6 docs tagged with "Agents"

A repeatable agent pattern — ontology first, query spec second, constrained execution, and failure recovery when labels or fields are wrong.

Ingest tickets, docs, decisions, incidents, and feature requests into a connected graph so your team can retrieve context instead of isolated documents.

Store goals, intermediate observations, tool outputs, and decisions as linked records so long-running agents can resume with context instead of stateless prompts.

Go beyond installation — learn the operator workflow for grounded, hallucination-resistant agent queries with the RushDB MCP server.

Model persistent facts, episodic interactions, and linked reference material as a graph so agents and applications can retrieve and reason over connected context.

End-to-end guide — connect RushDB to OpenClaw, install the RushDB skills pack, and let your AI assistant query, store, and model structured data without writing a single line of code.