LangGraphFreemium
A low-level orchestration framework for building stateful, multi-step agent workflows with explicit control over state, branching, and human-in-the-loop steps.
Agent Development comparison
LangGraph vs LlamaIndex
Pricing, pros, cons, and ideal use cases — side by side.
LlamaIndexFreemium
A data framework for connecting LLMs to private and enterprise data — ingestion, indexing, retrieval, and agent workflows over your own content.
At a glance
| LangGraph | LlamaIndex | |
|---|---|---|
| Pricing | FreemiumOpen-source framework, free to use. Paid tiers apply to the surrounding LangSmith platform, not the framework itself. | FreemiumOpen-source framework is free. LlamaCloud (managed parsing and indexing) is sold on usage-based paid plans. |
| Category | Agent Development | Agent Development |
| Ideal for | Engineering teams building multi-step agentsEnterprise platform teamsWorkflows needing human-in-the-loop control | Teams building RAG over internal dataEngineering orgs standardizing ingestion and retrievalEnterprises with large, messy document sets |
Pros & cons
LangGraph
Pros
- Explicit control over agent state and branching
- First-class human-in-the-loop and checkpointing
- Strong fit for durable, long-running workflows
- Large ecosystem and active development
Cons
- Requires real engineering investment
- Lower-level than no-code agent builders
- You own deployment and observability
LlamaIndex
Pros
- Deep, well-documented retrieval toolkit
- Handles complex and messy document types
- Managed LlamaCloud option for scale
- Large ecosystem of connectors
Cons
- Retrieval quality still depends on your tuning and evals
- Overlapping features with LangChain can confuse
- Fast-moving API surface
Which should you choose?
LangGraph is built around engineering teams building multi-step agents; LlamaIndex leans more toward teams building rag over internal data. Shortlist the one whose strengths line up with your biggest constraint.
Still deciding between LangGraph and LlamaIndex?
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