# ChatGPT Answer LangGraph is a graph-based language model developed by [[Langchain]]. It functions as the primary technology behind LangChain's natural language translation and generation capabilities. It uses a graph database to represent various aspects of language such as vocabulary, grammar, contextual usage, and more. This model allows for advanced understanding and manipulation of language data. LangChain is a platform that utilizes blockchain technology to provide decentralized translation services. The LangGraph is an integral part of this system, enabling high-quality machine translations on the platform. Thus, LangGraph and LangChain work together to offer efficient and accurate translation services. # Bard Answer LangGraph and LangChain are both tools related to building applications with large language models (LLMs). However, they serve different purposes and have a complementary relationship: **LangChain:** - **Purpose:** Build Directed Acyclic Graphs (DAGs) for LLM workflows. - **Function:** Orchestrates sequences of LLM calls with defined outputs and inputs. - **Benefits:** Easier to manage multi-step tasks, modular approach, good for building linear workflows. - **Limitations:** Cannot handle tasks with cycles or feedback loops. **LangGraph:** - **Purpose:** Build stateful, multi-actor applications with LLMs. - **Function:** Introduces cycles and loops into LLM workflows, allowing for agent-like behaviors. - **Benefits:** Enables tasks with feedback loops, ideal for interactive applications or agents. - **Limitations:** Not ideal for simple linear workflows where LangChain excels. **Relationship:** - LangGraph is built on top of LangChain and extends its functionality. - You can use LangChain Expression Language within LangGraph to define steps in the chain. - LangGraph adds syntax for handling cycles and coordinating multiple actors. **When to use:** - **LangChain:** Use for simple linear workflows where you don't need loops or cycles. - **LangGraph:** Use for tasks with feedback loops or agent-like interactions. In summary, LangChain and LangGraph are complementary tools. LangChain is the foundation for building basic sequences of LLM calls, while LangGraph adds the ability to handle more complex applications with cycles and state management. # References ```dataview Table title as Title, authors as Authors where contains(subject, "LangGraph") ```