ChatGPT 
Summary
Key takeaways
- The practical choice is not “LlamaIndex for RAG” versus “LangChain for agents” because both ecosystems now cover retrieval and agent workflows. The real decision depends on whether your hardest problem is retrieval quality, stateful orchestration, or both.
- LlamaIndex is usually the stronger starting point for document-heavy RAG, enterprise search, knowledge bases, and document Q&A systems built over private or connected data.
- LangChain with LangGraph is usually the better fit for multi-step agents that use tools, branch by condition, retain state, require approvals, and must resume safely after interruption.
- LlamaIndex is retrieval-first, with built-in abstractions for ingestion, indexing, query engines, retrievers, response synthesis, document parsing, and advanced retrieval patterns.
- LangGraph is orchestration-first, providing state graphs, persistence, checkpoints, interrupts, and human approval controls for durable agent workflows.
- A hybrid architecture is often the cleanest production option: LlamaIndex manages ingestion and retrieval, while LangGraph coordinates state, tools, decision logic, and approval gates.
- Retrieval quality often becomes the first real production bottleneck, especially when teams have inconsistent documents, weak metadata, poor chunking, duplicate records, or missing reranking and evaluation.
- Stateful agents introduce a different set of risks, including lost context, repeated actions, incomplete workflows, and unsafe behavior when state exists only in memory.
- Observability should be designed from the start so teams can trace failures across ingestion, retrieval, prompts, model calls, tool usage, and orchestration decisions.
- The best framework is the one that makes the most difficult part of the system easier to design, test, operate, and improve over time.
When this applies
This applies when you are building an AI application that must work with private documents, connected business data, enterprise knowledge bases, or multi-step agent workflows. It is especially relevant for teams deciding between RAG architecture, document intelligence, internal search, copilots, operational agents, or AI systems that must call APIs, use tools, maintain state, and wait for human approval. It also applies when a prototype is moving toward production and the team needs to decide how retrieval, orchestration, evaluation, observability, and permissions should be separated.
When this does not apply
This does not apply as directly when the product only needs a simple single-prompt chatbot, a small static FAQ assistant, or basic semantic search without advanced document processing, multi-step actions, or long-running workflow logic. It is also less relevant when the main challenge is choosing a foundation model, vector database, cloud provider, or UI framework rather than building the application layer around data retrieval and agent behavior. For a small experiment with no need for durable state, complex retrieval, production observability, or approval gates, either framework may add more abstraction than necessary.
Checklist
- Define whether the main product problem is retrieval, orchestration, or a combination of both.
- Choose LlamaIndex first when document ingestion, search quality, chunking, metadata, and grounded answers are the main engineering challenges.
- Choose LangChain with LangGraph first when the system must use multiple tools, branch, retain state, and recover after interruption.
- Identify all relevant data sources, including PDFs, office files, scans, tables, databases, APIs, and connected business systems.
- Define document parsing, extraction, normalization, and refresh workflows before building the assistant interface.
- Establish chunking rules, metadata fields, permission filters, and document hierarchy for the retrieval layer.
- Test retrieval quality with realistic questions before adding agent workflows or tool execution.
- Add reranking, citations, and evaluation datasets where basic top-k retrieval is not reliable enough.
- Define explicit state schemas for every agent workflow that has multiple steps or external actions.
- Add checkpoints and durable persistence for workflows that may be interrupted or resumed later.
- Introduce human approval gates before any agent performs consequential actions.
- Keep retrieval responsibilities separate from orchestration responsibilities in hybrid systems.
- Select one observability strategy and trace the entire request path from ingestion through final output.
- Monitor model usage, retrieved context size, tool calls, retries, latency, and persistent-state costs.
- Validate the architecture against real production failure scenarios before expanding the system to more users or data sources.
Common pitfalls
- Choosing a framework based on an outdated assumption that LlamaIndex only supports RAG and LangChain only supports agents.
- Starting with a generic agent when the actual problem is weak document ingestion, poor chunking, or unreliable retrieval.
- Treating a vector database and top-k search as a complete RAG architecture.
- Building document workflows without preserving metadata, hierarchy, source references, and permission controls.
- Using multi-step agents without explicit state management, checkpoints, or recovery behavior.
- Allowing agents to take actions without approval gates in workflows that involve sensitive or consequential decisions.
- Mixing retrieval logic, tool orchestration, and business rules into one tightly coupled workflow that becomes difficult to test and maintain.
- Adding observability only after the system starts producing incorrect answers, excessive costs, or inconsistent actions.
- Ignoring evaluation until after launch instead of continuously measuring retrieval quality, answer grounding, latency, and cost.
- Forcing one framework to imitate the other instead of using a hybrid architecture when both retrieval quality and durable orchestration are essential.
The old 2023 shorthand—LangChain for orchestration and LlamaIndex for retrieval—no longer explains the real decision. Both frameworks now cover retrieval and agent workflows. The practical question is where your hardest problem lives: retrieving reliable context from private data, coordinating stateful agent actions, or doing both in one production system.
For document-heavy RAG, enterprise search, and knowledge-base applications, LlamaIndex is usually the stronger starting point. For long-running, stateful, multi-step agents with approval gates and durable state, LangChain together with LangGraph is usually the better fit. In many production architectures, they complement rather than replace each other.
Key takeaways
- Choose by bottleneck. Use LlamaIndex when retrieval quality is the central challenge; use LangChain and LangGraph when complex orchestration is the central challenge.
- The category split has converged. Both ecosystems now support retrieval and agent workflows, so “RAG framework” versus “agent framework” is too simplistic.
- LlamaIndex is retrieval-first. It provides document ingestion, indexing, query engines, advanced retrieval patterns, and document parsing in one ecosystem.
- LangGraph is orchestration-first. It is designed for durable execution, stateful workflows, persistence, and human-in-the-loop controls.
- Hybrid architectures are often the clearest option. LlamaIndex can own ingestion and retrieval while LangGraph coordinates decisions, tools, state, and approvals.
The verdict, up front
Choose LlamaIndex when retrieval is the core problem: RAG, document Q&A, enterprise search, or a knowledge system that must reliably find and synthesize information across private files and connected data sources. Choose LangChain with LangGraph when the core problem is orchestration: an agent that branches, uses several tools, carries state across steps, waits for approval, and resumes safely after an interruption.
For serious production RAG, a hybrid can be the most maintainable design: LlamaIndex handles ingestion and retrieval, LangGraph runs the broader workflow, and one observability layer traces the entire request.
A decision guide for LlamaIndex vs LangChain: route by where your hardest problem lives — retrieval to LlamaIndex, agent orchestration to LangGraph, and most production RAG systems to a hybrid of both.
At a glance: LlamaIndex vs LangChain
| Dimension | LlamaIndex | LangChain and LangGraph |
|---|---|---|
| Primary centre of gravity | Data ingestion, indexing, retrieval, and RAG | Agent development, integrations, and stateful orchestration |
| Best starting point | Document Q&A, enterprise search, knowledge bases, retrieval-heavy assistants | Multi-step agents, tool use, approvals, workflows with durable state |
| Orchestration model | Event-driven, async-first Workflows | LangGraph state graphs, persistence, interrupts, and human-in-the-loop controls |
| Retrieval tooling | High-level query engines, document-aware retrieval, auto-merging patterns, and data connectors | Composable loaders, splitters, vector stores, retrievers, rerankers, and tools |
| Document parsing | LlamaParse for document parsing and agentic OCR workflows | Usually assembled through chosen loaders and integrations |
| Observability | Callbacks and third-party tooling | First-party LangSmith, plus third-party tooling where required |
| Strongest fit | Document-heavy RAG systems | Stateful, multi-step agent systems |
What each framework is—and is not
LlamaIndex
LlamaIndex is a framework for connecting language models to private and operational data. Its core workflow is straightforward: ingest documents and data from connected systems, index them, retrieve relevant context, and use that context to produce grounded answers or actions. It is not limited to basic vector search. The ecosystem includes query engines, retrievers, routing, post-processing, and retrieval patterns that help teams move beyond a simple top-k search.
Its document capabilities are also broader than the name “Index” suggests. LlamaParse supports more than 130 document and image formats, making it a practical option when a RAG system must work with PDFs, office documents, scans, tables, and mixed-format enterprise content. LlamaIndex Workflows add event-driven, async-first orchestration for teams that need document-centric agents rather than a retrieval library alone.
LangChain and LangGraph
LangChain is a general-purpose framework for building language-model applications and agents. Retrieval is one capability inside the ecosystem, alongside model integrations, tools, structured outputs, prompt handling, and agent loops. Its modular approach can require more assembly for RAG, but that extra composition gives teams direct control over each part of the pipeline.
For production agent workflows, LangChain is commonly paired with LangGraph. LangGraph is built for long-running, stateful workflows with durable execution, persistence, streaming, and human-in-the-loop controls. LangChain’s own documentation describes LangChain agents as running on top of the LangGraph runtime, which makes the division of responsibilities clearer: LangChain provides higher-level agent building blocks, while LangGraph provides the lower-level orchestration runtime.
Why the old split no longer works
It is still useful to describe LlamaIndex as retrieval-first and LangGraph as orchestration-first, but it is no longer accurate to treat either ecosystem as confined to one category. LlamaIndex can coordinate complex document-centric workflows. LangChain can build capable retrieval pipelines. The difference is not whether a framework can complete a task; it is how directly its default abstractions map to the task.
That distinction matters in delivery. A team building a document assistant may spend most of its effort on parsing, chunking, metadata, search, reranking, citations, and evaluation. A team building an operational agent may spend most of its effort on tool selection, state transitions, error recovery, permissions, approval gates, and auditability. The framework should reduce complexity in the part of the system that is genuinely difficult for your product.
Retrieval and indexing
LlamaIndex is the more natural default when retrieval quality is the main engineering problem. Its ecosystem is organized around connecting data to LLM applications, with higher-level abstractions for loaders, indexes, query engines, retrievers, and response synthesis. It also provides documented retrieval patterns such as auto-merging retrieval, which can consolidate related child chunks into a larger parent context when the retrieval result calls for it.
This can reduce the amount of glue code required when a system needs more than a vector store and a top-k query. It is particularly useful when you need to tune chunking, preserve document hierarchy, apply metadata filters, introduce reranking, or handle questions that span multiple source documents.
Data ingestion is another area where LlamaIndex has a strong default. Its LlamaHub connector ecosystem provides loaders that bring external sources into a normalized document representation, while the LlamaIndex package documentation notes that the ecosystem includes more than 300 integration packages. That does not eliminate the need for data ownership, synchronization, permission, and quality controls, but it can shorten the path to a working ingestion pipeline.
Agents, orchestration, and durable state
LangGraph is the stronger fit when the application must coordinate a sequence of actions rather than answer a question from retrieved context. Examples include an agent that classifies a request, searches internal documents, calls an API, performs a business-rule check, requests approval, and resumes later with the same state.
The key distinction is durable state. LangGraph persistence supports checkpoints and stored state so a workflow can continue after an interruption or failure. Its interrupt model is designed for human-in-the-loop patterns, including approval and review steps. Those capabilities are important in workflows where an agent should not be allowed to act autonomously without a controlled pause.
LlamaIndex Workflows can also coordinate asynchronous, event-driven work, and they are a strong option for document-centred agents. The choice becomes clearer when you ask whether the agent exists mainly to retrieve and reason over information, or mainly to manage a longer-running stateful process.
Observability is not optional in production
A production RAG or agent system needs request-level visibility. When an answer is wrong, a team must be able to tell whether the failure came from ingestion, retrieval, reranking, prompt construction, model output, tool use, or an orchestration decision. Without this trail, reliability problems are difficult to diagnose and impossible to improve systematically.
LangChain’s first-party advantage is LangSmith, which combines tracing, evaluation workflows, datasets, and prompt management. It is particularly convenient when the application is already built around LangChain and LangGraph. For systems that combine multiple frameworks, a cross-framework option such as Langfuse can be useful for tracing calls across retrieval, orchestration, and model layers.
The important architectural rule is simple: choose one observability strategy early, instrument the full request path, and make evaluation part of the release process rather than a late-stage debugging exercise.
GitHub stars in mid-2026: LangChain leads on raw community size, with LlamaIndex and LangGraph behind — a momentum signal, not an adoption metric.
Use LlamaIndex when
- Your central challenge is RAG, enterprise search, document Q&A, or a knowledge base over private data.
- You need to iterate quickly on loaders, chunking, metadata, document hierarchy, reranking, or query engines.
- You work with complex files such as PDFs, tables, scans, office documents, or mixed-format content.
- You want retrieval-focused abstractions before building a custom pipeline from individual components.
- Your agent is primarily document-centric and retrieval quality matters more than a complex state machine.
Use LangChain and LangGraph when
- Your application must execute several tools, branch by condition, preserve state, and recover from interruption.
- You need durable workflows with checkpoints, human approvals, and controlled handoffs.
- Retrieval is one tool among many rather than the system’s main capability.
- You value an agent-oriented ecosystem with first-party tracing and evaluation workflows.
- Your team needs fine-grained control over state transitions and orchestration behavior.
Use both when
A hybrid architecture is often the cleanest answer for a production system that must both retrieve reliably and take action safely. In this model, LlamaIndex ingests and retrieves from the document corpus. LangGraph controls the workflow: deciding when to search, when to call a tool, when to ask for clarification, and when to pause for human approval.
Keep the responsibilities clear. Let LlamaIndex own the data and retrieval layer. Let LangGraph own stateful orchestration. Use one model-client abstraction and one observability layer across both. This separation avoids a brittle architecture in which one framework is forced to imitate the other’s strengths.
What breaks first at scale
The first production failure is often not infrastructure throughput. It is retrieval quality. As the document corpus grows, naive chunking can surface near-duplicates, miss relationships across files, and return context that sounds relevant but does not answer the user’s question. Solving that problem requires disciplined ingestion, metadata, evaluation, and retrieval design.
The next common failure is state management. Agents that call tools and run across multiple steps can lose context, repeat actions, or stop halfway through an important workflow if state lives only in memory. Durable checkpointing and explicit state schemas are essential for workflows that must recover safely.
Cost and debugging follow closely behind. Multi-step agents can multiply model calls, tool calls, and token usage. A system without traceable requests cannot reliably explain why its cost, latency, or answer quality changed. Teams should design for evaluation and observability before they move from a prototype to live users.
Cost at scale
| Cost driver | What drives it | What to design for |
|---|---|---|
| Model usage | Number of calls, prompt size, retrieved context, and agent loops | Keep retrieval precise, control retries, and avoid unnecessary agent hops |
| Data and vector storage | Document volume, embeddings, metadata, and query traffic | Set retention rules, index only useful content, and monitor query patterns |
| Document parsing | File complexity, OCR needs, refresh frequency, and throughput | Separate initial backfills from incremental updates and validate extracted content |
| Orchestration and observability | Persistent state, trace retention, evaluations, and workflow runtime | Trace every production request and keep checkpoints only as long as the business process requires |
Our data: lines of code for a minimal RAG pipeline
In Uvik’s minimal comparison, both implementations performed the same high-level task: load a folder of documents, create a retrievable index, and answer a question from retrieved context. We counted non-blank, non-comment lines in each baseline.
| Task: minimal RAG over a folder of documents | LlamaIndex | LangChain baseline |
|---|---|---|
| Total lines | 7 | 20 |
| Lines excluding imports | 6 | 13 |
This is a directional implementation comparison, not a universal performance benchmark. Production code changes the result: custom loaders, chunking, embedding models, vector stores, rerankers, authentication, error handling, evaluation, and observability all add complexity. The practical takeaway is that LlamaIndex can provide a shorter path to a working retrieval baseline, while LangChain exposes more of the pipeline as composable building blocks.
Decision scorecard
| If your priority is… | Choose | Why |
|---|---|---|
| Retrieval quality with less custom assembly | LlamaIndex | Retrieval-first abstractions, document-aware tooling, and advanced retrieval patterns |
| The fastest route to a small RAG proof of concept | LlamaIndex | High-level ingestion, indexing, and query-engine abstractions |
| Document parsing, tables, scans, and OCR-oriented workflows | LlamaIndex | LlamaParse and a document-centred ecosystem |
| Stateful multi-step agents | LangChain and LangGraph | State graphs, persistence, interrupts, and approval-oriented workflows |
| First-party tracing and evaluation workflows | LangChain and LangGraph | LangSmith integration for tracing, evaluation, datasets, and prompt management |
| A production RAG system with complex actions | Both | LlamaIndex retrieves; LangGraph orchestrates; one observability layer connects the full path |
From the field
The reframe that saves the most time is to stop asking, “Which framework wins?” and ask, “Where is the bottleneck?” When answers are unreliable, the issue is usually retrieval quality: document parsing, chunking, metadata, search, reranking, or evaluation. When the system needs to carry state, take actions, and stop for approval, the issue is orchestration.
Choose the framework that makes your hardest problem easier to reason about, test, and operate. For a retrieval-heavy assistant, start with LlamaIndex. For a durable operational agent, start with LangGraph. For a system that needs both, design the boundary deliberately and use a unified observability strategy from the first production release.
Related reading
Planning a production RAG or agent system? Explore RAG development services, AI development services, and data engineering services from Uvik Software. For a broader view of the Python tools behind modern AI systems, read The Python Ecosystem Scorecard 2026.
Sources and references
- LlamaIndex on GitHub
- LlamaIndex Workflows documentation
- LlamaParse supported document types
- LlamaHub data connector documentation
- LangChain on GitHub
- LangGraph on GitHub
- LangGraph overview documentation
- LangSmith evaluation documentation
Frequently asked questions
Can I use LlamaIndex and LangChain together?
Yes. A common architecture uses LlamaIndex for ingestion and retrieval, while LangGraph orchestrates the wider agent workflow. Keep the boundary explicit so retrieval logic does not become entangled with state management and tool orchestration.
Which is better for RAG, LlamaIndex or LangChain?
LlamaIndex is usually the better default when RAG is the central product capability because its abstractions are organized around ingestion, indexing, retrieval, and query engines. LangChain can build RAG systems too, but it is often a better choice when retrieval is one component in a larger agent workflow.
Which is better for agents?
LangChain and LangGraph are the stronger fit for complex, stateful agents that need durable execution, branching, persistence, and human approval. LlamaIndex Workflows are a strong alternative when the agent is mainly centred on documents and retrieval.
Is LlamaIndex still only a RAG library?
No. Retrieval remains its centre of gravity, but its workflow and document-processing capabilities make it suitable for document agents, structured extraction, and event-driven AI applications as well.
Is LangChain dead?
No. Its role has become clearer. LangChain provides the higher-level agent framework and integrations, while LangGraph supplies the runtime for durable, stateful orchestration. That split is useful for teams building production agents rather than simple prompt chains.
What is LlamaIndex used for?
LlamaIndex is used to connect LLM applications to private data. Typical use cases include RAG, document Q&A, enterprise search, knowledge assistants, document parsing, and retrieval-backed workflows.
