| Conductance | Kannan, Vempala & Vetta, JACM 2004 |

sqlite-muninn

Odin's mythic raven of Memory.

This project aims to build agentic memory and knowledge graph primitives for sqlite as a native C extension and also made available for Python, Node.JS and WASM.

It is an advanced collection of knowledge graph primitives like Vector Similarity Search, HNSW Indexes, Graph database, Community Detection, Node2Vec capabilities and loading GGUF models via llama.cpp integration.

Huginn and Muninn fly each day over the wide world.
I fear for Huginn that he may not return,
yet I worry more for Muninn.

- Poetic Edda (Grimnismal, stanza 20)

Odin fears losing Memory more than Thought.

Huginn and Muninn are the two ravens of Odin and their names translate to Thoughts and Memory.

Documentation | GitHub

Package Index	Published Version	Downloads
PyPI
npm

Features

• HNSW Vector Index - O(log N) approximate nearest neighbor search with incremental insert/delete
• Graph Traversal - BFS, DFS, shortest path, connected components, PageRank on any edge table, dbt syntax graph node selection.
• llama.cpp native models; Load and use GGUF LLM models natively in sqlite.
• Centrality Measures - Degree, betweenness (Brandes), and closeness centrality with weighted/temporal support
• Community Detection - Leiden algorithm for discovering graph communities with modularity scoring, plus per-group conductance for scoring any partition
• Node2Vec - Learn structural node embeddings from graph topology, store in HNSW for similarity search
• Zero dependencies - compiles to a single .dylib/.so/.dll
• SIMD accelerated - ARM NEON and x86 SSE distance functions

Research References

License

MIT. See LICENSE.