{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-mastra","slug":"mastra","name":"Mastra","type":"framework","url":"https://mastra.ai","page_url":"https://unfragile.ai/mastra","categories":["app-builders"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-mastra__cap_0","uri":"capability://planning.reasoning.typescript.native.agent.orchestration.with.llm.provider.abstraction","name":"typescript-native agent orchestration with llm provider abstraction","description":"Mastra provides a unified TypeScript runtime for defining and executing AI agents that abstract over multiple LLM providers (OpenAI, Anthropic, etc.) through a provider-agnostic interface. Agents are defined as TypeScript classes with methods that map to LLM tool calls, enabling type-safe agent logic without provider lock-in. The framework handles provider-specific protocol differences (function calling schemas, streaming formats, token counting) transparently.","intents":["Build multi-provider AI agents without rewriting logic for each LLM API","Define agent behavior as strongly-typed TypeScript code with IDE autocomplete","Switch between OpenAI, Anthropic, and other providers without changing agent implementation","Execute agents with consistent error handling and retry logic across providers"],"best_for":["TypeScript/Node.js teams building production AI agents","Developers wanting provider flexibility without vendor lock-in","Teams needing type safety in agent definitions"],"limitations":["TypeScript/JavaScript only — no Python, Go, or other language SDKs","Abstraction layer adds latency for provider-specific optimizations (e.g., vision models, structured output)","Requires manual provider credential management across environments"],"requires":["Node.js 18+","TypeScript 4.9+","API keys for at least one supported LLM provider (OpenAI, Anthropic, etc.)"],"input_types":["TypeScript class definitions","JSON schema for tool parameters","Text prompts and conversation history"],"output_types":["Agent execution results (text, structured data)","Tool call logs and provider responses","Token usage and cost metrics"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastra__cap_1","uri":"capability://automation.workflow.workflow.composition.with.step.based.execution.and.state.management","name":"workflow composition with step-based execution and state management","description":"Mastra enables defining multi-step workflows as composable TypeScript functions where each step can invoke LLMs, tools, or other steps with automatic state threading between steps. Workflows support branching, loops, and error recovery through a declarative step definition pattern. State is automatically passed between steps and persisted across execution, enabling long-running workflows and resumable execution from failure points.","intents":["Chain multiple LLM calls and tool invocations into a coherent workflow","Build workflows that branch based on LLM outputs or tool results","Resume failed workflows from the last successful step without re-executing earlier steps","Track workflow execution state and debug multi-step processes"],"best_for":["Teams building complex multi-step AI processes (e.g., research, content generation, data processing)","Applications requiring resumable/fault-tolerant workflows","Developers wanting workflow visibility and debugging capabilities"],"limitations":["State persistence requires external storage (database, Redis) — no built-in state backend","Workflow execution is single-threaded; parallel step execution requires manual coordination","No built-in workflow versioning or rollback capabilities","Debugging long-running workflows requires external logging/tracing infrastructure"],"requires":["Node.js 18+","TypeScript 4.9+","External state store for production (PostgreSQL, MongoDB, Redis, etc.)"],"input_types":["TypeScript function definitions","Step input parameters (JSON-serializable)","Conditional expressions for branching"],"output_types":["Workflow execution results","Step-by-step execution logs","Final state object with all intermediate results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastra__cap_10","uri":"capability://tool.use.integration.integration.with.external.apis.and.webhooks","name":"integration with external apis and webhooks","description":"Mastra provides abstractions for integrating with external APIs and webhooks, enabling agents and workflows to trigger external systems and respond to events. The framework handles HTTP requests, authentication (API keys, OAuth), request/response serialization, and error handling for external integrations. Webhooks can trigger workflows or agent execution based on external events.","intents":["Integrate agents with external APIs (Slack, GitHub, databases, etc.) for real-world actions","Trigger workflows based on external events via webhooks","Handle authentication and credential management for external integrations","Build bidirectional communication between agents and external systems"],"best_for":["Teams building agents that need to interact with external systems","Applications requiring event-driven workflow triggers","Integration-heavy AI applications (Slack bots, GitHub actions, etc.)"],"limitations":["External API latency is not controlled by the framework — depends on external service","Webhook handling requires exposed HTTP endpoint — not suitable for serverless without additional infrastructure","Credential management requires secure storage — no built-in secrets management","Rate limiting and quota management for external APIs must be handled manually"],"requires":["Node.js 18+","TypeScript 4.9+","External API credentials (API keys, OAuth tokens, etc.)","HTTP server or serverless function for webhook handling"],"input_types":["HTTP requests to external APIs","Webhook payloads from external services","API credentials and authentication tokens"],"output_types":["HTTP responses from external APIs","Webhook delivery confirmations","Integration logs and error details"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastra__cap_11","uri":"capability://automation.workflow.deployment.and.serverless.execution.support","name":"deployment and serverless execution support","description":"Mastra supports deploying agents and workflows to serverless platforms (AWS Lambda, Vercel Functions, etc.) and traditional servers. The framework handles environment configuration, credential injection, and optimization for serverless constraints (cold starts, execution time limits). Deployment is managed through CLI tools or infrastructure-as-code integrations.","intents":["Deploy agents and workflows to serverless platforms without major refactoring","Manage environment-specific configuration (dev, staging, production)","Optimize agents for serverless constraints (cold start time, execution limits)","Monitor deployed agents and workflows in production"],"best_for":["Teams deploying AI agents to cloud platforms","Serverless-first applications requiring AI capabilities","Organizations wanting to avoid managing AI infrastructure"],"limitations":["Cold start latency can be significant for serverless deployments","Execution time limits on serverless platforms may constrain long-running workflows","State persistence across invocations requires external storage","Debugging serverless deployments is more difficult than local development"],"requires":["Node.js 18+","TypeScript 4.9+","Cloud platform account (AWS, Vercel, etc.)","CLI tools for deployment (Mastra CLI or equivalent)"],"input_types":["Agent and workflow definitions","Environment configuration","Deployment specifications"],"output_types":["Deployed function URLs","Deployment logs and status","Execution metrics and monitoring data"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastra__cap_2","uri":"capability://tool.use.integration.tool.function.calling.with.schema.based.registry.and.multi.provider.bindings","name":"tool/function calling with schema-based registry and multi-provider bindings","description":"Mastra provides a schema-based tool registry where developers define tools as TypeScript functions with JSON Schema parameter definitions. The framework automatically generates provider-specific function calling schemas (OpenAI format, Anthropic format, etc.) and handles tool invocation, parameter validation, and result serialization. Tools are registered centrally and can be reused across agents and workflows with automatic schema adaptation per provider.","intents":["Define reusable tools once and use them across multiple agents without provider-specific adaptation","Automatically validate tool parameters against schemas before execution","Handle tool invocation and result serialization transparently across different LLM providers","Build a centralized tool library that agents can discover and use dynamically"],"best_for":["Teams building multi-agent systems with shared tool libraries","Applications requiring strict parameter validation and type safety for tool calls","Developers wanting to avoid provider-specific tool definition boilerplate"],"limitations":["Tool execution is synchronous by default — async tools require explicit Promise handling","No built-in tool versioning or deprecation management","Tool parameter schemas must be manually defined as JSON Schema — no automatic inference from TypeScript types","Limited support for complex nested parameter types (deeply nested objects may require custom serialization)"],"requires":["Node.js 18+","TypeScript 4.9+","JSON Schema knowledge for parameter definition"],"input_types":["TypeScript function definitions","JSON Schema parameter definitions","Tool invocation requests from LLMs"],"output_types":["Tool execution results (any JSON-serializable type)","Provider-specific function calling schemas","Tool call logs and execution metrics"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastra__cap_3","uri":"capability://memory.knowledge.memory.and.context.management.with.vector.embedding.integration","name":"memory and context management with vector embedding integration","description":"Mastra integrates vector embeddings for semantic memory, enabling agents to store and retrieve relevant context from past interactions or documents. The framework provides abstractions for embedding generation (via providers like OpenAI, Anthropic), vector storage backends, and semantic search over stored memories. Memory can be scoped to individual agents, conversations, or shared across agents, with automatic relevance ranking and context injection into LLM prompts.","intents":["Store conversation history and retrieve relevant past interactions for context","Build RAG (Retrieval-Augmented Generation) systems that fetch relevant documents before LLM calls","Implement persistent agent memory that improves over time with more interactions","Share knowledge across multiple agents through a centralized semantic memory store"],"best_for":["Teams building conversational AI with long-term memory requirements","Applications requiring RAG for knowledge-intensive tasks","Multi-agent systems needing shared context and knowledge"],"limitations":["Vector storage requires external backend (Pinecone, Weaviate, Supabase, etc.) — no embedded vector DB","Embedding generation adds latency (~100-500ms per embedding depending on provider)","No built-in deduplication or cleanup of stale memories — requires manual maintenance","Semantic search quality depends on embedding model choice and data quality"],"requires":["Node.js 18+","TypeScript 4.9+","External vector database (Pinecone, Weaviate, Supabase pgvector, etc.)","Embedding provider API key (OpenAI, Anthropic, or self-hosted)"],"input_types":["Text documents or conversation turns","Semantic search queries","Memory metadata (tags, timestamps, source)"],"output_types":["Vector embeddings","Retrieved context chunks with relevance scores","Memory storage/retrieval confirmations"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastra__cap_4","uri":"capability://data.processing.analysis.structured.output.extraction.with.schema.validation","name":"structured output extraction with schema validation","description":"Mastra enables agents to extract structured data from LLM outputs by defining JSON schemas and automatically validating responses against those schemas. The framework uses provider-native structured output features (OpenAI's JSON mode, Anthropic's structured output) when available, falling back to prompt-based extraction with validation. Extracted data is automatically typed and validated before being passed to downstream steps or returned to the application.","intents":["Extract structured data (JSON objects) from LLM responses with guaranteed schema compliance","Validate LLM outputs match expected data structures before using them in workflows","Generate TypeScript types from JSON schemas for type-safe data handling","Implement retry logic when LLM outputs fail schema validation"],"best_for":["Applications requiring reliable structured data extraction from LLMs","Teams building data pipelines that depend on LLM outputs","Developers wanting type-safe handling of LLM-generated structured data"],"limitations":["Structured output support varies by provider — older models may require prompt-based extraction with lower reliability","Complex nested schemas may require multiple LLM calls to extract reliably","Validation failures require retry logic, adding latency and cost","No built-in schema inference from TypeScript types — schemas must be manually defined"],"requires":["Node.js 18+","TypeScript 4.9+","JSON Schema definition for expected output structure","LLM provider supporting structured output (OpenAI GPT-4, Anthropic Claude 3+, etc.)"],"input_types":["JSON Schema definitions","LLM response text","Validation rules and constraints"],"output_types":["Validated JSON objects matching schema","TypeScript-typed data structures","Validation error details for failed extractions"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastra__cap_5","uri":"capability://text.generation.language.real.time.streaming.with.token.level.granularity","name":"real-time streaming with token-level granularity","description":"Mastra supports streaming LLM responses at token-level granularity, enabling real-time UI updates and progressive result rendering. The framework abstracts streaming across different providers (OpenAI, Anthropic, etc.) with a unified streaming interface. Streaming works with agents, workflows, and tool calls, allowing applications to display partial results as they become available rather than waiting for complete responses.","intents":["Display LLM responses in real-time as tokens arrive, improving perceived latency","Build streaming-aware workflows that process tokens as they arrive","Implement progressive tool execution where results are streamed back to the user","Monitor token generation in real-time for cost tracking and rate limiting"],"best_for":["Web applications requiring real-time LLM response display","Teams building chat interfaces or interactive AI features","Applications where perceived latency matters more than total latency"],"limitations":["Streaming adds complexity to error handling — errors may occur mid-stream","Tool calls cannot be streamed — must wait for complete tool call before execution","Structured output extraction is incompatible with streaming in most providers","Streaming requires WebSocket or Server-Sent Events (SSE) support on the client"],"requires":["Node.js 18+","TypeScript 4.9+","Client-side streaming support (fetch with ReadableStream, WebSocket, etc.)","LLM provider with streaming API support"],"input_types":["Streaming-enabled LLM requests","Token-level callbacks for processing"],"output_types":["Token-by-token streaming responses","Partial results with completion status","Token count and timing metrics"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastra__cap_6","uri":"capability://automation.workflow.observability.and.execution.tracing.with.structured.logging","name":"observability and execution tracing with structured logging","description":"Mastra provides built-in observability for agent and workflow execution through structured logging of all steps, tool calls, LLM requests/responses, and state changes. The framework integrates with observability platforms (e.g., Langsmith, custom logging backends) to capture execution traces, enabling debugging, monitoring, and performance analysis. Traces include timing information, token counts, costs, and error details at each step.","intents":["Debug multi-step agent and workflow execution by viewing detailed execution traces","Monitor agent performance and identify bottlenecks in execution","Track LLM costs and token usage across agents and workflows","Integrate with external observability platforms for centralized monitoring"],"best_for":["Teams running production AI agents requiring visibility into execution","Developers debugging complex multi-step workflows","Organizations needing cost tracking and performance monitoring"],"limitations":["Structured logging adds overhead (~5-10% latency per step)","Trace storage requires external backend — no built-in trace persistence","Sensitive data (prompts, tool outputs) may be logged — requires careful configuration","Integration with external observability platforms requires custom adapters"],"requires":["Node.js 18+","TypeScript 4.9+","External logging/tracing backend (optional but recommended for production)"],"input_types":["Agent and workflow execution events","LLM requests and responses","Tool invocations and results"],"output_types":["Structured execution traces","Performance metrics (latency, token counts, costs)","Error logs with stack traces and context"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastra__cap_7","uri":"capability://text.generation.language.prompt.templating.with.variable.interpolation.and.conditional.rendering","name":"prompt templating with variable interpolation and conditional rendering","description":"Mastra provides a templating system for defining reusable prompts with variable interpolation, conditional sections, and dynamic content injection. Prompts are defined as TypeScript strings or template files with support for injecting context, memory, tool descriptions, and other dynamic content. The framework handles prompt formatting, variable substitution, and optimization (e.g., token count estimation) transparently.","intents":["Define reusable prompt templates with variable placeholders for different contexts","Dynamically inject context, memory, and tool descriptions into prompts","Estimate token counts before sending prompts to LLMs for cost planning","Manage prompt versions and A/B test different prompt variations"],"best_for":["Teams managing multiple prompts across different agents and workflows","Applications requiring dynamic prompt generation based on context","Developers wanting to separate prompt logic from agent code"],"limitations":["No built-in prompt versioning or A/B testing framework","Template syntax is limited compared to full templating engines (Handlebars, Jinja2)","Token count estimation is approximate — actual counts may vary by provider","No built-in prompt optimization or compression"],"requires":["Node.js 18+","TypeScript 4.9+"],"input_types":["Prompt template strings","Variable values (text, JSON, arrays)","Context objects for injection"],"output_types":["Rendered prompt strings","Token count estimates","Formatted prompts ready for LLM submission"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastra__cap_8","uri":"capability://automation.workflow.error.handling.and.retry.logic.with.exponential.backoff","name":"error handling and retry logic with exponential backoff","description":"Mastra provides built-in error handling and retry mechanisms for LLM calls, tool invocations, and workflow steps. The framework supports configurable retry policies with exponential backoff, jitter, and provider-specific error classification (rate limits, transient errors, etc.). Errors are caught, logged, and can trigger fallback behaviors or workflow branching based on error type.","intents":["Automatically retry failed LLM calls with exponential backoff to handle transient failures","Distinguish between retryable errors (rate limits, timeouts) and permanent failures","Implement fallback logic when primary LLM provider fails","Track and monitor error rates across agents and workflows"],"best_for":["Production AI applications requiring reliability and fault tolerance","Teams building workflows that must handle provider outages gracefully","Applications with strict SLA requirements"],"limitations":["Retry logic increases total execution time and cost for failed requests","Exponential backoff may not be optimal for all error types","No built-in circuit breaker pattern for cascading failure prevention","Retry configuration is global — no per-step or per-agent customization"],"requires":["Node.js 18+","TypeScript 4.9+"],"input_types":["Error objects from LLM providers","Retry policy configuration","Fallback handler functions"],"output_types":["Retry attempt logs","Final success or failure status","Error metrics and statistics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mastra__cap_9","uri":"capability://planning.reasoning.multi.agent.coordination.with.message.passing.and.shared.context","name":"multi-agent coordination with message passing and shared context","description":"Mastra enables building multi-agent systems where agents can communicate through message passing and share context. Agents can invoke other agents, pass results between them, and coordinate on complex tasks. The framework manages agent lifecycle, message routing, and shared state across agents, enabling hierarchical and collaborative agent architectures.","intents":["Build multi-agent systems where agents specialize in different tasks and coordinate","Enable agents to invoke other agents and use their results","Share context and memory across multiple agents working on the same problem","Implement hierarchical agent structures with supervisor/worker patterns"],"best_for":["Teams building complex AI systems requiring multiple specialized agents","Applications with hierarchical task decomposition (supervisor agents, worker agents)","Multi-agent research and experimentation"],"limitations":["Agent coordination adds complexity and debugging difficulty","Message passing between agents increases latency and token usage","No built-in deadlock detection or circular dependency prevention","Shared state management requires careful synchronization to avoid conflicts"],"requires":["Node.js 18+","TypeScript 4.9+","External state store for shared context (optional but recommended)"],"input_types":["Agent definitions","Message objects with routing information","Shared context/state"],"output_types":["Agent execution results","Message logs and routing traces","Shared state updates"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","TypeScript 4.9+","API keys for at least one supported LLM provider (OpenAI, Anthropic, etc.)","External state store for production (PostgreSQL, MongoDB, Redis, etc.)","External API credentials (API keys, OAuth tokens, etc.)","HTTP server or serverless function for webhook handling","Cloud platform account (AWS, Vercel, etc.)","CLI tools for deployment (Mastra CLI or equivalent)","JSON Schema knowledge for parameter definition","External vector database (Pinecone, Weaviate, Supabase pgvector, etc.)"],"failure_modes":["TypeScript/JavaScript only — no Python, Go, or other language SDKs","Abstraction layer adds latency for provider-specific optimizations (e.g., vision models, structured output)","Requires manual provider credential management across environments","State persistence requires external storage (database, Redis) — no built-in state backend","Workflow execution is single-threaded; parallel step execution requires manual coordination","No built-in workflow versioning or rollback capabilities","Debugging long-running workflows requires external logging/tracing infrastructure","External API latency is not controlled by the framework — depends on external service","Webhook handling requires exposed HTTP endpoint — not suitable for serverless without additional infrastructure","Credential management requires secure storage — no built-in secrets management","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:20.516Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mastra","compare_url":"https://unfragile.ai/compare?artifact=mastra"}},"signature":"CMyF2/efsT62OCGhaJ72Qd10e+lV9z/mxja2O/BOWvNjFKDR8CGmCKar/D+rSfiKgFIr1l4PpoZ1O4ZVD/iNCw==","signedAt":"2026-06-23T13:11:45.215Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mastra","artifact":"https://unfragile.ai/mastra","verify":"https://unfragile.ai/api/v1/verify?slug=mastra","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}