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.
Build LangGraph workflows that pause for human approval, accept user corrections, and resume execution -- essential for safe, trustworthy AI agents.
Implement token-level and event-level streaming in LangGraph to deliver real-time responses in chat interfaces and production applications.
Understand how LangGraph handles cycles in graphs, set recursion limits to prevent runaway agents, and implement graceful exit strategies.
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.
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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