LangGraph vs Semantic Kernel: Which One for Side Projects?

March 23, 2026

Alright, so you’re working on a side project, probably juggling APIs, integrations, or building some AI-powered mojo. You stumble upon two popular frameworks: LangGraph and Semantic Kernel. Both promise to simplify working with large language models and AI agents, but which one is actually better for your side gigs? I’ve been building, breaking, and messing with both for a while now, so here’s my take on langgraph vs semantic kernel.

Setting the Stage: What Are LangGraph and Semantic Kernel?

Quickly, before we get into the nitty-gritty, here’s what you’re dealing with:

• LangGraph: This is a Python-first toolkit focused on designing and executing AI workflows as graph structures. It’s especially handy if you want to design modular language product pipelines, chaining models, tools, and human inputs without fighting glue code.
• Semantic Kernel: Created by Microsoft, this SDK emphasizes .NET-first clients (but it’s expanding) to build AI-driven apps using plug-and-play AI skills and memory. It’s tailored to build “copilot” style apps, integrating models with contextual memory and programmable skills.

So through that lens, LangGraph feels a bit more experimental and dataflow-driven, while Semantic Kernel is designed for building AI “apps” or agents with a focus on skills and memory.

LangGraph vs Semantic Kernel: Head-to-Head Comparison Table

Feature	LangGraph	Semantic Kernel
Primary Language	Python	.NET (C#), Python support evolving
Model Support	Any LLM with API access (OpenAI, HuggingFace etc.)	OpenAI, Azure OpenAI, and more with plug-ins
Workflow Style	Graph-based, modular pipelines	Skill-based, memory-augmented agent design
Memory Management	Custom nodes required; less opinionated	Built-in semantic memory (vector stores, chat history)
Ease of Use for Side Projects	Lightweight & flexible; low setup overhead	Requires more initial setup but structured
Extensibility	Easily add new nodes and dataflow patterns	Rich skill ecosystem; pluggable connectors
Community & Ecosystem	Growing, mostly Python AI enthusiasts	Strong backing by Microsoft; enterprise ready
Documentation & Learning Curve	Concise docs; friendly to Python devs	thorough docs but steeper learning curve

Code Examples: LangGraph and Semantic Kernel Side By Side

LangGraph Example: Simple Chatbot Pipeline

from langgraph import Graph, Node
from langgraph.llms import OpenAI

# Initialize LLM node
llm_node = Node(OpenAI(api_key="YOUR_OPENAI_KEY"), name="SimpleGPT")

# A node to process the user prompt
def preprocess(prompt: str) -> str:
 return prompt.strip() + " Please answer conversationally."

preprocess_node = Node(preprocess, name="PrepPrompt")

# Build the graph
g = Graph()
g.add_nodes([preprocess_node, llm_node])
g.add_edge(preprocess_node, llm_node)

# Run
input_prompt = "What's the weather today?"
result = g.run(input_prompt)
print(f"LangGraph response: {result}")

This example shows how LangGraph lets you build a simple processing flow chaining a preprocessing node to an LLM node. It’s very minimal and lets you control every step explicitly.

Semantic Kernel Example: Simple Chatbot with Memory

// Install Microsoft.SemanticKernel package first

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Memory;
using Microsoft.SemanticKernel.Orchestration;
using Microsoft.SemanticKernel.Connectors.AI.OpenAI;

var kernel = Kernel.Builder.Build();

kernel.Config.AddOpenAITextCompletionService("default", "YOUR_OPENAI_KEY");

var memory = new VolatileMemoryStore();
kernel.Memory.RegisterMemory(memory);

var promptTemplate = @"{{$input}}
Answer conversationally.";

var chatFunction = kernel.CreateSemanticFunction(promptTemplate);

var context = kernel.CreateNewContext();
context["input"] = "What's the weather today?";

var result = await chatFunction.InvokeAsync(context);

Console.WriteLine($"Semantic Kernel response: {result.Result}");

Semantic Kernel’s C# API emphasizes memory and structured skills. You get integrated memory, stateful contexts, and skill-based functions, which is great if you want more app-like control over AI responses.

Performance & Practical Considerations

Honestly, the performance difference between LangGraph and Semantic Kernel mostly depends on the model providers (OpenAI or others) and your API usage patterns, but a few points to consider:

• Startup & Dev Cycle: LangGraph starts faster. Since it’s pure Python and lightweight, you don’t have the .NET runtime overhead. For quick prototyping, LangGraph feels snappier.
• Execution Efficiency: Both frameworks incur roughly the same LLM API latency. Semantic Kernel’s memory and skill orchestration add some overhead, but negligible unless you’re running complex multi-hop chains.
• Scalability: Semantic Kernel’s architecture fits better for scaling AI “bots” with managed skills and memory in production-level apps. LangGraph is excellent for experimental workflows or data pipelines but lacks some operational bells and whistles out of the box.
• Memory Handling: If your side project needs to remember user context across sessions or documents, Semantic Kernel offers built-in semantic memory support. You can replicate this in LangGraph but with more plumbing.

In my side project tests, LangGraph projects booted and iterated faster, while Semantic Kernel felt smoother once the skillset was defined and memory used. The choice depends heavily on what you want to build.

Migration Guide: Moving Your Project From One to the Other

If you’re starting with LangGraph but itching for more app-like memory and skill orchestration, or if you have a Semantic Kernel prototype that feels heavyweight, migrating between the two is worth considering. Here’s a rough roadmap for both directions.

From LangGraph to Semantic Kernel

1. Re-structure Your Pipeline into Skills: Semantic Kernel organizes logic into “skills” (units of semantic functions). Identify workflow steps in LangGraph nodes and convert them into skill methods.
2. Integrate Semantic Memory: Replace ephemeral state or stateless nodes with Kernel’s memory. You can use the built-in vector stores or plug into your preferred database for persistent memory.
3. Adopt Skills SDK: Use semantic functions instead of opaque node processing functions. This means defining prompts as templates and invoking them with context.
4. Rebuild Orchestration: Use the Kernel’s orchestration to chain skills and memory rather than explicit graph edges.

From Semantic Kernel to LangGraph

1. Extract Skills Into Nodes: Decompose your skill methods or semantic functions into independent functions or callable Python classes.
2. Recreate Workflows As Graphs: Map your orchestration sequence into LangGraph nodes and edges. This offers more explicit control than the built-in skill chaining.
3. Implement Memory Yourself: Since LangGraph doesn’t have native memory, you’d implement your own context or state tracking, possibly calling external vector databases manually.
4. Simplify Where Possible: LangGraph lends well to simple experiments. Cut down on enterprise features or advanced orchestration for faster prototyping.

FAQ: Clearing Up Confusions for Side Project Devs

Q: Can I use Semantic Kernel with Python?

Yes, there is growing Python support in Semantic Kernel, but the ecosystem is more mature in .NET/C#. If you’re a full-time Python dev, LangGraph feels more natural.

Q: Which is easier to learn quickly?

LangGraph wins the speed-to-prototype race simply because it’s Pythonic, minimal, and less opinionated. Semantic Kernel requires understanding its memory and skill abstractions first.

Q: Which one has better community support?

Semantic Kernel benefits from Microsoft’s backing and has lively discussions on GitHub and forums, but LangGraph is growing fast in the AI/ML Python space. So for side projects, both have good but differing support channels.

Q: Can I mix both in the same project?

Technically, yes, especially if you separate concerns—LangGraph can handle dataflow-heavy parts while Semantic Kernel manages memory or skill-heavy components. Expect some integration effort.

Q: Are both production-ready?

Semantic Kernel is more geared toward production and enterprise AI apps, thanks to built-in resiliency and memory. LangGraph is more experimental and ideal for research, prototypes, and casual tinkering.

Final Thoughts

Here’s the deal: for side projects focused on quick iterations, experimenting with AI workflows, and minimal friction, LangGraph is better. It puts you in the driver’s seat with graph-based chaining without a lot of ceremony.

However, if you want your side project to feel more like an AI assistant with memory, skills, and some thoughtful statefulness, Semantic Kernel is the better pick. It’s a bit heavier weight up front but pays off if your app needs to remember and act over longer sessions.

Personally, I gravitate toward LangGraph when prototyping small utilities or data pipelines and switch to Semantic Kernel when I want more structured apps or richer AI context. You’ll want to pick based on how deep your project’s AI logic needs to be and your language comfort zone.

Before you jump in, check out their official docs:

• LangGraph Documentation
• Semantic Kernel Official Site

Happy coding!

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🕒 Last updated: March 26, 2026  ·  Originally published: March 17, 2026