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 LangGraph pipeline that autonomously scrapes websites, extracts structured data, handles pagination and errors, and produces analytical summaries.
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 complete AI application from scratch — a FastAPI backend powered by a LangGraph agent with streaming, persistence, auth, and a chat frontend.
Force LangGraph agents to produce validated structured output using Pydantic models, with automatic retry and self-correction on validation failures.
Build a LangGraph agent that writes Python code, executes it in a sandbox, inspects the output, and iterates until the code works correctly.
Trace, debug, and monitor your LangGraph agents in production using LangSmith — visualize graph execution, inspect state transitions, and identify failures.
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.
Build a full retrieval-augmented generation agent in LangGraph that decides when to retrieve, evaluates relevance, and synthesizes answers from your documents.
Implement different memory architectures in LangGraph — windowed message history, summary memory, and persistent long-term memory across sessions.
Run graph branches in parallel using LangGraph's Send API and map-reduce pattern to process multiple items concurrently and aggregate results.
Add human approval gates to LangGraph agents. Learn the interrupt mechanism, tool call review, selective approval, multi-step review chains, and state editing — with...
Master all five LangGraph streaming modes — values, updates, messages, custom, and debug. Learn token-level streaming, tool call handling, and how to build responsive...
Learn how LangGraph cycles power agent loops, how recursion_limit prevents runaway execution, and three exit strategies to stop agents gracefully — with full working...
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 memory to LangGraph agents with checkpointers. Learn MemorySaver, SqliteSaver, and PostgresSaver — plus state inspection, time travel, and real-world resumable workflows.
Build resilient LangGraph agents that handle tool failures, LLM errors, and unexpected states with retry logic, fallback paths, and graceful degradation.
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