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

Consolidated from Code/agents/ (learning notebooks + CrewAI-in-LangGraph hybrids)

File	Concept
LangGraphLearning_menloparklab.ipynb	General LangGraph learning notes
YT_01_Learning_LangGraph_Agent_Executor_SamWitteveen.ipynb	Agent executor pattern
YT_02_Learning_LangGraph_Chat_Executor_SamWitteveen.ipynb	Chat executor pattern
YT_03_Learning_LangGraph_Agent_Supervisor__SamWitteveen.ipynb	Multi-agent supervisor pattern
langgraph_multiagent.py	Multi-agent graph example
langgraph_rajibdeb_01_how_to_langgraph_example_01.py	Basic LangGraph walkthrough
langgraph_rajibdeb_01_how_to_use_crew_ai_for_image_understanding.py	CrewAI-as-a-tool inside LangGraph (image understanding)
langgraph_rajibdeb_01_how_to_use_crewai_to_solve_math.py	CrewAI-as-a-tool inside LangGraph (math solving)
langgraph_rajibdeb_03_how_to_persist_shared_state_lg.py	Shared state persistence

The two ..._crew* files need crewai (in environment.yml) since they demo CrewAI running as a tool inside a LangGraph graph — LangGraph is the driving framework, hence living here rather than in Code/crewai/.

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.