Compare ยท updated 2026-05-18

LangChain / LangGraph vs LlamaIndex

Compare graph-oriented agent orchestration with a data-oriented framework for retrieval and private-data workflows.

agent-framework

LangChain / LangGraph

Agent and graph runtime ecosystem for building tool-using, stateful, and observable LLM applications.

tool-callinggraph-runtimetracingevaluation

agent-framework

LlamaIndex

Data-oriented agent and workflow framework for building LLM agents over private data, tools, retrieval, and workflows.

tool-callingretrieval-contextworkflow-orchestrationmulti-agent-workflow

Recommendation

Use LangChain / LangGraph when graph-style control flow and broad integrations dominate the design. Use LlamaIndex when retrieval, document context, and data-connected workflows are the center of the application.

Comparison criteria

graph runtimeretrieval contextworkflow orchestrationecosystem breadthRAG fit

Decision matrix

Criterion LangChain / LangGraph LlamaIndex Winner Summary
Graph runtime Strong graph-oriented workflow model through LangGraph. Workflow support exists, but positioning is more data and retrieval oriented. left LangChain / LangGraph is the clearer fit for graph-native agent control flow.
Retrieval context Broad integration ecosystem for retrieval workflows. Data and retrieval are central to the framework identity. right LlamaIndex is the stronger default when document and data context are the primary concern.
Ecosystem breadth Large ecosystem across providers, tools, and workflows. Strong ecosystem around data connectors and retrieval patterns. depends Breadth depends on whether the project optimizes for general agent integrations or data workflows.
Deployment fit Suitable for server, container, and workflow runtime deployments. Suitable for server, container, and workflow runtime deployments. tie Both can fit production service deployment patterns with source-specific validation.

Related compatibility facts

Source Target Status Evidence
langchain-langgraph serverless-container-runtime supported LangChain and LangGraph applications commonly run in server, container, and workflow runtime deployments.
llamaindex serverless-container-runtime supported LlamaIndex agent and workflow applications can run as service or container deployments for data-connected agent workflows.

When should teams choose LangChain / LangGraph over LlamaIndex?

Choose LangChain / LangGraph when graph-style control flow and broad integrations dominate. Choose LlamaIndex when document context, private data, and retrieval-first workflows are the center of the system.