LangGraph vs LlamaIndex: How They Differ
- Leanware Editorial Team
- 6 hours ago
- 9 min read
When you start building real AI systems, you quickly realize it’s not just about sending prompts and getting answers. You have to manage how components talk to each other, keep track of what’s happening between steps, and pull the right data when it’s needed. That’s where frameworks like LangGraph and LlamaIndex fit in.
LangGraph focuses on coordinating multiple agents and maintaining state across workflows. LlamaIndex connects models to external data through retrieval and indexing.
This guide explains how they differ, where each works best, and how to choose the right one for your setup.
Key Takeaways
LangGraph focuses on multi-agent orchestration and stateful workflows.
LlamaIndex specializes in RAG pipelines and knowledge retrieval.
LangGraph evolved from LangChain, designed for complex state management.
LlamaIndex started as GPT Index, optimized for indexing and query systems.
LangGraph offers strong observability and debugging tools via LangSmith.
LlamaIndex has a modular architecture for vector stores and retrieval engines.
They can complement each other: LangGraph can orchestrate agents that call LlamaIndex for retrieval.
Framework Maturity and Lineage
LangGraph originated from LangChain in early 2024 to handle workflows that traditional DAG frameworks couldn’t - cyclical, multi-turn processes where agents need to revisit states and maintain context.
Built on the Runnable API, it gives full control over execution flow through a graph-based architecture, where nodes are computational steps and edges define transitions. Visual tools like LangGraph Studio help design and debug workflows before deployment.
LlamaIndex started as GPT Index to connect LLMs to private or domain-specific data and evolved into a full framework for ingestion, indexing, and retrieval. Its high-level API enables quick prototypes, while lower-level APIs allow full customization of connectors, indices, retrievers, and query engines.
The ecosystem, including LlamaHub and LlamaLab, provides modular integrations and tools for both production and experimental use.
Both frameworks see active development. LangGraph focuses on multi-agent orchestration, state management, and observability, while LlamaIndex expands connectors, improves retrieval, and supports new vector databases. Both maintain active communities, extensive documentation, and resources for developers at different skill levels.
Features Comparison
These tools operate at different layers of the AI stack. LangGraph sits at the orchestration layer, managing how agents interact and maintain state.
LlamaIndex operates at the data layer, handling how information gets indexed and retrieved. This distinction shapes every aspect of their design.
1. Agent Orchestration
Agent orchestration determines how autonomous components coordinate to complete tasks. This becomes important when multiple agents need to work together, share state, or handle complex decision trees.
LangGraph
LangGraph models agent workflows as graphs, where nodes run code and edges define state transitions. This lets agents loop back, run parallel processes, or pause for human input.
Durable execution ensures workflows persist through failures, resuming from checkpointed state rather than starting over.
State management tracks conversation history, tool outputs, and intermediate steps, letting you control how updates are merged or accumulated. The graph structure also makes workflows explicit, helping with debugging and system visibility. Integration with LangChain allows use of existing components, while prebuilt functions like create_react_agent simplify common patterns.
LlamaIndex
LlamaIndex focuses on retrieval rather than orchestration. It can integrate with agent frameworks, but for multi-step workflows with persistent state, you’d pair it with LangGraph or LangChain agents. It supports basic query routing to different retrievers based on metadata or query type, which handles simpler branching logic.
2. Retrieval-Augmented Generation (RAG) Pipelines
RAG connects LLMs to external knowledge by retrieving relevant information and including it in prompts. This helps ground responses and reduces errors.
LangGraph
LangGraph can support RAG pipelines by including retrieval as nodes in the graph. For example, one node might query a vector store, another filters results, and a final node generates the response.
The framework orchestrates these steps but doesn’t provide retrieval infrastructure - you need your own vector stores, embeddings, and indexing logic.
LlamaIndex
LlamaIndex is built for RAG. It handles the full pipeline: loading documents, chunking text, generating embeddings, storing vectors, and running semantic queries.
You can start with the starter package or use llama-index-core and add only the integrations you need from LlamaHub. The framework supports multiple vector stores like Pinecone, Weaviate, Chroma, and Qdrant, and query engines allow hybrid search, metadata filtering, and multi-step retrieval.
Indexing is configurable, including chunking strategies, embedding models, and metadata extraction, and data persists to disk to avoid reindexing on restart.
3. Observability and Tracing
LangGraph
LangGraph provides built-in observability through its state transitions. Every node execution, edge traversal, and state update gets captured. This creates a complete audit trail showing exactly how your agent system arrived at its output.
LangSmith (LangChain's observability platform) integrates deeply with LangGraph to visualize execution paths, capture intermediate states, and provide runtime metrics. You can inspect what each agent saw, what tools it called, and how state evolved across the workflow.
The "time-travel" debugging feature lets you roll back to any previous state and explore alternative execution paths. This helps when an agent made a wrong decision and you need to understand why.
LlamaIndex
LlamaIndex offers basic logging that captures query execution and retrieval results. You can see which documents matched a query, what embeddings were generated, and how the final context was constructed.
The framework supports integration with third-party observability tools, but doesn't provide the same level of execution tracing that LangGraph offers. For most RAG applications, seeing query results and retrieved contexts provides sufficient debugging information.
4. State and Memory Management
LangGraph
State is central to LangGraph's design. The framework uses a shared state object that flows through the graph, with each node able to read and update specific fields. This enables persistent memory that spans multiple conversation turns.
The state model supports both short-term working memory for ongoing reasoning and long-term persistent memory across sessions. You can implement checkpointing to save state at specific points, allowing agents to resume after failures or human interruptions.
The event-driven architecture handles async operations naturally. Nodes can trigger events that other nodes respond to, creating reactive systems where components coordinate without tight coupling.
LlamaIndex
LlamaIndex manages document context rather than conversational state. Its focus is on maintaining indexes, tracking metadata, and organizing retrieved information. While this works well for long-context retrieval, it doesn't provide mechanisms for multi-turn conversation memory.
If you need conversational state on top of LlamaIndex retrieval, you'll implement that separately or use a framework that provides it.
Integration Capabilities
Both frameworks connect with the broader LLM ecosystem but take different approaches to integration.
LangGraph
LangGraph integrates tightly with LangChain, so you can use models from OpenAI, Anthropic, Google, Hugging Face, and others.
Any component that implements LangChain’s Runnable interface works within your graphs. You can compose workflows using retrievers, tools, and memory modules from LangChain’s ecosystem.
Deployment options include LangSmith Deployment for managed production hosting, or self-hosting on your own infrastructure.
LlamaIndex
LlamaIndex takes a more agnostic approach to integration. It works with LangChain when needed but doesn't require it. The framework connects to retrieval ecosystems like Haystack and integrates with various LLM providers.
Vector store support is extensive. Beyond the major hosted options like Pinecone and Weaviate, LlamaIndex supports local alternatives like Chroma and FAISS. This flexibility matters when you have specific infrastructure requirements or want to avoid vendor lock-in.
Data connector integrations cover common sources: databases, APIs, file systems, cloud storage, and web scraping. The connector ecosystem makes it straightforward to pull in data from wherever it lives.
Pricing
Both frameworks are open source, so the software itself is free. Costs come from infrastructure and optional commercial services.
LangGraph
LangGraph is completely open source and available on GitHub. You can use it without paying LangChain Inc. anything. However, the company offers commercial services built around LangGraph.
LangSmith Deployment provides managed hosting for LangGraph applications. Pricing tiers include Plus and Enterprise plans with different deployment options: fully managed cloud hosting, hybrid setups where data stays in your VPC, or self-hosted with managed control plane.
LlamaIndex
The core framework is open source. LlamaCloud is a managed service for parsing, indexing, and retrieval, including LlamaParse for advanced document extraction.
It provides free credits for testing, with usage-based pricing beyond that. Many teams run LlamaIndex entirely on open-source components without using LlamaCloud.
Choosing Between LangGraph and LlamaIndex
When to Use LangGraph
Use LangGraph when your complexity comes from agent coordination and state management:
Multi-agent workflows: Agents handle different tasks and share state.
Human-in-the-loop: Users approve actions or correct agents mid-execution.
Cyclical workflows: Agents retry, loop, or revisit steps.
Long-running tasks: Durable execution resumes from checkpoints.
Observability: Trace decisions and debug workflows.
When to Use LlamaIndex
Use LlamaIndex when your focus is on retrieval and data-driven functionality:
Document Q&A: Retrieves relevant passages efficiently.
Semantic search: Handles large document sets.
Mixed data types: Works with databases, PDFs, spreadsheets, APIs.
Enterprise knowledge management: Indexes internal documentation for queries.
Flexible vector stores: Supports Pinecone, Weaviate, FAISS, etc.
RAG pipelines: Optimizes retrieval with metadata filters and query strategies.
Which Is Best for Your Use Case?
These frameworks complement each other rather than compete. Many systems use LlamaIndex for retrieval and LangGraph for orchestration. If you mainly need to retrieve and synthesize documents, LlamaIndex handles it without extra orchestration.
For workflows where agents coordinate, maintain state, or involve human feedback, LangGraph manages the orchestration, with LlamaIndex used for retrieval if needed.
They also fit differently depending on your stack: LangGraph integrates naturally with LangChain, while LlamaIndex works with other frameworks. For scaling, LangGraph supports long-running, stateful processes, and LlamaIndex scales efficiently through indexing and vector stores.
The right choice depends on the role each framework plays in your system. You can also reach out to our team for guidance on integrating LangGraph and LlamaIndex, optimizing workflows, and making sure each part fits your system efficiently.
Frequently Asked Questions
What is the difference between LangGraph and LlamaIndex?
LangGraph orchestrates multi-agent workflows with graph-based state management. LlamaIndex specializes in retrieval-augmented generation by indexing documents and providing query interfaces. LangGraph handles how agents coordinate and maintain state, while LlamaIndex handles how data gets indexed and retrieved.
Can you use LangGraph and LlamaIndex together?
Yes. LlamaIndex can function as a retrieval component within a LangGraph workflow. You might create a LangGraph node that queries a LlamaIndex retriever, processes the results, and passes them to other nodes. This combines LlamaIndex's retrieval capabilities with LangGraph's orchestration features.
Is LangGraph better than LlamaIndex for RAG?
LlamaIndex is purpose-built for RAG pipelines with comprehensive indexing, vector store management, and query engines. LangGraph can support RAG by integrating retrieval tools, but doesn't provide the specialized RAG infrastructure that LlamaIndex offers. For RAG-focused applications, LlamaIndex provides more complete functionality out of the box.
Is LangGraph open source?
Yes, LangGraph is fully open source and available on GitHub under the MIT license. You can use it freely without cost. LangChain Inc. offers commercial services like LangSmith Deployment for managed hosting, but the core framework remains open source.
What is LlamaIndex used for?
LlamaIndex builds RAG applications by indexing documents, managing vector stores, and providing query interfaces. Common use cases include document Q&A systems, semantic search engines, enterprise knowledge bases, and any application where you need to ground LLM responses in external data sources.
How much does it cost to run LangGraph vs LlamaIndex in production?
Infrastructure costs depend on your deployment. For LangGraph, you'll pay for compute resources to run your agents (AWS Lambda, EC2, or container services) plus LangSmith Deployment if you use managed hosting.
For LlamaIndex, costs include vector database hosting (Pinecone, Weaviate pricing), compute for indexing and queries, and optionally LlamaCloud for managed parsing. A typical LangGraph deployment on AWS might run $200-500 monthly for moderate traffic, while LlamaIndex costs depend heavily on your vector store choice and document volume. Self-hosted vector stores reduce costs but require more operational effort.
How do I migrate from LlamaIndex to LangGraph (or vice versa)?
These frameworks serve different purposes, so "migration" usually means integration rather than replacement. If you're using LlamaIndex for retrieval and want to add agent orchestration, wrap your LlamaIndex queries in LangGraph nodes while keeping the indexing layer unchanged.
If you're using LangGraph and need better retrieval, integrate LlamaIndex as a component without rebuilding your orchestration logic. The key is recognizing that LlamaIndex handles data retrieval while LangGraph handles workflow coordination.
How do I debug common errors in LangGraph?
Start with LangSmith's trace visualization to see execution paths and state transitions. Common issues include: missing state transitions (check your edge definitions), memory context errors (verify state schema matches what nodes expect), and event handler failures (examine async operation timing).
Use checkpointing to capture state at problematic points, then replay execution with different inputs. The LangGraph documentation provides debugging guides for specific error types.
Which has better support for multimodal inputs (images, audio)?
Both frameworks support multimodal data through integration with appropriate models and processors. LangGraph can orchestrate workflows that include image or audio processing nodes by connecting to models like GPT-4V or Whisper.
LlamaIndex provides multimodal document ingestion pipelines that extract text from images and transcribe audio before indexing. Neither framework is inherently better at multimodal support, it depends on which models and processors you integrate.
How do backup and disaster recovery differ between them?
For LangGraph, DR focuses on state persistence. Use checkpointing to save state at critical points, store checkpoints in durable storage (databases or S3), and implement state snapshotting for recovery.
For LlamaIndex, DR centers on your indexed data. Back up vector store data, maintain document source references, and implement index versioning. LlamaIndex applications can rebuild indexes from source documents if needed, while LangGraph applications need state continuity to resume workflows correctly.
