05. Advanced Features

Advanced features and implementation patterns.

Overview

By leveraging LangGraph's advanced features, you can build more sophisticated agent systems.

Key Features

1. Human-in-the-Loop (Approval Flow)

Pause graph execution and request human intervention:

Dynamic interrupt
Static interrupt
Approval, editing, and rejection flows

2. Streaming

Monitor progress in real-time:

LLM token streaming
State update streaming
Custom event streaming

3. Map-Reduce (Parallel Processing Pattern)

Parallel processing of large datasets:

Dynamic worker generation with Send API
Result aggregation with Reducers
Hierarchical parallel processing

Feature Comparison

Feature	Use Case	Implementation Complexity
Human-in-the-Loop	Approval flows, quality control	Medium
Streaming	Real-time monitoring, UX improvement	Low
Map-Reduce	Large-scale data processing	High

Combination Patterns

Human-in-the-Loop + Streaming

# Stream while requesting approval
for chunk in graph.stream(input, config, stream_mode="values"):
    print(chunk)

    # Pause at interrupt
    if chunk.get("__interrupt__"):
        approval = input("Approve? (y/n): ")
        graph.invoke(None, config, resume=approval == "y")

Map-Reduce + Streaming

# Stream progress of parallel processing
for chunk in graph.stream(
    {"items": large_dataset},
    stream_mode="updates",
    subgraphs=True  # Also show worker progress
):
    print(f"Progress: {chunk}")

Next Steps

For details on each feature, refer to the following pages:

05_advanced_features_human_in_the_loop.md - Implementation of approval flows
05_advanced_features_streaming.md - How to use streaming
05_advanced_features_map_reduce.md - Map-Reduce pattern

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