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https://github.com/formulahendry/semantic-kernel-vs-langchain
Compare Semantic Kernel and LangChain
https://github.com/formulahendry/semantic-kernel-vs-langchain
Last synced: 27 days ago
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Compare Semantic Kernel and LangChain
- Host: GitHub
- URL: https://github.com/formulahendry/semantic-kernel-vs-langchain
- Owner: formulahendry
- Created: 2023-08-22T07:36:24.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-02-20T13:34:20.000Z (4 months ago)
- Last Synced: 2024-04-14T13:01:36.437Z (3 months ago)
- Size: 709 KB
- Stars: 90
- Watchers: 8
- Forks: 8
- Open Issues: 1
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Metadata Files:
- Readme: README.md
Lists
- awesome-semantickernel - Semantic Kernel vs LangChain
README
# 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 |
## Initial Release Date
LangChain: Oct, 2022
Semantic Kernel: Mar, 2023
## Some Numbers
Semantic Kernel: 
[](https://www.nuget.org/packages/Microsoft.SemanticKernel) [](https://pepy.tech/project/semantic-kernel) 
LangChain: 
[](https://pepy.tech/project/langchain) 
## 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](https://js.langchain.com/docs/modules/data_connection/document_loaders/integrations/file_loaders/) (CSV, Docx, EPUB, JSON, PDF, Markdown...) and [Web Loaders](https://js.langchain.com/docs/modules/data_connection/document_loaders/integrations/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) | ✅ | ❌ |