#langchain

Build AI agents with LangChain. Components: 1. LLM wrapper (OpenAI, Anthropic, local). 2. Prompt templates with variables. 3. Chains for sequential operations. 4. Agents with tool selection. 5. Memory for conversation context. 6. Vector stores for embeddings. 7. Document loaders and splitters. 8. Output parsers for structured data. Use LCEL (LangChain Expression Language) for complex flows and implement human-in-the-loop patterns.

Visualize a complex LangChain agent flow. Flow components: 1. User Input -> Embedding Model. 2. Vector DB (Pinecone) retrieval. 3. LLM (GPT-4) reasoning step. 4. Tool execution (Google Search, Python Repl). 5. Final Output. Use a node-based diagram style with directed arrows and color-coded component boxes.