How dooml/langchain-langgraph-tutorial works
This repository is an educational tutorial series, not a shared application: learners choose and run self-contained notebooks to explore LangChain and LangGraph patterns. The main path runs from foundational model and document examples through agents and graph workflows to advanced state, orchestration, and interoperability techniques. Notebook contents were not sampled, so relationships between individual examples are omitted; the README documents Groq as the model provider throughout and identifies the tutorial capabilities represented below.
Main entry points: Tutorial_1_Introduction_to_LangChain.ipynb Chains and prompts · Tutorial_2_Working_with_Language_Models.ipynb Models and parsers · Tutorial_3_Document_Processing.ipynb Documents and CRAG · Tutorial_4_Agents_in_LangChain.ipynb Tool-using agents
Components 27
LangChain foundations
- Chains and prompts
Tutorial01/Tutorial_1_Introduction_to_LangChain.ipynb - Models and parsers
Tutorial02/Tutorial_2_Working_with_Language_Models.ipynb - Documents and CRAG
Tutorial03/Tutorial_3_Document_Processing.ipynb - Tool-using agents
Tutorial04/Tutorial_4_Agents_in_LangChain.ipynb - Research agents
Tutorial05/Tutorial_5_Advanced_Agent_Techniques.ipynb - Memory systems
Tutorial06/Tutorial_6_Memory_Systems_in_LangChain.ipynb
Graph applications
- Graph fundamentals
Tutorial07/Tutorial_7_introduction_to_langgraph.ipynb - Conditional graph flows
Tutorial08/Tutorial_8_complex_flows_langgraph.ipynb - LangChain in graphs
Tutorial09/Tutorial_9_combining_langchain_langgraph.ipynb - Application patterns
Tutorial10/Tutorial_10_real_world_applications.ipynb - Structured outputs
Tutorial11/Tutorial_11_structured_data.ipynb - Advanced chains
Tutorial12/Tutorial_12_advanced_langchain_techniques.ipynb - Async and deployment
Tutorial13/Tutorial_13_best_practices_and_advanced_topics.ipynb
Agent orchestration
- Human approval
Tutorial14/Tutorial_14_human_in_the_loop.ipynb - Supervisor agents
Tutorial15/Tutorial_15_supervisor_agent.ipynb - Swarm agents
Tutorial16/Tutorial_16_swarm_agents.ipynb - Nested subgraphs
Tutorial17/Tutorial_17_subgraphs.ipynb - Map and reduce
Tutorial18/Tutorial_18_parallelization_map_reduce.ipynb
State and control
- Persistent memory
Tutorial19/Tutorial_19_long_term_memory.ipynb - Graph time travel
Tutorial20/Tutorial_20_time_travel.ipynb - Functional API
Tutorial21/Tutorial_21_functional_api.ipynb
Advanced integrations
- Deep agents
Tutorial22/Tutorial_22_deep_agents.ipynb - Framework utilities
Tutorial23/Tutorial_23_langchain_utilities.ipynb - Agent middleware
Tutorial24/Tutorial_24_agent_middleware.ipynb - MCP and A2A
Tutorial25/Tutorial_25_mcp_a2a_protocol.ipynb
Other components
- Learner
- Groq models
Connections 48
- Learner → Chains and prompts: opens and runs
- Learner → Models and parsers: opens and runs
- Learner → Documents and CRAG: opens and runs
- Learner → Tool-using agents: opens and runs
- Learner → Research agents: opens and runs
- Learner → Memory systems: opens and runs
- Learner → Graph fundamentals: opens and runs
- Learner → Conditional graph flows: opens and runs
- Learner → LangChain in graphs: opens and runs
- Learner → Application patterns: opens and runs
- Learner → Structured outputs: opens and runs
- Learner → Advanced chains: opens and runs
- Learner → Async and deployment: opens and runs
- Learner → Human approval: opens and runs
- Learner → Supervisor agents: opens and runs
- Learner → Swarm agents: opens and runs
- Learner → Nested subgraphs: opens and runs
- Learner → Map and reduce: opens and runs
- Learner → Persistent memory: opens and runs
- Learner → Graph time travel: opens and runs
- Learner → Functional API: opens and runs
- Learner → Deep agents: opens and runs
- Learner → Framework utilities: opens and runs
- Learner → Agent middleware: opens and runs
- Learner → MCP and A2A: opens and runs
- Chains and prompts → Groq models: requests inference
- Models and parsers → Groq models: requests inference
- Documents and CRAG → Groq models: requests inference
- Tool-using agents → Groq models: requests inference
- Research agents → Groq models: requests inference
- Memory systems → Groq models: requests inference
- Graph fundamentals → Groq models: requests inference
- Conditional graph flows → Groq models: requests inference
- LangChain in graphs → Groq models: requests inference
- Application patterns → Groq models: requests inference
- Structured outputs → Groq models: requests inference
- Advanced chains → Groq models: requests inference
- Async and deployment → Groq models: requests inference
- Supervisor agents → Groq models: requests inference
- Swarm agents → Groq models: requests inference
- Nested subgraphs → Groq models: requests inference
- Map and reduce → Groq models: requests inference
- Persistent memory → Groq models: requests inference
- Graph time travel → Groq models: requests inference
- Functional API → Groq models: requests inference
- Deep agents → Groq models: requests inference
- Framework utilities → Groq models: requests inference
- Agent middleware → Groq models: requests inference