langgraph

LangGraph Examples

LangGraph code samples covering the core concepts of stateful, graph-based agent orchestration.

Setup

conda env create -f environment.yml
conda activate langgraph
cp .env.example .env   # if present, add your API keys

Contents

Root-level files (CodeBasics series)

Step-by-step progression from simple graphs to production agent patterns:

File	Concept
codebasics_1_simple_graph.ipynb	Graph definition, nodes, edges
codebasics_2_graph_with_condition.ipynb	Conditional routing
codebasics_3_chatbot.ipynb	Stateful chatbot with memory
codebasics_4_tool_call.ipynb	Integrating tools
codebasics_5_tool_call_agent.ipynb	Full ReAct agent
codebasics_6_memory.ipynb	Persistent memory across turns
codebasics_7_langsmith_tracing.ipynb	Observability with LangSmith
codebasics_8_HITL.py	Human-in-the-loop interrupts
jamesbriggs_01_gpt_4o_research_agent.ipynb	GPT-4o powered research agent
agentcon_workflow_automation.py	AgentCon workflow demo

Sub-projects

• langgraph-harishneel1/ — structured course (Introduction → Reflection → ReAct → HITL → Multi-agent → Streaming)
• langgraphgroq/ — LangGraph examples using Groq as the LLM backend
• open_deep_research-langcahin-ai/ — production deep research agent (has its own CLAUDE.md and README)

Key Concept

LangGraph models agent logic as a directed graph where nodes are Python functions and edges are routing conditions. StateGraph holds shared state between nodes; END terminates the graph. This is the recommended pattern for multi-step, multi-tool agents over plain LangChain chains.