create-agent-graph

Build multi-agent workflow graphs with SimpleAgentGraphBuilder and ConditionalAgentGraphBuilder.

1|Updated Jul 28, 2025

Search Tags:#workflow #multi-agent #orchestration #kotlin #conditional routing #agent graph

Ready to run

npx skills add https://github.com/anoopg87/KAgentic --skill create-agent-graph

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: create-agent-graph
Source: https://github.com/anoopg87/KAgentic/tree/main/.claude/skills/create-agent-graph
Command: npx skills add https://github.com/anoopg87/KAgentic --skill create-agent-graph

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Designing and managing complex multi-agent workflows is difficult, requiring manual orchestration of sequential and conditional steps. This Skill simplifies building such pipelines by providing clear guidance and code patterns for agent graphs in KAgentic.

Core Features & Use Cases

Graph Types: Supports SimpleAgentGraph for linear pipelines and ConditionalAgentGraph for dynamic branching.
Builder APIs: Provides SimpleAgentGraphBuilder and ConditionalAgentGraphBuilder to construct graphs programmatically.
Best Practices: Includes patterns for error handling, retries, fan‑out/fan‑in, and validation.
Use Cases: Data processing pipelines, customer support routing, content creation workflows, and multi‑stage approval processes.

Quick Start

Ask the skill to build a sequential sales analysis pipeline using three specialized agents.

Frequently Asked Questions about create-agent-graph

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a multi-agent workflow with conditional routing in Kotlin?▼

Build multi-agent workflows with conditional routing in Kotlin using the ConditionalAgentGraphBuilder class, which enables dynamic branching and decision-making. It supports programmatic graph construction for deterministic execution across complex AI pipelines.

What is the best way to orchestrate sequential multi-agent pipelines in Kotlin?▼

The best way to orchestrate sequential multi-agent pipelines is using the SimpleAgentGraphBuilder class to construct linear execution graphs. This approach handles deterministic data processing pipelines, content creation workflows, and multi-stage approval processes.

Can I implement fan-out and fan-in patterns for dynamic decision-making in agent graphs?▼

Yes, you can implement fan-out and fan-in patterns for dynamic decision-making using the ConditionalAgentGraphBuilder class. It provides specific code patterns for conditional branching, validation, error handling, and retries within complex agent workflows.

Does this multi-agent orchestration approach support customer support routing workflows?▼

Yes, this multi-agent orchestration approach supports customer support routing workflows through the ConditionalAgentGraphBuilder class. It enables dynamic branching to route tasks across specialized agents based on conditional logic and validation.

What are the limitations when building agent graphs for complex AI applications?▼

A key limitation is that agent graphs require manual orchestration design for complex sequential and conditional steps. While builder APIs provide error handling and retry patterns, graph execution remains deterministic, limiting highly unpredictable dynamic routing scenarios.

Why use KAgentic for multi-agent workflow orchestration instead of manual orchestration?▼

Use KAgentic for multi-agent workflow orchestration to simplify the difficult manual orchestration of sequential and conditional steps. It provides clear guidance, builder APIs, and best practice patterns for error handling, retries, and validation in agent graphs.