langchain and LangChain-for-LLM-Application-Development
These are ecosystem siblings—one is a language-specific implementation (Elixir) of the LangChain framework architecture, while the other is a Python educational resource for applying LangChain's core patterns (agents, chains, memories) to LLM applications.
About langchain
brainlid/langchain
Elixir implementation of a LangChain style framework that lets Elixir projects integrate with and leverage LLMs.
This project helps Elixir developers integrate advanced AI capabilities into their applications. It takes input from various large language models (LLMs) like OpenAI, Anthropic, or locally hosted models and allows you to chain them together with other application logic. The result is more intelligent, data-aware, and agentic Elixir applications that can understand and interact with their environment.
About LangChain-for-LLM-Application-Development
ksm26/LangChain-for-LLM-Application-Development
Apply LLMs to your data, build personal assistants, and expand your use of LLMs with agents, chains, and memories.
This course teaches developers how to build powerful applications using large language models (LLMs) with the LangChain framework. It covers how to connect LLMs to your own data, manage conversation history, and chain multiple operations together to create sophisticated tools like personalized assistants or specialized chatbots. It's for software developers looking to integrate and enhance LLM capabilities in their applications.
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