Continuo Memory System
Persistent memory and hierarchical compression for development environments
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
git clone https://github.com/GtOkAi/continuo-memory-mcp-memory-mcp.git
cd continuo
./scripts/setup_memory.sh
Usage
- Start the memory server:
./scripts/run_memory_server.sh
- Configure your IDE (Qoder/Cursor):
Create .qoder/mcp.json (or .cursor/mcp.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"
]
}
}
}
- 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_memoryandstore_memorytools - Hierarchical Compression - Multi-level context optimization (N0/N1/N2)
- Autonomous Mode - Optional automation with Observe → Plan → Act → Reflect cycle
Configuration
Local Embeddings (Free)
python src/mcp/memory/mcp_memory_server.py \
--provider local \
--db-path ./memory_db
OpenAI Embeddings (Low-cost)
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/ directory:
basic_usage.py- Simple store/retrieve operationshierarchical_demo.py- Multi-level compression examplesauto_mode_demo.py- Autonomous mode demonstration
Documentation
- Setup Guide - Detailed installation instructions
- Architecture Specification - Complete technical documentation
- Code of Conduct - 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 for details.
Contributing
Contributions are welcome! Please read 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 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 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 - Protocol specification
- MCP Python SDK - MCP implementation
- ChromaDB - Vector database
- Sentence Transformers - 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.