Project — Build a Document Processing Agent with Multi-Modal Inputs
Build a LangGraph agent that processes PDFs, images, and text documents — extracting information, cross-referencing sources, and generating structured reports.
Build a LangGraph agent that processes PDFs, images, and text documents — extracting information, cross-referencing sources, and generating structured reports.
Build a production-ready customer support agent in LangGraph that handles queries, searches a knowledge base, escalates to humans, and logs interactions.
Build a multi-agent research system in LangGraph where a planner, researcher, and writer collaborate to produce comprehensive research reports from web sources.
Build a LangGraph agent that writes Python code, executes it in a sandbox, inspects the output, and iterates until the code works correctly.
Deploy your LangGraph agents as production APIs using LangGraph Platform — covering LangGraph Server, the SDK client, and cloud deployment options.
Build a LangGraph agent that converts natural language questions to SQL, queries your database, handles errors, and presents results in clear summaries.
Implement different memory architectures in LangGraph — windowed message history, summary memory, and persistent long-term memory across sessions.
Break large LangGraph applications into modular subgraphs that can be developed, tested, and reused independently, then composed into larger systems.
Design multi-agent systems in LangGraph using supervisor, swarm, and network topologies where specialized agents collaborate to solve complex tasks.
Add persistence to your LangGraph agents using checkpointers so conversations survive restarts and long-running workflows can resume from any point.
Get up to speed with LangChain essentials -- chat models, prompt templates, output parsers, and chains -- the building blocks you need before LangGraph.
Build a ReAct agent from scratch in LangGraph — implement the think-act-observe loop, add tools, debug agent cycles, and compare with create_react_agent().
Learn what LangGraph is, how it differs from LangChain, and why graph-based orchestration is the right paradigm for building reliable AI agents.
Learn how LangGraph manages state across graph execution using TypedDict schemas, reducer functions, add_messages, and built-in message history tracking.
Get the exact 10-course programming foundation that Data Science professionals use.