得分
60
用戶評分
3.0
支援免費/試用
不支援
起始價格
--
最近更新
9月 11, 2026
概覽
HydraDB 是什麼？
HydraDB is an open-source, native graph database designed as an AI context infrastructure layer for agent memory, ontologies, company brains, GraphRAG, and connected application data. Built directly on object storage, it combines graph traversal, vector search integrations, exact-match search, temporal versioning, and structured relationships to help AI systems retrieve precise, relevant context. HydraDB provides multi-tenant storage, strongly consistent queries, tiered storage across memory, NVMe SSD, and object storage, sub-200ms latency, OpenCypher support, Neo4j-compatible Bolt connectivity, and HTTP APIs.
主要功能
- Object-storage-native graph database with tiered hot, warm, and cold storage
- Graph-native AI context retrieval combining relationships, temporal state, vector search, and exact matching
- Multi-tenant architecture with strongly consistent queries and sub-200ms latency
- OpenCypher, Neo4j-compatible Bolt, and HTTP API support
- Temporal versioning, entity resolution, preference-aware retrieval, and agent memory
- Connectors for workplace, email, CRM, document, and other business data sources
- Observability and trace dashboards for understanding why agents act
如何使用 HydraDB？
Sign up for HydraDB, install its SDK or connect through Neo4j-compatible Bolt and HTTP APIs, and ingest agent logs, documents, workplace data, user memories, and other knowledge sources. Build or query context graphs using OpenCypher, optionally combine graph traversal with vector search, BM25, reranking, and database filters, then provide the retrieved structured context to your AI agents.
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使用場景
- Build persistent memory systems for AI agents that retain user preferences, interactions, and traces
- Create company brains and enterprise knowledge graphs from documents, workplace applications, chats, and business data
- Develop GraphRAG systems that retrieve connected context instead of isolated similar documents
- Power customer support agents with relationship-aware and temporally accurate context
- Build AI coding assistants that understand repositories, issues, dependencies, and prior agent actions
- Create research intelligence systems that connect entities, events, sources, and evolving knowledge
- Implement agentic actions and context engineering workflows for workplace applications
用戶評價
3.0
滿分5分
3.0
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