We ship production RAG and agent systems on both LangChain (with LangGraph) and LlamaIndex. This is not a feature-list roundup written by someone who tried both for an afternoon. It is an opinionated take from a team that runs both frameworks in shipped products, including VakeelSaathi legal RAG on 50,000 plus Indian court judgments. Both are excellent. They solve overlapping but distinct problems. The right choice depends on whether your product is retrieval-heavy or agent-heavy, and often the honest answer is to use them together.
If you only have a minute, here is where we land after two years of building on both frameworks across eight-plus production deployments.
Before comparing them, it helps to name what each is really trying to do. Marketing pages blur this.
LangChain started in October 2022 as a general-purpose framework for building applications with LLMs. Its scope is wide: prompt templates, output parsers, memory, chains, retrievers, embeddings, vector store wrappers, tool integrations, callbacks, and dozens of connectors. It is closer to a Swiss Army knife than a scalpel. The framework has evolved through a few painful iterations (the legacy AgentExecutor, the LCEL rewrite, and now LangGraph) and the 0.2 and 0.3 series in 2025-2026 are the stable modern surface. LangGraph is the newer, more opinionated framework from the same team for stateful, graph-based agent workflows. Think of LangGraph as a state machine you compose from nodes and edges, where each node is typically an LLM call or a tool call, and edges route based on state. For anything more complex than a linear chain, LangGraph is now the recommended path.
LlamaIndex (formerly GPT Index) started in November 2022 with a much narrower goal: connect your private data to LLMs. That focus shows. LlamaIndex has the most polished data connectors ecosystem (LlamaHub covers 300+ sources from Notion to Slack to Airtable to your custom API), the most sophisticated node-parsing and chunking strategies (sentence-window, hierarchical, semantic splitters, document summary indices), and the most thoughtful query engines (router, sub-question, multi-document, recursive). It has since expanded into agents (LlamaAgents, workflows) and structured outputs, but its centre of gravity remains retrieval and query planning over documents.
The overlap is real. Both can do RAG. Both can do agents. Both wrap the same underlying LLM providers and vector stores. But their default patterns and their opinions diverge in useful ways.
Snapshot as of July 2026. Both projects ship weekly, so verify on their docs before committing.
| Capability | LangChain (+ LangGraph) | LlamaIndex |
|---|---|---|
| Primary focus | General LLM app framework, agent orchestration | Data ingestion, indexing, query planning over private data |
| Retrieval sophistication | Good, standard vector plus BM25 hybrid | Excellent, includes sub-question, recursive, router, multi-document |
| Data connectors | ~200 via LangChain community | ~300 via LlamaHub, more polished ingestion |
| Node parsing / chunking | Basic text splitters | Sentence-window, hierarchical, semantic, JSON, HTML-aware |
| Agent framework | LangGraph, mature and expressive | Workflows and LlamaAgents, catching up but younger |
| Multi-agent orchestration | LangGraph strong, community patterns established | Newer, less battle-tested |
| Memory / persistence | Multiple options, checkpointing in LangGraph | Simpler chat store abstractions |
| Structured output | Pydantic, JSON schema, output parsers | Pydantic programs, structured LLM interface |
| Streaming | Full support, token, event, and node-level | Full support at query engine level |
| Evaluation tools | LangSmith (paid), Ragas integration | Native evals module, Ragas, DeepEval, TruLens |
| Observability integrations | LangSmith native, Langfuse, Arize, Phoenix | Langfuse, Arize, Phoenix, W&B, native tracing |
| TypeScript / JS SDK | LangChain.js mature, close to Python parity | LlamaIndex.TS exists, meaningfully behind Python |
| Docs quality | Improved significantly since 0.2, still large surface | Cleaner, more example-driven, easier to skim |
| Production readiness | Battle-tested at scale, LangGraph proven in 2026 | Battle-tested for retrieval, agent workflows newer |
| Managed / hosted offering | LangGraph Platform, LangSmith | LlamaCloud (parsing, ingestion, hosted indexes) |
| Community / ecosystem size | Larger, more Stack Overflow answers, more integrations | Smaller but higher signal, dedicated Discord |
| Typical build effort for basic RAG | 2-4 days | 1-3 days |
| Typical build effort for complex agent | 1-2 weeks with LangGraph | 2-3 weeks (younger patterns) |
| License | MIT | MIT |
LlamaIndex documentation is easier to skim if you know what you want. LangChain documentation is more comprehensive but the sheer surface area makes it harder to find the current best pattern (LCEL vs legacy chains vs LangGraph). If you are new to both, expect a steeper learning curve on LangChain simply because there is more to learn.
Three concrete scenarios where LangChain is our default recommendation.
You are building an agent that reads emails from Gmail, checks CRM records in Salesforce, drafts a reply, waits for human approval, updates the CRM, and schedules a follow-up in Calendar. Six tools, multi-step, needs memory and human-in-the-loop. LangGraph's state machine model plus checkpointing plus the existing ecosystem of tool wrappers make this a two-week build. Doing the same in LlamaIndex is possible but you will hand-write more glue.
You want to route simple queries to a cheap model, escalate to a flagship on complex ones, and fall back to a different provider when one has an outage. LangChain's Runnable interface plus with_fallbacks and configurable_alternatives make this a config-driven exercise. LlamaIndex can wrap the same LLMs but its abstractions are less agent-first.
A research agent that plans a query, searches web plus internal docs, evaluates the results, decides whether it has enough evidence, loops back if not, then writes a report. That control flow is what LangGraph was designed for. Nodes, conditional edges, cycles, checkpoints. Trying to model this in LlamaIndex workflows works but you are fighting a younger abstraction.
Three concrete scenarios where LlamaIndex is our default recommendation.
You have 10,000 contracts, judgments, or research papers. You need clause-level retrieval, faithful citations, and the ability to answer questions that require reasoning across multiple documents. LlamaIndex's node parsers, document summary index, sub-question query engine, and recursive retrievers were built for this exact workload. On VakeelSaathi we tried both and LlamaIndex was faster to a defensible answer with less code.
You want to build a queryable knowledge graph from a mix of Notion pages, Airtable records, and PDF reports. LlamaHub connectors ingest most sources cleanly, and the property graph index plus knowledge graph query engine give you graph-aware retrieval without hand-writing much. LangChain has GraphRAG support but LlamaIndex has been iterating on this longer.
You are building internal search that spans Confluence, SharePoint, Slack, Google Drive, Salesforce, and Zendesk. LlamaHub's 300 plus connectors plus router query engine (pick the right sub-index per query) give you the shortest path to a working search. This is LlamaIndex's home turf.
15-minute call. Tell us your top 3 use cases, your data shape, and your team's Python and TypeScript skills. We will tell you which framework to lead with, whether to combine them, and what the first prototype should look like.
Book Free 15-min CallThis is the pattern we default to for anything beyond a small prototype, and it is what most experienced teams we have compared notes with also run.
LlamaIndex owns the retrieval layer. Data connectors ingest source systems. Node parsers chunk documents intelligently based on structure. Indexes get built and maintained (vector, keyword, graph, or hybrid). Query engines expose the retrieval as clean interfaces. This is a good fit because LlamaIndex's abstractions map cleanly to how you actually think about documents (nodes, indexes, query engines) rather than how you think about LLM calls.
LangGraph owns the orchestration layer. The user query hits the agent. The agent decides which tool to call (one of the LlamaIndex query engines, plus other tools like a web search, a calendar, a database). Multi-step reasoning happens as nodes in the state graph. Human-in-the-loop pauses are checkpoints. Memory persists across sessions via LangGraph's built-in state store.
The bridge is a one-liner. LlamaIndex exposes each query engine as a LangChain-compatible tool via QueryEngineTool or a small wrapper. LangGraph consumes it like any other tool. You keep the strengths of both frameworks and avoid the weaknesses.
What this looks like in code, conceptually. LlamaIndex code lives in a `retrieval/` module that exposes `build_query_engine(name)` returning a query engine per index. LangGraph code lives in an `agent/` module that constructs a StateGraph, imports the query engines as tools, and wires up the flow. Provider-neutral LLM access via LiteLLM sits under both. Langfuse observability is initialised once and both frameworks emit traces to it. This is the structure we use on VakeelSaathi and on most client builds.
Keep your LlamaIndex code in one directory and your LangGraph code in another, with a thin adapter layer between them. When the framework wars flare up and one library ships a breaking change, you only refactor one side. This has saved us multiple weeks of maintenance across our production stack.
If you already picked one and want to switch, or if you want to hedge against framework risk, here is what actually works.
LangChain to LlamaIndex for retrieval. Straightforward. Re-ingest your documents through LlamaIndex node parsers, rebuild the index (usually on the same underlying vector store, so this is a re-embed job, not a full data migration), and swap the retriever in your chain for a LlamaIndex query engine wrapper. Budget two days for a mid-sized index.
LlamaIndex to LangGraph for orchestration. Also straightforward. Keep your LlamaIndex query engines, wrap them as LangChain tools, and rebuild the workflow as a LangGraph StateGraph. Budget three to five days depending on workflow complexity.
Either framework to raw provider SDK. Harder but doable if you kept your business logic separate from the framework glue. If you sprinkled LangChain calls all through your codebase, this is a rewrite. Lesson: keep a thin service layer between your framework code and your app code from day one.
Framework-neutral escape hatch. LiteLLM for LLM calls (100 plus providers behind one API). Langfuse for observability. A vector store you own (Qdrant self-hosted or Pinecone). If you commit to these three, switching frameworks becomes a bounded refactor rather than a rewrite. This is our default architectural principle regardless of which framework you pick.
Both frameworks have real competition in 2026. None of these are drop-in replacements but each is a legitimate choice for a specific profile.
Haystack (deepset). Mature, more opinionated pipeline framework. Popular in European enterprises for its clean abstractions and its native support for on-prem model serving via deepset Cloud. Good choice if you want a more Django-like framework with fewer moving parts.
DSPy (Stanford). A different paradigm. Treats prompts as programs you optimise via metrics rather than hand-tune. Excellent for research and for teams that want to squeeze quality out of smaller models. Steeper learning curve. Worth exploring if you have an evaluation dataset and want to automate prompt optimisation.
Pydantic AI. Newer, from the Pydantic team. Type-safe, minimal, focused on agents with structured outputs. Loved by teams who find LangChain too heavy. Smaller ecosystem, fewer integrations, but the API is clean and stable. Good for greenfield agent projects where you want typed Python and not much else.
LlamaStack (Meta). Newer, opinionated stack from Meta covering inference, agents, tools, and evals. Interesting but young. Worth watching if you are Meta-aligned or heavily using Llama models.
Raw provider SDK plus a small library. The most underrated option. For a straightforward RAG chatbot on one vector store with one LLM, you can ship a clean production system in 400 lines of Python using the OpenAI SDK, a vector store client, and a small orchestration function. Framework choice becomes overhead for small, focused projects.
Our default stack across 8 plus production deployments in 2025-2026.
Concrete example: VakeelSaathi legal RAG. LlamaIndex handles ingestion of 50,000 plus Indian court judgments with hierarchical parsing that preserves the judgment structure (facts, arguments, ratio, holdings). Hybrid retrieval with a legal-domain reranker. LangGraph orchestrates the multi-step agent that plans a query (identify statute, retrieve precedents, retrieve related case law, cross-reference, synthesise). Claude 3.5 Sonnet as primary LLM. The combined stack lets us swap models per node (cheaper model for classification, flagship for synthesis) without rewriting the retrieval side.
Framework maintenance overhead. Both frameworks require attention. We budget roughly two engineering days per quarter per project for framework upgrades, breaking-change fixes, and API deprecation handling. This is our reality across the stack. It has decreased notably from the chaos of 2023-2024 but is not zero.
If you are building a serious production system, plan to use both. If you are building a single-purpose prototype, pick whichever your team already knows. If nobody on your team knows either, start with LlamaIndex for RAG or LangGraph for agents, whichever matches your first use case. Do not over-optimise the framework decision. The business logic, evaluation harness, and data quality matter more.
Questions we hear on nearly every discovery call about RAG framework choice.
Tell us your top 3 use cases, your data shape, and your team's stack. We come back within 48 hours with a framework recommendation, a proposed architecture sketch, and a fixed prototype scope you can execute against.