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---
name: "Build planner-driven agent workflows with Microsoft Semantic Kernel"
slug: "build-planner-driven-agent-workflows-with-microsoft-semantic-kernel"
description: "Compose prompts, plugins, memory, planners, and connectors into repeatable Python, .NET, or Java agent workflows with Microsoft Semantic Kernel."
github_stars: 27861
verification: "security_reviewed"
source: "https://github.com/microsoft/semantic-kernel"
author: "Microsoft"
publisher_type: "organization"
category: "Developer Tools"
framework: "Multi-Framework"
tool_ecosystem:
github_repo: "microsoft/semantic-kernel"
github_stars: 27861
---
# Build planner-driven agent workflows with Microsoft Semantic Kernel
Compose prompts, plugins, memory, planners, and connectors into repeatable Python, .NET, or Java agent workflows with Microsoft Semantic Kernel.
## Prerequisites
Microsoft Semantic Kernel; Python 3.10+, .NET 10.0+, or Java 17+ depending on runtime; LLM provider credentials as required by the chosen connector
## Installation
Use the upstream install or setup path that matches your environment:
- pip install semantic-kernel
Requirements and caveats from upstream:
- [](https://pypi.org/project/semantic-kernel/)
- **Python**: 3.10+
- ### Python
Basic usage or getting-started notes:
- **Local Deployment**: Run with [Ollama](https://ollama.com/), [LMStudio](https://lmstudio.ai/), or [ONNX](https://onnx.ai/)
- First, set the environment variable for your AI Services:
- **Azure OpenAI:**
- Source: https://github.com/microsoft/semantic-kernel
- Extracted from upstream docs: https://raw.githubusercontent.com/microsoft/semantic-kernel/HEAD/README.md
## Documentation
- https://learn.microsoft.com/semantic-kernel/
## Source
- [Agent Skill Exchange](https://agentskillexchange.com/skills/build-planner-driven-agent-workflows-with-microsoft-semantic-kernel/)