README.md•7.36 kB
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<img src="https://shigoto.me/continuo.jpeg" alt="Continuo">
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## Continuo Memory System
**Persistent memory and hierarchical compression for development environments**
[](https://www.python.org/downloads/)
[](https://www.gnu.org/licenses/agpl-3.0.en.html)
[](COMMERCIAL_LICENSE.md)
[](https://modelcontextprotocol.io)
## Overview
Continuo is a **persistent memory system** that provides semantic search and storage capabilities for development workflows. By separating reasoning (LLM) from long-term memory (Vector DB + hierarchical compression), the system maintains knowledge indefinitely, circumventing context window limitations.
### Key Features
- **Persistent Memory** - Store and retrieve development knowledge across sessions
- **Semantic Search** - Find relevant information using natural language queries
- **Hierarchical Compression** - N0 (chunks) → N1 (summaries) → N2 (meta-summaries)
- **MCP Integration** - Seamless integration with IDEs via Model Context Protocol
- **Cost Effective** - 100% local (free) or hybrid (low-cost) deployment options
- **FastMCP** - Built on the modern MCP server framework
## Quick Start
### Installation
```bash
git clone https://github.com/GtOkAi/continuo-memory-mcp-memory-mcp.git
cd continuo
./scripts/setup_memory.sh
```
### Usage
1. **Start the memory server:**
```bash
./scripts/run_memory_server.sh
```
2. **Configure your IDE (Qoder/Cursor):**
Create `.qoder/mcp.json` (or `.cursor/mcp.json`):
```json
{
"mcpServers": {
"continuo-memory": {
"command": "/absolute/path/to/continuo/venv_memory/bin/python",
"args": [
"/absolute/path/to/continuo/src/mcp/memory/mcp_memory_server.py",
"--provider",
"local",
"--db-path",
"/absolute/path/to/memory_db"
]
}
}
}
```
3. **Use in your IDE:**
```
@continuo-memory search_memory("authentication implementation")
@continuo-memory store_memory("Fixed JWT validation bug", {"file": "auth.py"})
@continuo-memory get_memory_stats()
```
## Architecture
```
IDE Chat ──► MCP Adapter ──► Memory Server ──► ChromaDB
▲ ▲ │ │
│ └──── tools ◄─────┘ │
└───── response ◄──── context ◄───────────────┘
```
### Components
- **Memory Server** - ChromaDB + sentence-transformers for embeddings
- **MCP Adapter** - FastMCP server exposing `search_memory` and `store_memory` tools
- **Hierarchical Compression** - Multi-level context optimization (N0/N1/N2)
- **Autonomous Mode** - Optional automation with Observe → Plan → Act → Reflect cycle
## Configuration
### Local Embeddings (Free)
```bash
python src/mcp/memory/mcp_memory_server.py \
--provider local \
--db-path ./memory_db
```
### OpenAI Embeddings (Low-cost)
```bash
python src/mcp/memory/mcp_memory_server.py \
--provider openai \
--api-key sk-your-key \
--db-path ./memory_db
```
## API
### Tools
**`search_memory(query: str, top_k: int = 5, level: str | None = None) -> str`**
- Semantic search in persistent memory
- Returns relevant documents with similarity scores
**`store_memory(text: str, metadata: dict | None = None, level: str = "N0") -> str`**
- Store content in persistent memory
- Supports metadata tagging and hierarchical levels
**`get_memory_stats() -> str`**
- Get memory statistics (total documents, levels, etc.)
### Hierarchical Levels
- **N0** - Raw chunks (code snippets, conversations)
- **N1** - Micro-summaries (5-10 chunks compressed)
- **N2** - Meta-summaries (5-10 summaries compressed)
## Examples
See the [`examples/memory/`](examples/memory/) directory:
- `basic_usage.py` - Simple store/retrieve operations
- `hierarchical_demo.py` - Multi-level compression examples
- `auto_mode_demo.py` - Autonomous mode demonstration
## Documentation
- [Setup Guide](SETUP.md) - Detailed installation instructions
- [Architecture Specification](continuo.markdown) - Complete technical documentation
- [Code of Conduct](CODE_OF_CONDUCT.md) - Community guidelines
## Technology Stack
- **Python 3.9+** - Core implementation
- **ChromaDB** - Vector database for embeddings
- **Sentence Transformers** - Local embedding generation (all-MiniLM-L6-v2)
- **FastMCP** - MCP server framework
- **Model Context Protocol** - IDE integration standard
## Cost & Licensing
### Embedding Providers
| Provider | Storage | Search | Monthly (1000 queries) |
|----------|---------|--------|------------------------|
| Local (sentence-transformers) | Free | Free | $0 |
| OpenAI embeddings | Free | ~$0.0001/query | ~$0.10 |
### Software License
| Use Case | License | Cost |
|----------|---------|------|
| Individual/Research | AGPL v3 | **Free** |
| Startup (<$1M, <10 employees) | AGPL v3 | **Free** |
| Non-profit/Education | AGPL v3 | **Free** |
| Commercial (≥$1M OR ≥10 employees) | Commercial | **From $2,500/year** |
See [COMMERCIAL_LICENSE.md](COMMERCIAL_LICENSE.md) for details.
## Contributing
Contributions are welcome! Please read [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
## License
Continuo Memory System is **dual-licensed**:
### 📖 Open Source (AGPL v3)
**FREE for:**
- ✅ Individual developers and researchers
- ✅ Non-profit organizations and educational institutions
- ✅ Companies with **<$1M revenue** AND **<10 employees**
- ✅ Development, testing, and evaluation
- ✅ Open source projects (AGPL-compatible)
**Requirements**: Share source code of modifications under AGPL v3
See [LICENSE](LICENSE) for full AGPL v3 terms.
### 💼 Commercial License
**REQUIRED for:**
- ❌ Companies with **≥$1M revenue** OR **≥10 employees**
- ❌ Proprietary/closed-source products
- ❌ SaaS offerings without source disclosure
**Benefits:**
- ✅ No AGPL copyleft obligations
- ✅ Proprietary use rights
- ✅ Priority support (optional)
- ✅ Custom deployment assistance (optional)
**Pricing**: From **$2,500/year** (Bronze) to custom Enterprise
See [COMMERCIAL_LICENSE.md](COMMERCIAL_LICENSE.md) for pricing and details.
### 💡 Why AGPL + Commercial?
- **Sustainable Development**: Commercial users fund ongoing maintenance
- **Open Source Protection**: AGPL prevents proprietary forks
- **Fair Use**: Small teams and non-profits use free indefinitely
- **Community First**: Core features always open source
**Contact**: gustavo@shigoto.me for commercial inquiries
## Acknowledgments
Built using:
- [Model Context Protocol](https://modelcontextprotocol.io) - Protocol specification
- [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk) - MCP implementation
- [ChromaDB](https://www.trychroma.com/) - Vector database
- [Sentence Transformers](https://www.sbert.net/) - Embedding models
## Authors
- **D.D. & Gustavo Porto**
---
**Note**: This project implements the architecture described in `continuo.markdown`. For academic context and detailed specifications, refer to that document.