LangGraph score factors
Editorial quality82
Practical utility92
Trust & transparency85
Freshness90
Engagement quality0
Momentum50
Decision guide · Reviewed Sep 8, 2026 · AI Agent & Chatbot Builders, Agent Platforms & Frameworks, AI Coding & Development
LangGraph
77.4
LlamaIndex
76.5Choose LangGraph when you need building complex, custom, and multi-actor agent workflows that require explicit state management, human approvals, and real-time streaming. Choose LlamaIndex when you need developers and technical teams building production RAG pipelines and autonomous agents over complex unstructured documents.
At a glance
The practical differences
| Decision factor | LangGraph | LlamaIndex |
|---|---|---|
| Best for | building complex, custom, and multi-actor agent workflows that require explicit state management, human approvals, and real-time streaming | developers and technical teams building production RAG pipelines and autonomous agents over complex unstructured documents |
| Not ideal for | non-technical users looking for no-code bot builders or projects requiring only basic, linear LLM call chains | non-technical business users seeking a visual no-code document dashboard or simple desktop file reader |
| Pricing | At the review date, the LangGraph orchestration library is open source under an MIT license and free to use. Optional hosted infrastructure, tracing, and managed deployment through LangSmith offer a free Developer tier (1 seat, 5,000 monthly traces) and a paid Plus tier at $39 per seat per month plus compute and storage usage. | At the review date (September 2026), LlamaIndex provides 10,000 free credits per month (~1,000 basic pages). Paid tiers include Starter at $50/month (40,000 credits) and Pro at $500/month (400,000 credits). Extra credit consumption is billed at $1.25 per 1,000 credits. Enterprise plans offer custom limits and VPC deployment. Verify current rates on the official pricing page. |
| Key difference | Unlike generic black-box autonomous loops, LangGraph models agent workflows as explicit stateful graphs with fine-grained control over cyclical transitions, state persistence, and human-in-the-loop interruptions. | Unlike conventional OCR APIs or standard PDF text extractors, LlamaIndex pairs developer orchestration frameworks with multi-modal agentic parsing, using recursive error-checking loops and specialized models to accurately interpret dense tables, charts, and handwritten inputs. |
| Overall rank | #88 | #128 |
| Review verdict | LangGraph suits software engineers who need precise, low-level control over multi-agent workflows, state persistence, and human approval steps. It is not designed for non-technical users seeking no-code drag-and-drop builders or teams that only need standard, non-cyclical prompt chaining. | LlamaIndex suits engineers and data teams building production RAG systems, complex ingestion pipelines, and agentic workflows over challenging unstructured files. It is not intended for non-technical users looking for a fully managed, no-code document viewer or business dashboard. |
LlamaIndex score factors
Editorial quality82
Practical utility88
Trust & transparency85
Freshness90
Engagement quality0
Momentum50
LangGraph strengths
- Open-source and MIT-licensed with no licensing cost for the core framework
- Provides fine-grained state machine control rather than rigid black-box agent loops
- Native token-by-token streaming for both intermediate actions and final outputs
- Seamless optional observability and evaluation integration with LangSmith
LlamaIndex strengths
- High parsing fidelity on complex layouts, irregular tables, charts, and handwritten text.
- Generous free tier with 10,000 monthly credits and open-source tools like LiteParse.
- Enterprise deployment flexibility including private VPC installations, AWS/Azure marketplace availability, and SOC 2 Type II compliance.
Frequently asked
LangGraph vs LlamaIndex
Should I choose LangGraph or LlamaIndex?+
Choose LangGraph when you need building complex, custom, and multi-actor agent workflows that require explicit state management, human approvals, and real-time streaming. Choose LlamaIndex when you need developers and technical teams building production RAG pipelines and autonomous agents over complex unstructured documents. ToolsRank scores LangGraph 77.4 and LlamaIndex 76.5; the gap reflects editorial quality, utility, trust, and freshness, not popularity or payment.
Is LangGraph cheaper than LlamaIndex?+
LangGraph: At the review date, the LangGraph orchestration library is open source under an MIT license and free to use. Optional hosted infrastructure, tracing, and managed deployment through LangSmith offer a free Developer tier (1 seat, 5,000 monthly traces) and a paid Plus tier at $39 per seat per month plus compute and storage usage. LlamaIndex: At the review date (September 2026), LlamaIndex provides 10,000 free credits per month (~1,000 basic pages). Paid tiers include Starter at $50/month (40,000 credits) and Pro at $500/month (400,000 credits). Extra credit consumption is billed at $1.25 per 1,000 credits. Enterprise plans offer custom limits and VPC deployment. Verify current rates on the official pricing page. Compare the plan you would actually use and verify current prices on each vendor's pricing page before purchasing.
Which is better for ai agent & chatbot builders?+
LangGraph currently scores higher for ai agent & chatbot builders work. LlamaIndex remains the stronger pick when your priority is developers and technical teams building production RAG pipelines and autonomous agents over complex unstructured documents. Avoid LangGraph if you are non-technical users looking for no-code bot builders or projects requiring only basic, linear LLM call chains.
Can I use LangGraph and LlamaIndex together?+
Yes. LangGraph stands out for unlike generic black-box autonomous loops, LangGraph models agent workflows as explicit stateful graphs with fine-grained control over cyclical transitions, state persistence, and human-in-the-loop interruptions. LlamaIndex stands out for unlike conventional OCR APIs or standard PDF text extractors, LlamaIndex pairs developer orchestration frameworks with multi-modal agentic parsing, using recursive error-checking loops and specialized models to accurately interpret dense tables, charts, and handwritten inputs. Pairing them makes sense when one workflow needs both strengths; otherwise pick the tool that matches your primary job.