Semantic Kernel vs LangChain

The repo tries to compare Semantic Kernel and LangChain to show the difference and similarity between them.

LangChain	Semantic Kernel	Note
Chains	Kernel	Construct sequences of calls
Agents	Planner	Auto create chains to address novel needs for a user
Tools	Plugins (semantic functions + native function)	Custom components that can be reused across different apps
Memory	Memory	Store context and embeddings in memory or other storage

Supported languages

Language	LangChain	Semantic Kernel
Python	✅	✅
JavaScript	✅	❌
C#	❌	✅
Java	❌	✅

Data connection (Retrieval)

Many LLM applications require user-specific data that is not part of the model's training set. The primary way of accomplishing this is through Retrieval Augmented Generation (RAG). In this process, external data is retrieved and then passed to the LLM when doing the generation step.

Building block	LangChain	Semantic Kernel
Document loaders: Load documents from many different sources	Over 100 document loaders: File Loaders (CSV, Docx, EPUB, JSON, PDF, Markdown...) and Web Loaders (Azure Storage, S3, GitHub, Figma...)	Word
Document transformers: Split documents, drop redundant documents, and more	Multiple Split methods	❌
Text embedding models: Take unstructured text and turn it into a list of floating point numbers	Over 25 different embedding providers: OpenAI, Azure OpenAI, Hugging Face, Cohere, Google PaLM, Google Vertex AI, TensorFlow...	OpenAI, Azure OpenAI, Hugging Face
Vector stores: Store and search over embedded data	Over 50 vector stores	About 10 vector stores
Retrievers: Query your data	Simple semantic search, Contextual compression, Time-weighted vector store retriever, Parent Document Retriever, Self Query Retriever, Ensemble Retriever, and more.	Simple semantic search

Automatically orchestrate AI

Type	LangChain's Agents	Semantic Kernel's Planner
Conversational	✅	❌
Plan and execute	✅	✅ (SequentialPlanner)
ReAct	✅	✅ (StepwisePlanner)
Tree of Thoughts (ToT)	✅	❌

agoncal/semantic-kernel-vs-langchain

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