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Plearn-agentic-ai
Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kubernetes.
Mmirrord
Run any process, on your machine or in an AI agent's environment, as if it were a pod in your Kubernetes cluster: real env vars, DNS, network, traffic.
Popularity
Repository stars4,3945,401Highest
7-day net stars——
Fork1,006Highest226
30-day trendInsufficient historyInsufficient history
Maintenance
Last push2025-10-262026-11-03
Maintenance notesNot verifiedNot verified
Repository created2024-06-122022-02-01
License & use
LicenseMIT · PermissiveMIT · Permissive
PricingPricing not verifiedPricing not verified
Basics
Browse by purposeKubernetes operationsAgent skill extensions · Command-line tools · Coding assistants · Kubernetes operations
TypeFrameworks & SDKsAgents
SubcategoryAgent frameworksCoding agents
PlatformNot verifiedNot verified
Cost detailsCheck current official software, hosting, model and infrastructure pricing.Check current official software, hosting, model and infrastructure pricing.
Pricing source——
First collected2026-11-032026-11-03
MetricRepository stars, not unique users or individual-skill installsRepository stars, not unique users or individual-skill installs
LanguageJupyter NotebookRust
About this projectLearn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kubernetes.Run any process, on your machine or in an AI agent's environment, as if it were a pod in your Kubernetes cluster: real env vars, DNS, network, traffic.
Measured at2026-11-03 09:37 UTC2026-11-03 08:24 UTC
License tiers are derived from metadata. Check obligations for your use; unknown does not mean prohibited. Ranked by repository stars, one position per repository. Stars and forks are not user counts.