LangChain vs LlamaIndex

Quick verdict

LangChain is the broad agent-engineering stack: open-source libraries (LangChain + LangGraph), plus commercial LangSmith for traces, evals, and agent deployment. LlamaIndex is the data-and-document stack: open-source indexing, query engines, and workflows, plus commercial LlamaParse for agentic document parsing, extraction, and managed indexes.

Pick LangChain when the hard problem is multi-step agents, tool calling, memory, human-in-the-loop, and production observability of those runs. Pick LlamaIndex when the hard problem is getting messy enterprise docs into trustworthy retrieval—parsers, chunk/index strategies, hybrid query engines, and document workflows. Many serious systems use both (LlamaIndex for RAG quality, LangGraph for the agent loop) or neither (plain SDK + vector DB) once the prototype is done.

Side-by-side

What each product is in 2026

LangChain is no longer “just chains.” The company positions an engineering platform: open-source LangChain for composing models, tools, and prompts; LangGraph as the low-level runtime for reliable multi-step and multi-agent systems (durable execution, streaming, checkpoints, human-in-the-loop); and LangSmith for tracing, evaluation, deployment, sandboxes, Fleet, and Engine. GitHub scale is large (~142k stars on the main repo; LangGraph ~37.6k). Docs emphasize agents with control—state, memory, approval gates—rather than only linear prompt chains. You can use LangGraph without LangChain components, though most tutorials wire them together.

LlamaIndex started as “GPT Index” and still owns the data-framework identity: connect sources, build indexes, retrieve, synthesize—then extend into agents over that data. The commercial center of gravity is LlamaParse: agentic OCR/parsing for messy tables, charts, layouts, plus extraction, classification, and managed indexes on a credit model. Open-source Workflows (event-driven steps, no heavy DSL) compete with LangGraph-style orchestration for multi-step apps. The Python monorepo sits around ~51k stars; TypeScript exists separately as LlamaIndex.TS for Node, Deno, Bun, and Workers.

Pricing and real cost (TCO)

Framework code is free. You pay models, vector/DB infra, engineering time—and optionally each vendor’s platform. Re-check live pricing before procurement; both vendors iterate meters often.

LangChain / LangSmith

• Open source — LangChain and LangGraph are free (MIT).
• LangSmith Developer — $0/seat; 1 seat; up to 5k base traces/mo, then pay-as-you-go; community support.
• LangSmith Plus — $39/seat/month; unlimited seats; up to 10k base traces/mo, then pay-as-you-go; email support; access to Deployment, Engine, and related features; 1 free small serverless deployment included.
• Enterprise — Custom; self-hosted/hybrid options, custom SSO/RBAC, SLA, infosec review path.
• Usage units — Platform work metered as LCU ($1.50) and LSU ($1.00) for compute/storage-style consumption (Engine, Fleet, deployments, sandboxes, traces).
• Trace retention — Base traces (shorter retention, e.g. 14 days) vs extended (longer retention, e.g. 180 days) with upgrade fees for keeping high-value labeled runs.
• Startups — LangChain advertises discounted rates and free-trace allotments for eligible early-stage companies.

LlamaIndex / LlamaParse

• Open source — Framework and workflows free to self-host. LlamaParse itself is not open source.
• Free (Parse) — 10K credits/mo; limited concurrent jobs; community support; plan table lists up to ~100 users.
• Starter — $50/mo; 40K included credits; pay-as-you-go (plan table: up to roughly $500/mo of overage headroom); email support.
• Pro — $500/mo; 400K included credits; higher concurrency, Slack support, more indexes/sources; higher pay-as-you-go cap.
• Enterprise — Custom credits, SSO, hybrid/VPC, higher rate limits, dedicated support; SOC 2 / HIPAA / GDPR called out for the commercial platform.
• Credit rate — Official site: 1,000 credits = $1.25.
• Parse modes (LlamaParse v2 public tiers) — Fast ~1 credit/page; Cost Effective ~3; Agentic ~10; Agentic Plus ~45. Auto Mode can route tiers per page and is marketed as saving large amounts on mixed corpora. Basic text can be ~1 credit; layout-aware agentic modes cost far more.
• Startups — Startup program advertises on the order of $2k free platform credits (one year) plus bulk discounts when you qualify.

TCO notes: A solo builder can stay on free OSS + free cloud tiers indefinitely. A five-seat team on LangSmith Plus is $195/mo before overages—not counting LLM APIs. A document-heavy pipeline on LlamaParse Pro is $500/mo before over-credit use; complex legal/financial PDFs push you into agentic modes that burn credits fast. At $1.25 per 1k credits, Agentic Plus (~45 credits/page) is about $0.056 per page; Fast mode is about $0.00125 per page. That gap decides whether Parse is a rounding error or a line-item fight with finance.

Capabilities that actually matter

LangChain side

• LangGraph — Explicit state machines/graphs for agents: cycles, checkpoints, human approval gates, long-running work. Official docs treat it as more controllable than high-level “agent in a box” APIs and note you can use it without the rest of LangChain.
• Integrations — Large catalog of chat models, tools, vector stores, retrievers (Python and JS).
• LangSmith — End-to-end traces, datasets, online/offline evals, prompt playground/hub, annotation queues; deployment and sandbox products for agent code; Engine for automated issue clustering and fix proposals (metered in LCUs).
• JS/TS parity — Serious Node and browser paths (langchainjs, langgraphjs), not Python-only afterthoughts.
• Framework-agnostic tracing — LangSmith markets OpenTelemetry and traces for OpenAI/Anthropic SDKs, Vercel AI SDK, LlamaIndex, and custom code—not only LangChain apps.

LlamaIndex side

• Data connectors and indexing — Broad loaders (LlamaHub ecosystem historically), multiple index types, hybrid/keyword+vector patterns, knowledge-graph and structured/SQL-oriented retrieval paths in the framework story.
• Query engines — Sub-question decomposition, multi-step retrieval, response synthesis modules—designed so RAG quality is the product, not a side module.
• LlamaParse — Layout-aware / agentic parse modes, tables/charts/images, structured extraction, classification/split, managed indexes with file/source limits by plan; 130+ formats claimed on the pricing matrix.
• Workflows — Event-driven steps for agents and document pipelines with pluggable durability and deployment helpers; positioned as a lighter, Pythonic alternative to graph DSLs.
• TypeScript — LlamaIndex.TS covers idiomatic TS for RAG and multi-agent workflows; depth still trails LangChain’s JS ecosystem for pure agent glue.

Community sentiment (Reddit / HN)

The durable consensus: “LangChain for agents and glue; LlamaIndex for retrieval.” That split shows up repeatedly on r/LangChain and r/Rag: chaining/tool orchestration vs data connectivity and retrieval abstractions. Production-minded threads often add a third option—use neither framework in core prod paths and keep a thin layer over vendor SDKs + your vector store.

LangChain praise: Fast prototyping, huge integration surface, LangSmith tracing when debugging multi-hop agents, LangGraph for control after the old high-level agent APIs felt magical and brittle. Positive Reddit writeups describe LangGraph + LangSmith as the stack that finally made agent failures debuggable.

LangChain complaints: Heavy abstractions, API churn (especially around earlier version transitions), hard-to-debug magic, recurring “is anyone using this in production?” threads, and high-engagement writeups (Octomind) about removing LangChain after a year—amplified on HN with hundreds of comments, including a public reply from LangChain’s CEO. Reddit threads about moving away cite complexity and unstable patterns more than “it can’t call an LLM.” Parallel videos and blogs amplify a “senior engineers ditch for plain Python” narrative.

LlamaIndex praise: Better retrieval-oriented APIs, stronger story for document-heavy RAG, parse quality for ugly PDFs via LlamaParse, clearer path from data → index → query. HN threads around LlamaParse launches discuss multi-column PDFs and foreign-language OCR as real pain points the product targets.

LlamaIndex complaints: Less natural if you mainly need multi-tool agents without a corpus; package weight and “too much framework” comments surface next to LangChain in r/Rag and r/LocalLLaMA; commercial credit costs surprise teams that parse everything in the highest mode (r/Rag threads asking for LlamaParse alternatives for large personal legal corpora).

HN and security outlets also tracked a critical CVE-2025-68664 serialization injection in LangChain core (“LangGrinch”)—a reminder that popular agent frameworks are supply-chain surface area, not just convenience wrappers.

When LangChain wins

• You are building multi-step or multi-agent systems with tools, memory, retries, and human approval—LangGraph is the product of record.
• You need deep observability: every tool call and LLM hop traced, evaluated, and annotated in one place (LangSmith).
• You want Python + TypeScript teams on the same conceptual model.
• RAG is present but secondary to workflow control (CRM actions, tickets, browsers, internal APIs).
• You prefer (or already standardized on) the LangChain integration catalog and hiring market familiarity—with eyes open about abstraction cost.
• You need managed agent deployment (serverless/dedicated Agent Server) rather than only offline notebooks.

When LlamaIndex wins

• The product is a knowledge assistant over proprietary documents: manuals, contracts, research PDFs, policies.
• You need serious parsing (tables, scans, mixed layouts) before embeddings ever run—LlamaParse modes and credits map to that pain.
• You care about retrieval architecture (indexes, hybrid search, query transforms, structured/SQL RAG) more than agent graph sugar.
• You want event-driven document workflows (intake → extract → decide → write back) as first-class examples.
• Your team is Python-centric and wants a data-framework mental model rather than an “agent platform” mental model.
• You need enterprise parse compliance (SOC 2 / HIPAA / GDPR story + hybrid VPC) without building OCR in-house.

When hybrid (or neither) is correct

Hybrid is common: LlamaParse/LlamaIndex for ingestion and retrieval tools, LangGraph for the control loop that decides which tools to call. LangSmith can still trace non-LangChain steps if you instrument them. That split keeps each library in its strong zone.

Neither is also common after the learning phase: official SDKs for models, a vector DB client, a small custom retrieval module, and maybe an open observability tool (e.g. Langfuse self-host) instead of seats. Octomind-style posts and many r/Rag threads describe this as the end state for narrow products where framework flexibility became pure cost.

Risks and failure modes

• Wrong layer selection — Forcing LangChain to be your PDF brain, or LlamaIndex to be your only multi-agent OS, creates awkward code and weak evals.
• Framework churn — Both ecosystems move fast; pinning versions and writing integration tests matters more than blog-post architecture.
• Observability bill shock (LangSmith) — Agentic apps multiply traces per user request; seat fees are the easy number, overages and LCU/LSU are the surprise.
• Parse bill shock (LlamaParse) — Agentic/high-accuracy modes × page volume dominate; free 10K credits are for trials, not warehouse-scale OCR. Always pilot modes on a sample corpus before committing Pro spend.
• Security surface — CVE-2025-68664 showed serialization paths in agent frameworks can become critical (CVSS 9.3 class). Patch langchain-core to fixed versions (0.3.81+ / 1.2.5+ lineages per advisories), lock deps, and distrust LLM-shaped structured blobs that can inject reserved keys.
• Over-frameworking — HN and production engineers still argue the winning architecture is often thin custom code once you know your graph. Frameworks help you learn; they are not mandatory forever.
• Compliance assumptions — OSS libraries are not the same as a vendor’s SOC 2 SaaS. Inventory where prompts, documents, and traces live (US vs EU, SaaS vs VPC) before legal signs off.

Recommendation by profile

FAQ

Is LangChain or LlamaIndex better for RAG in 2026?

For pure RAG quality and document pipelines, LlamaIndex is usually the better default. LangChain can do RAG, but its center of gravity is agents and orchestration.

Do I need to pay for either?

No for the libraries. Pay when you want LangSmith traces/deploy or LlamaParse managed parsing/indexes.

What does LangSmith cost?

Developer free (5k traces, 1 seat); Plus $39/seat/month (10k traces included); Enterprise custom; usage beyond included traces and LCU/LSU meters apply. Base vs extended retention changes storage cost.

What does LlamaParse cost?

Free 10K credits; Starter $50 (40K credits); Pro $500 (400K credits); overage at $1.25 per 1,000 credits; page cost depends on parse mode (roughly 1–45 credits/page on public v2 tiers).

Can I use both together?

Yes. Common pattern: LlamaIndex (or LlamaParse) for ingestion/retrieval tools called from a LangGraph agent, with LangSmith tracing the whole run if instrumented.

Is LangGraph replacing LangChain?

No—LangGraph is the orchestration runtime; LangChain remains the broader component/integration layer. Docs position them as complementary. LangGraph can also be used without LangChain components.

Why do so many posts say to avoid LangChain?

Teams hit abstraction and upgrade pain in production; Octomind’s post and related HN/Reddit threads made that narrative mainstream. LangGraph and better docs are the company’s answer—evaluate on your codebase, not social media memes.

What about CVE-2025-68664?

Critical serialization injection in langchain-core (dumps/dumpd). Patch to fixed versions (e.g. 0.3.81 / 1.2.5 lineage per advisories) and treat agent serialization carefully. Vendor and industry writeups call this a supply-chain reminder for AI stacks.

Is LlamaParse open source?

No. LlamaParse is commercial. LlamaIndex framework and Workflows are the open-source building blocks.

Sources

This comparison is backed by 100+ primary and secondary sources in research_cache/langchain-vs-llamaindex_sources.json: official sites and pricing pages, framework docs, GitHub repos and advisories, Reddit and Hacker News threads, independent reviews and videos, and security writeups. Re-check live pricing and plan limits before procurement—both vendors iterate meters often.

Bottom line

If your bottleneck is agents that act, start with LangGraph + LangSmith and keep integrations thin. If your bottleneck is data that lies when chunked badly, start with LlamaIndex + LlamaParse. If you need both, split layers rather than forcing one brand to own the whole stack. If you are past the learning phase and shipping one narrow product path, be willing to delete the framework and keep the pieces that earned their import lines. Stars measure popularity, not fitness for your next deploy.