LangChain
4.9Paid460
Editorial matchup · June 2026
LangChain vs LlamaIndex: Which AI Tool Is Better in 2026?
Side-by-side comparison of LangChain and LlamaIndex — pricing, features, and use cases. Reviewed by our editorial team in Jun 2026.
Use-case score 2–1Updated Jun 2026
AI/ML Models
LlamaIndex
4.9Freemium400
As of mid-2026, LangChain and LlamaIndex have sharpened their identities considerably since the last time this comparison was authored. LangChain has evolved from a broad LLM toolkit into a structured agent engineering platform, anchored by the stable release of LangChain 1.0 and LangGraph 1.0 in October 2025. LlamaIndex, meanwhile, has doubled down on its document-intelligence identity, shipping LlamaParse v2 in late 2025 alongside a full product suite — LlamaSheets, LlamaSplit, LlamaExtract, and LlamaAgents — that positions it squarely as the agentic document-processing layer for enterprise AI stacks.
The architectural difference is sharper than it looks on the surface. LangChain 1.0 is explicitly the "fastest way to build an AI agent" — it provides high-level abstractions for tool-calling, middleware, and provider-agnostic model switching. Underneath it, LangGraph 1.0 is the durable execution engine: state persists across server restarts, human-in-the-loop interrupts are a first-class API primitive, and the graph runtime is already in production at Uber, LinkedIn, Klarna, and JP Morgan. With 138,000 GitHub stars, 28 million monthly downloads, and 90 million cumulative downloads across Python and JavaScript, LangChain has the largest installed base of any LLM framework by a meaningful margin. The framework core — LangChain, LangGraph, LangServe — is MIT-licensed and costs nothing. The commercial surface is LangSmith, which offers a free Developer tier (5,000 traces per month) and a paid Plus tier for production teams.
LlamaIndex takes a different angle. Its open-source Python and TypeScript framework is also free, with roughly 40,000 GitHub stars and 3 million monthly downloads. The managed commercial product, LlamaCloud, operates on a credit-based model with a free tier offering 10,000 credits per month. LlamaParse v2 — released December 2025 — simplified the parsing surface to four tiers (Fast, Cost Effective, Agentic, Agentic Plus) and delivered up to a 50% reduction in parsing costs. New integrations with GPT-5 and Gemini 2.5 Pro in LlamaParse's agentic modes arrived in mid-2025. LlamaExtract gained TypeScript SDK support and page-level granularity. LlamaSheets hit public beta for spreadsheet intelligence. The entire suite is designed for one problem: turning messy documents — PDFs, Word files, PowerPoints, spreadsheets, scanned forms — into structured, model-ready context.
For teams choosing between them, the deciding question is what problem sits at the center of the application. If you are orchestrating multi-step agent workflows with branching logic, tools, long-running state, human checkpoints, and cross-session memory, LangChain plus LangGraph is the mature, production-proven answer, with enterprises like Ally Financial and Snowflake already in production on it. If your core problem is connecting an LLM to a large body of complex proprietary documents — financial filings, legal contracts, technical manuals — and you need accurate parsing, configurable retrieval, and minimal pipeline engineering, LlamaIndex wins on specificity and depth. Many production stacks in 2026 use both: LlamaIndex as the document ingestion and retrieval layer, LangChain/LangGraph as the orchestration and agent execution layer. The combination is documented and officially supported.
Best for durable, stateful agent workflows
LangChain
LangGraph 1.0 — which reached stable GA in October 2025 and powers agents at Uber, LinkedIn, and Klarna — provides built-in state persistence, human-in-the-loop interrupts, and graph-based execution that LlamaIndex's Workflows library does not match in depth.
Best for complex document ingestion and RAG
LlamaIndex
LlamaParse v2 with four parsing tiers, LlamaExtract for schema-based field extraction, LlamaSplit for document separation, and 160-plus LlamaHub data connectors give LlamaIndex a specialized document pipeline that LangChain cannot replicate with comparable precision.
Best ecosystem and integration breadth
LangChain
LangChain's 138,000 GitHub stars, 28 million monthly downloads, and 1,000-plus community integrations across Python and TypeScript represent the largest surface area in the LLM framework space, and LangSmith adds framework-agnostic observability on top.
Section 01
Best for what
4 use cases scored. LangChain wins 2, LlamaIndex wins 1.
- EvenPricing value
Neither tool publishes a starting price.
- LlamaIndexFree tier
LlamaIndex offers a free tier; LangChain is paid only.
- LangChainUser ratings
LangChain averages 4.9 / 5 vs 4.9 / 5 on the other side.
- LangChainReview volume
LangChain has 212 ratings vs 186 on the other.
Section 02
Pros & cons
Where each tool earns its rating — and where it falls short.
LangChain
AI/ML ModelsPros
- LangGraph 1.0 (GA October 2025) delivers durable agent state — execution survives server restarts mid-workflow — plus built-in persistence, human-in-the-loop interrupts, and graph-based branching and retry logic proven in production at Uber, LinkedIn, Klarna, and JP Morgan.
- LangChain 1.0 introduced a middleware architecture and a create_agent() abstraction that lets teams prototype quickly with high-level APIs, then drop down to raw LangGraph primitives when they need finer control, with no lock-in between the two layers.
- LangSmith provides end-to-end observability — step-by-step trace inspection, token usage, latency P50/P99, error rates, pairwise annotation queues, and online evals — and works via OpenTelemetry with any LLM framework, not just LangChain stacks.
- First-class Python and TypeScript/JavaScript support across the entire stack (LangChain, LangGraph, LangServe), with all three MIT-licensed and free to use without any commercial dependency.
- 138,000 GitHub stars and 28 million monthly downloads (as of early 2026) give LangChain the largest developer community of any LLM framework, translating to more answered Stack Overflow questions, tutorials, and third-party integrations.
Cons
- The API surface has been large and fast-moving since launch; although LangChain 1.0 commits to no breaking changes until 2.0, the ecosystem migration from legacy LCEL patterns to LangGraph-backed agents still imposes real upgrade costs on teams with existing production code.
- RAG-specific primitives — document chunking strategies, multi-index routing, query transformation, reranking — are less optimized than LlamaIndex's purpose-built pipeline; teams building retrieval-heavy apps will write more glue code.
- LangSmith costs scale with trace volume and team size, and the 14-day data retention on the free Developer tier makes month-over-month debugging difficult without upgrading to a paid plan.
- The langchain-community integration package is being sunset as of 2026, shifting responsibility for third-party integrations to individual partner packages; teams depending on community-maintained connectors may encounter gaps during the transition.
- LangChain's abstraction overhead — chain-of-thought steps, retrieval loops, and agent iterations each generate additional LLM API calls — can noticeably inflate provider costs compared to lower-level alternatives for simple use cases.
LangChain
AI/ML ModelsPros
- LangGraph 1.0 (GA October 2025) delivers durable agent state — execution survives server restarts mid-workflow — plus built-in persistence, human-in-the-loop interrupts, and graph-based branching and retry logic proven in production at Uber, LinkedIn, Klarna, and JP Morgan.
- LangChain 1.0 introduced a middleware architecture and a create_agent() abstraction that lets teams prototype quickly with high-level APIs, then drop down to raw LangGraph primitives when they need finer control, with no lock-in between the two layers.
- LangSmith provides end-to-end observability — step-by-step trace inspection, token usage, latency P50/P99, error rates, pairwise annotation queues, and online evals — and works via OpenTelemetry with any LLM framework, not just LangChain stacks.
- First-class Python and TypeScript/JavaScript support across the entire stack (LangChain, LangGraph, LangServe), with all three MIT-licensed and free to use without any commercial dependency.
- 138,000 GitHub stars and 28 million monthly downloads (as of early 2026) give LangChain the largest developer community of any LLM framework, translating to more answered Stack Overflow questions, tutorials, and third-party integrations.
Cons
- The API surface has been large and fast-moving since launch; although LangChain 1.0 commits to no breaking changes until 2.0, the ecosystem migration from legacy LCEL patterns to LangGraph-backed agents still imposes real upgrade costs on teams with existing production code.
- RAG-specific primitives — document chunking strategies, multi-index routing, query transformation, reranking — are less optimized than LlamaIndex's purpose-built pipeline; teams building retrieval-heavy apps will write more glue code.
- LangSmith costs scale with trace volume and team size, and the 14-day data retention on the free Developer tier makes month-over-month debugging difficult without upgrading to a paid plan.
- The langchain-community integration package is being sunset as of 2026, shifting responsibility for third-party integrations to individual partner packages; teams depending on community-maintained connectors may encounter gaps during the transition.
- LangChain's abstraction overhead — chain-of-thought steps, retrieval loops, and agent iterations each generate additional LLM API calls — can noticeably inflate provider costs compared to lower-level alternatives for simple use cases.
LlamaIndex
AI InfrastructurePros
- LlamaParse v2 (released December 2025) handles PDFs, PowerPoints, Word files, spreadsheets, and scanned documents with automatic model routing across four tiers — Fast, Cost Effective, Agentic, Agentic Plus — at up to 50% lower cost than v1, with version pinning for production stability.
- LlamaHub provides 160-plus pre-built data connectors spanning S3, Postgres, APIs, file systems, and more; combined with LlamaExtract's schema-based structured extraction and LlamaSplit's AI-powered document separation, the ingestion pipeline is deeper than anything LangChain ships natively.
- Sophisticated index types — vector, summary, tree, keyword, and graph — with query transformation, node reranking, and post-processing give LlamaIndex the most specialized retrieval optimization layer available in an open-source RAG framework.
- LlamaCloud's freemium model (10,000 credits per month on the free tier) lets teams validate document parsing quality on real production documents before committing to paid tiers, lowering the evaluation barrier.
- LlamaParse gained GPT-5 and Gemini 2.5 Pro support in agentic parsing modes during 2025, offering best-in-class accuracy for complex documents like multi-page financial reports, scanned legal filings, and layout-heavy technical PDFs.
Cons
- The Workflows event-driven orchestration engine is less mature than LangGraph for non-RAG agentic use cases — there is no equivalent to LangGraph's durable state persistence, checkpoint-based recovery, or human-in-the-loop interrupt API.
- TypeScript SDK support is actively improving but still trails the Python library in coverage of advanced features; teams building TypeScript-first stacks may encounter gaps in LlamaExtract and advanced retrieval primitives.
- LlamaCloud's credit-based pricing model can be difficult to budget accurately at scale; parsing costs vary dramatically between tiers (roughly 1 credit per page at Fast versus 45 credits per page at Agentic Plus), and high-volume document workflows can accumulate credit spend quickly.
- LlamaIndex's GitHub star count (roughly 40,000 as of 2026) and monthly download volume are meaningfully smaller than LangChain's, which translates to fewer community tutorials, fewer answered questions in forums, and a narrower pool of contributors maintaining integrations.
- The observability story is weaker than LangSmith — LlamaIndex does not ship a first-party trace and evaluation platform, meaning teams must integrate a third-party tool (Langfuse, Arize Phoenix, etc.) to get production-grade debugging and eval workflows.
Section 03
At a glance
Every spec on one page. Live-pulled from each tool's detail page.
- PricingInquireFree credits + paid
- Pricing modelPaidFreemium
- Free tierNoYes
- Free trialNoNo
- Rating4.9 / 5 (212 ratings)4.9 / 5 (186 ratings)
- Saves460400
- CategoriesAI/ML ModelsAI Infrastructure, Developer Tools
- VerifiedNoNo
- Top 100 tier——
- Last updatedJun 2026Jun 2026
Frequently asked
LangChain vs LlamaIndex FAQs
Quick answers to the questions readers ask before picking between these two.
Can I use LlamaIndex and LangChain together in the same project?
Yes, using both together is a documented and officially supported pattern. LlamaIndex handles document ingestion, indexing, and retrieval while LangChain (or LangGraph) handles agent orchestration and multi-step workflow execution. Many production stacks in 2026 explicitly use LlamaIndex as the knowledge layer and LangChain as the orchestration layer, and LangChain context and models can integrate directly with LlamaIndex retrievers.
Which framework is better for production RAG pipelines with complex documents?
LlamaIndex wins for production RAG over complex documents. LlamaParse v2 (released December 2025) handles PDFs, PowerPoints, spreadsheets, and scanned files with automatic model routing and version pinning for production stability; LlamaExtract adds schema-based structured field extraction; and the framework ships purpose-built index types (vector, tree, keyword, graph) with query transformation and reranking that LangChain does not match natively.
Is LangChain free to use?
Yes, the core frameworks — LangChain, LangGraph, and LangServe — are MIT-licensed and entirely free. The commercial product is LangSmith, which offers a free Developer tier (5,000 traces per month) and paid tiers for production teams needing observability, evaluation, and managed agent deployment. Your largest ongoing cost is always LLM provider API usage, not the LangChain framework itself.
What did LangGraph 1.0 add compared to earlier versions?
LangGraph 1.0 reached stable GA in October 2025 and was the first major stable release in the durable agent framework space. Key additions included durable agent state (execution resumes exactly where it stopped after a server restart), built-in persistence for multi-day workflows, and first-class human-in-the-loop interrupt APIs. The release also committed to no breaking changes until LangGraph 2.0 and consolidated prebuilt agent patterns into langchain.agents.
Does LlamaIndex have an observability platform like LangSmith?
No, LlamaIndex does not ship a first-party observability and evaluation platform. Teams running LlamaIndex in production typically integrate third-party tools such as Langfuse (open-source, self-hostable), Arize Phoenix, or LangSmith itself — which explicitly supports tracing LlamaIndex applications via OpenTelemetry. This is a meaningful gap compared to LangChain's tightly integrated LangSmith offering.
Which framework has more integrations and community support?
LangChain wins by a large margin on both counts. As of mid-2026, LangChain's main GitHub repository has 138,000 stars, 28 million monthly downloads, and over 1,000 community integrations across Python and TypeScript. LlamaIndex has roughly 40,000 GitHub stars and 3 million monthly downloads. LangChain also has a larger Discord community, more Stack Overflow coverage, and more third-party tutorials.
What is LlamaParse v2 and how does it differ from the original?
LlamaParse v2, released in December 2025, replaced the original's complex configuration system with four simple tiers: Fast, Cost Effective, Agentic, and Agentic Plus. It added version pinning so production pipelines do not change unexpectedly when LlamaIndex updates its underlying models, delivered up to 50% lower parsing costs, and added support for GPT-5 and Gemini 2.5 Pro in agentic modes. Developers no longer need to choose between parsing modes and model providers manually — LlamaParse routes automatically based on the chosen tier.
Bottom line
Choose LangChain when the core challenge is orchestrating multi-step, long-running agent workflows: branching logic, tool calling, cross-session memory, human approval gates, and durable execution that survives interruptions. As of mid-2026, LangGraph 1.0 — LangChain's graph-based execution engine — is the most production-proven framework in this space, with Uber, JP Morgan, and Klarna among documented enterprise deployments. Teams that want a single stack that covers prototyping through production monitoring, with LangSmith for observability and LangGraph Cloud for managed deployment, will find LangChain the most complete answer.
Choose LlamaIndex when the primary problem is document intelligence — turning messy, complex documents into accurate, model-ready context. LlamaParse v2's four-tier parsing system, LlamaExtract's schema-based structured extraction, LlamaSplit for multi-document separation, and LlamaSheets for spreadsheet normalization form a document AI pipeline that LangChain cannot replicate with equivalent precision. For enterprises processing financial filings, legal contracts, medical records, or technical manuals at scale, LlamaIndex's managed LlamaCloud platform (with SaaS and VPC deployment options) significantly reduces the engineering cost of building production RAG.
For many production stacks, the answer is both. LlamaIndex handles ingestion and retrieval; LangChain/LangGraph handles orchestration and agent execution. The combination is actively documented and supported, and it avoids the tradeoffs of forcing either framework to do something it was not designed for.
Solo developers and small teams building proof-of-concept RAG should start with LlamaIndex's open-source framework or its free LlamaCloud tier — the focused API surface and purpose-built retrieval primitives reduce boilerplate. Teams building production agents with complex state, tool use, and multiple LLM providers should start with LangChain 1.0 and LangGraph 1.0, which committed to API stability through the 2.0 release cycle.
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