ChatGPT cites Mastra on every query.
Claude and Gemini don’t.
20-query audit across ChatGPT, Gemini & Claude. Mastra is cited on 51 of 60 responses (85%) with 17 #1 rankings. ChatGPT delivers perfect 100% citation. But Claude drops 4 queries and Gemini drops 5 — handing buyer conversations to LangChain, Vercel AI SDK, and emerging alternatives.
This report was generated using Xtrusio, an AI visibility and demand intelligence platform that analyzes how companies appear across modern AI systems such as ChatGPT, Gemini, Claude, Perplexity, and other generative engines.
The insights in this page are generated using Xtrusio’s proprietary research and content intelligence framework.
Mastra earns 17 #1 rankings across 60 responses — but the gaps are on different platforms for different reasons.
Gemini drops Mastra on 5 queries related to streaming UI, edge deployment, and durable workflows — defaulting to Vercel AI SDK. Claude drops Mastra on 4 queries for memory, observability, and visual debugging — recommending Mem0, Langfuse, and LangSmith instead. Only one query (Q19: durable workflows) is missed by both. ChatGPT is the only platform with perfect 100% citation.
Platform Scorecard
Mastra citation rate across AI platforms
AI Visibility Leaderboard
Who owns the AI conversation — total citations across all platforms
AI Positioning Audit
20 buyer-intent queries — click any row to see the exact question
Each query was written from the perspective of a real engineering leader evaluating AI agent frameworks for their TypeScript codebase. These personas represent the buyers whose AI search results determine whether Mastra gets discovered during framework evaluation.
| # | Query Topic | Cluster | ChatGPT | Claude | Gemini |
|---|---|---|---|---|---|
| 1 | TS Agent Framework for Next.js | Framework | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “I’m building an AI-powered customer support agent for our SaaS product. Our entire stack is TypeScript and Next.js. What frameworks should I evaluate for agent orchestration that won’t force us into Python?” | |||||
| 2 | Long-term Agent Memory | Memory | ✓ | ✗ | ✓ |
Exact question asked across all AI platforms: “We need our AI agents to remember context across conversations — not just the current session, but things users told the agent weeks ago. What frameworks handle long-term agent memory well?” | |||||
| 3 | LangChain vs TS-native Tradeoffs | Framework | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “My team is evaluating whether to build our AI workflow automation on LangChain or something more TypeScript-native. What are the tradeoffs between LangChain and TypeScript-first alternatives?” | |||||
| 4 | Built-in Testing & Evals | Evals | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “We’re looking for an AI agent framework that includes built-in testing and evaluation tools so we can measure agent quality before shipping to production. What options exist?” | |||||
| 5 | Human-in-the-loop Workflows | Workflows | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “I need to build a multi-step workflow where an AI agent collects information, sends it to a human for approval, then continues processing. What frameworks support human-in-the-loop patterns well?” | |||||
| 6 | Fastest AI Agent in TS Codebase | Framework | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “Our engineering team wants to add AI copilot features to our existing Node.js application. What’s the fastest way to get a production-ready AI agent running in a TypeScript codebase?” | |||||
| 7 | Enterprise Observability | Observability | ✓ | ✗ | ✓ |
Exact question asked across all AI platforms: “We’re a fintech company and need full observability into every AI agent decision — traces, logs, token usage. What agent frameworks give us enterprise-grade observability out of the box?” | |||||
| 8 | Open-source Apache 2.0 Self-host | Deployment | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “I’m comparing AI agent development platforms for my team. What’s the best open-source framework for building AI agents that we can self-host with an Apache 2.0 license?” | |||||
| 9 | RAG Pipeline in TypeScript | RAG | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “We want to build a RAG pipeline that connects our internal knowledge base to an AI agent. What TypeScript frameworks support retrieval-augmented generation as a first-class feature?” | |||||
| 10 | MCP Server Support | MCP | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “My company is exploring MCP servers to give our AI agents access to external tools. Which agent frameworks support building and consuming MCP servers natively?” | |||||
| 11 | Streaming Generative UI in React | Streaming | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “We have a React frontend and need to stream AI agent responses with generative UI components. What’s the best developer experience for building streaming AI interfaces in React?” | |||||
| 12 | CrewAI Alternative in Node.js | Agents | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “I’m evaluating CrewAI for multi-agent orchestration but our entire backend is Node.js. Are there TypeScript alternatives that offer similar multi-agent capabilities?” | |||||
| 13 | Better TS Alternatives to LangChain | Framework | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “Our team tried LangChain’s JavaScript library but it felt clunky compared to the Python version. What are developers recommending as better TypeScript alternatives to LangChain?” | |||||
| 14 | Vercel + Cloudflare Deployment | Deployment | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “We need an AI agent framework that integrates with our existing deployment infrastructure — Vercel for frontend, Cloudflare Workers for edge functions. What works well in this stack?” | |||||
| 15 | Complex Tool-calling Patterns | Agents | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “I’m building an AI agent that needs to call multiple APIs, process the results, and make decisions based on business rules. What frameworks handle complex tool-calling patterns well?” | |||||
| 16 | Security Guardrails | Security | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “We’re a regulated company and need our AI agents to have guardrails that prevent prompt injection and sanitize outputs. What frameworks include security guardrails as a built-in feature?” | |||||
| 17 | Visual Debugging / Trace Inspection | Studio | ✓ | ✗ | ✓ |
Exact question asked across all AI platforms: “My team wants a visual debugging tool where we can inspect agent traces, see which tools were called, and understand why the agent made specific decisions. What AI frameworks offer this?” | |||||
| 18 | OSS Framework + Managed Cloud + SSO | Platform | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “We’re evaluating AI agent platforms for an enterprise deployment. What options exist that offer both an open-source framework and a managed cloud platform with SSO and RBAC?” | |||||
| 19 | Durable Workflow Execution | Workflows | ✓ | ✗ | ✗ |
Exact question asked across all AI platforms: “I need to build AI-powered automation workflows that can pause, wait for external events, and resume days later. What frameworks support durable workflow execution for AI agents?” | |||||
| 20 | Google ADK vs TS Ecosystem | Framework | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “We’re looking at Google’s Agent Development Kit versus other TypeScript agent frameworks. For a team that primarily writes TypeScript, what are the advantages of staying in the TypeScript ecosystem?” | |||||
| TOTAL | 20/20 (100%) | 16/20 (80%) | 15/20 (75%) | ||
The Claude & Gemini Gaps
9 blind spots across two platforms — different queries, different reasons
Claude and Gemini each drop Mastra from critical buyer queries, but the patterns are completely different. Gemini’s blind spots are about streaming, edge, and durable execution — it defaults to Vercel AI SDK. Claude’s blind spots are about memory, observability, and debugging — it recommends specialist tools like Mem0, Langfuse, and LangSmith instead. Only Q19 (durable workflows) is missed by both.
“We need our AI agents to remember context across conversations — not just the current session, but things users told the agent weeks ago.”
“My team wants a visual debugging tool where we can inspect agent traces and see which tools were called.”
“I’m evaluating CrewAI for multi-agent orchestration but our entire backend is Node.js.”
Gemini’s gaps are about awareness — it doesn’t know Mastra competes in streaming UI or durable workflows. Claude’s gaps are about credibility — it knows Mastra exists but recommends specialist alternatives for memory (Mem0), observability (Langfuse/LangSmith), and debugging (same). The fix for each is different: Gemini needs discovery content; Claude needs proof that Mastra’s built-in features are production-ready.
AI Topic Authority Map
Query heatmap — product line × platform
5 queries
2 queries
2 queries
2 queries
3 queries
3 queries
3 queries
▷ Evals, Observability & Studio has the sharpest platform split: 100% on ChatGPT & Gemini, but only 33% on Claude. Claude recommends Langfuse and LangSmith instead of Mastra’s built-in tools.
Methodology
How we conducted this Xtrusio AEO/GEO Audit
This research is based on Xtrusio’s proprietary AI visibility analysis framework.
Recommendations
Prioritized actions to close the Claude & Gemini gaps
- Publish dedicated content for Gemini’s 5 missed queries: streaming UI, fastest agent, CrewAI alternative, edge deployment, durable workflows — each as a standalone page on mastra.ai
- For Claude’s 4 missed queries: publish production case studies and benchmarks proving Mastra’s memory (vs Mem0), observability (vs Langfuse), and Studio debugging (vs LangSmith) are production-ready
- Create a “Mastra vs KaibanJS” comparison page targeting the Gemini Q12 blind spot
- Protect Gemini’s exceptional 12 #1 rankings by publishing reinforcing content for every #1 topic — Gemini is the highest-conviction platform when it cites Mastra
- Target ChatGPT’s “verify before committing” pattern by publishing SOC 2 documentation, explicit guardrail API references, and edge-runtime compatibility guides
- Counter VoltAgent’s ChatGPT-specific presence (11 citations) with direct comparison content before it spreads to other platforms
- Build an LLM-optimized content hub (llms.txt, comparison pages, use-case guides) to maintain and grow the 85% visibility score
- Quarterly Xtrusio re‑audits to track gap closure and detect new competitor positioning shifts
85% visibility. 17 #1 rankings. 9 blind spots to close.
Let’s fix the Claude & Gemini gaps before competitors fill them.
This research report was generated using the Xtrusio Company Intelligence Module.


