Evangstav Python Memory MCP Server

The Evangstav Python Memory MCP Server turns a local SQLite database into an AI‑ready memory store that Claude and other Model Context Protocol (MCP) clients can query, update, and reason over. It solves the common pain point of persisting conversational context beyond a single session: developers can now offload long‑term knowledge, facts, and relationships to a structured graph while still enjoying the speed of in‑memory access through MCP. By exposing a lightweight HTTP interface that follows the MCP specification, the server becomes a drop‑in component in any Claude‑based workflow, allowing assistants to reference external knowledge without compromising privacy or latency.

What the Server Does

At its core, the server implements a knowledge graph manager that maps entities and edges into a SQLite schema. It exposes MCP endpoints for the standard operations—reading, writing, and querying nodes and relationships—while also providing batch‑optimized routes for bulk updates. The graph can be queried using simple key/value lookups or more expressive traversal queries, enabling the assistant to answer fact‑based questions, maintain state across sessions, or perform inference over connected data. The server’s design keeps the graph entirely local, eliminating external dependencies and making it ideal for on‑premise or offline use cases.

Key Features & Capabilities

• Persistent Knowledge Graph: Stores entities, attributes, and relationships in a durable SQLite database that survives restarts.
• Batch Operations: Optimized endpoints allow bulk insertion or deletion of nodes and edges, reducing round‑trip overhead for large data imports.
• MCP Compliance: Implements the full MCP protocol, ensuring seamless integration with Claude Desktop and other MCP‑aware clients.
• Environment‑Driven Configuration: A single variable points the server to any SQLite instance, making deployment flexible.
• Test‑Driven Development: A comprehensive pytest suite guarantees reliability across graph operations and server behaviour.

Real‑World Use Cases

• Personal Knowledge Management: Store notes, contacts, and project details that the assistant can recall during conversations.
• Domain‑Specific Expertise: Load industry data (e.g., medical ontologies, legal statutes) into the graph so Claude can reference up‑to‑date facts.
• Multi‑Session Dialogue: Persist user preferences, past interactions, and evolving context so each new session feels continuous.
• Compliance & Privacy: Keep all data on‑premise, ensuring sensitive information never leaves the local environment.

Integration Into AI Workflows

Developers add the server to their Claude Desktop configuration by specifying a command that launches the MCP process. Once running, any MCP‑enabled client can invoke graph queries through standard JSON payloads. The server’s lightweight nature means it can be spun up on demand, integrated into CI pipelines for testing conversational agents, or embedded within larger microservice architectures. Because the graph is local, latency stays low, and developers can instrument logging or monitoring around the MCP calls without affecting user experience.

Unique Advantages

The standout benefit of this server is its balance between persistence and speed. Unlike in‑memory caches, the graph survives across restarts; unlike remote APIs, it introduces no network hop. Its pure Python implementation keeps dependencies minimal, and the use of SQLite ensures compatibility across operating systems. For teams building AI assistants that need reliable long‑term memory without external cloud services, the Evangstav Python Memory MCP Server offers a pragmatic, standards‑compliant solution.