LlamaIndex is a Python framework designed to connect Large Language Models with your private or domain-specific data. It provides tools for data ingestion, indexing, and querying, enabling the creation of RAG pipelines, autonomous agents, and multi-step LLM workflows. The framework supports both high-level rapid prototyping and low-level customization for complex applications.

Mastra is a TypeScript framework for building and shipping AI agents, focusing on rapid prototyping and production readiness. It provides a structured approach for defining agents, tools, and integrating with various LLM providers through a unified model router. The framework includes a Studio UI for managing development workflows and supports integration into existing web frameworks.

• +Leading framework for building LLM-powered agents over your data with LLMs and workflows.
• +Provides tools to ingest, parse, index, and process your data from various sources and formats.
• +Supports building various context-augmented LLM applications like RAG, chatbots, and autonomous agents.
• +Offers high-level APIs for quick setup and low-level APIs for extensive customization of modules.
• +Facilitates creation of multi-step, event-driven workflows combining agents and data sources with reflection and error-correction.
• +Includes managed services (LlamaCloud) for enterprise document parsing, extraction, indexing, and retrieval.

• +Provides a structured TypeScript framework for building AI agents, enabling fast prototyping and reliable deployment.
• +Offers an integrated model router that abstracts LLM providers (OpenAI, Anthropic, Google, xAI) using a `provider/model` string format and environment variables.
• +Enforces a clear structure for defining tools via `createTool()` including Zod schemas for input/output, preventing silent execution failures.
• +Includes Mastra Studio, an interactive UI for building, testing, and managing agents, workflows, and tools.
• +Supports integration with popular web frameworks like Next.js, React, Astro, Express, SvelteKit, and Hono.
• +Enables a wide range of use cases, including customer-facing assistants, internal copilots, data analysis, content automation, and DevOps.
• +Provides access to a large selection of models through its model router, beyond a short list of known IDs.

• −Plain object tool definitions silently fail to execute; tools must be strictly defined via `createTool()` with specific properties.
• −Requires a `package.json` file with `"type": "module"` for project setup.
• −Requires Node.js 22.18.0 or later for direct TypeScript file execution.

LlamaIndex manages state implicitly through its higher-level abstractions like chat engines, agents, and event-driven workflows, facilitating multi-step and conversational interactions.

Mastra manages state through explicitly defined Agent and Tool objects, configured within a central Mastra instance, which then handles model routing and execution context via environment variables.