Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mnemo remember that the auth service uses JWT tokens"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Autonomous bitemporal memory engine and AST-based knowledge graph for AI agents via Model Context Protocol (MCP). Zero external infrastructure, single-file SQLite storage.
Bitemporal Fact Store: Separates validity timeline (valid_time) from system recording time (ingest_time) using SQLite WAL mode.
3-Channel Hybrid Retrieval: Fuses dense vector embeddings, FTS5 full-text search, and recursive CTE graph traversal via Reciprocal Rank Fusion (RRF).
Ebbinghaus Memory Decay: Four dynamic tiers (Core, Working, Peripheral, Archived) prevent context bloat through piecewise exponential decay.
AST Knowledge Graph: Parses project syntax trees, tracks cross-file class/function relationships, and reconciles deleted modules automatically.
Zero Infrastructure: Operates locally inside a single embedded database file without requiring Docker, external vector databases, or cloud accounts.
Related MCP server: Menhir
Install the package directly from PyPI into your environment:
pip install mnemo-agentmcpOr run commands directly via uvx without global installation:
uvx --from mnemo-agentmcp mnemo --helpConnect Mnemo to your preferred AI environment (Cursor, Claude Desktop, Antigravity, Windsurf) by updating your client settings.
.cursor/mcp.json or mcp_config.json){
"mcpServers": {
"mnemo": {
"command": "python",
"args": ["-m", "mnemo.mcp.server"]
}
}
}uvx (Cross-Platform){
"mcpServers": {
"mnemo": {
"command": "uvx",
"args": ["--from", "mnemo-agentmcp", "mnemo", "serve"]
}
}
}Initialize the memory database in your project directory:
mnemo initAnalyze the repository topology and populate the initial knowledge graph:
mnemo scan .Generate an interactive dark-themed Sankey visualization of your memory tiers and graph connections:
mnemo visualizeVerify your environment dependencies and database health:
mnemo doctorAgents communicate with Mnemo over stdio JSON-RPC using these tools:
mnemo_remember: Classify and persist facts using AUDN operations (Add, Update, Delete, Noop).
mnemo_search: Retrieve facts using hybrid vector, lexical, and graph ranking.
mnemo_invalidate: Soft-retire facts by ending their validity window without losing history.
mnemo_reinforce: Boost fact salience upon confirmation or repeated access.
mnemo_inspect: View full metadata, source trace, and bitemporal intervals for any fact.
mnemo_get_debt_ledger: Audit active architectural tensions, deprecated items, and unresolved debt.
mnemo_scan_project: Trigger project-wide AST dependency re-indexing directly from the agent.
Component | Technology | Responsibility |
Storage Engine | SQLite (WAL mode) | ACID guarantees, bitemporal schema, and lightweight blob arrays |
Lexical Search | SQLite FTS5 | Sub-millisecond keyword matching across architectural decisions |
Vector Search | Cosine Similarity | Semantic retrieval and similarity clustering |
Graph Traversal | Recursive CTEs | Fast multi-hop entity dependency traversal |
Agent Interface | FastMCP (stdio) | Protocol-compliant JSON-RPC layer isolated on |
This project is licensed under the MIT License — see the LICENSE file for details.
This server cannot be deployed
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