# Mastra

> Mastra is an open-source TypeScript agent framework designed to provide the essential primitives for building AI applications. It enables developers to create AI agents with memory and tool-calling capabilities, implement deterministic LLM workflows, and leverage RAG for knowledge integration. With features like model routing, workflow graphs, and automated evals, Mastra provides a complete toolkit for developing, testing, and deploying AI applications.

This documentation covers everything from getting started to advanced features, APIs, and best practices for working with Mastra's agent-based architecture.

The documentation is organized into key sections:
- **docs**: Core documentation covering concepts, features, and implementation details
- **examples**: Practical examples and use cases demonstrating Mastra's capabilities
- **showcase**: A showcase of applications built using Mastra

Each section contains detailed markdown files that provide comprehensive information about Mastra's features and how to use them effectively.


## EN - docs
- [Adding Voice to Agents | Agents](https://mastra.ai/docs/agents/adding-voice)
- [Agent Memory | Agents](https://mastra.ai/docs/agents/agent-memory): Learn how to add memory to agents to store conversation history and maintain context across interactions.
- [Guardrails | Agents](https://mastra.ai/docs/agents/guardrails): Learn how to implement guardrails using input and output processors to secure and control AI interactions.
- [Agent Networks | Agents](https://mastra.ai/docs/agents/networks): Learn how to coordinate multiple agents, workflows, and tools using agent networks for complex, non-deterministic task execution.
- [Using Agents | Agents](https://mastra.ai/docs/agents/overview): Overview of agents in Mastra, detailing their capabilities and how they interact with tools, workflows, and external systems.
- [Using Tools | Agents](https://mastra.ai/docs/agents/using-tools): Learn how to create tools and add them to agents to extend capabilities beyond text generation.
- [MastraAuthAuth0 Class | Auth](https://mastra.ai/docs/auth/auth0): Documentation for the MastraAuthAuth0 class, which authenticates Mastra applications using Auth0 authentication.
- [MastraAuthClerk Class | Auth](https://mastra.ai/docs/auth/clerk): Documentation for the MastraAuthClerk class, which authenticates Mastra applications using Clerk authentication.
- [MastraAuthFirebase Class | Auth](https://mastra.ai/docs/auth/firebase): Documentation for the MastraAuthFirebase class, which authenticates Mastra applications using Firebase Authentication.
- [Auth Overview | Auth](https://mastra.ai/docs/auth): Learn about different Auth options for your Mastra applications
- [MastraJwtAuth Class | Auth](https://mastra.ai/docs/auth/jwt): Documentation for the MastraJwtAuth class, which authenticates Mastra applications using JSON Web Tokens.
- [MastraAuthSupabase Class | Auth](https://mastra.ai/docs/auth/supabase): Documentation for the MastraAuthSupabase class, which authenticates Mastra applications using Supabase Auth.
- [MastraAuthWorkos Class | Auth](https://mastra.ai/docs/auth/workos): Documentation for the MastraAuthWorkos class, which authenticates Mastra applications using WorkOS authentication.
- [Contributing Templates | Community](https://mastra.ai/docs/community/contributing-templates): How to contribute your own templates to the Mastra ecosystem
- [Discord Community | Community](https://mastra.ai/docs/community/discord): Information about the Mastra Discord community and MCP bot.
- [License | Community](https://mastra.ai/docs/community/licensing): Mastra License
- [Building Mastra | Deployment](https://mastra.ai/docs/deployment/building-mastra): Learn how to build a Mastra server with build settings and deployment options.
- [Amazon EC2 | Deployment](https://mastra.ai/docs/deployment/cloud-providers/amazon-ec2): Deploy your Mastra applications to Amazon EC2.
- [AWS Lambda | Deployment](https://mastra.ai/docs/deployment/cloud-providers/aws-lambda): Deploy your Mastra applications to AWS Lambda using Docker containers and the AWS Lambda Web Adapter.
- [Azure App Services | Deployment](https://mastra.ai/docs/deployment/cloud-providers/azure-app-services): Deploy your Mastra applications to Azure App Services.
- [CloudflareDeployer | Deployment](https://mastra.ai/docs/deployment/cloud-providers/cloudflare-deployer): Learn how to deploy a Mastra application to Cloudflare using the Mastra CloudflareDeployer
- [Digital Ocean | Deployment](https://mastra.ai/docs/deployment/cloud-providers/digital-ocean): Deploy your Mastra applications to Digital Ocean.
- [Cloud Providers | Deployment](https://mastra.ai/docs/deployment/cloud-providers): Deploy your Mastra applications to popular cloud providers.
- [NetlifyDeployer | Deployment](https://mastra.ai/docs/deployment/cloud-providers/netlify-deployer): Learn how to deploy a Mastra application to Netlify using the Mastra NetlifyDeployer
- [VercelDeployer | Deployment](https://mastra.ai/docs/deployment/cloud-providers/vercel-deployer): Learn how to deploy a Mastra application to Vercel using the Mastra VercelDeployer
- [Navigating the Dashboard | Mastra Cloud](https://mastra.ai/docs/deployment/mastra-cloud/dashboard): Details of each feature available in Mastra Cloud
- [Understanding Tracing and Logs | Mastra Cloud](https://mastra.ai/docs/deployment/mastra-cloud/observability): Monitoring and debugging tools for Mastra Cloud deployments
- [Mastra Cloud | Mastra Cloud](https://mastra.ai/docs/deployment/mastra-cloud/overview): Deployment and monitoring service for Mastra applications
- [Setting Up and Deploying | Mastra Cloud](https://mastra.ai/docs/deployment/mastra-cloud/setting-up): Configuration steps for Mastra Cloud projects
- [Monorepo Deployment | Deployment](https://mastra.ai/docs/deployment/monorepo): Learn how to deploy Mastra applications that are part of a monorepo setup
- [Deployment Overview | Deployment](https://mastra.ai/docs/deployment/overview): Learn about different deployment options for your Mastra applications
- [Web Framework Integration | Deployment](https://mastra.ai/docs/deployment/web-framework): Learn how Mastra can be deployed when integrated with a Web Framework
- [Using Vercel AI SDK | Frameworks](https://mastra.ai/docs/frameworks/agentic-uis/ai-sdk): Learn how Mastra leverages the Vercel AI SDK library and how you can leverage it further with Mastra
- [Using with Assistant UI | Frameworks](https://mastra.ai/docs/frameworks/agentic-uis/assistant-ui): Learn how to integrate Assistant UI with Mastra
- [Integrate Cedar-OS with Mastra | Frameworks](https://mastra.ai/docs/frameworks/agentic-uis/cedar-os): Build AI-native frontends for your Mastra agents with Cedar-OS
- [Integrate CopilotKit with Mastra | Frameworks](https://mastra.ai/docs/frameworks/agentic-uis/copilotkit): Learn how Mastra leverages the CopilotKits AGUI library and how you can leverage it to build user experiences
- [Use OpenRouter with Mastra | Frameworks](https://mastra.ai/docs/frameworks/agentic-uis/openrouter): Learn how to integrate OpenRouter with Mastra
- [Integrate Mastra in your Express project | Frameworks](https://mastra.ai/docs/frameworks/servers/express): A step-by-step guide to integrating Mastra with an Express backend.
- [Integrate Mastra in your Astro project | Frameworks](https://mastra.ai/docs/frameworks/web-frameworks/astro): A step-by-step guide to integrating Mastra with Astro.
- [Integrate Mastra in your Next.js project | Frameworks](https://mastra.ai/docs/frameworks/web-frameworks/next-js): A step-by-step guide to integrating Mastra with Next.js.
- [Integrate Mastra in your SvelteKit project | Frameworks](https://mastra.ai/docs/frameworks/web-frameworks/sveltekit): A step-by-step guide to integrating Mastra with SvelteKit.
- [Integrate Mastra in your Vite/React project | Frameworks](https://mastra.ai/docs/frameworks/web-frameworks/vite-react): A step-by-step guide to integrating Mastra with Vite and React.
- [Install Mastra | Getting Started](https://mastra.ai/docs/getting-started/installation): Guide on installing Mastra and setting up the necessary prerequisites for running it with various LLM providers.
- [Mastra Docs Server | Getting Started](https://mastra.ai/docs/getting-started/mcp-docs-server): Learn how to use the Mastra MCP documentation server in your IDE to turn it into an agentic Mastra expert.
- [Project Structure | Getting Started](https://mastra.ai/docs/getting-started/project-structure): Guide on organizing folders and files in Mastra, including best practices and recommended structures.
- [Studio | Getting Started](https://mastra.ai/docs/getting-started/studio): Get started with Mastra Studio, a local UI and API to build, test, debug, and inspect agents and workflows in real time.
- [Templates | Getting Started](https://mastra.ai/docs/getting-started/templates): Pre-built project structures that demonstrate common Mastra use cases and patterns
- [About Mastra](https://mastra.ai/docs): Mastra is an all-in-one framework for building AI-powered applications and agents with a modern TypeScript stack.
- [MCP Overview | MCP](https://mastra.ai/docs/mcp/overview): Learn about the Model Context Protocol (MCP), how to use third-party tools via MCPClient, connect to registries, and share your own tools using MCPServer.
- [Publishing an MCP Server | MCP](https://mastra.ai/docs/mcp/publishing-mcp-server): Guide to setting up and building a Mastra MCP server using the stdio transport, and publishing it to NPM.
- [Conversation History | Memory](https://mastra.ai/docs/memory/conversation-history): Learn how to configure conversation history in Mastra to store recent messages from the current conversation.
- [Memory Processors | Memory](https://mastra.ai/docs/memory/memory-processors): Learn how to use memory processors in Mastra to filter, trim, and transform messages before theyre sent to the language model to manage context window limits.
- [Memory overview | Memory](https://mastra.ai/docs/memory/overview): Learn how Mastras memory system works with working memory, conversation history, and semantic recall.
- [Semantic Recall | Memory](https://mastra.ai/docs/memory/semantic-recall): Learn how to use semantic recall in Mastra to retrieve relevant messages from past conversations using vector search and embeddings.
- [Memory with LibSQL | Memory](https://mastra.ai/docs/memory/storage/memory-with-libsql): Example for how to use Mastras memory system with LibSQL storage and vector database backend.
- [Example: Memory with MongoDB | Memory](https://mastra.ai/docs/memory/storage/memory-with-mongodb): Example for how to use Mastras memory system with MongoDB storage and vector capabilities.
- [Memory with Postgres | Memory](https://mastra.ai/docs/memory/storage/memory-with-pg): Example for how to use Mastras memory system with PostgreSQL storage and vector capabilities.
- [Memory with Upstash | Memory](https://mastra.ai/docs/memory/storage/memory-with-upstash): Example for how to use Mastras memory system with Upstash Redis storage and vector capabilities.
- [Memory threads and resources | Memory](https://mastra.ai/docs/memory/threads-and-resources): Use memory threads and resources in Mastra to control conversation scope, persistence, and recall behavior.
- [Working Memory | Memory](https://mastra.ai/docs/memory/working-memory): Learn how to configure working memory in Mastra to store persistent user data, preferences.
- [Arize Exporter | AI Tracing | Observability](https://mastra.ai/docs/observability/ai-tracing/exporters/arize): Send AI traces to Arize Phoenix or Arize AX using OpenTelemetry and OpenInference
- [Braintrust Exporter | AI Tracing | Observability](https://mastra.ai/docs/observability/ai-tracing/exporters/braintrust): Send AI traces to Braintrust for evaluation and monitoring
- [Cloud Exporter | AI Tracing | Observability](https://mastra.ai/docs/observability/ai-tracing/exporters/cloud): Send traces to Mastra Cloud for production monitoring
- [Default Exporter | AI Tracing | Observability](https://mastra.ai/docs/observability/ai-tracing/exporters/default): Store traces locally for development and debugging
- [Langfuse Exporter | AI Tracing | Observability](https://mastra.ai/docs/observability/ai-tracing/exporters/langfuse): Send AI traces to Langfuse for LLM observability and analytics
- [LangSmith Exporter | AI Tracing | Observability](https://mastra.ai/docs/observability/ai-tracing/exporters/langsmith): Send AI traces to LangSmith for LLM observability and evaluation
- [OpenTelemetry Exporter | AI Tracing | Observability](https://mastra.ai/docs/observability/ai-tracing/exporters/otel): Send AI traces to any OpenTelemetry-compatible observability platform
- [AI Tracing | Observability](https://mastra.ai/docs/observability/ai-tracing/overview): Set up AI tracing for Mastra applications
- [Sensitive Data Filter | Processors | Observability](https://mastra.ai/docs/observability/ai-tracing/processors/sensitive-data-filter): Protect sensitive information in your AI traces with automatic data redaction
- [Logging | Observability](https://mastra.ai/docs/observability/logging): Learn how to use logging in Mastra to monitor execution, capture application behavior, and improve the accuracy of AI applications.
- [Next.js Tracing | Observability](https://mastra.ai/docs/observability/nextjs-tracing): Set up OpenTelemetry tracing for Next.js applications
- [OTEL Tracing (Deprecated) | Observability](https://mastra.ai/docs/observability/otel-tracing): Set up OpenTelemetry tracing for Mastra applications
- [Observability Overview | Observability](https://mastra.ai/docs/observability/overview): Monitor and debug applications with Mastras Observability features.
- [Chunking and Embedding Documents | RAG | Mastra Docs](https://mastra.ai/docs/rag/chunking-and-embedding): Guide on chunking and embedding documents in Mastra for efficient processing and retrieval.
- [RAG (Retrieval-Augmented Generation) in Mastra | RAG](https://mastra.ai/docs/rag/overview): Overview of Retrieval-Augmented Generation (RAG) in Mastra, detailing its capabilities for enhancing LLM outputs with relevant context.
- [Retrieval, Semantic Search, Reranking | RAG](https://mastra.ai/docs/rag/retrieval): Guide on retrieval processes in Mastras RAG systems, including semantic search, filtering, and re-ranking.
- [Storing Embeddings in A Vector Database | RAG](https://mastra.ai/docs/rag/vector-databases): Guide on vector storage options in Mastra, including embedded and dedicated vector databases for similarity search.
- [Built-in Scorers | Scorers](https://mastra.ai/docs/scorers/built-in-scorers): Overview of Mastras ready-to-use scorers for evaluating AI outputs across quality, safety, and performance dimensions.
- [Custom Scorers | Scorers](https://mastra.ai/docs/scorers/custom-scorers)
- [Create a Custom Eval | Scorers](https://mastra.ai/docs/scorers/evals-legacy/custom-eval): Mastra allows you to create your own evals, here is how.
- [Evals Overview | Evals](https://mastra.ai/docs/scorers/evals-legacy/overview): Overview of evals in Mastra, detailing their capabilities for evaluating AI outputs and measuring performance.
- [Running Evals in CI | Scorers](https://mastra.ai/docs/scorers/evals-legacy/running-in-ci): Learn how to run Mastra evals in your CI/CD pipeline to monitor agent quality over time.
- [Textual Evals | Scorers](https://mastra.ai/docs/scorers/evals-legacy/textual-evals): Understand how Mastra uses LLM-as-judge methodology to evaluate text quality.
- [Scorers Overview | Scorers](https://mastra.ai/docs/scorers/overview): Overview of scorers in Mastra, detailing their capabilities for evaluating AI outputs and measuring performance.
- [Custom API Routes | Server & DB](https://mastra.ai/docs/server-db/custom-api-routes): Expose additional HTTP endpoints from your Mastra server.
- [Mastra Client SDK | Server & DB](https://mastra.ai/docs/server-db/mastra-client): Learn how to set up and use the Mastra Client SDK
- [Mastra Server | Server & DB](https://mastra.ai/docs/server-db/mastra-server): Learn how to configure and deploy a production-ready Mastra server with custom settings for APIs, CORS, and more
- [Middleware | Server & DB](https://mastra.ai/docs/server-db/middleware): Apply custom middleware functions to intercept requests.
- [Runtime Context | Server & DB](https://mastra.ai/docs/server-db/runtime-context): Learn how to use Mastras RuntimeContext to provide dynamic, request-specific configuration to agents.
- [MastraStorage | Server & DB](https://mastra.ai/docs/server-db/storage): Overview of Mastras storage system and data persistence capabilities.
- [Streaming Events | Streaming](https://mastra.ai/docs/streaming/events): Learn about the different types of streaming events in Mastra, including text deltas, tool calls, step events, and how to handle them in your applications.
- [Streaming Overview | Streaming](https://mastra.ai/docs/streaming/overview): Streaming in Mastra enables real-time, incremental responses from both agents and workflows, providing immediate feedback as AI-generated content is produced.
- [Tool streaming | Streaming](https://mastra.ai/docs/streaming/tool-streaming): Learn how to use tool streaming in Mastra, including handling tool calls, tool results, and tool execution events during streaming.
- [Workflow streaming | Streaming](https://mastra.ai/docs/streaming/workflow-streaming): Learn how to use workflow streaming in Mastra, including handling workflow execution events, step streaming, and workflow integration with agents and tools.
- [Voice in Mastra | Voice](https://mastra.ai/docs/voice/overview): Overview of voice capabilities in Mastra, including text-to-speech, speech-to-text, and real-time speech-to-speech interactions.
- [Speech-to-Speech Capabilities in Mastra | Voice](https://mastra.ai/docs/voice/speech-to-speech): Overview of speech-to-speech capabilities in Mastra, including real-time interactions and event-driven architecture.
- [Speech-to-Text (STT) | Voice](https://mastra.ai/docs/voice/speech-to-text): Overview of Speech-to-Text capabilities in Mastra, including configuration, usage, and integration with voice providers.
- [Text-to-Speech (TTS) | Voice](https://mastra.ai/docs/voice/text-to-speech): Overview of Text-to-Speech capabilities in Mastra, including configuration, usage, and integration with voice providers.
- [Agents and Tools | Workflows](https://mastra.ai/docs/workflows/agents-and-tools): Learn how to call agents and tools from workflow steps and choose between execute functions and step composition.
- [Control Flow | Workflows](https://mastra.ai/docs/workflows/control-flow): Control flow in Mastra workflows allows you to manage branching, merging, and conditions to construct workflows that meet your logic requirements.
- [Error Handling | Workflows](https://mastra.ai/docs/workflows/error-handling): Learn how to handle errors in Mastra workflows using step retries, conditional branching, and monitoring.
- [Human-in-the-loop (HITL) | Workflows](https://mastra.ai/docs/workflows/human-in-the-loop): Human-in-the-loop workflows in Mastra allow you to pause execution for manual approvals, reviews, or user input before continuing.
- [Inngest Workflow | Workflows](https://mastra.ai/docs/workflows/inngest-workflow): Inngest workflow allows you to run Mastra workflows with Inngest
- [Workflows overview | Workflows](https://mastra.ai/docs/workflows/overview): Workflows in Mastra help you orchestrate complex sequences of tasks with features like branching, parallel execution, resource suspension, and more.
- [Snapshots | Workflows](https://mastra.ai/docs/workflows/snapshots): Learn how to save and resume workflow execution state with snapshots in Mastra
- [Suspend & Resume | Workflows](https://mastra.ai/docs/workflows/suspend-and-resume): Suspend and resume in Mastra workflows allows you to pause execution while waiting for external input or resources.
- [Time Travel | Workflows](https://mastra.ai/docs/workflows/time-travel): Re-execute workflow steps from a specific point using time travel debugging in Mastra
- [Workflow state | Workflows](https://mastra.ai/docs/workflows/workflow-state): Share values across workflow steps using global state that persists through the entire workflow run.
- [Control Flow in Legacy Workflows: Branching, Merging, and Conditions | Workflows (Legacy)](https://mastra.ai/docs/workflows-legacy/control-flow): Control flow in Mastra legacy workflows allows you to manage branching, merging, and conditions to construct legacy workflows that meet your logic requirements.
- [Dynamic Workflows (Legacy) | Workflows (Legacy)](https://mastra.ai/docs/workflows-legacy/dynamic-workflows): Learn how to create dynamic workflows within legacy workflow steps, allowing for flexible workflow creation based on runtime conditions.
- [Error Handling in Workflows (Legacy) | Workflows (Legacy)](https://mastra.ai/docs/workflows-legacy/error-handling): Learn how to handle errors in Mastra legacy workflows using step retries, conditional branching, and monitoring.
- [Nested Workflows (Legacy) | Workflows (Legacy)](https://mastra.ai/docs/workflows-legacy/nested-workflows)
- [Handling Complex LLM Operations with Workflows (Legacy) | Workflows (Legacy)](https://mastra.ai/docs/workflows-legacy/overview): Workflows in Mastra help you orchestrate complex sequences of operations with features like branching, parallel execution, resource suspension, and more.
- [Workflow Runtime Variables (Legacy) | Workflows (Legacy)](https://mastra.ai/docs/workflows-legacy/runtime-variables): Learn how to use Mastras dependency injection system to provide runtime configuration to workflows and steps.
- [Defining Steps in a Workflow (Legacy) | Workflows (Legacy)](https://mastra.ai/docs/workflows-legacy/steps): Steps in Mastra workflows provide a structured way to manage operations by defining inputs, outputs, and execution logic.
- [Suspend and Resume in Workflows (Legacy) | Workflows (Legacy)](https://mastra.ai/docs/workflows-legacy/suspend-and-resume): Suspend and resume in Mastra workflows (Legacy) allows you to pause execution while waiting for external input or resources.
- [Data Mapping with Workflow Variables | Workflows (Legacy)](https://mastra.ai/docs/workflows-legacy/variables): Learn how to use workflow variables to map data between steps and create dynamic data flows in your Mastra workflows.

## EN - examples
- [Example: AI SDK v5 Integration | Agents](https://mastra.ai/examples/agents/ai-sdk-v5-integration): Example of integrating Mastra agents with AI SDK v5 for streaming chat interfaces with memory and tool integration.
- [Example: Calling Agents | Agents](https://mastra.ai/examples/agents/calling-agents): Example for how to call agents.
- [Example: Image Analysis | Agents](https://mastra.ai/examples/agents/image-analysis): Example of using a Mastra AI Agent to analyze images from Unsplash to identify objects, determine species, and describe locations.
- [Example: Runtime Context | Agents](https://mastra.ai/examples/agents/runtime-context): Learn how to create and configure dynamic agents using runtime context to adapt behavior based on user subscription tiers.
- [Example: Supervisor Agent | Agents](https://mastra.ai/examples/agents/supervisor-agent): Example of creating a supervisor agent using Mastra, where agents interact through tool functions.
- [Example: Changing the System Prompt | Agents](https://mastra.ai/examples/agents/system-prompt): Example of creating an AI agent in Mastra with a system prompt to define its personality and capabilities.
- [Example: WhatsApp Chat Bot | Agents](https://mastra.ai/examples/agents/whatsapp-chat-bot): Example of creating a WhatsApp chat bot using Mastra agents and workflows to handle incoming messages and respond naturally via text messages.
- [Example: Answer Relevancy Evaluation | Evals](https://mastra.ai/examples/evals/answer-relevancy): Example of using the Answer Relevancy metric to evaluate response relevancy to queries.
- [Example: Bias Evaluation | Evals](https://mastra.ai/examples/evals/bias): Example of using the Bias metric to evaluate responses for various forms of bias.
- [Example: Completeness Evaluation | Evals](https://mastra.ai/examples/evals/completeness): Example of using the Completeness metric to evaluate how thoroughly responses cover input elements.
- [Example: Content Similarity Evaluation | Evals](https://mastra.ai/examples/evals/content-similarity): Example of using the Content Similarity metric to evaluate text similarity between content.
- [Example: Context Position Evaluation | Evals](https://mastra.ai/examples/evals/context-position): Example of using the Context Position metric to evaluate sequential ordering in responses.
- [Example: Context Precision Evaluation | Evals](https://mastra.ai/examples/evals/context-precision): Example of using the Context Precision metric to evaluate how precisely context information is used.
- [Example: Context Relevancy Evaluation | Evals](https://mastra.ai/examples/evals/context-relevancy): Example of using the Context Relevancy metric to evaluate how relevant context information is to a query.
- [Example: Contextual Recall Evaluation | Evals](https://mastra.ai/examples/evals/contextual-recall): Example of using the Contextual Recall metric to evaluate how well responses incorporate context information.
- [Example: LLM as a Judge Evaluation | Evals](https://mastra.ai/examples/evals/custom-llm-judge-eval): Example of creating a custom LLM-based evaluation metric.
- [Example: Custom Native JavaScript Evaluation | Evals](https://mastra.ai/examples/evals/custom-native-javascript-eval): Example of creating a custom native JavaScript evaluation metric.
- [Example: Faithfulness Evaluation | Evals](https://mastra.ai/examples/evals/faithfulness): Example of using the Faithfulness metric to evaluate how factually accurate responses are compared to context.
- [Example: Hallucination Evaluation | Evals](https://mastra.ai/examples/evals/hallucination): Example of using the Hallucination metric to evaluate factual contradictions in responses.
- [Example: Keyword Coverage Evaluation | Evals](https://mastra.ai/examples/evals/keyword-coverage): Example of using the Keyword Coverage metric to evaluate how well responses cover important keywords from input text.
- [Example: Prompt Alignment Evaluation | Evals](https://mastra.ai/examples/evals/prompt-alignment): Example of using the Prompt Alignment metric to evaluate instruction adherence in responses.
- [Example: Summarization Evaluation | Evals](https://mastra.ai/examples/evals/summarization): Example of using the Summarization metric to evaluate how well LLM-generated summaries capture content while maintaining factual accuracy.
- [Example: Textual Difference Evaluation | Evals](https://mastra.ai/examples/evals/textual-difference): Example of using the Textual Difference metric to evaluate similarity between text strings by analyzing sequence differences and changes.
- [Example: Tone Consistency Evaluation | Evals](https://mastra.ai/examples/evals/tone-consistency): Example of using the Tone Consistency metric to evaluate emotional tone patterns and sentiment consistency in text.
- [Example: Toxicity Evaluation | Evals](https://mastra.ai/examples/evals/toxicity): Example of using the Toxicity metric to evaluate responses for harmful content and toxic language.
- [Examples List: Workflows, Agents, RAG](https://mastra.ai/examples): Explore practical examples of AI development with Mastra, including text generation, RAG implementations, structured outputs, and multi-modal interactions. Learn how to build AI applications using OpenAI, Anthropic, and Google Gemini.
- [Example: Working Memory with Schema | Memory](https://mastra.ai/examples/memory/working-memory-schema): Example showing how to use Zod schema to structure and validate working memory data.
- [Example: Working Memory with Template | Memory](https://mastra.ai/examples/memory/working-memory-template): Example showing how to use Markdown template to structure working memory data.
- [Example: Basic AI Tracing Example | Observability](https://mastra.ai/examples/observability/basic-ai-tracing): Get started with AI tracing in your Mastra application
- [Example: Message Length Limiter | Processors](https://mastra.ai/examples/processors/message-length-limiter): Example of creating a custom input processor that limits message length before sending to the language model.
- [Example: Response Length Limiter | Processors](https://mastra.ai/examples/processors/response-length-limiter): Example of creating a custom output processor that limits AI response length during streaming to prevent excessively long outputs.
- [Example: Response Validator | Processors](https://mastra.ai/examples/processors/response-validator): Example of creating a custom output processor that validates AI responses contain required keywords before returning them to users.
- [Example: Adjust Chunk Delimiters | RAG](https://mastra.ai/examples/rag/chunking/adjust-chunk-delimiters): Adjust chunk delimiters in Mastra to better match your content structure.
- [Example: Adjust Chunk Size | RAG](https://mastra.ai/examples/rag/chunking/adjust-chunk-size): Adjust chunk size in Mastra to better match your content and memory requirements.
- [Example: Semantically Chunking HTML | RAG](https://mastra.ai/examples/rag/chunking/chunk-html): Chunk HTML content in Mastra to semantically chunk the document.
- [Example: Semantically Chunking JSON | RAG](https://mastra.ai/examples/rag/chunking/chunk-json): Chunk JSON data in Mastra to semantically chunk the document.
- [Example: Chunk Markdown | RAG](https://mastra.ai/examples/rag/chunking/chunk-markdown): Example of using Mastra to chunk markdown documents for search or retrieval purposes.
- [Example: Chunk Text | RAG](https://mastra.ai/examples/rag/chunking/chunk-text): Example of using Mastra to split large text documents into smaller chunks for processing.
- [Example: Embed Chunk Array | RAG](https://mastra.ai/examples/rag/embedding/embed-chunk-array): Example of using Mastra to generate embeddings for an array of text chunks for similarity search.
- [Example: Embed Text Chunk | RAG](https://mastra.ai/examples/rag/embedding/embed-text-chunk): Example of using Mastra to generate an embedding for a single text chunk for similarity search.
- [Example: Embed Text with Cohere | RAG](https://mastra.ai/examples/rag/embedding/embed-text-with-cohere): Example of using Mastra to generate embeddings using Coheres embedding model.
- [Example: Metadata Extraction | RAG](https://mastra.ai/examples/rag/embedding/metadata-extraction): Example of extracting and utilizing metadata from documents in Mastra for enhanced document processing and retrieval.
- [Example: Hybrid Vector Search | RAG](https://mastra.ai/examples/rag/query/hybrid-vector-search): Example of using metadata filters with PGVector to enhance vector search results in Mastra.
- [Example: Retrieving Top-K Results | RAG](https://mastra.ai/examples/rag/query/retrieve-results): Example of using Mastra to query a vector database and retrieve semantically similar chunks.
- [Example: Re-ranking Results with Tools | RAG](https://mastra.ai/examples/rag/rerank/rerank-rag): Example of implementing a RAG system with re-ranking in Mastra using OpenAI embeddings and PGVector for vector storage.
- [Example: Re-ranking Results | RAG](https://mastra.ai/examples/rag/rerank/rerank): Example of implementing semantic re-ranking in Mastra using OpenAI embeddings and PGVector for vector storage.
- [Example: Reranking with Cohere | RAG](https://mastra.ai/examples/rag/rerank/reranking-with-cohere): Example of using Mastra to improve document retrieval relevance with Coheres reranking service.
- [Example: Reranking with ZeroEntropy | RAG](https://mastra.ai/examples/rag/rerank/reranking-with-zeroentropy): Example of using Mastra to improve document retrieval relevance with ZeroEntropys reranking service.
- [Example: Upsert Embeddings | RAG](https://mastra.ai/examples/rag/upsert/upsert-embeddings): Examples of using Mastra to store embeddings in various vector databases for similarity search.
- [Example: Using the Vector Query Tool | RAG](https://mastra.ai/examples/rag/usage/basic-rag): Example of implementing a basic RAG system in Mastra using OpenAI embeddings and PGVector for vector storage.
- [Example: Optimizing Information Density | RAG](https://mastra.ai/examples/rag/usage/cleanup-rag): Example of implementing a RAG system in Mastra to optimize information density and deduplicate data using LLM-based processing.
- [Example: Chain of Thought Prompting | RAG](https://mastra.ai/examples/rag/usage/cot-rag): Example of implementing a RAG system in Mastra with chain-of-thought reasoning using OpenAI and PGVector.
- [Example: Structured Reasoning with Workflows | RAG](https://mastra.ai/examples/rag/usage/cot-workflow-rag): Example of implementing structured reasoning in a RAG system using Mastras workflow capabilities.
- [Example: Database-Specific Configurations | RAG](https://mastra.ai/examples/rag/usage/database-specific-config): Learn how to use database-specific configurations to optimize vector search performance and leverage unique features of different vector stores.
- [Example: Agent-Driven Metadata Filtering | RAG](https://mastra.ai/examples/rag/usage/filter-rag): Example of using a Mastra agent in a RAG system to construct and apply metadata filters for document retrieval.
- [Example: Graph RAG | RAG](https://mastra.ai/examples/rag/usage/graph-rag): Example of implementing a Graph RAG system in Mastra using OpenAI embeddings and PGVector for vector storage.
- [Example: Call Analysis with Mastra | Voice](https://mastra.ai/examples/voice/speech-to-speech): Example of using Mastra to create a speech to speech application.
- [Example: Smart Voice Memo App | Voice](https://mastra.ai/examples/voice/speech-to-text): Example of using Mastra to create a speech to text application.
- [Example: Interactive Story Generator | Voice](https://mastra.ai/examples/voice/text-to-speech): Example of using Mastra to create a text to speech application.
- [Example: AI Debate with Turn Taking | Voice](https://mastra.ai/examples/voice/turn-taking): Example of using Mastra to create a multi-agent debate with turn-taking conversation flow.
- [Example: Inngest Workflow | Workflows](https://mastra.ai/examples/workflows/inngest-workflow): Example of building an inngest workflow with Mastra
- [Example: Branching Paths | Workflows (Legacy)](https://mastra.ai/examples/workflows_legacy/branching-paths): Example of using Mastra to create legacy workflows with branching paths based on intermediate results.
- [Example: Calling an Agent From a Workflow (Legacy) | Workflows (Legacy)](https://mastra.ai/examples/workflows_legacy/calling-agent): Example of using Mastra to call an AI agent from within a legacy workflow step.
- [Example: Workflow (Legacy) with Conditional Branching (experimental) | Workflows (Legacy)](https://mastra.ai/examples/workflows_legacy/conditional-branching): Example of using Mastra to create conditional branches in legacy workflows using if/else statements.
- [Example: Creating a Simple Workflow (Legacy) | Workflows (Legacy)](https://mastra.ai/examples/workflows_legacy/creating-a-workflow): Example of using Mastra to define and execute a simple workflow with a single step.
- [Example: Workflow (Legacy) with Cyclical dependencies | Workflows (Legacy)](https://mastra.ai/examples/workflows_legacy/cyclical-dependencies): Example of using Mastra to create legacy workflows with cyclical dependencies and conditional loops.
- [Example: Human in the Loop Workflow (Legacy) | Workflows (Legacy)](https://mastra.ai/examples/workflows_legacy/human-in-the-loop): Example of using Mastra to create legacy workflows with human intervention points.
- [Example: Parallel Execution with Steps | Workflows (Legacy)](https://mastra.ai/examples/workflows_legacy/parallel-steps): Example of using Mastra to execute multiple independent tasks in parallel within a workflow.
- [Example: Workflow (Legacy) with Sequential Steps | Workflows (Legacy)](https://mastra.ai/examples/workflows_legacy/sequential-steps): Example of using Mastra to chain legacy workflow steps in a specific sequence, passing data between them.
- [Example: Workflow (Legacy) with Suspend and Resume | Workflows (Legacy)](https://mastra.ai/examples/workflows_legacy/suspend-and-resume): Example of using Mastra to suspend and resume legacy workflow steps during execution.
- [Example: Tool as a Workflow step (Legacy) | Workflows (Legacy)](https://mastra.ai/examples/workflows_legacy/using-a-tool-as-a-step): Example of using Mastra to integrate a custom tool as a step in a legacy workflow.
- [Example: Data Mapping with Workflow Variables (Legacy) | Workflows (Legacy)](https://mastra.ai/examples/workflows_legacy/workflow-variables): Learn how to use workflow variables to map data between steps in Mastra workflows.

## EN - guides
- [Guide: Building an AI Recruiter | Mastra Workflows | Guides](https://mastra.ai/guides/guide/ai-recruiter): Guide on building a recruiter workflow in Mastra to gather and process candidate information using LLMs.
- [Guide: Building an AI Chef Assistant | Mastra Agent Guides](https://mastra.ai/guides/guide/chef-michel): Guide on creating a Chef Assistant agent in Mastra to help users cook meals with available ingredients.
- [Guide: Building a Notes MCP Server | Mastra Guide](https://mastra.ai/guides/guide/notes-mcp-server): A step-by-step guide to creating a fully-featured MCP (Model Context Protocol) server for managing notes using the Mastra framework.
- [Guide: Building a Research Paper Assistant with RAG | Mastra RAG Guides](https://mastra.ai/guides/guide/research-assistant): Guide on creating an AI research assistant that can analyze and answer questions about academic papers using RAG.
- [Guide: Building an AI Stock Agent | Mastra Agents | Guides](https://mastra.ai/guides/guide/stock-agent): Guide on creating a simple stock agent in Mastra to fetch the last days closing stock price for a given symbol.
- [Guide: Building an Agent that can search the web | Mastra Guide](https://mastra.ai/guides/guide/web-search): A step-by-step guide to creating an agent that can search the web.
- [Overview](https://mastra.ai/guides): Guides on building with Mastra
- [Migration: AgentNetwork to .network() | Migration Guide](https://mastra.ai/guides/migrations/agentnetwork): Learn how to migrate from AgentNetwork primitives to .network() in Mastra.
- [Migration: Upgrade to Latest 0.x | Migration Guide](https://mastra.ai/guides/migrations/upgrade-to-latest-0x): Learn how to upgrade through breaking changes in pre-v1 versions of Mastra to reach the latest 0.x release.
- [Migration: VNext to Standard APIs | Migration Guide](https://mastra.ai/guides/migrations/vnext-to-standard-apis): Learn how to migrate from VNext methods to the new standard agent APIs in Mastra.
- [Next.js Quickstart](https://mastra.ai/guides/quickstarts/nextjs): Get started with Mastra, Next.js, and AS SDK UI. Quickly.

## EN - models
- [Embedding Models](https://mastra.ai/models/embeddings): Use embedding models through Mastras model router for semantic search and RAG.
- [Custom Gateways | Models | Mastra](https://mastra.ai/models/gateways/custom-gateways): Create custom model gateways for private or specialized LLM deployments
- [Gateways](https://mastra.ai/models/gateways): Access AI models through gateway providers with caching, rate limiting, and analytics.
- [Netlify | Models | Mastra](https://mastra.ai/models/gateways/netlify): Use AI models through Netlify.
- [OpenRouter | Models | Mastra](https://mastra.ai/models/gateways/openrouter): Use AI models through OpenRouter.
- [Vercel | Models | Mastra](https://mastra.ai/models/gateways/vercel): Use AI models through Vercel.
- [Models](https://mastra.ai/models): Access 53+ AI providers and 1113+ models through Mastras model router.
- [AIHubMix](https://mastra.ai/models/providers/aihubmix): Use AIHubMix models via the AI SDK.
- [Alibaba (China) | Models | Mastra](https://mastra.ai/models/providers/alibaba-cn): Use Alibaba (China) models with Mastra. 61 models available.
- [Alibaba | Models | Mastra](https://mastra.ai/models/providers/alibaba): Use Alibaba models with Mastra. 39 models available.
- [Amazon Bedrock](https://mastra.ai/models/providers/amazon-bedrock): Use Amazon Bedrock models via the AI SDK.
- [Anthropic | Models | Mastra](https://mastra.ai/models/providers/anthropic): Use Anthropic models with Mastra. 20 models available.
- [Azure](https://mastra.ai/models/providers/azure): Use Azure models via the AI SDK.
- [Baseten | Models | Mastra](https://mastra.ai/models/providers/baseten): Use Baseten models with Mastra. 4 models available.
- [Cerebras | Models | Mastra](https://mastra.ai/models/providers/cerebras): Use Cerebras models with Mastra. 3 models available.
- [Chutes | Models | Mastra](https://mastra.ai/models/providers/chutes): Use Chutes models with Mastra. 52 models available.
- [Cloudflare Workers AI](https://mastra.ai/models/providers/cloudflare-workers-ai): Use Cloudflare Workers AI models via the AI SDK.
- [Cohere](https://mastra.ai/models/providers/cohere): Use Cohere models via the AI SDK.
- [Cortecs | Models | Mastra](https://mastra.ai/models/providers/cortecs): Use Cortecs models with Mastra. 11 models available.
- [Deep Infra | Models | Mastra](https://mastra.ai/models/providers/deepinfra): Use Deep Infra models with Mastra. 6 models available.
- [DeepSeek | Models | Mastra](https://mastra.ai/models/providers/deepseek): Use DeepSeek models with Mastra. 2 models available.
- [FastRouter | Models | Mastra](https://mastra.ai/models/providers/fastrouter): Use FastRouter models with Mastra. 14 models available.
- [Fireworks AI | Models | Mastra](https://mastra.ai/models/providers/fireworks-ai): Use Fireworks AI models with Mastra. 12 models available.
- [GitHub Models | Models | Mastra](https://mastra.ai/models/providers/github-models): Use GitHub Models models with Mastra. 55 models available.
- [Vertex](https://mastra.ai/models/providers/google-vertex): Use Vertex models via the AI SDK.
- [Google | Models | Mastra](https://mastra.ai/models/providers/google): Use Google models with Mastra. 25 models available.
- [Groq | Models | Mastra](https://mastra.ai/models/providers/groq): Use Groq models with Mastra. 17 models available.
- [Hugging Face | Models | Mastra](https://mastra.ai/models/providers/huggingface): Use Hugging Face models with Mastra. 14 models available.
- [iFlow | Models | Mastra](https://mastra.ai/models/providers/iflowcn): Use iFlow models with Mastra. 18 models available.
- [Inception | Models | Mastra](https://mastra.ai/models/providers/inception): Use Inception models with Mastra. 2 models available.
- [Providers](https://mastra.ai/models/providers): Direct access to AI model providers.
- [Inference | Models | Mastra](https://mastra.ai/models/providers/inference): Use Inference models with Mastra. 9 models available.
- [Llama | Models | Mastra](https://mastra.ai/models/providers/llama): Use Llama models with Mastra. 7 models available.
- [LMStudio | Models | Mastra](https://mastra.ai/models/providers/lmstudio): Use LMStudio models with Mastra. 3 models available.
- [LucidQuery AI | Models | Mastra](https://mastra.ai/models/providers/lucidquery): Use LucidQuery AI models with Mastra. 2 models available.
- [Minimax | Models | Mastra](https://mastra.ai/models/providers/minimax): Use Minimax models with Mastra. 1 model available.
- [Mistral | Models | Mastra](https://mastra.ai/models/providers/mistral): Use Mistral models with Mastra. 19 models available.
- [ModelScope | Models | Mastra](https://mastra.ai/models/providers/modelscope): Use ModelScope models with Mastra. 7 models available.
- [Moonshot AI (China) | Models | Mastra](https://mastra.ai/models/providers/moonshotai-cn): Use Moonshot AI (China) models with Mastra. 5 models available.
- [Moonshot AI | Models | Mastra](https://mastra.ai/models/providers/moonshotai): Use Moonshot AI models with Mastra. 5 models available.
- [Morph | Models | Mastra](https://mastra.ai/models/providers/morph): Use Morph models with Mastra. 3 models available.
- [Nebius Token Factory | Models | Mastra](https://mastra.ai/models/providers/nebius): Use Nebius Token Factory models with Mastra. 15 models available.
- [Nvidia | Models | Mastra](https://mastra.ai/models/providers/nvidia): Use Nvidia models with Mastra. 20 models available.
- [Ollama](https://mastra.ai/models/providers/ollama): Use Ollama models via the AI SDK.
- [OpenAI | Models | Mastra](https://mastra.ai/models/providers/openai): Use OpenAI models with Mastra. 35 models available.
- [OpenCode Zen | Models | Mastra](https://mastra.ai/models/providers/opencode): Use OpenCode Zen models with Mastra. 21 models available.
- [OVHcloud AI Endpoints | Models | Mastra](https://mastra.ai/models/providers/ovhcloud): Use OVHcloud AI Endpoints models with Mastra. 15 models available.
- [Perplexity | Models | Mastra](https://mastra.ai/models/providers/perplexity): Use Perplexity models with Mastra. 4 models available.
- [Poe | Models | Mastra](https://mastra.ai/models/providers/poe): Use Poe models with Mastra. 100 models available.
- [Requesty | Models | Mastra](https://mastra.ai/models/providers/requesty): Use Requesty models with Mastra. 17 models available.
- [Scaleway | Models | Mastra](https://mastra.ai/models/providers/scaleway): Use Scaleway models with Mastra. 13 models available.
- [SiliconFlow | Models | Mastra](https://mastra.ai/models/providers/siliconflow): Use SiliconFlow models with Mastra. 72 models available.
- [submodel | Models | Mastra](https://mastra.ai/models/providers/submodel): Use submodel models with Mastra. 9 models available.
- [Synthetic | Models | Mastra](https://mastra.ai/models/providers/synthetic): Use Synthetic models with Mastra. 23 models available.
- [Together AI | Models | Mastra](https://mastra.ai/models/providers/togetherai): Use Together AI models with Mastra. 6 models available.
- [Upstage | Models | Mastra](https://mastra.ai/models/providers/upstage): Use Upstage models with Mastra. 2 models available.
- [Venice AI | Models | Mastra](https://mastra.ai/models/providers/venice): Use Venice AI models with Mastra. 14 models available.
- [Vultr | Models | Mastra](https://mastra.ai/models/providers/vultr): Use Vultr models with Mastra. 5 models available.
- [Weights & Biases | Models | Mastra](https://mastra.ai/models/providers/wandb): Use Weights & Biases models with Mastra. 10 models available.
- [xAI | Models | Mastra](https://mastra.ai/models/providers/xai): Use xAI models with Mastra. 22 models available.
- [Z.AI Coding Plan | Models | Mastra](https://mastra.ai/models/providers/zai-coding-plan): Use Z.AI Coding Plan models with Mastra. 5 models available.
- [Z.AI | Models | Mastra](https://mastra.ai/models/providers/zai): Use Z.AI models with Mastra. 5 models available.
- [ZenMux | Models | Mastra](https://mastra.ai/models/providers/zenmux): Use ZenMux models with Mastra. 21 models available.
- [Zhipu AI Coding Plan | Models | Mastra](https://mastra.ai/models/providers/zhipuai-coding-plan): Use Zhipu AI Coding Plan models with Mastra. 5 models available.
- [Zhipu AI | Models | Mastra](https://mastra.ai/models/providers/zhipuai): Use Zhipu AI models with Mastra. 5 models available.

## EN - reference
- [Reference: Agent Class | Agents](https://mastra.ai/reference/agents/agent): Documentation for the `Agent` class in Mastra, which provides the foundation for creating AI agents with various capabilities.
- [Reference: Agent.generate() | Agents](https://mastra.ai/reference/agents/generate): Documentation for the `Agent.generate()` method in Mastra agents, which enables non-streaming generation of responses with enhanced capabilities.
- [Reference: Agent.generateLegacy() (Legacy) | Agents](https://mastra.ai/reference/agents/generateLegacy): Documentation for the legacy `Agent.generateLegacy()` method in Mastra agents. This method is deprecated and will be removed in a future version.
- [Reference: Agent.getDefaultGenerateOptions() | Agents](https://mastra.ai/reference/agents/getDefaultGenerateOptions): Documentation for the `Agent.getDefaultGenerateOptions()` method in Mastra agents, which retrieves the default options used for generate calls.
- [Reference: Agent.getDefaultStreamOptions() | Agents](https://mastra.ai/reference/agents/getDefaultStreamOptions): Documentation for the `Agent.getDefaultStreamOptions()` method in Mastra agents, which retrieves the default options used for stream calls.
- [Reference: Agent.getDescription() | Agents](https://mastra.ai/reference/agents/getDescription): Documentation for the `Agent.getDescription()` method in Mastra agents, which retrieves the agents description.
- [Reference: Agent.getInstructions() | Agents](https://mastra.ai/reference/agents/getInstructions): Documentation for the `Agent.getInstructions()` method in Mastra agents, which retrieves the instructions that guide the agents behavior.
- [Reference: Agent.getLLM() | Agents](https://mastra.ai/reference/agents/getLLM): Documentation for the `Agent.getLLM()` method in Mastra agents, which retrieves the language model instance.
- [Reference: Agent.getMemory() | Agents](https://mastra.ai/reference/agents/getMemory): Documentation for the `Agent.getMemory()` method in Mastra agents, which retrieves the memory system associated with the agent.
- [Reference: Agent.getModel() | Agents](https://mastra.ai/reference/agents/getModel): Documentation for the `Agent.getModel()` method in Mastra agents, which retrieves the language model that powers the agent.
- [Reference: Agent.getScorers() | Agents](https://mastra.ai/reference/agents/getScorers): Documentation for the `Agent.getScorers()` method in Mastra agents, which retrieves the scoring configuration.
- [Reference: Agent.getTools() | Agents](https://mastra.ai/reference/agents/getTools): Documentation for the `Agent.getTools()` method in Mastra agents, which retrieves the tools that the agent can use.
- [Reference: Agent.getVoice() | Agents](https://mastra.ai/reference/agents/getVoice): Documentation for the `Agent.getVoice()` method in Mastra agents, which retrieves the voice provider for speech capabilities.
- [Reference: Agent.getWorkflows() | Agents](https://mastra.ai/reference/agents/getWorkflows): Documentation for the `Agent.getWorkflows()` method in Mastra agents, which retrieves the workflows that the agent can execute.
- [Reference: Agent.listAgents() | Agents](https://mastra.ai/reference/agents/listAgents): Documentation for the `Agent.listAgents()` method in Mastra agents, which retrieves the sub-agents that the agent can access.
- [Reference: Agent.listScorers() | Agents](https://mastra.ai/reference/agents/listScorers): Documentation for the `Agent.listScorers()` method in Mastra agents, which retrieves the scoring configuration.
- [Reference: Agent.listWorkflows() | Agents](https://mastra.ai/reference/agents/listWorkflows): Documentation for the `Agent.listWorkflows()` method in Mastra agents, which retrieves the workflows that the agent can execute.
- [Reference: Agent.network() | Agents](https://mastra.ai/reference/agents/network): Documentation for the `Agent.network()` method in Mastra agents, which enables multi-agent collaboration and routing.
- [Reference: MastraAuthAuth0 Class | Auth](https://mastra.ai/reference/auth/auth0): API reference for the MastraAuthAuth0 class, which authenticates Mastra applications using Auth0 authentication.
- [Reference: MastraAuthClerk Class | Auth](https://mastra.ai/reference/auth/clerk): API reference for the MastraAuthClerk class, which authenticates Mastra applications using Clerk authentication.
- [Reference: MastraAuthFirebase Class | Auth](https://mastra.ai/reference/auth/firebase): API reference for the MastraAuthFirebase class, which authenticates Mastra applications using Firebase Authentication.
- [Reference: MastraJwtAuth Class | Auth](https://mastra.ai/reference/auth/jwt): API reference for the MastraJwtAuth class, which authenticates Mastra applications using JSON Web Tokens.
- [Reference: MastraAuthSupabase Class | Auth](https://mastra.ai/reference/auth/supabase): API reference for the MastraAuthSupabase class, which authenticates Mastra applications using Supabase Auth.
- [Reference: MastraAuthWorkos Class | Auth](https://mastra.ai/reference/auth/workos): API reference for the MastraAuthWorkos class, which authenticates Mastra applications using WorkOS authentication.
- [Reference: create-mastra | CLI](https://mastra.ai/reference/cli/create-mastra): Documentation for the create-mastra command, which creates a new Mastra project with interactive setup options.
- [Reference: CLI Commands | CLI](https://mastra.ai/reference/cli/mastra): Documentation for the Mastra CLI to develop, build, and start your project.
- [Reference: Agents API | Client SDK](https://mastra.ai/reference/client-js/agents): Learn how to interact with Mastra AI agents, including generating responses, streaming interactions, and managing agent tools using the client-js SDK.
- [Reference: Error Handling | Client SDK](https://mastra.ai/reference/client-js/error-handling): Learn about the built-in retry mechanism and error handling capabilities in the Mastra client-js SDK.
- [Reference: Logs API | Client SDK](https://mastra.ai/reference/client-js/logs): Learn how to access and query system logs and debugging information in Mastra using the client-js SDK.
- [Reference: Mastra Client SDK | Client SDK](https://mastra.ai/reference/client-js/mastra-client): Learn how to interact with Mastra using the client-js SDK.
- [Reference: Memory API | Client SDK](https://mastra.ai/reference/client-js/memory): Learn how to manage conversation threads and message history in Mastra using the client-js SDK.
- [Reference: Observability API | Client SDK](https://mastra.ai/reference/client-js/observability): Learn how to retrieve AI traces, monitor application performance, and score traces using the client-js SDK.
- [Reference: Telemetry API | Client SDK](https://mastra.ai/reference/client-js/telemetry): Learn how to retrieve and analyze traces from your Mastra application for monitoring and debugging using the client-js SDK.
- [Reference: Tools API | Client SDK](https://mastra.ai/reference/client-js/tools): Learn how to interact with and execute tools available in the Mastra platform using the client-js SDK.
- [Reference: Vectors API | Client SDK](https://mastra.ai/reference/client-js/vectors): Learn how to work with vector embeddings for semantic search and similarity matching in Mastra using the client-js SDK.
- [Reference: Workflows (Legacy) API | Client SDK](https://mastra.ai/reference/client-js/workflows-legacy): Learn how to interact with and execute automated legacy workflows in Mastra using the client-js SDK.
- [Reference: Workflows API | Client SDK](https://mastra.ai/reference/client-js/workflows): Learn how to interact with and execute automated workflows in Mastra using the client-js SDK.
- [Reference: Mastra.getAgent() | Core](https://mastra.ai/reference/core/getAgent): Documentation for the `Agent.getAgent()` method in Mastra, which retrieves an agent by name.
- [Reference: Mastra.getAgentById() | Core](https://mastra.ai/reference/core/getAgentById): Documentation for the `Mastra.getAgentById()` method in Mastra, which retrieves an agent by its ID.
- [Reference: Mastra.getAgents() | Core](https://mastra.ai/reference/core/getAgents): Documentation for the `Mastra.getAgents()` method in Mastra, which retrieves all configured agents.
- [Reference: Mastra.getDeployer() | Core](https://mastra.ai/reference/core/getDeployer): Documentation for the `Mastra.getDeployer()` method in Mastra, which retrieves the configured deployer instance.
- [Reference: Mastra.getLogger() | Core](https://mastra.ai/reference/core/getLogger): Documentation for the `Mastra.getLogger()` method in Mastra, which retrieves the configured logger instance.
- [Reference: Mastra.getLogs() | Core](https://mastra.ai/reference/core/getLogs): Documentation for the `Mastra.getLogs()` method in Mastra, which retrieves all logs for a specific transport ID.
- [Reference: Mastra.getLogsByRunId() | Core](https://mastra.ai/reference/core/getLogsByRunId): Documentation for the `Mastra.getLogsByRunId()` method in Mastra, which retrieves logs for a specific run ID and transport ID.
- [Reference: Mastra.getMCPServer() | Core](https://mastra.ai/reference/core/getMCPServer): Documentation for the `Mastra.getMCPServer()` method in Mastra, which retrieves a specific MCP server instance by ID and optional version.
- [Reference: Mastra.getMCPServers() | Core](https://mastra.ai/reference/core/getMCPServers): Documentation for the `Mastra.getMCPServers()` method in Mastra, which retrieves all registered MCP server instances.
- [Reference: Mastra.getMemory() | Core](https://mastra.ai/reference/core/getMemory): Documentation for the `Mastra.getMemory()` method in Mastra, which retrieves the configured memory instance.
- [Reference: getScorer() | Core](https://mastra.ai/reference/core/getScorer): Documentation for the `getScorer()` method in Mastra, which retrieves a specific scorer by its registration key.
- [Reference: getScorerByName() | Core](https://mastra.ai/reference/core/getScorerByName): Documentation for the `getScorerByName()` method in Mastra, which retrieves a scorer by its name property rather than registration key.
- [Reference: getScorers() | Core](https://mastra.ai/reference/core/getScorers): Documentation for the `getScorers()` method in Mastra, which returns all registered scorers for evaluating AI outputs.
- [Reference: Mastra.getServer() | Core](https://mastra.ai/reference/core/getServer): Documentation for the `Mastra.getServer()` method in Mastra, which retrieves the configured server configuration.
- [Reference: Mastra.getStorage() | Core](https://mastra.ai/reference/core/getStorage): Documentation for the `Mastra.getStorage()` method in Mastra, which retrieves the configured storage instance.
- [Reference: Mastra.getTelemetry() | Core](https://mastra.ai/reference/core/getTelemetry): Documentation for the `Mastra.getTelemetry()` method in Mastra, which retrieves the configured telemetry instance.
- [Reference: Mastra.getVector() | Core](https://mastra.ai/reference/core/getVector): Documentation for the `Mastra.getVector()` method in Mastra, which retrieves a vector store by name.
- [Reference: Mastra.getVectors() | Core](https://mastra.ai/reference/core/getVectors): Documentation for the `Mastra.getVectors()` method in Mastra, which retrieves all configured vector stores.
- [Reference: Mastra.getWorkflow() | Core](https://mastra.ai/reference/core/getWorkflow): Documentation for the `Mastra.getWorkflow()` method in Mastra, which retrieves a workflow by ID.
- [Reference: Mastra.getWorkflows() | Core](https://mastra.ai/reference/core/getWorkflows): Documentation for the `Mastra.getWorkflows()` method in Mastra, which retrieves all configured workflows.
- [Reference: Mastra.listLogs() | Core](https://mastra.ai/reference/core/listLogs): Documentation for the `Mastra.listLogs()` method in Mastra, which retrieves all logs for a specific transport ID.
- [Reference: Mastra.listLogsByRunId() | Core](https://mastra.ai/reference/core/listLogsByRunId): Documentation for the `Mastra.listLogsByRunId()` method in Mastra, which retrieves logs for a specific run ID and transport ID.
- [Reference: listScorers() | Core](https://mastra.ai/reference/core/listScorers): Documentation for the `listScorers()` method in Mastra, which returns all registered scorers for evaluating AI outputs.
- [Reference: Mastra.listWorkflows() | Core](https://mastra.ai/reference/core/listWorkflows): Documentation for the `Mastra.listWorkflows()` method in Mastra, which retrieves all configured workflows.
- [Reference: Mastra Class | Core](https://mastra.ai/reference/core/mastra-class): Documentation for the `Mastra` class in Mastra, the core entry point for managing agents, workflows, MCP servers, and server endpoints.
- [Reference: MastraModelGateway | Core](https://mastra.ai/reference/core/mastra-model-gateway): Base class for creating custom model gateways
- [Reference: Mastra.setLogger() | Core](https://mastra.ai/reference/core/setLogger): Documentation for the `Mastra.setLogger()` method in Mastra, which sets the logger for all components (agents, workflows, etc.).
- [Reference: Mastra.setStorage() | Core](https://mastra.ai/reference/core/setStorage): Documentation for the `Mastra.setStorage()` method in Mastra, which sets the storage instance for the Mastra instance.
- [Reference: Mastra.setTelemetry() | Core](https://mastra.ai/reference/core/setTelemetry): Documentation for the `Mastra.setTelemetry()` method in Mastra, which sets the telemetry configuration for all components.
- [Reference: CloudflareDeployer | Deployer](https://mastra.ai/reference/deployer/cloudflare): Documentation for the CloudflareDeployer class, which deploys Mastra applications to Cloudflare Workers.
- [Reference: Deployer | Deployer](https://mastra.ai/reference/deployer/deployer): Documentation for the Deployer abstract class, which handles packaging and deployment of Mastra applications.
- [Reference: NetlifyDeployer | Deployer](https://mastra.ai/reference/deployer/netlify): Documentation for the NetlifyDeployer class, which deploys Mastra applications to Netlify Functions.
- [Reference: VercelDeployer | Deployer](https://mastra.ai/reference/deployer/vercel): Documentation for the VercelDeployer class, which deploys Mastra applications to Vercel.
- [Reference: AnswerRelevancyMetric | Evals](https://mastra.ai/reference/evals/answer-relevancy): Documentation for the Answer Relevancy Metric in Mastra, which evaluates how well LLM outputs address the input query.
- [Reference: BiasMetric | Evals](https://mastra.ai/reference/evals/bias): Documentation for the Bias Metric in Mastra, which evaluates LLM outputs for various forms of bias, including gender, political, racial/ethnic, or geographical bias.
- [Reference: CompletenessMetric | Evals](https://mastra.ai/reference/evals/completeness): Documentation for the Completeness Metric in Mastra, which evaluates how thoroughly LLM outputs cover key elements present in the input.
- [Reference: ContentSimilarityMetric | Evals](https://mastra.ai/reference/evals/content-similarity): Documentation for the Content Similarity Metric in Mastra, which measures textual similarity between strings and provides a matching score.
- [Reference: ContextPositionMetric | Evals](https://mastra.ai/reference/evals/context-position): Documentation for the Context Position Metric in Mastra, which evaluates the ordering of context nodes based on their relevance to the query and output.
- [Reference: ContextPrecisionMetric | Evals](https://mastra.ai/reference/evals/context-precision): Documentation for the Context Precision Metric in Mastra, which evaluates the relevance and precision of retrieved context nodes for generating expected outputs.
- [Reference: ContextRelevancyMetric | Evals](https://mastra.ai/reference/evals/context-relevancy): Documentation for the Context Relevancy Metric, which evaluates the relevance of retrieved context in RAG pipelines.
- [Reference: ContextualRecallMetric | Evals](https://mastra.ai/reference/evals/contextual-recall): Documentation for the Contextual Recall Metric, which evaluates the completeness of LLM responses in incorporating relevant context.
- [Reference: FaithfulnessMetric Reference | Evals](https://mastra.ai/reference/evals/faithfulness): Documentation for the Faithfulness Metric in Mastra, which evaluates the factual accuracy of LLM outputs compared to the provided context.
- [Reference: HallucinationMetric | Evals](https://mastra.ai/reference/evals/hallucination): Documentation for the Hallucination Metric in Mastra, which evaluates the factual correctness of LLM outputs by identifying contradictions with provided context.
- [Reference: KeywordCoverageMetric | Evals](https://mastra.ai/reference/evals/keyword-coverage): Documentation for the Keyword Coverage Metric in Mastra, which evaluates how well LLM outputs cover important keywords from the input.
- [Reference: PromptAlignmentMetric | Evals](https://mastra.ai/reference/evals/prompt-alignment): Documentation for the Prompt Alignment Metric in Mastra, which evaluates how well LLM outputs adhere to given prompt instructions.
- [Reference: SummarizationMetric | Evals](https://mastra.ai/reference/evals/summarization): Documentation for the Summarization Metric in Mastra, which evaluates the quality of LLM-generated summaries for content and factual accuracy.
- [Reference: TextualDifferenceMetric | Evals](https://mastra.ai/reference/evals/textual-difference): Documentation for the Textual Difference Metric in Mastra, which measures textual differences between strings using sequence matching.
- [Reference: ToneConsistencyMetric | Evals](https://mastra.ai/reference/evals/tone-consistency): Documentation for the Tone Consistency Metric in Mastra, which evaluates emotional tone and sentiment consistency in text.
- [Reference: ToxicityMetric | Evals](https://mastra.ai/reference/evals/toxicity): Documentation for the Toxicity Metric in Mastra, which evaluates LLM outputs for racist, biased, or toxic elements.
- [Reference: Overview](https://mastra.ai/reference): Reference documentation on Mastras APIs and tools
- [Reference: .after() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/after): Documentation for the `after()` method in workflows (legacy), enabling branching and merging paths.
- [Reference: afterEvent() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/afterEvent): Reference for the afterEvent method in Mastra workflows that creates event-based suspension points.
- [Reference: Workflow.commit() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/commit): Documentation for the `.commit()` method in workflows, which re-initializes the workflow machine with the current step configuration.
- [Reference: Workflow.createRun() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/createRun): Documentation for the `.createRun()` method in workflows (legacy), which initializes a new workflow run instance.
- [Reference: Workflow.else() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/else): Documentation for the `.else()` method in Mastra workflows, which creates an alternative branch when an if condition is false.
- [Reference: Event-Driven Workflows | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/events): Learn how to create event-driven workflows using afterEvent and resumeWithEvent methods in Mastra.
- [Reference: Workflow.execute() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/execute): Documentation for the `.execute()` method in Mastra workflows, which runs workflow steps and returns results.
- [Reference: Workflow.if() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/if): Documentation for the `.if()` method in Mastra workflows, which creates conditional branches based on specified conditions.
- [Reference: run.resume() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/resume): Documentation for the `.resume()` method in workflows, which continues execution of a suspended workflow step.
- [Reference: resumeWithEvent() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/resumeWithEvent): Reference for the resumeWithEvent method that resumes suspended workflows using event data.
- [Reference: Snapshots | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/snapshots): Technical reference on snapshots in Mastra - the serialized workflow state that enables suspend and resume functionality
- [Reference: start() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/start): Documentation for the `start()` method in workflows, which begins execution of a workflow run.
- [Reference: Step | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/step-class): Documentation for the Step class, which defines individual units of work within a workflow.
- [Reference: StepCondition | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/step-condition): Documentation for the step condition class in workflows, which determines whether a step should execute based on the output of previous steps or trigger data.
- [Reference: Workflow.step() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/step-function): Documentation for the `.step()` method in workflows, which adds a new step to the workflow.
- [Reference: StepOptions | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/step-options): Documentation for the step options in workflows, which control variable mapping, execution conditions, and other runtime behavior.
- [Reference: Step Retries | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/step-retries): Automatically retry failed steps in Mastra workflows with configurable retry policies.
- [Reference: suspend() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/suspend): Documentation for the suspend function in Mastra workflows, which pauses execution until resumed.
- [Reference: Workflow.then() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/then): Documentation for the `.then()` method in workflows, which creates sequential dependencies between steps.
- [Reference: Workflow.until() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/until): Documentation for the `.until()` method in Mastra workflows, which repeats a step until a specified condition becomes true.
- [Reference: run.watch() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/watch): Documentation for the `.watch()` method in workflows, which monitors the status of a workflow run.
- [Reference: Workflow.while() | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/while): Documentation for the `.while()` method in Mastra workflows, which repeats a step as long as a specified condition remains true.
- [Reference: Workflow Class | Legacy Workflows](https://mastra.ai/reference/legacyWorkflows/workflow): Documentation for the Workflow class in Mastra, which enables you to create state machines for complex sequences of operations with conditional branching and data validation.
- [Reference: Memory.createThread() | Memory](https://mastra.ai/reference/memory/createThread): Documentation for the `Memory.createThread()` method in Mastra, which creates a new conversation thread in the memory system.
- [Reference: Memory.deleteMessages() | Memory](https://mastra.ai/reference/memory/deleteMessages): Documentation for the `Memory.deleteMessages()` method in Mastra, which deletes multiple messages by their IDs.
- [Reference: Memory.getThreadById() | Memory](https://mastra.ai/reference/memory/getThreadById): Documentation for the `Memory.getThreadById()` method in Mastra, which retrieves a specific thread by its ID.
- [Reference: Memory.getThreadsByResourceId() | Memory](https://mastra.ai/reference/memory/getThreadsByResourceId): Documentation for the `Memory.getThreadsByResourceId()` method in Mastra, which retrieves all threads that belong to a specific resource.
- [Reference: Memory.getThreadsByResourceIdPaginated() | Memory](https://mastra.ai/reference/memory/getThreadsByResourceIdPaginated): Documentation for the `Memory.getThreadsByResourceIdPaginated()` method in Mastra, which retrieves threads associated with a specific resource ID with pagination support.
- [Reference: Memory Class | Memory](https://mastra.ai/reference/memory/memory-class): Documentation for the `Memory` class in Mastra, which provides a robust system for managing conversation history and thread-based message storage.
- [Reference: Memory.query() | Memory](https://mastra.ai/reference/memory/query): Documentation for the `Memory.query()` method in Mastra, which retrieves messages from a specific thread with support for pagination, filtering options, and semantic search.
- [Reference: AITracing | Observability](https://mastra.ai/reference/observability/ai-tracing/ai-tracing): Core AI Tracing classes and methods
- [Reference: Configuration | Observability](https://mastra.ai/reference/observability/ai-tracing/configuration): AI Tracing configuration types and registry functions
- [Reference: ArizeExporter | Observability](https://mastra.ai/reference/observability/ai-tracing/exporters/arize): Arize exporter for AI tracing using OpenInference
- [Reference: BraintrustExporter | Observability](https://mastra.ai/reference/observability/ai-tracing/exporters/braintrust): Braintrust exporter for AI tracing
- [Reference: CloudExporter | Observability](https://mastra.ai/reference/observability/ai-tracing/exporters/cloud-exporter): API reference for the CloudExporter
- [Reference: ConsoleExporter | Observability](https://mastra.ai/reference/observability/ai-tracing/exporters/console-exporter): API reference for the ConsoleExporter
- [Reference: DefaultExporter | Observability](https://mastra.ai/reference/observability/ai-tracing/exporters/default-exporter): API reference for the DefaultExporter
- [Reference: LangfuseExporter | Observability](https://mastra.ai/reference/observability/ai-tracing/exporters/langfuse): Langfuse exporter for AI tracing
- [Reference: LangSmithExporter | Observability](https://mastra.ai/reference/observability/ai-tracing/exporters/langsmith): LangSmith exporter for AI tracing
- [Reference: OtelExporter | Observability](https://mastra.ai/reference/observability/ai-tracing/exporters/otel): OpenTelemetry exporter for AI tracing
- [Reference: Interfaces | Observability](https://mastra.ai/reference/observability/ai-tracing/interfaces): AI Tracing type definitions and interfaces
- [Reference: SensitiveDataFilter | Observability](https://mastra.ai/reference/observability/ai-tracing/processors/sensitive-data-filter): API reference for the SensitiveDataFilter processor
- [Reference: Span | Observability](https://mastra.ai/reference/observability/ai-tracing/span): Span interfaces, methods, and lifecycle events
- [Reference: PinoLogger | Observability](https://mastra.ai/reference/observability/logging/pino-logger): Documentation for PinoLogger, which provides methods to record events at various severity levels.
- [Reference: `OtelConfig` | Observability](https://mastra.ai/reference/observability/otel-tracing/otel-config): Documentation for the OtelConfig object, which configures OpenTelemetry instrumentation, tracing, and exporting behavior.
- [Reference: Arize AX | Observability](https://mastra.ai/reference/observability/otel-tracing/providers/arize-ax): Documentation for integrating Arize AX with Mastra, a comprehensive AI observability platform for monitoring and evaluating LLM applications.
- [Reference: Arize Phoenix | Observability](https://mastra.ai/reference/observability/otel-tracing/providers/arize-phoenix): Documentation for integrating Arize Phoenix with Mastra, an open-source AI observability platform for monitoring and evaluating LLM applications.
- [Reference: Braintrust | Observability](https://mastra.ai/reference/observability/otel-tracing/providers/braintrust): Documentation for integrating Braintrust with Mastra, an evaluation and monitoring platform for LLM applications.
- [Reference: Dash0 | Observability](https://mastra.ai/reference/observability/otel-tracing/providers/dash0): Documentation for integrating Mastra with Dash0, an Open Telemetry native observability solution.
- [Reference: OTLP Providers | Observability](https://mastra.ai/reference/observability/otel-tracing/providers): Overview of OTLP observability providers.
- [Reference: Keywords AI Integration | Mastra Observability Docs](https://mastra.ai/reference/observability/otel-tracing/providers/keywordsai): Documentation for integrating Keywords AI (an observability platform for LLM applications) with Mastra.
- [Reference: Laminar | Observability](https://mastra.ai/reference/observability/otel-tracing/providers/laminar): Documentation for integrating Laminar with Mastra, a specialized observability platform for LLM applications.
- [Reference: Langfuse | Observability](https://mastra.ai/reference/observability/otel-tracing/providers/langfuse): Documentation for integrating Langfuse with Mastra, an open-source observability platform for LLM applications.
- [Reference: LangSmith | Observability](https://mastra.ai/reference/observability/otel-tracing/providers/langsmith): Documentation for integrating LangSmith with Mastra, a platform for debugging, testing, evaluating, and monitoring LLM applications.
- [Reference: LangWatch | Observability](https://mastra.ai/reference/observability/otel-tracing/providers/langwatch): Documentation for integrating LangWatch with Mastra, a specialized observability platform for LLM applications.
- [Reference: New Relic | Observability](https://mastra.ai/reference/observability/otel-tracing/providers/new-relic): Documentation for integrating New Relic with Mastra, a comprehensive observability platform supporting OpenTelemetry for full-stack monitoring.
- [Reference: SigNoz | Observability](https://mastra.ai/reference/observability/otel-tracing/providers/signoz): Documentation for integrating SigNoz with Mastra, an open-source APM and observability platform providing full-stack monitoring through OpenTelemetry.
- [Reference: Traceloop | Observability](https://mastra.ai/reference/observability/otel-tracing/providers/traceloop): Documentation for integrating Traceloop with Mastra, an OpenTelemetry-native observability platform for LLM applications.
- [Reference: Batch Parts Processor | Processors](https://mastra.ai/reference/processors/batch-parts-processor): Documentation for the BatchPartsProcessor in Mastra, which batches multiple stream parts together to reduce frequency of emissions.
- [Reference: Language Detector | Processors](https://mastra.ai/reference/processors/language-detector): Documentation for the LanguageDetector in Mastra, which detects language and can translate content in AI responses.
- [Reference: Moderation Processor | Processors](https://mastra.ai/reference/processors/moderation-processor): Documentation for the ModerationProcessor in Mastra, which provides content moderation using LLM to detect inappropriate content across multiple categories.
- [Reference: PII Detector | Processors](https://mastra.ai/reference/processors/pii-detector): Documentation for the PIIDetector in Mastra, which detects and redacts personally identifiable information (PII) from AI responses.
- [Reference: Prompt Injection Detector | Processors](https://mastra.ai/reference/processors/prompt-injection-detector): Documentation for the PromptInjectionDetector in Mastra, which detects prompt injection attempts in user input.
- [Reference: System Prompt Scrubber | Processors](https://mastra.ai/reference/processors/system-prompt-scrubber): Documentation for the SystemPromptScrubber in Mastra, which detects and redacts system prompts from AI responses.
- [Reference: Token Limiter Processor | Processors](https://mastra.ai/reference/processors/token-limiter-processor): Documentation for the TokenLimiterProcessor in Mastra, which limits the number of tokens in AI responses.
- [Reference: Unicode Normalizer | Processors](https://mastra.ai/reference/processors/unicode-normalizer): Documentation for the UnicodeNormalizer in Mastra, which normalizes Unicode text to ensure consistent formatting and remove potentially problematic characters.
- [Reference: Reference: .chunk() | RAG](https://mastra.ai/reference/rag/chunk): Documentation for the chunk function in Mastra, which splits documents into smaller segments using various strategies.
- [Reference: DatabaseConfig | RAG](https://mastra.ai/reference/rag/database-config): API reference for database-specific configuration types used with vector query tools in Mastra RAG systems.
- [Reference: MDocument | Document Processing | RAG](https://mastra.ai/reference/rag/document): Documentation for the MDocument class in Mastra, which handles document processing and chunking.
- [Reference: Embed | RAG](https://mastra.ai/reference/rag/embeddings): Documentation for embedding functionality in Mastra using the AI SDK.
- [Reference: ExtractParams | RAG](https://mastra.ai/reference/rag/extract-params): Documentation for metadata extraction configuration in Mastra.
- [Reference: GraphRAG | RAG](https://mastra.ai/reference/rag/graph-rag): Documentation for the GraphRAG class in Mastra, which implements a graph-based approach to retrieval augmented generation.
- [Reference: Metadata Filters | RAG](https://mastra.ai/reference/rag/metadata-filters): Documentation for metadata filtering capabilities in Mastra, which allow for precise querying of vector search results across different vector stores.
- [Reference: rerank() | RAG](https://mastra.ai/reference/rag/rerank): Documentation for the rerank function in Mastra, which provides advanced reranking capabilities for vector search results.
- [Reference: rerankWithScorer() | RAG](https://mastra.ai/reference/rag/rerankWithScorer): Documentation for the rerankWithScorer function in Mastra, which provides advanced reranking capabilities for vector search results.
- [Reference: Answer Relevancy Scorer | Scorers](https://mastra.ai/reference/scorers/answer-relevancy): Documentation for the Answer Relevancy Scorer in Mastra, which evaluates how well LLM outputs address the input query.
- [Reference: Answer Similarity Scorer | Scorers](https://mastra.ai/reference/scorers/answer-similarity): Documentation for the Answer Similarity Scorer in Mastra, which compares agent outputs against ground truth answers for CI/CD testing.
- [Reference: Bias Scorer | Scorers](https://mastra.ai/reference/scorers/bias): Documentation for the Bias Scorer in Mastra, which evaluates LLM outputs for various forms of bias, including gender, political, racial/ethnic, or geographical bias.
- [Reference: Completeness Scorer | Scorers](https://mastra.ai/reference/scorers/completeness): Documentation for the Completeness Scorer in Mastra, which evaluates how thoroughly LLM outputs cover key elements present in the input.
- [Reference: Content Similarity Scorer | Scorers](https://mastra.ai/reference/scorers/content-similarity): Documentation for the Content Similarity Scorer in Mastra, which measures textual similarity between strings and provides a matching score.
- [Reference: Context Precision Scorer | Scorers](https://mastra.ai/reference/scorers/context-precision): Documentation for the Context Precision Scorer in Mastra. Evaluates the relevance and precision of retrieved context for generating expected outputs using Mean Average Precision.
- [Reference: Context Relevance Scorer | Scorers](https://mastra.ai/reference/scorers/context-relevance): Documentation for the Context Relevance Scorer in Mastra. Evaluates the relevance and utility of provided context for generating agent responses using weighted relevance scoring.
- [Reference: createScorer | Scorers](https://mastra.ai/reference/scorers/create-scorer): Documentation for creating custom scorers in Mastra, allowing users to define their own evaluation logic using either JavaScript functions or LLM-based prompts.
- [Reference: Faithfulness Scorer | Scorers](https://mastra.ai/reference/scorers/faithfulness): Documentation for the Faithfulness Scorer in Mastra, which evaluates the factual accuracy of LLM outputs compared to the provided context.
- [Reference: Hallucination Scorer | Scorers](https://mastra.ai/reference/scorers/hallucination): Documentation for the Hallucination Scorer in Mastra, which evaluates the factual correctness of LLM outputs by identifying contradictions with provided context.
- [Reference: Keyword Coverage Scorer | Scorers](https://mastra.ai/reference/scorers/keyword-coverage): Documentation for the Keyword Coverage Scorer in Mastra, which evaluates how well LLM outputs cover important keywords from the input.
- [Reference: MastraScorer | Scorers](https://mastra.ai/reference/scorers/mastra-scorer): Documentation for the MastraScorer base class in Mastra, which provides the foundation for all custom and built-in scorers.
- [Reference: Noise Sensitivity Scorer (CI/Testing Only) | Scorers](https://mastra.ai/reference/scorers/noise-sensitivity): Documentation for the Noise Sensitivity Scorer in Mastra. A CI/testing scorer that evaluates agent robustness by comparing responses between clean and noisy inputs in controlled test environments.
- [Reference: Prompt Alignment Scorer | Scorers](https://mastra.ai/reference/scorers/prompt-alignment): Documentation for the Prompt Alignment Scorer in Mastra. Evaluates how well agent responses align with user prompt intent, requirements, completeness, and appropriateness using multi-dimensional analysis.
- [Reference: runExperiment | Scorers](https://mastra.ai/reference/scorers/run-experiment): Documentation for the runExperiment function in Mastra, which enables batch evaluation of agents and workflows using multiple scorers.
- [Reference: Textual Difference Scorer | Scorers](https://mastra.ai/reference/scorers/textual-difference): Documentation for the Textual Difference Scorer in Mastra, which measures textual differences between strings using sequence matching.
- [Reference: Tone Consistency Scorer | Scorers](https://mastra.ai/reference/scorers/tone-consistency): Documentation for the Tone Consistency Scorer in Mastra, which evaluates emotional tone and sentiment consistency in text.
- [Reference: Tool Call Accuracy Scorers | Scorers](https://mastra.ai/reference/scorers/tool-call-accuracy): Documentation for the Tool Call Accuracy Scorers in Mastra, which evaluate whether LLM outputs call the correct tools from available options.
- [Reference: Toxicity Scorer | Scorers](https://mastra.ai/reference/scorers/toxicity): Documentation for the Toxicity Scorer in Mastra, which evaluates LLM outputs for racist, biased, or toxic elements.
- [Reference: Cloudflare D1 Storage | Storage](https://mastra.ai/reference/storage/cloudflare-d1): Documentation for the Cloudflare D1 SQL storage implementation in Mastra.
- [Reference: Cloudflare Storage | Storage](https://mastra.ai/reference/storage/cloudflare): Documentation for the Cloudflare KV storage implementation in Mastra.
- [Reference: DynamoDB Storage | Storage](https://mastra.ai/reference/storage/dynamodb): Documentation for the DynamoDB storage implementation in Mastra, using a single-table design with ElectroDB.
- [Reference: LanceDB Storage | Storage](https://mastra.ai/reference/storage/lance): Documentation for the LanceDB storage implementation in Mastra.
- [Reference: LibSQL Storage | Storage](https://mastra.ai/reference/storage/libsql): Documentation for the LibSQL storage implementation in Mastra.
- [Reference: MongoDB Storage | Storage](https://mastra.ai/reference/storage/mongodb): Documentation for the MongoDB storage implementation in Mastra.
- [Reference: MSSQL Storage | Storage](https://mastra.ai/reference/storage/mssql): Documentation for the MSSQL storage implementation in Mastra.
- [Reference: PostgreSQL Storage | Storage](https://mastra.ai/reference/storage/postgresql): Documentation for the PostgreSQL storage implementation in Mastra.
- [Reference: Upstash Storage | Storage](https://mastra.ai/reference/storage/upstash): Documentation for the Upstash storage implementation in Mastra.
- [Reference: ChunkType | Streaming](https://mastra.ai/reference/streaming/ChunkType): Documentation for the ChunkType type used in Mastra streaming responses, defining all possible chunk types and their payloads.
- [Reference: MastraModelOutput | Streaming](https://mastra.ai/reference/streaming/agents/MastraModelOutput): Complete reference for MastraModelOutput - the stream object returned by agent.stream() with streaming and promise-based access to model outputs.
- [Reference: Agent.stream() | Streaming](https://mastra.ai/reference/streaming/agents/stream): Documentation for the `Agent.stream()` method in Mastra agents, which enables real-time streaming of responses with enhanced capabilities.
- [Reference: Agent.streamLegacy() (Legacy) | Streaming](https://mastra.ai/reference/streaming/agents/streamLegacy): Documentation for the legacy `Agent.streamLegacy()` method in Mastra agents. This method is deprecated and will be removed in a future version.
- [Reference: Run.observeStream() | Streaming](https://mastra.ai/reference/streaming/workflows/observeStream): Documentation for the `Run.observeStream()` method in workflows, which enables reopening the stream of an already active workflow run.
- [Reference: Run.observeStreamVNext() (Experimental) | Streaming](https://mastra.ai/reference/streaming/workflows/observeStreamVNext): Documentation for the `Run.observeStreamVNext()` method in workflows, which enables reopening the stream of an already active workflow run.
- [Reference: Run.resumeStreamVNext() (Experimental) | Streaming](https://mastra.ai/reference/streaming/workflows/resumeStreamVNext): Documentation for the `Run.resumeStreamVNext()` method in workflows, which enables real-time resumption and streaming of suspended workflow runs.
- [Reference: Run.stream() | Streaming](https://mastra.ai/reference/streaming/workflows/stream): Documentation for the `Run.stream()` method in workflows, which allows you to monitor the execution of a workflow run as a stream.
- [Reference: Run.streamVNext() (Experimental) | Streaming](https://mastra.ai/reference/streaming/workflows/streamVNext): Documentation for the `Run.streamVNext()` method in workflows, which enables real-time streaming of responses.
- [Reference: Run.timeTravelStream() | Streaming](https://mastra.ai/reference/streaming/workflows/timeTravelStream): Documentation for the `Run.timeTravelStream()` method for streaming workflow time travel execution.
- [Reference: LLM provider API keys (choose one or more) | Templates](https://mastra.ai/reference/templates/overview): Complete guide to creating, using, and contributing Mastra templates
- [Reference: MastraMCPClient (Deprecated) | Tools & MCP](https://mastra.ai/reference/tools/client): API Reference for MastraMCPClient - A client implementation for the Model Context Protocol.
- [Reference: createTool() | Tools & MCP](https://mastra.ai/reference/tools/create-tool): Documentation for the `createTool()` function in Mastra, used to define custom tools for agents.
- [Reference: createDocumentChunkerTool() | Tools & MCP](https://mastra.ai/reference/tools/document-chunker-tool): Documentation for the Document Chunker Tool in Mastra, which splits documents into smaller chunks for efficient processing and retrieval.
- [Reference: createGraphRAGTool() | Tools & MCP](https://mastra.ai/reference/tools/graph-rag-tool): Documentation for the Graph RAG Tool in Mastra, which enhances RAG by building a graph of semantic relationships between documents.
- [Reference: MCPClient | Tools & MCP](https://mastra.ai/reference/tools/mcp-client): API Reference for MCPClient - A class for managing multiple Model Context Protocol servers and their tools.
- [Reference: MCPServer | Tools & MCP](https://mastra.ai/reference/tools/mcp-server): API Reference for MCPServer - A class for exposing Mastra tools and capabilities as a Model Context Protocol server.
- [Reference: createVectorQueryTool() | Tools & MCP](https://mastra.ai/reference/tools/vector-query-tool): Documentation for the Vector Query Tool in Mastra, which facilitates semantic search over vector stores with filtering and reranking capabilities.
- [Reference: Astra Vector Store | Vectors](https://mastra.ai/reference/vectors/astra): Documentation for the AstraVector class in Mastra, which provides vector search using DataStax Astra DB.
- [Reference: Chroma Vector Store | Vectors](https://mastra.ai/reference/vectors/chroma): Documentation for the ChromaVector class in Mastra, which provides vector search using ChromaDB.
- [Reference: Couchbase Vector Store | Vectors](https://mastra.ai/reference/vectors/couchbase): Documentation for the CouchbaseVector class in Mastra, which provides vector search using Couchbase Vector Search.
- [Reference: Lance Vector Store | Vectors](https://mastra.ai/reference/vectors/lance): Documentation for the LanceVectorStore class in Mastra, which provides vector search using LanceDB, an embedded vector database based on the Lance columnar format.
- [Reference: LibSQLVector Store | Vectors](https://mastra.ai/reference/vectors/libsql): Documentation for the LibSQLVector class in Mastra, which provides vector search using LibSQL with vector extensions.
- [Reference: MongoDB Vector Store | Vectors](https://mastra.ai/reference/vectors/mongodb): Documentation for the MongoDBVector class in Mastra, which provides vector search using MongoDB Atlas and Atlas Vector Search.
- [Reference: OpenSearch Vector Store | Vectors](https://mastra.ai/reference/vectors/opensearch): Documentation for the OpenSearchVector class in Mastra, which provides vector search using OpenSearch.
- [Reference: PG Vector Store | Vectors](https://mastra.ai/reference/vectors/pg): Documentation for the PgVector class in Mastra, which provides vector search using PostgreSQL with pgvector extension.
- [Reference: Pinecone Vector Store | Vectors](https://mastra.ai/reference/vectors/pinecone): Documentation for the PineconeVector class in Mastra, which provides an interface to Pinecones vector database.
- [Reference: Qdrant Vector Store | Vectors](https://mastra.ai/reference/vectors/qdrant): Documentation for integrating Qdrant with Mastra, a vector similarity search engine for managing vectors and payloads.
- [Reference: Amazon S3 Vectors Store | Vectors](https://mastra.ai/reference/vectors/s3vectors): Documentation for the S3Vectors class in Mastra, which provides vector search using Amazon S3 Vectors (Preview).
- [Reference: Turbopuffer Vector Store | Vectors](https://mastra.ai/reference/vectors/turbopuffer): Documentation for integrating Turbopuffer with Mastra, a high-performance vector database for efficient similarity search.
- [Reference: Upstash Vector Store | Vectors](https://mastra.ai/reference/vectors/upstash): Documentation for the UpstashVector class in Mastra, which provides vector search using Upstash Vector.
- [Reference: Cloudflare Vector Store | Vectors](https://mastra.ai/reference/vectors/vectorize): Documentation for the CloudflareVector class in Mastra, which provides vector search using Cloudflare Vectorize.
- [Reference: Azure | Voice](https://mastra.ai/reference/voice/azure): Documentation for the AzureVoice class, providing text-to-speech and speech-to-text capabilities using Azure Cognitive Services.
- [Reference: Cloudflare | Voice](https://mastra.ai/reference/voice/cloudflare): Documentation for the CloudflareVoice class, providing text-to-speech capabilities using Cloudflare Workers AI.
- [Reference: CompositeVoice | Voice](https://mastra.ai/reference/voice/composite-voice): Documentation for the CompositeVoice class, which enables combining multiple voice providers for flexible text-to-speech and speech-to-text operations.
- [Reference: Deepgram | Voice](https://mastra.ai/reference/voice/deepgram): Documentation for the Deepgram voice implementation, providing text-to-speech and speech-to-text capabilities with multiple voice models and languages.
- [Reference: ElevenLabs | Voice](https://mastra.ai/reference/voice/elevenlabs): Documentation for the ElevenLabs voice implementation, offering high-quality text-to-speech capabilities with multiple voice models and natural-sounding synthesis.
- [Reference: Google Gemini Live Voice | Voice](https://mastra.ai/reference/voice/google-gemini-live): Documentation for the GeminiLiveVoice class, providing real-time multimodal voice interactions using Googles Gemini Live API with support for both Gemini API and Vertex AI.
- [Reference: Google | Voice](https://mastra.ai/reference/voice/google): Documentation for the Google Voice implementation, providing text-to-speech and speech-to-text capabilities.
- [Reference: MastraVoice | Voice](https://mastra.ai/reference/voice/mastra-voice): Documentation for the MastraVoice abstract base class, which defines the core interface for all voice services in Mastra, including speech-to-speech capabilities.
- [Reference: Murf | Voice](https://mastra.ai/reference/voice/murf): Documentation for the Murf voice implementation, providing text-to-speech capabilities.
- [Reference: OpenAI Realtime Voice | Voice](https://mastra.ai/reference/voice/openai-realtime): Documentation for the OpenAIRealtimeVoice class, providing real-time text-to-speech and speech-to-text capabilities via WebSockets.
- [Reference: OpenAI | Voice](https://mastra.ai/reference/voice/openai): Documentation for the OpenAIVoice class, providing text-to-speech and speech-to-text capabilities.
- [Reference: PlayAI | Voice](https://mastra.ai/reference/voice/playai): Documentation for the PlayAI voice implementation, providing text-to-speech capabilities.
- [Reference: Sarvam | Voice](https://mastra.ai/reference/voice/sarvam): Documentation for the Sarvam class, providing text-to-speech and speech-to-text capabilities.
- [Reference: Speechify | Voice](https://mastra.ai/reference/voice/speechify): Documentation for the Speechify voice implementation, providing text-to-speech capabilities.
- [Reference: voice.addInstructions() | Voice](https://mastra.ai/reference/voice/voice.addInstructions): Documentation for the addInstructions() method available in voice providers, which adds instructions to guide the voice models behavior.
- [Reference: voice.addTools() | Voice](https://mastra.ai/reference/voice/voice.addTools): Documentation for the addTools() method available in voice providers, which equips voice models with function calling capabilities.
- [Reference: voice.answer() | Voice](https://mastra.ai/reference/voice/voice.answer): Documentation for the answer() method available in real-time voice providers, which triggers the voice provider to generate a response.
- [Reference: voice.close() | Voice](https://mastra.ai/reference/voice/voice.close): Documentation for the close() method available in voice providers, which disconnects from real-time voice services.
- [Reference: voice.connect() | Voice](https://mastra.ai/reference/voice/voice.connect): Documentation for the connect() method available in real-time voice providers, which establishes a connection for speech-to-speech communication.
- [Reference: Voice Events | Voice](https://mastra.ai/reference/voice/voice.events): Documentation for events emitted by voice providers, particularly for real-time voice interactions.
- [Reference: voice.getSpeakers() | Voice Providers](https://mastra.ai/reference/voice/voice.getSpeakers): Documentation for the getSpeakers() method available in voice providers, which retrieves available voice options.
- [Reference: voice.listen() | Voice](https://mastra.ai/reference/voice/voice.listen): Documentation for the listen() method available in all Mastra voice providers, which converts speech to text.
- [Reference: voice.off() | Voice](https://mastra.ai/reference/voice/voice.off): Documentation for the off() method available in voice providers, which removes event listeners for voice events.
- [Reference: voice.on() | Voice](https://mastra.ai/reference/voice/voice.on): Documentation for the on() method available in voice providers, which registers event listeners for voice events.
- [Reference: voice.send() | Voice](https://mastra.ai/reference/voice/voice.send): Documentation for the send() method available in real-time voice providers, which streams audio data for continuous processing.
- [Reference: voice.speak() | Voice](https://mastra.ai/reference/voice/voice.speak): Documentation for the speak() method available in all Mastra voice providers, which converts text to speech.
- [Reference: voice.updateConfig() | Voice](https://mastra.ai/reference/voice/voice.updateConfig): Documentation for the updateConfig() method available in voice providers, which updates the configuration of a voice provider at runtime.
- [Reference: Run.cancel() | Workflows](https://mastra.ai/reference/workflows/run-methods/cancel): Documentation for the `Run.cancel()` method in workflows, which cancels a workflow run.
- [Reference: Run.restart() | Workflows](https://mastra.ai/reference/workflows/run-methods/restart): Documentation for the `Run.restart()` method in workflows, which restarts an active workflow run that lost connection to the server.
- [Reference: Run.resume() | Workflows](https://mastra.ai/reference/workflows/run-methods/resume): Documentation for the `Run.resume()` method in workflows, which resumes a suspended workflow run with new data.
- [Reference: Run.start() | Workflows](https://mastra.ai/reference/workflows/run-methods/start): Documentation for the `Run.start()` method in workflows, which starts a workflow run with input data.
- [Reference: Run.timeTravel() | Workflows](https://mastra.ai/reference/workflows/run-methods/timeTravel): Documentation for the `Run.timeTravel()` method in workflows, which re-executes a workflow from a specific step.
- [Reference: Run.watch() | Workflows](https://mastra.ai/reference/workflows/run-methods/watch): Documentation for the `Run.watch()` method in workflows, which allows you to monitor the execution of a workflow run.
- [Reference: Run Class | Workflows](https://mastra.ai/reference/workflows/run): Documentation for the Run class in Mastra, which represents a workflow execution instance.
- [Reference: Step Class | Workflows](https://mastra.ai/reference/workflows/step): Documentation for the Step class in Mastra, which defines individual units of work within a workflow.
- [Reference: Workflow.branch() | Workflows](https://mastra.ai/reference/workflows/workflow-methods/branch): Documentation for the `Workflow.branch()` method in workflows, which creates conditional branches between steps.
- [Reference: Workflow.commit() | Workflows](https://mastra.ai/reference/workflows/workflow-methods/commit): Documentation for the `Workflow.commit()` method in workflows, which finalizes the workflow and returns the final result.
- [Reference: Workflow.createRunAsync() | Workflows](https://mastra.ai/reference/workflows/workflow-methods/create-run): Documentation for the `Workflow.createRunAsync()` method in workflows, which creates a new workflow run instance.
- [Reference: Workflow.dountil() | Workflows](https://mastra.ai/reference/workflows/workflow-methods/dountil): Documentation for the `Workflow.dountil()` method in workflows, which creates a loop that executes a step until a condition is met.
- [Reference: Workflow.dowhile() | Workflows](https://mastra.ai/reference/workflows/workflow-methods/dowhile): Documentation for the `Workflow.dowhile()` method in workflows, which creates a loop that executes a step while a condition is met.
- [Reference: Workflow.foreach() | Workflows](https://mastra.ai/reference/workflows/workflow-methods/foreach): Documentation for the `Workflow.foreach()` method in workflows, which creates a loop that executes a step for each item in an array.
- [Reference: Workflow.map() | Workflows](https://mastra.ai/reference/workflows/workflow-methods/map): Documentation for the `Workflow.map()` method in workflows, which maps output data from a previous step to the input of a subsequent step.
- [Reference: Workflow.parallel() | Workflows](https://mastra.ai/reference/workflows/workflow-methods/parallel): Documentation for the `Workflow.parallel()` method in workflows, which executes multiple steps in parallel.
- [Reference: Workflow.sendEvent() | Workflows](https://mastra.ai/reference/workflows/workflow-methods/sendEvent): Documentation for the `Workflow.sendEvent()` method in workflows, which resumes execution when an event is sent.
- [Reference: Workflow.sleep() | Workflows](https://mastra.ai/reference/workflows/workflow-methods/sleep): Documentation for the `Workflow.sleep()` method in workflows, which pauses execution for a specified number of milliseconds.
- [Reference: Workflow.sleepUntil() | Workflows](https://mastra.ai/reference/workflows/workflow-methods/sleepUntil): Documentation for the `Workflow.sleepUntil()` method in workflows, which pauses execution until a specified date.
- [Reference: Workflow.then() | Workflows](https://mastra.ai/reference/workflows/workflow-methods/then): Documentation for the `Workflow.then()` method in workflows, which creates sequential dependencies between steps.
- [Reference: Workflow.waitForEvent() | Workflows](https://mastra.ai/reference/workflows/workflow-methods/waitForEvent): Documentation for the `Workflow.waitForEvent()` method in workflows, which pauses execution until an event is received.
- [Reference: Workflow Class | Workflows](https://mastra.ai/reference/workflows/workflow): Documentation for the `Workflow` class in Mastra, which enables you to create state machines for complex sequences of operations with conditional branching and data validation.