LangGraph vs LlamaIndex: Which AI Agent Framework Wins?

How to develop AI agents involves different engineering systems; we are currently in a chaotic "Warring States" era where AI practitioners must choose frameworks carefully.

We compare two concrete development frameworks, LangGraph and LlamaIndex, building on the previous article that dissected the essence of agents, providing a high‑level outline rather than detailed code.

The market offers many AI development frameworks, but LangGraph and LlamaIndex are chosen for comparison because they are established, widely used, and allow a focused discussion.

What abstractions of agents do LangGraph and LlamaIndex each adopt? How do these reflect their underlying design philosophies?

How to build multi‑agent Agentic Systems on top of each framework?

Briefly compare key features such as state management, interface usability, concurrency, and streaming support.

Two Frameworks' Abstractions of Agents

Both frameworks share a unified concept of an

Agentic System

, encompassing workflows, autonomous agents, and hybrids. However, their concrete implementations differ.

LangGraph is a new framework built after LangChain, introducing a graph‑based orchestration layer. Nodes represent any logic (program code, LLM‑driven decisions), and edges define partial order execution. This graph can express cycles, unlike traditional DAG‑based workflows.

Key capabilities of LangGraph’s graph orchestration include:

Specifying partial‑order execution relationships between nodes.

Passing or sharing data between nodes.

Concurrent execution of nodes with synchronization after completion.

Dynamic logic and dynamic partial‑order changes to enable loops.

LlamaIndex treats Agentic Systems as either highly autonomous

Agent

or more controllable

Workflow

. Internally, its Workflow is an event‑driven system where each

step

processes events and may emit new events, forming a graph of steps and events.

The three implementations of the ReAct pattern in LlamaIndex illustrate this:

LlamaIndex’s Workflow ultimately also forms a graph of steps (nodes) and events (edges), though the documentation may obscure this.

Support for Multi‑Agent Systems

LangGraph’s multi‑agent solution is illustrated by a graph where nodes can be LLMs, tools, custom logic, or nested Agentic System sub‑graphs.

LlamaIndex builds multi‑agent systems using its Workflow with

@step

decorators. Each step handles tool calls, model calls, routing, etc.

Interface Usability

LlamaIndex’s Workflow creates a step simply by adding

@step

to a method, offering flexibility but requiring implicit event matching for execution order.

LangGraph requires explicit

add_node

and

add_edge

calls, making developers handle graph concepts directly.

State Management

LangGraph shares data via a global state; LlamaIndex uses events between steps and also supports a global Context, which can become complex in large systems.

Concurrency

Both frameworks support asynchronous, concurrent execution of eligible nodes/steps.

For synchronization after concurrent execution, LangGraph uses explicit waiting edges, while LlamaIndex relies on Context to wait for multiple events.

Streaming Support

Both frameworks allow

async for

loops to stream execution progress—LangGraph streams supersteps, while LlamaIndex streams events.

Summary

LangGraph and LlamaIndex are large projects; this article only covered a small portion concerning agent development. Their underlying design philosophies differ: LangGraph adopts an explicit graph‑based orchestration, whereas LlamaIndex uses an event‑driven workflow that implicitly forms a graph. Both aim to support varying degrees of autonomy in Agentic Systems.

References

How to think about agent frameworks. (https://blog.langchain.dev/how-to-think-about-agent-frameworks/)

Agents (LlamaIndex). (https://docs.llamaindex.ai/en/stable/use_cases/agents/)

Agents (LlamaIndex). (https://docs.llamaindex.ai/en/stable/module_guides/deploying/agents/)

Workflows (LlamaIndex). (https://docs.llamaindex.ai/en/stable/module_guides/workflow/)

Shunyu Yao et al., 2022. ReAct: Synergizing Reasoning and Acting in Language Models. (https://arxiv.org/abs/2210.03629)

An example of multi‑agent orchestration with llama‑index. (https://github.com/run-llama/multi-agent-concierge/)

Grzegorz Malewicz et al., 2010. Pregel: a system for large‑scale graph processing. (https://research.google/pubs/pregel-a-system-for-large-scale-graph-processing/)

Multi‑agent systems (LangGraph). (https://langchain-ai.github.io/langgraph/concepts/multi_agent/)