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Neo4j-based organizational knowledge graph storing resources, relationships, and execution history.

Quick Start

Output:
Output:

Graph Statistics

Get Overall Statistics

Nodes

List Nodes

Get Node Details

Get Node Relationships

Search Nodes

Integrations

List all integrated data sources that contribute to the knowledge graph:

Custom Queries

Run custom Cypher queries to analyze your organizational knowledge graph:

Schema Discovery

List Labels

Output:

List Relationship Types

Output:

Subgraph Exploration

Extract a subgraph around a specific node:
AI-powered natural language search for the context graph using Claude:
Output:
Available Features:
  • AI-powered: Claude-based agent with specialized graph tools
  • Flexible: Configurable model, temperature, and search parameters
  • Smart tools: 10 graph operations (property search, relationships, subgraphs, etc.)
  • Streaming: Real-time progress updates (default: enabled)
  • Session continuity: Continue conversations with --session flag
Optional Flags:
  • --stream - Enable real-time streaming (default: true)
  • --model - LiteLLM model name (default: kubiya/claude-sonnet-4)
  • --temperature - Model temperature 0.0-2.0 (default: 0.7)
  • --max-turns - Maximum conversation turns 1-20 (default: 5)
  • --integration - Filter by integration (AWS, Azure, Slack, etc.)
  • --label - Filter by node label
  • --semantic - Enable semantic search
  • --cypher - Enable custom Cypher queries
  • --output - Output format (text, json)
Use intelligent search for exploratory queries. For structured data retrieval, use kubiya graph nodes commands or custom Cypher queries.

Best Practices

  • Use graph queries for resource discovery and pattern analysis
  • Monitor agent activity and execution success rates
  • Optimize graph queries with indexes and limits
  • Archive old execution data periodically
  • Implement regular Neo4j backups
  • Use intelligent search for exploration, structured queries for automation
  • Filter queries by integration or label for performance
  • Set appropriate depth limits for subgraph queries

Command Reference

Next Steps

Cognitive Memory

Store and recall contextual knowledge using semantic search

Core Resources

Create and manage agents, teams, and projects

On-Demand Execution

Execute tasks with intelligent planning

Capabilities

Configure skills, models, and policies