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zanetworker
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kagenti-a2a-adapter

Generate A2A protocol adapters and Kagenti deployment manifests for Python agents.

Updated Mar 13, 2026
Search Tags:#kubernetes#langgraph#crewai#agent deployment#adapter#a2a#kagenti
One-click install
npx skills add https://github.com/zanetworker/kagenti-a2a-adapter --skill kagenti-a2a-adapter
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kagenti-a2a-adapter
Source: https://github.com/zanetworker/kagenti-a2a-adapter/tree/main
Command: npx skills add https://github.com/zanetworker/kagenti-a2a-adapter --skill kagenti-a2a-adapter

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and assets (resource) components.

What problem does it solve?

This Skill bridges the gap between existing AI agent code and the A2A protocol, enabling seamless deployment on Kagenti and other Kubernetes-native platforms. It automates the creation of necessary adapter code and deployment manifests.

Core Features & Use Cases

  • A2A Protocol Integration: Wraps your agent code (CrewAI, LangGraph, OpenAI SDK, custom Python) with A2A endpoints.
  • Kagenti Manifest Generation: Creates Kubernetes Deployment, AgentCard, and optional AgentRuntime configurations.
  • Use Case: You have a CrewAI agent that performs market research. Use this Skill to generate the FastAPI server, Dockerfile, and Kagenti Kubernetes manifests to deploy it as an A2A-compatible service.

Quick Start

Use the kagenti-a2a-adapter skill to make my CrewAI agent A2A-ready for Kagenti.

Frequently Asked Questions about kagenti-a2a-adapter

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

FAQPage Schema
How do I deploy a CrewAI or LangGraph agent to Kubernetes using the A2A protocol?▼

To deploy CrewAI or LangGraph agents using the A2A protocol, you generate A2A protocol adapters and Kagenti deployment manifests. This wraps your agent code with A2A endpoints and automates the creation of FastAPI servers, Dockerfiles, and Kubernetes configurations.

What is an A2A protocol adapter and when do I need one for AI agent deployment?▼

An A2A protocol adapter bridges existing AI agent code and the A2A protocol, enabling deployment on Kagenti and Kubernetes-native platforms. You need one to wrap custom Python agents with A2A endpoints for deployment.

Does the Kagenti A2A adapter work with OpenAI Agents SDK and custom Python agents?▼

Yes, the Kagenti A2A adapter supports OpenAI Agents SDK and custom Python agents. It generates A2A endpoints and Kagenti deployment manifests for existing agent code across CrewAI, LangGraph, OpenAI Agents SDK, and custom Python implementations.

What Kubernetes manifests are generated for A2A-compatible agent deployment on Kagenti?▼

The adapter generates Kubernetes Deployment manifests, AgentCard configurations, and optional AgentRuntime configurations for A2A-compatible deployment on Kagenti. These manifests are created alongside FastAPI servers and Dockerfiles to containerize your AI agents.

Can I use this skill to create a FastAPI server and Dockerfile for an existing AI agent?▼

Yes, you can use this skill to create a FastAPI server and Dockerfile for an existing AI agent. It automates the creation of FastAPI servers and Dockerfiles to wrap your agent code with A2A endpoints for Kubernetes deployment on Kagenti.

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