openmemx 1.0.1

Intelligent, token-optimized memory stack for AI agents

OpenMemX: The Intelligent Memory Stack for AI Agents

Beyond Vector RAG. OpenMemX 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 OpenMemX?

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

1. Bayesian Surprise-Driven Forgetting

Instead of a simple "Last In, First Out" (LRU) buffer, OpenMemX 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, OpenMemX 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)	OpenMemX (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

1. Install the Core Engine

Open a terminal and run:

pip install openmemx

2. Connect Your Agents (Grounding)

OpenMemX works by connecting to your favorite AI tools. Choose your agent below to set up its "Brain":

💎 Gemini CLI (Automated)

Run this command to automatically configure your Gemini sessions:

openmemx instruct --gemini

Restart your Gemini terminal and type /openmemx_system_init to begin.

☁️ Claude Desktop (Automated)

Run this command to add OpenMemX to Claude's toolbelt:

openmemx instruct --claude

Restart Claude, click the paper icon (Prompts), and select openmemx_system_init.

🦀 OpenClaw (Automated)

Run this command to add OpenMemX to OpenClaw/Moltbot:

openmemx instruct --openclaw

Restart OpenClaw and trigger the openmemx_system_init prompt via your agent's interface.

🤖 Nanobot (Manual Skill Setup)

Nanobot uses a "Skills" system. To add OpenMemX:

1. Locate your Nanobot workspace: (Usually ~/.nanobot/workspace/)
2. Copy the OpenMemX Skill:

   mkdir -p ~/.nanobot/workspace/skills
   cp -r .agent/skills/openmemx ~/.nanobot/workspace/skills/
   

3. Use it: Simply tell Nanobot: "Use the OpenMemX skill to manage my memory."

🏗️ Generic / Other Agents

If your agent supports MCP (Model Context Protocol), point it to: Command: python -m openmemx.mcp_server

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📅 The "Daily Standup" (Universal Monitoring)

OpenMemX is now a Universal Activity Monitor. It can read logs from Nanobot, Gemini, and OpenClaw to give you a bird's-eye view of your productivity.

How to use it:

1. Initialize Patterns: Run openmemx instruct at least once to set up your global config.
2. Ask your Agent: "Give me a global activity report for the last 24 hours."
3. View Results: Your agent will aggregate actions from across your entire system, even if they happened in different tools or folders.

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🛠 Management CLI

# Ingest your existing notes/markdown files
openmemx migrate my_notes.md --conversation_id "research_project"

# Reset your local configuration
openmemx instruct

# Manage automatic memory logging
openmemx auto-ingest --status           # See if it's currently recording
openmemx auto-ingest --enable           # Start recording everything automatically
openmemx auto-ingest --disable          # Stop recording
openmemx auto-ingest --new-conversation # Start a fresh conversation session

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🤖 AI Agent Use Cases

1. The Coding Assistant

Use openmemx 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)

OpenMemX 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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Auto-Ingest & Project-Aware Memory (NEW)

OpenMemX is now context-aware. It automatically manages sessions based on your current working directory, allowing you to "resume" memory context simply by navigating to a project folder.

1. Project-Aware Sessions

• Automatic Context: When you run OpenMemX in /projects/backend, it loads that specific project's memory history.
• Seamless Resumption: Switch folders, and your agent switches context automatically.
• Registry System: Keeps track of which Conversation ID belongs to which directory.

2. Auto-Ingest

• Zero-Friction: Your queries to retrieve_memory are automatically logged.
• Smart Logging: Use configure_auto_ingest to toggle query/response logging preferences.

3. Global Awareness (The "Daily Standup")

Even with isolated project memories, your agent maintains a global view.

• get_recent_activity: This tool returns a summary of all interactions across all projects from the last 24 hours.
• Use Case: Ask your agent "What did I work on yesterday?" and it will aggregate context from your backend, frontend, and infrastructure projects into a single report.

MCP Tools:

Core Memory Operations:

• ingest_interaction - Store a new interaction in episodic memory with Bayesian Surprise scoring
• retrieve_memory - Semantic search across all conversation history
• consolidate_memory - Trigger "sleep cycle" to identify high-surprise items and prune low-surprise noise

Knowledge Graph (GraphRAG):

• add_knowledge_node - Add entities to the semantic knowledge graph
• add_knowledge_edge - Create relationships between entities
• traverse_knowledge_graph - Multi-hop reasoning over crystallized facts

Prompt Optimization:

• compress_prompt - Reduce prompt size using LLMLingua-2 (20-40% compression)
• snapshot_memory - Create Git-backed version checkpoint

Auto-Ingest & Sessions:

• get_recent_activity - Global report of actions across all project directories
• log_interaction - Quick logging without managing conversation IDs
• configure_auto_ingest - Customize auto-ingest behavior
• get_auto_ingest_status - Check current configuration
• start_new_conversation - Force a fresh conversation thread for the current project

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🧩 Architecture Details

• Location: All memory data is stored in the ~/.openmemx 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

OpenMemX 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.