NornicDB (orneryd/NornicDB) is an open-source AI project on GitHub. Repository summary: Nornicdb is a distributed low-latency, Graph+Vector, Temporal MVCC with all sub-ms HNSW search, graph traversal, and writes. Using Neo4j Bolt/Cypher and qdrant's gRPC means you can switch with no changes. Then, adding intelligent features like schemas, managed embeddings, LLM reranking+inferrence, GPU accel, Auto-TLP, Memory Decay, and MCP server. Its focus includes MCP and tool-calling integration, retrieval-augmented generation. It is suitable for extension, integration, and iterative delivery in real workflows.
License
MIT
Stars
727
Features
- Core capability: Nornicdb is a distributed low-latency, Graph+Vector, Temporal MVCC with all sub-ms HNSW search, graph traversal, and writes. Using Neo4j Bolt/Cypher and qdrant's gRPC means you can switch with no changes. Then, adding intelligent features like schemas, managed embeddings, LLM reranking+inferrence, GPU accel, Auto-TLP, Memory Decay, and MCP server.
- Provides MCP or tool-calling integration
- Supports vector retrieval and retrieval-augmented reasoning
- Repository: orneryd/NornicDB
- Primary language: Go
- Open-source license: MIT
Use Cases
- Connects external systems into agent workflows
- Builds enterprise knowledge Q&A and document retrieval systems
- Build internal AI workflow prototypes with NornicDB
- Validate NornicDB in production-like engineering scenarios
- Building AI development workflows
- Automating agent-based processes
FAQ
Teams should first define integration boundaries and call patterns, then map repository capabilities into concrete interfaces, parameters, and access rules. GitHub repository: https://github.com/orneryd/NornicDB. Community traction is around 725 stars. License: MIT.
It usually works as an execution component or capability layer, with common deployment fits such as: Connects external systems into agent workflows, Builds enterprise knowledge Q&A and document retrieval systems, Build internal AI workflow prototypes with NornicDB.
