The Digital Backbone
for AI Agents
Multi-model fusion data engine for AI applications. SQL·KV·TimeSeries·MQ·Vector·FTS·GEO·Graph + AI abstraction, 8+1 engines in one.
8+1 Engines, 1 Shared Storage.
Zero Latency.
A unified architecture replaces a fragmented data stack. Connect your agents to everything with zero overhead.
SQL Engine
ACID transactions, PK lookup <0.02ms, batch insert 165K rows/s. JOIN/CTE/window functions.
SELECT * FROM users
WHERE vector_similarity(embedding, ?) > 0.9
AND last_active > NOW() - INTERVAL '5m';
Vector Search
Custom HNSW index, KNN search P95=0.2ms, SQ8 quantization & metadata filtering.
Key-Value Store
GET Throughput
Time Series
Write Throughput
Geospatial
Geohash-based spatial indexing, Redis GEO command compatible.
AI Engine
Native Session/Context/Memory/RAG/Agent/Trace + Hybrid Recall Pipeline (BM25+Vec+Temporal+Rerank+Graph), Auto-Embedding, Auto-Summarize.
Message Queue
1.6M msg/s
Graph Engine
935K reads/s
Full-Text Search
BM25 scoring + inverted index with Chinese tokenizer & hybrid search.
Simplify Through Unification
Stop managing complex infrastructure glue code.
warning Traditional Stack (Fragmented)
check_circle Talon Stack (Unified)
Built for AI-Native
Core primitives for the modern AI stack.
Long-Term Memory
Persist agent state effortlessly. Auto-embed memories via configured LLM Provider. Semantic search with automatic query embedding.
Session Management
Handle millions of concurrent agent sessions with sub-millisecond latency. Smart context window with auto-summarize for long conversations.
Hybrid RAG
Combine keyword search and vector semantic search for the most accurate retrieval-augmented generation results.