./start.sh
   ```

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## 🤖 AI Agent Use Cases

### 1. The Coding Assistant
Use `opennexus migrate` to ingest your existing project documentation or Obsidian dev-notes. The agent gains a permanent, vectorized understanding of your coding style.

### 2. Personal Assistant (OpenClaw / Moltbot)
OpenNexus is the perfect companion for **OpenClaw (Moltbot)**. 
- **Persistence**: Your memories survive restarts.
- **Sync**: Use the Git layer to sync your agent's memory across multiple devices.
- **Efficiency**: Keep your local LLM snappy by compressing long chat histories.

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## 🛠 Management CLI

```bash
# Ingest your existing notes
opennexus migrate my_memories.md --conversation_id "deep_research"

# Initialize a new agent turn
opennexus instruct
```

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## 🧩 Architecture Details
- **Location**: All memory data is stored in the `~/.opennexus` directory (persistent across sessions).
- **Python 3.10+** (Optimized for Apple Silicon & NVIDIA RTX)
- **Model Context Protocol (MCP)** v1.0
- **Extractive Pruning**: LLMLingua-2
- **Vector Engine**: LanceDB (In-Process)

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## 📜 License & Research
OpenNexus is based on the research report: *Computational Architectures for Efficient Long-Term Memory in Localized Agentic Systems.* See the `doc/` folder for the full whitepaper.