Hypabase MCP Server

@hypabase

Basic information

About Hypabase MCP Server

Hypergraph database for AI agents. Store n-ary relationships (edges connecting 2+ nodes), track provenance (source and confidence), and query paths — all backed by local

#database #knowledge-graph #rag #sqlite#hypergraph#agent-memory#hyperrag#cog-rag

Hypabase MCP Server config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

Repository

Hypabase MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Hypabase?

Hypabase is a Python hypergraph library with provenance tracking and SQLite persistence. It provides an MCP server (14 tools + 2 resources), a CLI, and a Python API for storing and querying n‑ary relationships (hyperedges) where an edge can connect any number of nodes. It is intended for knowledge graphs, agent memory, and data integration scenarios where provenance (source and confidence) matters.

How to use Hypabase?

Install via uv add hypabase (or uv add hypabase[mcp] for the MCP server, uv add hypabase[cli] for CLI). Use the Python API to create a Hypabase("file.db") instance, then call .edge(), .edges(), .paths(), .node(), .nodes(), .sources(), etc. Run the MCP server with hypabase mcp. CLI commands include hypabase init, hypabase node, hypabase edge, hypabase query, and hypabase stats.

Key features of Hypabase

Hyperedges connecting 2+ nodes in a single relationship
Provenance tracking: every edge carries source and confidence
SQLite persistence to a local file automatically
O(1) vertex‑set lookup for exact node sets
Namespace isolation via .database("name") within a single file
MCP server with 14 tools and 2 resources for AI agents
CLI for initialisation, nodes, edges, and queries

Use cases of Hypabase

Representing complex real‑world relationships (e.g., medical events, academic papers) without decomposition
Structured, queryable memory for AI agents that persists across sessions
Storing extracted relationships for retrieval‑augmented generation (RAG)
Biomedical data modeling (drug interactions, clinical events, molecular pathways)
Supply chain and collaboration networks where relationships involve more than two entities

Frequently asked questions

What is a hypergraph and how does Hypabase differ from a regular graph database?

In a regular graph each edge connects exactly two nodes. Hypabase stores hypertedges that connect any number of nodes natively, preserving grouping without splitting into binary edges. It also tracks provenance (source and confidence) on every edge.

What are the runtime dependencies and installation requirements?

Hypabase requires Python 3.x. Install via pip (uv) – the base package pulls in SQLite (stdlib). For MCP capabilities add the `mcp` extra; for CLI add the `cli` extra.

Where does Hypabase store data?

Data is stored in a local SQLite database file specified when creating a `Hypabase` instance (e.g., `Hypabase("my.db")`). No external database server is needed.

What transport and authentication does the MCP server use?

The MCP server uses stdio transport (no network port) by default. There is no built‑in authentication; it is designed for local or trusted environments (e.g., Claude Desktop, Cursor, Windsurf).

What are the known limitations of Hypabase?

The README does not list explicit limitations. The library is a Python package and inherits SQLite single‑writer concurrency; very large graphs may require optimisation or sharding.