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.
