langchain-architecture

Design LLM applications using LangChain with agents, memory, and tool integration.

• Build AI agents that autonomously act on tasks
• Create chains to sequence LLM calls and operations
• Manage context with memory types across conversations
• Integrate tools and external data via retrieval and APIs

• Agents reason and act using tools and memory
• Chains enable step-by-step LLM workflows
• Memory retains context across interactions
• RAG pipelines retrieve relevant documents from vector stores
• Callbacks support logging, monitoring, and debugging
• Modular components support reuse and scalability
• Tool integration enables external system access

1. 1.Initialize LLM and define tools with load_tools
2. 2.Set memory type based on conversation length and needs
3. 3.Chain multiple LLM calls using SequentialChain or RouterChain
4. 4.Use RetrievalQA for RAG with document retrieval
5. 5.Implement custom agents with tools and memory
6. 6.Apply appropriate memory types for context retention