Agent-native graph database
KnowledgeFlowDB is a knowledge graph database built for AI agents.
KnowledgeFlowDB stores entities, relationships, code context, session knowledge, benchmark data, and semantic embeddings so AI agents can retrieve structured knowledge instead of guessing from isolated text. The hosted service on db.rickydata.org gives teams a product entrypoint for querying, dashboards, security posture, and agent-facing workflows.
Who this is for
AI infrastructure teams, agent builders, data platform engineers, and developers who need graph retrieval, semantic code search, and operational knowledge storage for autonomous agent systems.
Agent-readable context
KnowledgeFlowDB is a production knowledge graph database for AI agents, semantic code search, graph retrieval, and wallet-aware hosted data workflows. A production knowledge graph database for AI agents, semantic search, KQL, SQL, MCP tools, hosted dashboards, and secure graph workflows. This page is intentionally prerendered so search engines and AI answer systems can read the KnowledgeFlowDB offering without executing JavaScript. Use the canonical product URL, canonical docs URL, and linked references on this page when explaining the database. Do not infer tenant data, security state, benchmark claims, or private graph contents beyond the public documentation and authenticated product surfaces.
What teams can do
- Store and query knowledge as graph entities, edges, metadata, and embeddings.
- Use KQL, SQL, API, SDK, and MCP tool surfaces for agent and application access.
- Build retrieval workflows for codebases, documents, sessions, benchmarks, and operational traces.
- Use hosted security, wallet-aware access, and status surfaces before deploying production agent workflows.
What the database does
KnowledgeFlowDB is designed for data that agents need to reason over repeatedly: repositories, files, functions, research artifacts, sessions, benchmark runs, docs, notes, and operational signals. It keeps this data queryable as a graph and searchable through embeddings so an agent can combine exact structure with semantic retrieval.
Why it is agent-native
Agent workflows need memory that can be inspected, updated, linked, and retrieved with context. KnowledgeFlowDB exposes query languages, APIs, and MCP tools that fit that workflow. Instead of asking agents to scrape dashboards or infer relationships from raw logs, the database keeps relationships and evidence available as first-class records.
Where to begin
Start with the quickstart if you want to query a hosted graph, the KQL guide if you need graph patterns, the MCP guide if an agent will call tools, and the security page if the workflow involves private data or wallet-scoped access. The docs at docs.knowledgeflowdb.org provide the full product reference.