Comparison Preset
Mastra cannot be recommended for enterprise use due to its 'NOASSERTION' license, which introduces unacceptable legal and compliance risk. LlamaIndex is the clear choice here, featuring a permissive MIT license that is well-understood and enterprise-friendly. Its high bus factor score of 9/10, 100 maintainers, and long history demonstrate project stability and reduce long-term maintenance risk. Furthermore, its core strength in building agents over private, domain-specific data aligns directly with common enterprise requirements for security and control.
Overview
The bottom line — what this framework is, who it's for, and when to walk away.
Bottom Line Up Front
LlamaIndex is a Python framework designed to build LLM applications that integrate with your private or domain-specific data. It provides tools for data ingestion, indexing, querying, and orchestrating LLM-powered agents and multi-step workflows.
Mastra is a TypeScript framework designed for rapidly prototyping and deploying AI agents. It integrates with popular web frameworks and supports diverse applications like customer assistants, internal copilots, and data analysis.
Best For
Building LLM agents, RAG, and multi-step workflows with private or domain-specific data.
Rapidly prototyping and deploying AI agents for integration into products or internal workflows.
Avoid If
Your application does not require external data context augmentation or complex agentic workflows.
no data
Strengths
- +Provides a comprehensive framework for building LLM-powered agents over custom data.
- +Supports complex, event-driven workflows combining multiple agents and data sources with reflection and error-correction.
- +Offers robust tools for data ingestion, parsing, indexing, and processing from various sources (APIs, PDFs, SQL).
- +Features both high-level APIs for quick setup and lower-level APIs for extensive customization of core modules.
- +Includes engines for natural language access to data, such as query engines for RAG and chat engines for conversational interactions.
- +Integrates with observability and evaluation tools for rigorous experimentation and monitoring.
- +Offers LlamaCloud, a managed service for document parsing (LlamaParse), extraction, indexing, and retrieval.
- +Enables rapid prototyping and confident deployment of AI agents.
- +Offers broad integration with popular JavaScript/TypeScript web frameworks like Next.js, React, and Express.
- +Supports a wide array of AI agent applications, including customer support, data analysis, and DevOps automation.
- +Provides quick project setup with a single command and ready-to-use templates for common use cases.
Weaknesses
- −Advanced customization and complex workflow orchestration can introduce a steep learning curve.
- −The quickstart guide defaults to requiring an OpenAI API key, implying a common reliance on external LLM providers.
- −Building and managing robust, reflection-capable agentic workflows can be complex for production systems.
Project Health
Is this project alive, well-maintained, and safe to bet on long-term?
Bus Factor Score
Maintainers
Open Issues
Fit
Does it support the workflows, patterns, and capabilities your team actually needs?
State Management
LlamaIndex manages state through data indexes that structure external information for LLMs, and within conversational agents and event-driven workflows that maintain context across multi-step interactions.
no data
Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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