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Agentic AI: LangChain and LangGraph

13 articles·~275 min total·Updated
Ai Agents(11)
  1. LangGraph 101: Building Your First Stateful Agent01

    LangGraph 101: Building Your First Stateful Agent

    Learn LangGraph from scratch: StateGraph, typed state, nodes, edges, and your first LLM-agnostic stateful agent.

    •21 min read
  2. Deploying LangGraph Agents: LangServe, Docker, LangGraph Platform, and Production Observability02

    Deploying LangGraph Agents: LangServe, Docker, LangGraph Platform, and Production Observability

    Deploy LangGraph agents to production: LangServe, Docker, PostgresSaver, LangGraph Platform, and LangSmith observability.

    •27 min read
  3. 03

    Human-in-the-Loop Workflows with LangGraph: Interrupts, Approvals, and Async Execution

    Pause LangGraph agents mid-run for human approval: interrupt(), Command, update_state(), and async resume patterns.

    •18 min read
  4. 04

    LangGraph Memory and State Persistence: Checkpointers, Threads, and Cross-Session Memory

    Give LangGraph agents persistent memory: checkpointers, thread IDs, cross-session memory store, and context overflow strategies.

    •18 min read
  5. 05

    Multi-Agent Systems in LangGraph: Supervisor Pattern, Handoffs, and Agent Networks

    Build multi-agent systems in LangGraph: supervisor routing, worker handoffs, subgraphs, and the Send API for parallel agents.

    •27 min read
  6. 06

    The ReAct Agent Pattern in LangGraph: Think, Act, Observe, Repeat

    Build ReAct agents in LangGraph: prebuilt create_react_agent vs custom StateGraph loop, with a coding assistant example.

    •23 min read
  7. 07

    Streaming Agent Responses in LangGraph: Tokens, Events, and Real-Time UI Integration

    Stream LangGraph agents in real time: astream_events, token-level output, StreamWriter, and FastAPI SSE frontend integration.

    •20 min read
  8. LangGraph Tool Calling: ToolNode, Parallel Tools, and Custom Tools08

    LangGraph Tool Calling: ToolNode, Parallel Tools, and Custom Tools

    Wire real capabilities into LangGraph agents: @tool decorator, ToolNode, bind_tools, parallel execution, and error handling.

    •18 min read
  9. 09

    From LangChain to LangGraph: When Agents Need State Machines

    LangChain's AgentExecutor handles simple loops — but stateful branching, long-running tasks, and human-in-the-loop require LangGraph's graph model.

    •19 min read
  10. LangChain 101: Chains, Prompts, and LLM Integration10

    LangChain 101: Chains, Prompts, and LLM Integration

    Build your first LangChain pipeline: LCEL, ChatPromptTemplate, and LLM-agnostic model swapping from OpenAI to Anthropic to Ollama.

    •22 min read
  11. 13

    LangChain Tools and Agents: The Classic Agent Loop

    Build tool-using agents with LangChain: @tool decorator, AgentExecutor, ReAct reasoning, and when to reach for LangGraph instead.

    •22 min read
Conversation(1)
  1. LangChain Memory: Conversation History and Summarization11

    LangChain Memory: Conversation History and Summarization

    Keep context across turns: ConversationBufferMemory, ConversationSummaryMemory, and the LCEL memory pattern — before LangGraph checkpointing.

    •20 min read
Langchain(1)
  1. 12

    LangChain RAG: Retrieval-Augmented Generation in Practice

    Ground your LLM in real data: build a RAG pipeline with FAISS, Chroma, and LangChain retrievers — step by step.

    •20 min read