LlamaIndex vs Mastra
LlamaIndex and Mastra both answer the same question: how do you call models and build agents? Framework focused on getting your documents into a model's context: ingestion, chunking, indexing and retrieval. TypeScript agent framework with workflows, memory, tools and evaluation designed to run inside a normal Node app. Both cover the ai sdk layer competently, so the decision comes down to which set of tradeoffs you would rather live with for the next two years.
Verdict
Both cover the ai sdk layer competently, so the decision comes down to which set of tradeoffs you would rather live with for the next two years.
Pick LlamaIndex if
- ✓Document loaders and chunking strategies are the deepest of any framework.
- ✓Retrieval patterns like reranking and hybrid search come prebuilt.
- ✓Handles messy PDFs and tables better than rolling your own parser.
Pick Mastra if
- ✓Durable workflow steps with suspend and resume suit long-running agent tasks.
- ✓Agent memory and working state are first-class rather than hand-rolled.
- ✓Built by the Gatsby team with strong typing throughout the agent definition.
| Comparison | LlamaIndex | Mastra |
|---|---|---|
| Pricing shape | Free and open source, with hosted parsing and cloud indexing billed by usage. | Free and open source framework, with a paid cloud for deploying and observing agents. |
| Frameworks | Next.js, Django | Next.js |
| In one line | Framework focused on getting your documents into a model's context: ingestion, chunking, indexing and retrieval. | TypeScript agent framework with workflows, memory, tools and evaluation designed to run inside a normal Node app. |
Pricing described qualitatively because published plans change often. Checked 2026-08-23. Confirm current terms on LlamaIndex and Mastra.
LlamaIndex
Strengths
- ✓Document loaders and chunking strategies are the deepest of any framework.
- ✓Retrieval patterns like reranking and hybrid search come prebuilt.
- ✓Handles messy PDFs and tables better than rolling your own parser.
- ✓Query engines compose over multiple indexes without custom routing code.
Tradeoffs
- ✗Centred on retrieval, so general agent workflows fit less naturally.
- ✗Many overlapping abstractions make the right entry point unclear at first.
- ✗Best document parsing is a paid hosted service, not the open source path.
- ✗Python remains the primary target, with the TypeScript port lagging behind.
Mastra
Strengths
- ✓Durable workflow steps with suspend and resume suit long-running agent tasks.
- ✓Agent memory and working state are first-class rather than hand-rolled.
- ✓Built by the Gatsby team with strong typing throughout the agent definition.
- ✓Local playground lets you exercise agents without deploying anything.
Tradeoffs
- ✗Young project, so the API is still settling between releases.
- ✗TypeScript only, which rules out sharing agents with Python data teams.
- ✗Smaller integration catalogue than the established Python frameworks.
- ✗Opinionated workflow model that not every agent design fits comfortably.
Frequently asked questions
Is LlamaIndex or Mastra better?
Neither is better in the abstract. Both cover the ai sdk layer competently, so the decision comes down to which set of tradeoffs you would rather live with for the next two years. Pick the one whose downside you can absorb, because both upsides are real.
What is the main drawback of LlamaIndex?
Centred on retrieval, so general agent workflows fit less naturally. Many overlapping abstractions make the right entry point unclear at first.
What is the main drawback of Mastra?
Young project, so the API is still settling between releases. TypeScript only, which rules out sharing agents with Python data teams.
Can you switch from one to the other later?
Usually, at a cost that grows with how much of your product leans on the ai sdk layer. Keep the integration behind a thin module of your own and the migration stays a weekend rather than a quarter.