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README.md

@@ -3,204 +3,61 @@ license: mit
 library_name: mnemo
 tags:
   - memory
-  - retrieval
+  - ai-memory
+  - llm-memory
+  - semantic-memory
   - rag
-  - semantic-search
+  - retrieval
+  - langchain
+  - llamaindex
+  - mcp
+  - agent-memory
+  - chatbot-memory
+  - mem0
+  - vector-search
   - knowledge-graph
-  - faiss
-  - bm25
-language:
-  - en
 pipeline_tag: feature-extraction
 ---
 
-# Mnemo: Semantic-Loop Memory
-
-> *Named after Mnemosyne, Greek goddess of memory*
-
-**21x faster than mem0. No API keys. Fully local. Learns from feedback.**
-
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
-
-## 🎮 Try the Demo
-
-👉 [**Live Demo on HuggingFace Spaces**](https://huggingface.co/spaces/AthelaPerk/mnemo)
-
----
-
-## 🏆 Benchmarks vs mem0
-
-| Metric | mem0 | Mnemo | Winner |
-|--------|------|-------|--------|
-| **Search Latency** | 5.73ms | 0.27ms | **Mnemo (21x faster)** |
-| **Ingestion Latency** | 31.1ms | 0.8ms | **Mnemo (39x faster)** |
-| **API Key Required** | Yes (OpenAI) | No | **Mnemo** |
-| **Offline Capable** | No | Yes | **Mnemo** |
-| **Features** | 5/10 | 8/10 | **Mnemo** |
-
----
-
-## ✨ Features mem0 Doesn't Have
-
-| Feature | mem0 | Mnemo |
-|---------|------|-------|
-| Query Intent Detection | ❌ | ✅ |
-| Multi-Strategy Retrieval | ❌ | ✅ |
-| Knowledge Graph | ❌ | ✅ |
-| Feedback Learning | ❌ | ✅ |
-| Observability/Metrics | ❌ | ✅ |
-| Semantic Chunking | ❌ | ✅ |
-| Works Offline | ❌ | ✅ |
-| No API Dependencies | ❌ | ✅ |
+# 🧠 Mnemo - AI Memory System
 
----
+**Open-source memory for LLMs, chatbots, and AI agents**
 
-## 🚀 Quick Start
+## Install
 
 ```bash
-git clone https://huggingface.co/AthelaPerk/mnemo-memory
-cd mnemo-memory
-pip install -r requirements.txt
-```
-
-```python
-from mnemo import Mnemo
-
-# Initialize
-m = Mnemo()
-
-# Add memories
-m.add("User prefers dark mode and morning notifications")
-m.add("Project deadline is March 15th for the API redesign")
-m.add("Team meeting every Tuesday at 2pm")
-
-# Search (0.27ms average!)
-results = m.search("notification preferences")
-print(results[0].content)  # "User prefers dark mode..."
-
-# Feedback learning
-m.feedback("notification preferences", results[0].id, relevance=0.9)
-```
-
----
-
-## 🧠 Architecture
-
-```
-┌─────────────────────────────────────────────────────────────┐
-│                         MNEMO                                │
-├─────────────────────────────────────────────────────────────┤
-│  Query → [Intent Detection] → [Multi-Strategy Retrieval]    │
-│                                       │                      │
-│                         ┌─────────────┴─────────────┐       │
-│                         │ Semantic │ BM25 │ Graph   │       │
-│                         │   50%    │ 30%  │  20%    │       │
-│                         └─────────────┬─────────────┘       │
-│                                       │                      │
-│                              [Feedback Loop]                 │
-│                           (Learns & Improves)                │
-└─────────────────────────────────────────────────────────────┘
+pip install mnemo-memory
 ```
 
-### Core Components
-
-1. **FAISS Vector Store** - Scalable similarity search
-2. **Knowledge Graph** - Entity relationships (NetworkX)
-3. **Query Understanding** - Intent classification + NER
-4. **Multi-Strategy Retrieval** - BM25 + Semantic + Graph
-5. **Feedback Loop** - Learns from user interactions
-6. **Observability** - Built-in metrics and tracing
-
----
-
-## 📖 API Reference
-
-### Basic Usage
+## Quick Start
 
 ```python
 from mnemo import Mnemo
 
-m = Mnemo(
-    embedding_dim=384,      # BGE-small compatible
-    semantic_weight=0.5,    # Weight for semantic search
-    bm25_weight=0.3,        # Weight for keyword search
-    graph_weight=0.2        # Weight for graph traversal
-)
-
-# Add memory
-mem_id = m.add("content", metadata={"key": "value"})
-
-# Search
-results = m.search("query", top_k=5)
-for r in results:
-    print(r.id, r.score, r.content)
-
-# Feedback
-m.feedback("query", mem_id, relevance=0.9)  # -1 to 1
-
-# Stats
-print(m.get_stats())
+memory = Mnemo()
+memory.add("User prefers Python")
+results = memory.search("preferences")  # 0.27ms!
 ```
 
-### SearchResult Object
+## MCP Server (for Claude)
 
-```python
-result.id              # Memory ID
-result.content         # Memory content
-result.score           # Combined score
-result.strategy_scores # {'semantic': 0.5, 'bm25': 0.3, 'graph': 0.1}
-result.metadata        # Original metadata
+```json
+{
+  "mcpServers": {
+    "mnemo": {"command": "uvx", "args": ["mnemo-memory"]}
+  }
+}
 ```
 
----
-
-## 🏥 Built for Compliance
-
-Mnemo was designed with **EU AI Act Article 10** compliance in mind:
-
-- ✅ **Full audit trails** - Every operation logged
-- ✅ **Documented provenance** - Know where memories came from
-- ✅ **No external API calls** - Data never leaves your infrastructure
-- ✅ **Traceable reasoning** - Understand why results were returned
-
-*From the team behind NRA-19 medical AI benchmark.*
-
----
-
-## 🆚 Why Not mem0?
-
-| Issue with mem0 | Mnemo Solution |
-|-----------------|----------------|
-| Requires OpenAI API key even for local embeddings | Zero API dependencies |
-| Data sent to external services | Fully local, air-gapped capable |
-| Single retrieval strategy | Multi-strategy (21x faster) |
-| No learning from usage | Feedback loop improves over time |
-| No query understanding | Intent detection + NER |
-| Limited observability | Full metrics and tracing |
-
----
-
-## 🗺️ Roadmap
-
-- [ ] Sentence-transformers integration
-- [ ] Persistent storage (SQLite, PostgreSQL)
-- [ ] Multi-user support
-- [ ] REST API server
-- [ ] LangChain integration
-- [ ] LlamaIndex integration
-- [ ] ROS 2 integration (robotics)
-
----
-
-## 📜 License
-
-MIT License
-
----
+## Benchmarks vs mem0
 
-## 🙏 Etymology
+| Metric | mem0 | Mnemo |
+|--------|------|-------|
+| Search | 5.73ms | **0.27ms** |
+| API Keys | Required | **None** |
+| Offline | No | **Yes** |
 
-Named after **Mnemosyne** (Μνημοσύνη), the Greek goddess of memory and mother of the nine Muses. In Greek mythology, she was one of the Titans and personified memory itself.
+## Links
 
-*Mnemo: Memory that learns.*
+- [Demo](https://huggingface.co/spaces/AthelaPerk/mnemo)
+- [MCP Server](https://huggingface.co/spaces/AthelaPerk/mnemo-mcp)