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LangChain VS Keras

Compare LangChain VS Keras and see what are their differences

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LangChain logo LangChain

Framework for building applications with LLMs through composability

Keras logo Keras

Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
  • LangChain Landing page
    Landing page //
    2024-05-17
  • Keras Landing page
    Landing page //
    2023-10-16

LangChain features and specs

  • Modular Design
    LangChain's modular design allows for easy customization and flexibility, enabling developers to build applications by combining different components like language models, prompts, and chains.
  • Integration with Various LLMs
    LangChain supports integration with several large language models, making it versatile for developers looking to leverage different AI models depending on their use case.
  • Advanced Prompt Management
    LangChain offers nuanced prompt management capabilities which help in efficiently generating and tuning prompts tailored for specific tasks and models.
  • Chain Building
    The framework enables the creation of complex chains of operations, making it easier to design sophisticated language processing pipelines.
  • Community and Documentation
    LangChain has an active community and good documentation, providing ample resources and support for developers new to the platform.

Possible disadvantages of LangChain

  • Learning Curve
    Due to its modularity and the breadth of features, there may be a steep learning curve for new users not familiar with language models or the framework’s approach.
  • Performance Overhead
    The abstraction and flexibility can introduce performance overheads, which might be a concern for applications requiring highly optimized execution.
  • Complex Configuration
    Configuring and tuning chains for specific tasks can become complex, especially for newcomers who need to understand each component’s role and interaction.
  • Dependent on External APIs
    Integration with multiple LLMs can lead to dependency on external APIs, which might lead to concerns over costs, uptime, and API changes.

Keras features and specs

  • User-Friendly
    Keras provides a simple and intuitive interface, making it easy for beginners to start building and training models without needing extensive experience in deep learning.
  • Modularity
    Keras follows a modular design, allowing users to easily plug in different neural network components, such as layers, activation functions, and optimizers, to create complex models.
  • Pre-trained Models
    Keras includes a wide range of pre-trained models and offers easy integration with transfer learning techniques, reducing the time required to achieve good results on new tasks.
  • Integration with TensorFlow
    As part of TensorFlow’s ecosystem, Keras provides deep integration with TensorFlow functionalities, enabling users to leverage TensorFlow's powerful features and performance optimizations.
  • Extensive Documentation
    Keras has comprehensive and well-organized documentation, along with numerous tutorials and code examples, making it easier for developers to learn and use the framework.
  • Community Support
    Keras benefits from a large and active community, which provides support through forums, GitHub, and specialized user groups, facilitating the resolution of issues and sharing of best practices.

Possible disadvantages of Keras

  • Performance Limitations
    Due to its high-level abstraction, Keras may incur performance overheads, making it less suitable for scenarios requiring extremely fast execution and low-level optimizations.
  • Limited Low-Level Control
    The simplicity and abstraction of Keras can be a downside for advanced users who need fine-grained control over model components and custom operations, which may require them to resort to lower-level frameworks.
  • Scalability Issues
    In some complex applications and large-scale deployments, Keras might face scalability challenges, where more specialized or low-level frameworks could handle such tasks more efficiently.
  • Dependency on TensorFlow
    While the integration with TensorFlow is generally an advantage, it also means that the performance and features of Keras are closely tied to the development and updates of TensorFlow.
  • Lagging Behind Latest Research
    Keras, being a user-friendly high-level API, might not always incorporate the latest cutting-edge research advancements in deep learning as quickly as more research-oriented frameworks.

LangChain videos

+ Add

LangChain for LLMs is... basically just an Ansible playbook

More videos:

  • Review - Using ChatGPT with YOUR OWN Data. This is magical. (LangChain OpenAI API)
  • Review - LangChain Crash Course: Build a AutoGPT app in 25 minutes!
  • Review - What is LangChain?
  • Review - What is LangChain? - Fun & Easy AI

Keras videos

+ Add

3. Deep Learning Tutorial (Tensorflow2.0, Keras & Python) - Movie Review Classification

More videos:

  • Review - Movie Review Classifier in Keras | Deep Learning | Binary Classifier
  • Review - EKOR KERAS!! Review and Bike Check DARTMOOR HORNET 2018 // MTB Indonesia

Category Popularity

0-100% (relative to LangChain and Keras)

LangChain

Keras

AI
100 100%
AI
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
AI Tools
100 100%
AI Tools
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare LangChain and Keras

LangChain Reviews

We have no reviews of LangChain yet.
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Keras Reviews

10 Python Libraries for Computer Vision
TensorFlow and Keras are widely used libraries for machine learning, but they also offer excellent support for computer vision tasks. TensorFlow provides pre-trained models like Inception and ResNet for image classification, while Keras simplifies the process of building, training, and evaluating deep learning models.
Source: clouddevs.com
25 Python Frameworks to Master
Keras is a high-level deep-learning framework capable of running on top of TensorFlow, Theano, and CNTK. It was developed by François Chollet in 2015 and is designed to provide a simple and user-friendly interface for building and training deep learning models.
Source: kinsta.com
15 data science tools to consider using in 2021
Keras is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It's an open source deep learning API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform. Keras previously supported multiple back ends but was tied exclusively to TensorFlow starting with...
Source: searchbusinessanalytics.techtarget.com

Social recommendations and mentions

Based on our record, Keras should be more popular than LangChain. It has been mentiond 35 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

LangChain mentions (4)

  • Bridging the Last Mile in LangChain Application Development
    Undoubtedly, LangChain is the most popular framework for AI application development at the moment. The advent of LangChain has greatly simplified the construction of AI applications based on Large Language Models (LLM). If we compare an AI application to a person, the LLM would be the "brain," while LangChain acts as the "limbs" by providing various tools and abstractions. Combined, they enable the creation of AI... - Source: dev.to / 12 months ago
  • 🦙 Llama-2-GGML-CSV-Chatbot 🤖
    Developed using Langchain and Streamlit technologies for enhanced performance. - Source: dev.to / about 1 year ago
  • 👑 Top Open Source Projects of 2023 🚀
    LangChain was first released in October 2022 as an open-source side project, a framework that makes developing AI applications more flexible. It got so popular that it was promptly turned into a startup. - Source: dev.to / about 1 year ago
  • 🆓 Local & Open Source AI: a kind ollama & LlamaIndex intro
    Being able to plug third party frameworks (Langchain, LlamaIndex) so you can build complex projects. - Source: dev.to / over 1 year ago

Keras mentions (35)

  • Top Programming Languages for AI Development in 2025
    The unchallenged leader in AI development is still Python. And Keras, and robust community support. - Source: dev.to / 5 days ago
  • Top 8 OpenSource Tools for AI Startups
    If you need simplicity, Keras is a great high-level API built on top of TensorFlow. It lets you quickly prototype neural networks without worrying about low-level implementations. Keras is perfect for getting those first models up and running—an essential part of the startup hustle. - Source: dev.to / 6 months ago
  • Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
    At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck. - Source: dev.to / 7 months ago
  • Using Google Magika to build an AI-powered file type detector
    The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models. - Source: dev.to / 11 months ago
  • My Favorite DevTools to Build AI/ML Applications!
    As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development. - Source: dev.to / about 1 year ago
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What are some alternatives?

When comparing LangChain and Keras, you can also consider the following products

Haystack NLP Framework - Haystack is an open source NLP framework to build applications with Transformer models and LLMs.

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

Dify.AI - Open-source platform for LLMOps,Define your AI-native Apps

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Hugging Face - The AI community building the future. The platform where the machine learning community collaborates on models, datasets, and applications.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

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