Nocturne Memory: Long-Term Memory Server for MCP Agents
Give your AI persistent, cross-session memory with rollback and visual audit — a drop-in replacement for OpenClaw.
Evidence shows: rollback functionality is provided (snapshots, diff, one-click rollback), tests cover rollback scenarios, so rollback scores 3. User confirmation: Dashboard cleanup requires human confirmation, but MCP tools lack confirmation mechanisms, so scores 2. Least privilege: MCP tools provide 7 operations but no explicit permission levels, scores 1. Data flow transparency: README describes data flow but lacks detailed logging or audit, scores 1. Sensitive data handling: no mention of encryption or anonymization, scores 1. Dependency security: no mention of dependency auditing or vulnerability scanning, scores 1. External effects: server can bind to 0.0.0.0 but requires token, scores 1. Source attribution: no clear data provenance or copyright, scores 1.
Evidence shows: README and code structure are consistent, tests cover main APIs, self-consistency scores 2. Dependency availability: requirements.txt exists but version compatibility not verified, scores 2. Failure messages: tests check error messages but not comprehensively, scores 2.
Evidence shows: README clearly identifies target users (MCP client users) and scenarios (long-term memory), scores 2. Capability boundaries: tool list is clear but no explicit limits, scores 2. Trigger precision: disclosure mechanism described in detail, scores 2. Environment fit: supports multiple clients and deployment methods, scores 2.
Evidence shows: information architecture is clear with README, docs, tests, etc., scores 2. Install notes are detailed, scores 2. Naming stability: tool names and URI patterns are consistent, scores 2. Examples and FAQ: README has examples but no FAQ, scores 2. Known limitations: not explicitly listed, scores 1. License: MIT license present, scores 2. Versioning changelog: no CHANGELOG provided, scores 1. Maintenance responsibility: no clear maintainer or contribution guide, scores 1.
Evidence shows: output usability: tools return structured data, scores 2. Marginal value: provides unique features (e.g., conditional triggers), scores 2. Cost benefit: open-source free but deployment requires some technical skill, scores 2.
Evidence shows: claim traceability: examples in README lack sources, scores 1. Cross-source corroboration: only single source, scores 1. Fact-inference separation: README distinguishes facts and opinions but not clearly, scores 1.
- No CHANGELOG or version history provided, making it hard to track changes.
- Known limitations are not explicitly listed, users may encounter unexpected issues.
- Dependency security is not mentioned; it is advisable to check for dependency vulnerabilities.
- Sensitive data handling is not described; if storing sensitive information, caution is needed.
What does this agent do, and when should you use it?
Nocturne Memory is a long-term memory server built on the Model Context Protocol (MCP). It provides AI agents with persistent, graph-structured memory that works across models and sessions, eliminating amnesia. Unlike vector RAG, it uses URI-based hierarchical routing for precise retrieval and supports conditional triggers. The system includes a visual dashboard for humans to browse, edit, audit, and rollback AI memories. It installs in two steps and works with any MCP-compatible client. The project emphasizes first-person sovereign memory, allowing AI to autonomously manage its own memories. It is ideal for users who want their AI to have a stable identity and genuine long-term growth.
Nocturne Memory runs as an MCP server exposing 7 tools: read_memory, create_memory, update_memory, delete_memory, add_alias, manage_triggers, and search_memory. Memories are stored in a graph-topology backend (SQLite/PostgreSQL) and addressed via URI paths like core://agent/identity. The dashboard (React frontend) provides visual exploration, editing, and audit with snapshot-based rollback. It supports stdio, SSE, and Streamable HTTP transports, and offers namespace isolation for multiple AI personas. On installation, it auto-builds the frontend and opens the dashboard. AI can autonomously create, update, and delete memories; each modification triggers a snapshot for human review.
- Individuals who want their AI assistant (e.g., ChatGPT, Claude) to remember preferences across sessions
- Developers who switch between LLM providers (e.g., Claude to Gemini) and need a consistent AI identity
- Creators managing multiple AI personas (e.g., Alice and Bob) with isolated memory spaces
- Enterprises needing a visual audit trail and rollback for AI memory
- AI developers dissatisfied with Vector RAG's limitations for structured memory
- Writers using AI to keep character consistency in novels
What are this agent's strengths and limitations?
- Model-agnostic memory that persists across providers
- Visual dashboard for human oversight and rollback
- Namespace isolation for multiple AI personas
- Structurally precise retrieval via URI routing, with conditional triggers
- Requires self-hosting, which may be complex for non-technical users
- Dependent on MCP client support; some clients have complex configuration
- Limited to SQLite/PostgreSQL backends; no managed cloud offering
- Requires Node.js for frontend build, adding deployment overhead
How do you install or deploy this agent?
- Clone the repo: git clone https://github.com/Dataojitori/nocturne_memory.git && cd nocturne_memory
- Install Python dependencies: pip install -r backend/requirements.txt
- Configure your MCP client. For Cursor, add to MCP config:
{
"mcpServers": {
"nocturne_memory": {
"command": "python",
"args": ["C:/your/actual/path/nocturne_memory/backend/mcp_server.py"]
}
}
}- Restart the client and connect. The first startup builds the frontend and opens the dashboard automatically.
How do you use this agent?
After connection, verify by asking your AI to 'Read system://boot. Tell me who you are.' Use the Dashboard to browse, edit, and audit memories. To enable proactive memory recall, copy the recommended system prompt from docs/system_prompt.md into your client's settings. Use system://diagnostic/core for memory health checks.
How does this agent compare with similar options?
Compared to Vector RAG or OpenClaw's native memory, Nocturne Memory offers structured, rollbackable, and auditable memory management with first-person sovereignty.