Choose LlamaIndex when your main challenge is turning varied documents into useful retrieved context. Choose LangChain with LangGraph when your main challenge is coordinating a multi-step agent: routing, tool selection, retries, persistent state, or human approval. You do not have to choose one framework for every layer. A common hybrid is to expose a LlamaIndex query engine as a tool inside a LangGraph workflow.
What is the difference between LlamaIndex and LangChain?
LlamaIndex is organized around data: loading it, parsing and structuring it, indexing it, retrieving it, and evaluating the result. Its documentation also covers RAG pipelines, storage, structured extraction, agents, workflows, and integrations. It is a natural fit when the hard part of an LLM application is getting the right information out of heterogeneous sources.
LangChain is a general framework for building LLM applications. LangGraph is its lower-level orchestration runtime for building long-running, stateful agents. It is a natural fit when the difficult part is deciding what the application should do next, managing branches and tool calls, and maintaining state as a process runs.
The practical dividing line is where decisions happen. Deciding which documents matter, how they should be parsed, and how indexes should be combined is primarily a data and retrieval problem. Deciding which route or tool to use, whether to retry, and how to pause for a person is primarily an orchestration problem.
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Which should you use for RAG?
Choose LlamaIndex when retrieval quality is the central problem
Start with LlamaIndex if your application must ingest messy or varied inputs and retrieve relevant context reliably. Its retrieval patterns include hybrid search, recursive retrieval, query decomposition, sub-question generation, hierarchical node parsing, and auto-merging. Its named index types include VectorStoreIndex, SummaryIndex, TreeIndex, KeywordTableIndex, and PropertyGraphIndex.
Those options matter when a simple vector search is not enough—for example, when a question needs to be decomposed, when relevant information is nested in a hierarchy, or when multiple retrieval strategies need to contribute. The right choice depends on your data and query behavior; the list of available patterns is not evidence that any one pattern will improve every application.
LangChain also has substantial retrieval building blocks
LangChain is not limited to agent orchestration. Its retrieval primitives include EnsembleRetriever, ContextualCompressionRetriever, ParentDocumentRetriever, and MultiVectorRetriever. Its supported patterns include vector-store, graph, self-query, multi-query, time-weighted, parent-document, multi-vector, and contextual-compression retrieval. It can also integrate LlamaIndex retrievers.
So the choice is not “retrieval in LlamaIndex, no retrieval in LangChain.” A better question is whether you want a framework whose center of gravity is data ingestion and retrieval, or a broader application framework whose retrieval components sit alongside other application-building tools.
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Which is better for agents and workflows?
Choose LangGraph for orchestration-heavy agents
LangChain’s comparison describes LangGraph as a runtime for long-running, stateful agents. Persistence checkpoints graph state, allowing a run to pause for human approval and resume from that checkpoint. This makes LangGraph a strong fit when your system needs explicit control over its process: branching, tool selection, retries, durable state, or approval steps.
LangChain co-founder Harrison Chase defines an AI agent as “a system that uses an LLM to decide the control flow of an application.” That definition helps separate an agent loop from a retrieval system: the former decides what action to take, while the latter supplies information that can inform the action.
LlamaIndex supports multi-step work too
LlamaIndex provides event-driven Workflows and AgentWorkflow for multi-step and multi-agent applications. Checkpointing is available through WorkflowCheckpointer and is opt-in. If your workflow is centered on data operations and needs a workflow layer, LlamaIndex may cover both concerns without requiring a second framework.
Compare the actual control-flow requirements rather than deciding from the word “agent.” A workflow that retrieves documents and returns an answer has different orchestration needs from one that routes among tools, retries failed actions, retains state across a long run, and waits for human authorization.
How do their integrations compare?
| Framework | Reported integration figure | What the figure covers |
|---|---|---|
| LangChain | 1,000+ integrations, reported in 2026 | Models, vector stores, tools, embeddings, and document loaders |
| LlamaIndex | 300+ integration packages, reported in 2026 | Packages across the stack; 158 reader packages were verified in May 2026 |
These are publisher-reported, time-sensitive counts, not a permanent or independently normalized comparison. Package totals can count different categories and change over time, so they do not establish that one framework has a better integration for your specific provider. Check that the exact model, storage backend, loader, or tool your project needs is supported and maintained before choosing.
The LlamaIndex/LangChain comparison also reports that LlamaParse supports 130+ file formats and 100+ languages in 2026, including layout-aware extraction of charts, graphs, tables, and images. Treat these as reported capabilities, not a promise that every file or language will parse equally well. LlamaCloud is a separate managed service for parsing, indexing, and retrieval; the open-source LlamaIndex framework can be used without it.
Can you use LlamaIndex and LangChain together?
Yes. A useful boundary is to let LlamaIndex handle parsing, indexing, and retrieval, then wrap its query engine as a tool that a LangGraph node can invoke. LangGraph can own the agent loop, state, and tool calling, while LlamaIndex answers the narrower question: what relevant context can be retrieved for this query?
- Keep retrieval behind a clear interface. Define the query input and the result your agent needs—such as retrieved text and any source details your application chooses to pass along.
- Wrap the LlamaIndex query engine as a tool. The tool should accept the agent’s query and return the query-engine result in a form the workflow can use.
- Invoke that tool from the LangGraph workflow. Let the graph decide when retrieval is needed and how the result affects later steps.
- Handle failures at the boundary. Decide how retrieval errors, empty results, and timeouts should affect retries or alternate routes before putting the workflow into production.
For basic cases, the comparison names the community packages LlamaIndexRetriever and LlamaIndexGraphRetriever. If production behavior depends on controlled retries and error handling, a custom tool wrapper can give the team clearer ownership of that boundary. Confirm the current maintenance status and compatibility of any community package before adopting it; the package names alone do not establish support guarantees.
The hybrid approach is not automatically better. It introduces two frameworks to understand and operate. Use it when retrieval has enough specialized complexity to justify LlamaIndex and orchestration has enough complexity to justify LangGraph. If one side is modest, keeping the application in one framework may be simpler.
Deployment, observability, and operational complexity
LangSmith is described by LangChain as a framework-agnostic platform for observability, evaluation, and deployment across LangChain, LangGraph, LlamaIndex, several SDKs, and custom code. That makes it relevant even for a mixed stack, but the existence of a platform does not by itself settle whether it fits a team’s deployment or monitoring requirements.
LlamaCloud is a separate managed option for parsing, indexing, and retrieval. It may be useful when a team needs managed parsing for unstructured data at production scale; it is optional when the open-source LlamaIndex framework is sufficient. Decide whether a managed service solves a concrete operating need, and verify current service availability and pricing directly before making a procurement decision.
Operationally, a single-framework design usually means fewer interfaces to debug and fewer framework-specific concepts for a team to learn. A hybrid can create a cleaner separation of concerns, but adds an integration boundary where result formats, failures, and retries must be handled deliberately. Consider who will own retrieval quality, who will own workflow behavior, and how each will be tested and monitored.
Best Value
A practical decision checklist
- Pick LlamaIndex first if complex document ingestion, parsing, indexing, or retrieval is the work most likely to determine whether the product succeeds.
- Pick LangChain with LangGraph first if the central requirement is an agent that routes among tools, branches, retries, preserves state, or pauses for a human.
- Consider both if retrieval needs specialized data-layer behavior and the application also needs substantial stateful orchestration.
- Prefer the simpler stack when one framework already covers the requirements well; integration breadth and feature lists are not reasons on their own to add complexity.
- Evaluate with your own workload. No universal benchmark in the cited comparison establishes that one framework produces better answers for every application.
ScreenshotNeo: an alternative for website screenshots, not an LLM framework
ScreenshotNeo is not a substitute for LlamaIndex or LangChain: it is a website screenshot API and MCP server for developers. If the adjacent task in your project is capturing a web page as an image or PDF—not building RAG or agent workflows—it is an alternative to try first. A single GET request can return a PNG, JPEG, WebP, or PDF; its capture options include full-page shots, CSS-selected elements, device presets, custom CSS and JavaScript, and PDF settings. See ScreenshotNeo for the product overview.
For example, this cURL request captures a page to WebP. Use your own API key in place of the example value. See the ScreenshotNeo API documentation for request options.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpIts differentiators are specific: it accepts cookie or consent banners like a visitor and removes more than 60 known consent platforms, newsletter popups, and chat widgets before capture; each step can be turned off. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits cost nothing, and response headers report the page verdict and billing status. An MCP server provides take_screenshot, get_page_info, and capture_pdf tools for AI agents and MCP clients such as Claude and Cursor.
The free plan includes 1,000 screenshots per month with no card. Paid monthly plans are Starter at $5 for 3,000, Growth at $15 for 15,000, Pro at $39 for 60,000, Scale at $99 for 250,000, and Business at $249 for 1,000,000; yearly billing gives two months free. Every feature is on every plan. Sign up free for 1,000 screenshots a month, with no card required.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteFrequently Asked Questions
Does choosing LlamaIndex prevent a later move to LangChain or LangGraph?
No. The frameworks can interoperate through a retriever or query-engine tool boundary, though migration still requires adapting interfaces and workflow behavior.
Is the larger reported integration count proof that LangChain supports my stack better?
No. The totals are publisher-reported counts across broad categories. Verify support for the specific components and versions your application requires.
Does using both frameworks guarantee better RAG answers?
No. The hybrid is an architectural option, not a quality guarantee. Measure retrieval and end-to-end results on representative queries and data.
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