LlamaIndex
Also known as: LlamaParse, LlamaCloud
Data framework for building RAG pipelines and data agents with LlamaParse document ingestion and LlamaCloud managed platform.
LlamaIndex is an open-source Python framework, and a companion hosted platform, for building LLM applications that work over an organization's own data. Maintained by LlamaIndex Inc., the venture-backed company that grew out of the GPT Index project and was co-founded by Jerry Liu and Simon Suo in 2023, it is distributed under an MIT license and became one of the defining tools of the retrieval-augmented generation, or RAG, era.
The framework gives developers a clean data layer for LLMs. Readers ingest content from sources like PDFs, databases, and APIs; that content becomes Documents and Nodes; indexes structure it; and retrievers and query engines turn it into question-answering and chat over private data. A high-level API lets a beginner ingest and query data in a handful of lines, while lower-level APIs let advanced users customize parsers, retrievers, rerankers, and synthesis. Hundreds of integration packages connect it to popular models, embeddings, and vector stores such as Pinecone, Weaviate, and Qdrant.
Beyond retrieval, LlamaIndex builds agents and event-driven Workflows that combine multiple agents, data connectors, and tools into multi-step processes with reflection and error correction, which can be deployed as production microservices. Over 2025 and into 2026 the company sharpened its focus toward what it calls agentic document processing, on the premise that most enterprise knowledge is locked in PDFs and spreadsheets and extracting it reliably is the hard, high-value problem.
That focus lives in its hosted platform, centered on LlamaParse: agentic OCR and parsing across well over a hundred file formats, structured Extract for pulling specific fields, managed Index for ingestion and RAG, LlamaSheets for messy spreadsheets, and LlamaAgents for deploying document agents, including a builder that turns a plain-language description into a working agent. Word, line, and cell-level bounding boxes provide audit-grade citations back to the source.
LlamaIndex is often paired with orchestration frameworks, serving as the knowledge-and-data layer while another tool handles agent control flow. It is widely used for document-heavy work in finance and insurance, from research and due diligence to invoice and contract processing.
Vendor details
Canonical URL
https://www.llamaindex.ai/
Category
Agent infrastructure
Company status
independent
Use cases & customers
Target customers
Deployment options
In practice
You need a chatbot that answers from 200,000 of your own PDFs and filings, not the open web. LlamaIndex ingests, indexes, and retrieves over that private data, returning answers with citations to the source passages.
Your invoices and contracts are scanned PDFs and messy spreadsheets that standard OCR mangles. LlamaParse reads complex layouts and tables, and Extract pulls the specific fields you need into structured data.
You want a document agent without wiring the whole pipeline yourself. LlamaAgents Builder turns a plain-language description, like classifying deal memos and pulling key financials, into a deployed, testable agent.
Agentic Index coverage score
9.0 / 14 capabilities · 64%
| Integrations & Tool CallingAgentic AI Index Expansion Evidence Report | Full |
|---|---|
| Workflow OrchestrationAgentic AI Index Expansion Evidence Report | Full |
| Knowledge Grounding & RAGAgentic AI Index Expansion Evidence Report | Full |
| Human Oversight & GuardrailsAgentic AI Index Expansion Evidence Report | Partial |
| Security, Identity & GovernanceAgentic AI Index Expansion Evidence Report | Partial |
| Observability & AuditabilityAgentic AI Index Expansion Evidence Report | Partial |
| Memory & State PersistenceAgentic AI Index Expansion Evidence Report | Full |
| Deployment & Data ResidencyAgentic AI Index Expansion Evidence Report | Partial |
| Prebuilt Agents, Templates & PacksAgentic AI Index Expansion Evidence Report | Partial |
| Triggers & Channel CoverageAgentic AI Index Expansion Evidence Report | Unable to verify |
| Model Flexibility & RoutingAgentic AI Index Expansion Evidence Report | Full |
| APIs, SDKs & MCP ExtensibilityAgentic AI Index Expansion Evidence Report | Full |
| Testing, Debugging & OptimizationAgentic AI Index Expansion Evidence Report | Partial |
| Browser & Computer UseAgentic AI Index Expansion Evidence Report | Unable to verify |
The Agentic Index coverage score grades every vendor Full, Partial or Unable to verify against the same 14 buyer facing capabilities, from public evidence only. Each capability links to how all vendors in the index score on it. How this evidence is graded
Recent platform changes
LlamaIndex announced Retrieval Harness, giving agents filesystem-style primitives (grep, file read, directory listing) over document collections so they can navigate and fetch context on demand rather than relying solely on a prebuilt vector index.
Bears on: Agent capability
View sourceLlamaIndex shipped v5 and v6 of the LlamaParse Platform community node for n8n, now an officially verified n8n community node. The node brings LlamaParse's document parsing, extraction, classification, and splitting into n8n workflows.
Bears on: Integrations
View sourceLlamaParse added opt-in granular bounding boxes at line, word, and cell level, with beta availability across paid tiers.
Bears on: Observability / auditability
View sourcePricing
From $50/mo · free tier
credits
Included quota
OSS framework: free (Python/TS, 160+ LlamaHub connectors, indexing/query/agent engines). LlamaCloud Free: 10,000 credits/mo (~1,000 pages), agentic OCR, structured extraction, 1 user, 1 project, 5 indexes, 50 files/index, basic support. Starter ($50/mo): 40,000 credits, 5 users, 50 indexes, 250 files/index, PAYG to 400k. Pro ($500/mo): 400,000 credits, 10 users, 5 projects, 100 indexes, 1,250 files/index, Slack support, 5x rate limits, PAYG to 4M. Enterprise: custom credits, unlimited users/projects/indexes, SSO, VPC, dedicated support.
What is public
LlamaIndex (llamaindex.ai - dual model: a free open-source RAG/agent framework in Python/TS + the managed LlamaCloud platform for document parsing/extraction/indexing) prices LlamaCloud on CREDITS (1,000 credits = $1.25, current; $1.00 was the older rate). Four tiers, all with access to every module (Parse, Extract, Classify, Split, Index): Free (10,000 credits/mo, ~1,000 pages, 1 user/project), Starter ($50/mo, 40,000 credits, 5 users, PAYG to 400k capped ~$500), Pro ($500/mo, 400,000 credits, 10 users, Slack support, 5x rate limits, PAYG to 4M capped ~$5,000), and custom Enterprise (SSO, VPC, dedicated support; ~$30K/yr AWS Marketplace example). The OSS framework is free; LLM/embeddings/vector-DB costs are separate.
Billing mechanics
The framework is free OSS (you pay your own LLM/embedding/vector-DB/infra). LlamaCloud runs on credits ($1.25 per 1,000; ~$0.00125 per credit): every operation - parsing, extraction, indexing, classification - consumes credits, and the per-page cost scales with complexity (Fast/Basic ~1 credit/page -> Cost-effective ~3 -> Premium/Agentic ~45-60 credits/page; LlamaExtract Premium = 60 credits/page). The Starter ($50/mo) and Pro ($500/mo) subscriptions equal their included-credit value at $1.25/1,000 (40k and 400k credits); beyond the allotment you pay-as-you-go at the same rate (capped ~$500 Starter / ~$5,000 Pro). A 48-hour parse cache makes re-parsing the same file free.
Cost watchouts
Credit consumption swings wildly by document complexity (1 credit/page Fast vs 45-60 credits/page Agentic/Premium - a 100-page complex doc can cost ~$6+ just to parse); LLM API, embeddings, and vector-DB hosting are SEPARATE (~$150-$450/mo additional at 50K pages/mo); a busy month or large document dump can push you into PAYG; budgeting is hard without someone watching credit usage
Variable cost rationale
Fully consumption-based - LlamaCloud spend scales with pages processed x complexity tier (credits), and the separate LLM/embedding/vector-DB costs scale with usage too; the parse cache and tier choice are the main levers
Additional watchouts
Credit-based pricing is hard to budget (complexity-driven swings + separate LLM/embedding/vector costs); the OSS path requires significant engineering time to manage infra/scaling; not built for non-technical business users; LlamaCloud only handles parsing/indexing - you still assemble the rest of the stack
Overage / add-ons
Beyond a tier's included credits, you pay-as-you-go at the same $1.25/1,000 rate - capped at ~$500 (Starter) and ~$5,000 (Pro); Enterprise negotiates custom credits + volume discounts. Per-operation credit cost varies sharply by parsing/extraction complexity tier; the 48-hour parse cache zeroes out repeat parses of the same file.
Sales call required
Mixed (some tiers require a call)
Free / trial
Free (10k credits/mo)
Lowest paid plan
n/p (credit-based platform)
Commercial notes
Formerly GPT Index; the leading RAG-specialized framework (vs LangChain's broader orchestration) - LlamaParse/LlamaExtract are its document-parsing moat; 160+ LlamaHub connectors; used by 10,000+ production teams; LlamaCloud is the managed layer (parse/extract/index) with a credit model; competes with LangChain (general orchestration) and no-code RAG tools (Stack AI, which bundles LLM tokens)
Key ambiguities
Resolved: the current credit rate is $1.25/1,000 (LlamaIndex's own pricing page + dev FAQ; $1.00/1,000 was the older rate). Starter ($50/mo) and Pro ($500/mo) are fixed monthly subscriptions whose price equals their included-credit value at $1.25/1,000 (40k / 400k credits), with PAYG beyond the allotment (capped per tier). Exact per-operation credit costs still vary by parse/extract complexity tier.
Cancellation / refund
OSS is free (no contract); LlamaCloud Free is free; Starter/Pro are self-serve with included credits + capped PAYG; Enterprise is custom-contracted (SaaS or VPC; ~$30K/yr AWS Marketplace example); credits reset monthly
Support SLA / resale
Basic support (Free/Starter); Slack support (Pro); dedicated support (Enterprise); SaaS or VPC deployment for enterprise; Enterprise SSO; Python/TS SDKs + REST API + vector-store integrations; available via AWS Marketplace
Missing data
Resolved: credit rate is $1.25/1,000 (current; $1.00 older); Starter $50/mo and Pro $500/mo are fixed subscriptions (= included-credit value) + capped PAYG. Exact per-operation credit costs vary by parse/extract tier. Seed 'Free tier 10k credits/mo (LlamaCloud); OSS framework self-hostable' is accurate and current; LlamaCloud is credit-based (Free/Starter/Pro/Enterprise) and LLM/vector costs are separate.
Related vendors
- Acrab — Singapore compute infrastructure company building a full stack…
- AgentOps — Agent observability and reliability platform with broad model and…
- Agno — High-performance agent runtime and framework (formerly Phidata) with…
- AIsa — Unified resource and payment gateway for AI agents that lets them…
- AlphaBitCore — AI control plane that governs how models, agents, tools, and…
- Anchor Browser — Cloud hosted browser infrastructure that lets AI agents operate real…
Alternatives to LlamaIndex
The closest documented capability profiles to LlamaIndex among agent infrastructure platforms tracked by Agentic Index, ordered by similarity on the same 14 point evidence the rankings use. No vendor pays for placement.
- Letta10.0 / 14Adds documented Triggers & Channel Coverage
- Haystack9.5 / 14Adds documented Triggers & Channel CoverageLlamaIndex vs Haystack →
- Langbase7.5 / 14A lighter documented profile than LlamaIndex
- Hyperspell7.5 / 14Fuller documented coverage on Security, Identity & Governance
- LangChain11.5 / 14Adds documented Triggers & Channel CoverageLlamaIndex vs LangChain →
- nexos.ai9.5 / 14Adds documented Triggers & Channel Coverage
Similarity is computed from each vendor's Agentic Index coverage score evidence, axis by axis, not from the totals. How this evidence is graded