Programming

LangGraph Framework

Master the LangGraph Framework to build robust, stateful, and cyclical multi-agent AI systems. Learn StateGraph, state management (TypedDict, Dataclass), dynamic nodes, conditional edges, parallel execution, human-in-the-loop workflows, time-travel checkpointing, multi-agent supervisor architectures, streaming events, and production deployment. Duration: 20–25 Hours …

3 Months Duration
15 Modules
Paid Access

Course fee ~~₹20000.00~~ ₹10000.00

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Learning path

Course Modules

Work through each module and pass quizzes to unlock the next step in your journey.

Module 1 Locked

Introduction to LangGraph

Discover the LangGraph framework for orchestrating stateful, multi-actor AI workflows. Understand what LangGraph is, why cycles and loops are critical for modern agentic architectures, how LangGraph compares with traditional LangChain chains, explore its architecture, and …

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Learning objectives

• Understand what LangGraph is and how it powers agentic workflows
• Analyze why cyclical graphs are superior to linear DAG chains for agent reasoning
• Compare LangGraph with standard LangChain LCEL pipelines
• Explore core features: state machines, cycles, persistence, and human-in-the-loop
• Understand the internal architecture of LangGraph
• Install LangGraph and configure your Python environment
• Build and execute your first functional LangGraph application

Module 2 Locked

Understanding Graphs

Master foundational graph theory concepts applied to AI workflows. Learn the mechanics of Nodes, Edges, START, and END nodes, understand directed graph execution flows, and explore the complete lifecycle of a LangGraph execution step.

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Learning objectives

• Understand the graph computing model and how it maps to AI applications
• Define Nodes as units of discrete work or computation
• Define Edges as transition paths controlling execution flow
• Connect workflows using the special START and END markers
• Understand directed graph principles and topological execution order
• Trace the step-by-step lifecycle of graph execution and state transitions

Module 3 Locked

State Management

State is the single source of truth in LangGraph. Learn to design clear state schemas using TypedDict and Dataclass, understand state updates and readers, use reducer functions (Annotated) to control how state keys are merged, …

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Learning objectives

• Understand the role and structure of State in LangGraph workflows
• Define state schemas using TypedDict and Python Dataclasses
• Use typing.Annotated with reducers like add_messages and operator.add
• Update state keys immutably and incrementally from nodes
• Implement shared state across multiple cooperating nodes
• Validate state data structures to prevent runtime corruption
• Apply best practices for state modularity, cleanup, and schema design

Module 4 Locked

Nodes

Build reusable, robust, and clean computational nodes. Learn the node function contract, compare stateless vs stateful nodes, handle input unpacking and output dictionary formatting, design modular reusable node components, and implement defensive error handling inside …

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Learning objectives

• Understand the standard callable interface of LangGraph nodes
• Write node functions that accept state and configuration parameters
• Differentiate stateless pure transformations from stateful integrations
• Structure clean inputs and output update dictionaries
• Build modular, reusable nodes shared across different graphs
• Implement error handling and defensive recovery inside nodes
• Follow production best practices for node design and naming

Module 5 Locked

Edges

Connect your nodes into intelligent, reactive execution networks. Understand sequential edges, conditional edges, multiple dynamic routing pathways, conditional evaluation functions, cyclic looping between nodes, and edge design best practices.

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Learning objectives

• Master the distinction between standard fixed edges and dynamic conditional edges
• Connect sequential execution steps using add_edge()
• Implement dynamic routing with add_conditional_edges()
• Write condition functions that inspect state and determine next steps
• Build cyclic loops (node A -> node B -> node A) safely
• Map condition outputs to node names using path maps
• Avoid common edge pitfalls like unreachable nodes and infinite loops

Module 6 Locked

Graph Builder

Assemble, compile, execute, and debug end-to-end graphs with StateGraph. Master the step-by-step construction workflow: defining schema, registering nodes, connecting edges, establishing entry and finish points, compiling into runnables, executing invocations, and debugging with visual diagrams.

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Learning objectives

• Initialize and configure StateGraph with custom state types
• Register nodes using graph_builder.add_node()
• Wire sequential and conditional edges across all nodes
• Set entry points from START and exit points to END
• Compile the graph into a production-ready CompiledGraph
• Execute compiled graphs synchronously (invoke) and asynchronously (ainvoke)
• Visualize graphs using Mermaid markdown and ASCII representations

Module 7 Locked

Conditional Workflows

Implement intelligent decision-making logic and adaptive branching in your AI systems. Learn decision-making nodes, dynamic multi-condition evaluations, automated error recovery, self-correcting loops, intelligent retry strategies, and workflow optimization.

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Learning objectives

• Implement dynamic decision-making nodes that evaluate intermediate agent outputs
• Build multi-branch conditional routing based on complex state criteria
• Design self-healing pipelines that route back to fixers on validation errors
• Implement retry mechanisms with maximum attempt thresholds
• Handle fallbacks and degraded execution states gracefully
• Optimize conditional paths to minimize unnecessary LLM invocations

Module 8 Locked

Parallel Execution

Accelerate graph throughput by executing independent nodes concurrently. Understand parallel processing concepts, multiple branch execution (fan-out), synchronizing results (fan-in), parallel state updates with reducers, merge strategies, error handling in parallel branches, and best practices.

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Learning objectives

• Understand how LangGraph supports concurrent branch execution
• Implement fan-out patterns from a single node to multiple parallel workers
• Synchronize parallel execution branches using fan-in aggregation nodes
• Manage parallel state updates safely using Annotated reducer functions
• Design merge and aggregation strategies for multi-agent outputs
• Handle branch-specific errors without terminating the entire graph
• Optimize latency in research, multi-search, and evaluation pipelines

Module 9 Locked

Human-in-the-Loop

Integrate human oversight and interactive feedback directly into autonomous AI systems. Master graph interruption (interrupt_before, interrupt_after), waiting for user inputs, human approval checkpoints, resuming paused execution, state inspection and editing, and interactive agent applications.

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Learning objectives

• Understand the importance of Human-in-the-Loop (HITL) for safety and precision
• Configure graph compilation with interrupt_before and interrupt_after hooks
• Pause graph execution seamlessly before sensitive actions (e.g. payments, emails)
• Inspect and modify graph state while paused using app.update_state()
• Resume paused execution with human feedback or explicit approval
• Design production-ready interactive and collaborative agent workflows

Module 10 Locked

Memory & Checkpointing

Achieve complete workflow durability, long-term memory, and time travel with Checkpointing. Understand how LangGraph saves state snapshots across super-steps, compare InMemorySaver with PostgresSaver, manage conversational multi-turn sessions with thread_ids, support long-running workflows, and recover seamlessly …

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Learning objectives

• Understand the architecture and necessity of Checkpointing in stateful graphs
• Save and restore state snapshots across execution super-steps
• Use MemorySaver for in-memory development and automated testing
• Deploy persistent checkpointers (PostgresSaver, SqliteSaver) for production
• Isolate independent user conversations using thread_id configs
• Implement time-travel debugging: rewinding, forking, and inspecting past states
• Recover long-running multi-day workflows seamlessly after infrastructure crashes

Module 11 Locked

Tool Integration

Integrate external tools, APIs, and computational functions into LangGraph agents. Explore ToolNode, create custom tools with the @tool decorator, execute single and multiple tools, handle tool output messages, implement error handling and fallbacks, and follow …

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Learning objectives

• Understand how LLMs invoke external tools in LangGraph architectures
• Build custom tools using LangChain @tool and Pydantic schemas
• Integrate prebuilt ToolNode into the graph execution loop
• Connect model nodes and tool nodes using tools_condition
• Process tool execution results formatted as ToolMessages
• Implement defensive error handling for tool timeouts and exceptions
• Follow security and sandboxing best practices for tool execution

Module 12 Locked

Multi-Agent Systems

Design and build collaborative multi-agent architectures that divide complex tasks among specialized agents. Master the Supervisor Pattern, worker agent delegation, inter-agent communication, collaborative problem solving, coordination protocols, and building complete production multi-agent applications.

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Learning objectives

• Understand the advantages of multi-agent systems over single monolithic agents
• Implement the Supervisor Pattern to coordinate multiple specialized workers
• Build worker agents specialized for research, coding, writing, and review
• Establish clear communication channels via shared graph state
• Coordinate task delegation, status reporting, and handoffs between agents
• Prevent infinite loops and deadlocks in multi-agent conversations
• Construct an end-to-end multi-agent team capable of autonomous problem solving

Module 13 Locked

Streaming & Events

Deliver responsive, real-time user experiences with streaming and event handling. Master graph streaming modes (values, updates, messages), stream LLM tokens directly to frontends, configure event listeners, monitor execution steps in real time, and implement event …

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Learning objectives

• Understand how LangGraph streams state changes and tokens in real time
• Compare stream_mode="values" (full state) vs stream_mode="updates" (node diffs)
• Stream model tokens live using stream_mode="messages" or astream_events()
• Implement real-time progress indicators for multi-step agent reasoning
• Dispatch and consume custom application events from inside nodes
• Build interactive, real-time WebSocket and SSE streaming backends

Module 14 Locked

Debugging & Monitoring

Diagnose issues, inspect states, and monitor performance in complex LangGraph applications. Visualize graph topologies, trace execution graphs with LangSmith, inspect state histories, diagnose concurrency issues, analyze performance bottlenecks, and optimize execution flow.

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Learning objectives

• Visualize and audit graph topologies to identify unreachable nodes and deadlocks
• Trace end-to-end execution paths, node timings, and token costs with LangSmith
• Inspect state history across all historical checkpoints using app.get_state_history()
• Diagnose common concurrency bugs, state overwrite collisions, and recursion errors
• Profile node execution latency and identify optimization opportunities
• Implement structured logging and health metrics for production graphs

Module 15 Locked

Production Deployment

Deploy robust, scalable, and secure LangGraph applications to production. Master project organization, configuration management with environment variables, exception handling and retries, structured logging, performance optimization, LangGraph Cloud/Server deployment, and enterprise production architecture.

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Learning objectives

• Structure production LangGraph codebases cleanly with modular separation of nodes and edges
• Manage runtime configurations, secrets, and environment variables securely
• Implement defensive exception handling, timeouts, and fallback policies
• Deploy asynchronous graph services with persistent database checkpointers
• Explore LangGraph Server / LangGraph Cloud for enterprise deployment
• Monitor production health, token consumption, and rate limits
• Design scalable production architectures capable of handling concurrent users

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