LangGraph and LangChain are powerful frameworks designed to simplify the development of applications powered by large language models (LLMs).
LangGraph is a library for building stateful, multi-agent workflows with LLMs. It enables developers to create complex, graph-based conversational flows, supporting branching, memory, and agent collaboration.
Key Features:
- Graph-based workflow design
- Multi-agent orchestration
- Built-in memory and state management
- Extensible and modular
LangChain is a framework for developing applications with LLMs through composable chains. It provides tools to connect LLMs with external data, APIs, and user-defined logic.
Key Features:
- Chain and agent abstractions
- Integration with various LLM providers
- Tools for retrieval-augmented generation (RAG)
- Support for memory, tools, and custom logic
Both LangGraph and LangChain can be used to build sophisticated AI agents. By combining LangChain's composable chains and agent abstractions with LangGraph's graph-based workflow and multi-agent orchestration, developers can create agents that reason, remember, and interact with users or systems in complex ways.
Notes:
- Agents can leverage memory and tools to perform multi-step reasoning.
- LangGraph enables coordination between multiple agents, allowing for collaborative problem-solving.
- LangChain provides easy integration with external data sources, enhancing agent capabilities.
- Together, they support building conversational agents, autonomous workflows, and intelligent assistants.
- Conversational agents and chatbots
- Automated research assistants
- Workflow automation
- Knowledge retrieval and summarization