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Welcome to KnowledgeFlowDB

KnowledgeFlowDB is a next-generation distributed knowledge graph database written in Rust, optimized for AI agentic systems.

Try it now!

Want to get started immediately? Check out our Hosted Service for a fully managed database instance, or follow the quickstart below to self-host.

Status: Production-Ready 🚀​

  • 264 tests passing (100%)
  • Zero warnings across workspace
  • Performance: Up to 13.4x faster than targets
  • 5 production crates: core, storage, graph, vector, query
  • 3 storage backends: Memory, RocksDB, ScyllaDB
  • Horizontally scalable: Linear scaling with cluster nodes

Quick Start​

Get started with KnowledgeFlowDB in just a few commands:

# Clone the repository
git clone https://github.com/cambriannetwork/knowledgeflow_db.git
cd knowledgeflow_db

# Start the interactive CLI
cargo run -p kfdb

# Run a query
cargo run -p kfdb -- query "MATCH (f:File) RETURN f.path, f.size"

Key Features​

🚀 High Performance​

  • Storage: 45ns (memory), 8µs (RocksDB), 2ms (ScyllaDB)
  • Graph: BFS/DFS in <10µs for 1000 nodes
  • Vector Search: 13.4x faster than targets (<0.4ms for 10K vectors)

📈 Horizontal Scaling​

Throughput = 100,000 QPS × Number of Nodes
Storage = 500 GB × Number of Nodes

🔍 Powerful Query Language (KQL)​

MATCH (f:File)-[r:DEFINES]->(fn:Function)
WHERE fn.visibility = 'public'
RETURN f.path, fn.name
ORDER BY fn.name
LIMIT 10

🧠 AI-Native Features​

  • Dual embeddings (content + context)
  • Semantic code search
  • Session tracking
  • Self-improvement mechanisms

Architecture​

KnowledgeFlowDB is built with a layered architecture:

  1. kfdb-core: Core types and traits (NodeId, EdgeId, Value, Embedding)
  2. kfdb-storage: Storage engines (Memory, RocksDB, ScyllaDB)
  3. kfdb-graph: Graph storage and traversal algorithms
  4. kfdb-vector: HNSW vector search index
  5. kfdb-query: KQL parser and query executor
  6. kfdb: CLI tool for interactive queries

Next Steps​

Community​

License​

KnowledgeFlowDB is dual-licensed under MIT OR Apache-2.0.