# Trae AI Reviews **Vendor:** Trae **Category:** [Software de Generación de Código por IA](https://www.g2.com/es/categories/ai-code-generation) **Average Rating:** 3.5/5.0 **Total Reviews:** 5 ## About Trae AI Trae AI es un entorno de desarrollo integrado (IDE) avanzado impulsado por inteligencia artificial, diseñado para revolucionar el desarrollo de software actuando como un socio colaborativo para los desarrolladores. Permite a los usuarios describir los requisitos del proyecto en lenguaje natural, y la IA genera, refina y entrega de manera autónoma código funcional, agilizando el proceso de desarrollo y reduciendo significativamente el tiempo de lanzamiento al mercado. Características y Funcionalidades Clave: - Procesamiento de Lenguaje Natural: Permite a los desarrolladores ingresar las necesidades del proyecto en lenguaje cotidiano, que la IA interpreta para producir estructuras de código correspondientes. - Integración de Diseño: Soporta la carga de archivos de diseño, ayudando a traducir conceptos visuales en código limpio y funcional. - Aprendizaje Adaptativo: Aprende de los proyectos en curso para proporcionar sugerencias contextualmente relevantes, mejorando la eficiencia del código. - Acceso Multi-Modelo: Ofrece acceso gratuito a modelos de IA premium como Claude 3.5 Sonnet, Claude 3.7 Sonnet, Gemini 2.5 Pro y GPT-4.1, ampliando el conjunto de herramientas del desarrollador sin costos adicionales. - Agentes de IA Personalizados: Permite la creación de agentes de IA especializados con comportamientos definidos, acceso a herramientas y reglas específicas del proyecto para asistir en diversas tareas de desarrollo. Valor Principal y Soluciones para el Usuario: Trae AI aborda los desafíos de los largos ciclos de desarrollo y la necesidad de prototipado rápido automatizando la generación y refinamiento de código. Empodera a los desarrolladores, desde principiantes hasta expertos, para que se concentren en la resolución creativa de problemas y la planificación estratégica en lugar de tareas rutinarias de codificación. Al integrar modelos avanzados de IA y aprender de las interacciones del usuario, Trae AI mejora la productividad, asegura la calidad del código y acelera la entrega de soluciones de software. ## Trae AI Pros & Cons **What users like:** - Los usuarios valoran la **asistencia de codificación** de Trae AI, ya que acelera significativamente sus tareas de codificación en un 60%. (1 reviews) - Los usuarios valoran la **facilidad de uso** de Trae AI, mejorando significativamente la velocidad y eficiencia en la realización de tareas. (1 reviews) - Los usuarios aprecian la **automatización que ahorra tiempo** de Trae AI, lo que permite una finalización de tareas más rápida y procesos de codificación más eficientes. (1 reviews) - Los usuarios encuentran la **facilidad de implementación** de Trae AI excepcional, acelerando significativamente sus tareas de codificación y flujo de trabajo. (1 reviews) - Los usuarios encuentran que Trae AI ofrece una gran **variedad de modelos** , proporcionando una opción competitiva en calidad y rendimiento a precios más bajos. (1 reviews) - Los usuarios encuentran que Trae AI es **rápido y eficiente** , ofreciendo un rendimiento excelente a un precio más bajo que los competidores. (1 reviews) - Disponibilidad de soporte (1 reviews) - Ahorro de tiempo (1 reviews) **What users dislike:** - Los usuarios informan de **mala codificación** en Trae AI, lo que a menudo resulta en códigos complicados y cambios inexplicables. (2 reviews) - Los usuarios experimentan **soporte de idioma limitado** con respuestas retrasadas, a menudo recibiendo comunicaciones en chino mientras esperan ayuda. (1 reviews) - Los usuarios informan de **un mal servicio de atención al cliente** , citando respuestas tardías y correos electrónicos automáticos en chino durante los períodos de espera. (1 reviews) ## Trae AI Reviews ### 1. AI based IDE **Rating:** 4.0/5.0 stars **Reviewed by:** Luca P. | Chief Operations Officer DEQUA Studio | Formerly CTO in MarTech, Marketing y publicidad, Mediana Empresa (51-1000 empleados) **Reviewed Date:** May 26, 2026 **¿Qué es lo que más le gusta de Trae AI?** The dual-mode setup is what made TRAE stick with me after I stopped opening Cursor. IDE mode behaves like a VS Code fork with a strong agent panel beside the editor, and SOLO mode hands the whole development loop over to an autonomous agent that scaffolds, writes, runs, and iterates while I watch. The toggle between the two is what matters. I start a project in SOLO when I want a scaffold off a brief, then drop into IDE mode the moment I need to hand-edit something or follow a debug thread. Most agentic tools force you to commit to one posture. TRAE lets the posture shift with the task, which is closer to how I actually work. Builder, the in-IDE agent, is the part I open every working day. I describe a feature with the level of detail a junior dev would need (something like "add a settings page that persists user preferences in localStorage, with a toggle for dark mode and a select for language"), and Builder plans the change, edits the files it needs to edit, runs the dev server, and shows me the result in the in-app preview without my hand leaving the keyboard. The preview tab is the small touch that pulled me in. Other tools dump generated code into my editor and leave verification to me. TRAE shows me the page rendering while it works, which means I catch a bad assumption in seconds rather than after a manual run. SOLO mode goes further than I expected from something this cheap. I gave it a brief for a small internal tool last month, a simple form that posts to a Supabase table with auth, and it produced a working app, deployed, in a single multi-step run. The model planned the schema, wrote the migrations, wired the front end, and ran through enough of its own test loop that the first version actually loaded. I have done this exercise enough times with other agents to know how rare that is. It is not magic. SOLO still hallucinates the occasional library name and needs me to step in when a model loses the thread, but the rate at which it gets a v0 across the line is meaningfully ahead of what I was getting from a Cursor agent or a Cline-plus-BYOK setup six months ago. Free access to premium models is the part that still surprises me when I open the model picker. Claude 4 Sonnet, GPT-4o, DeepSeek R1, Claude 3.7 Sonnet, Gemini 2.5 Pro, GPT-4.1, all available without an API key, all included in a free tier that is not crippled in the usual ways. The autocomplete allowance covers a working week of normal use comfortably, and the chat and Builder usage is more generous than I expected from anything in this category. Even with the token-based metering that came in earlier this year, I have not hit a wall during a typical session. For a free tool to put Claude in my hands at all is unusual. For it to put Claude inside an agent loop with MCP support, in an editor that does not feel like a downgrade from VS Code, is closer to genuinely uncommon. Custom agents are where TRAE crosses from useful into shapeable to how I work. I can define an agent with its own system prompt, its own allowed tools, and its own MCP servers, then invoke it by name. I keep one called Debug that has terminal access, browser inspector tools, and a tight system prompt that tells it to read logs before suggesting code changes. Another one called Refactor has no terminal access at all and is constrained to read-and-suggest behavior, which I use when I am pairing with the agent rather than letting it drive. The ability to enforce these constraints by agent rather than per-conversation removes a lot of the negotiation that other tools require ("don't run anything, just suggest" repeated every session). The Builder with MCP variant takes the same idea further by dynamically calling whichever MCP server fits the current task, which I have come to rely on for things like database queries during development and Supabase admin operations. In-app preview deserves its own line. I have used enough AI coding tools that ship code I have to verify in a separate browser tab to appreciate what TRAE does here. The preview lives in the same window, refreshes on save, and the agent can read what is on the screen and the console output to debug a runtime error without me having to copy-paste a stack trace back into chat. On a recent build I left the agent running with auto-run on, watched it hit a CSS bug in the preview, read the computed styles, and patch the offending file without any prompt from me. The loop is closed where it needs to be closed. MCP support is mature enough that I have stopped writing one-off integrations. Adding a server is a config edit and a restart, and the agent will pull tools from any server I have configured and combine them inside a single task. I use the Supabase server most days, a filesystem server when I am moving things around outside the editor, and a couple of custom ones I wrote for internal tooling. The Builder with MCP mode chooses tools dynamically rather than forcing me to spell out the call, which is the right design for tools whose value is in being available without ceremony. Interface polish is the quiet selling point. If you have used VS Code you will be productive in TRAE inside the first hour. Settings are in the same place, keybindings transfer, the extensions you would expect work. The agent panel sits on the right, the preview slides in below it when relevant, and the model picker is a dropdown rather than a menu safari. The UI is the part of TRAE most often praised in the community, and after several months I understand why. It is the kind of polish that disappears when you are using it, which is what good UI is supposed to do. The pricing structure works in TRAE's favor too. The free tier is enough for solo developers and small side projects, the Lite plan at a few dollars a month is genuinely cheap for a primary IDE, and Pro at roughly ten dollars sits well below Cursor at twenty. I run on Pro because I want the larger token allocation for SOLO runs, but I would not have hesitated on Lite if I were doing lighter agent work. The cost ladder rewards the way developers actually consume these tools rather than locking core features behind a single high tier. **¿Qué es lo que no le gusta de Trae AI?** Pricing predictability is the one thing I would change. The shift to token-based metering earlier this year was a sensible move in principle, but in practice it makes it harder to know in advance whether a long SOLO run is going to eat a noticeable chunk of my monthly allocation or pass without me noticing. I find myself watching usage more closely than I would like, particularly on tasks that involve a lot of file reads or back-and-forth with a frontier model. A clearer in-editor estimate of what a given task is likely to cost before it runs would solve most of the friction. Right now I track it after the fact, which is the wrong direction. Support is the other rough edge, and it lines up with what I have seen in community discussions. When I have written in with an actual issue, the turnaround has been slower than I would expect from a paid tool, and the early replies have occasionally come through in a way that suggests the queue is being worked by an automated layer that is not yet good enough to answer the questions a developer would ask. I have always gotten an answer in the end, and the product itself has not failed me in a way that left me stuck without a workaround. But the support experience is the part of TRAE that feels most like a younger company than the rest of the product does. The fix here is partly hiring, partly tooling, and there is no shortcut. Context handling on very large codebases is where I have hit the most concrete technical limit. On a small to medium project the agent maintains context fine across a session. On a larger codebase, somewhere past a certain file count, I start seeing the agent forget files it touched earlier in the same conversation, which costs me a re-prompt and sometimes a partial redo. I have worked around this by being deliberate about what I include in a task ("only modify the files in /src/components/auth, do not touch anything else") and by breaking larger tasks into clearly scoped subtasks. The workaround is fine, but it is a workaround, and a smarter long-context retrieval layer would let me skip it. The privacy posture is worth a note for anyone evaluating the tool. TRAE is a ByteDance product, the telemetry is meaningful by default, and the privacy mode the team has added since I started using it is the right move but not yet a complete answer. For my own work this is acceptable, because the projects I run through TRAE are either open source or low-sensitivity client work, and the local-first storage model keeps the code on my machine. For anyone with stricter compliance requirements, or anyone whose employer has a position on ByteDance tools, this is the field that decides whether you can use TRAE at all. The product team is clearly aware of the concern and is iterating on it, but it is the kind of trade-off that does not go away through better engineering alone. The occasional over-engineering from the agent is worth flagging too. When I am working with Gemini as the backing model in particular, I will sometimes ask for a simple fix and get back a multi-file refactor that pulls in abstractions I did not ask for. The fix is to either re-prompt with a stricter scope or switch models for the task. It is not a deal-breaker, but it is the kind of behavior that means I do not yet leave the agent fully unsupervised on anything I care about shipping. **¿Qué problemas resuelve Trae AI y cómo le beneficia eso?** The core problem it addresses is the cost of switching between writing code and asking an AI to write code. Before TRAE, my workflow involved an editor, a separate chat window pointed at a model, copy-paste in both directions, and a browser tab for whatever the page actually looked like. Each switch was small. Together they fragmented every task. TRAE folds the model, the agent, the preview, and the editor into one window, and the friction that used to slow down a feature reduces to the time it actually takes the model to think. The before-state was a session that felt busy without being productive. The after-state is the same task done in less time with fewer dropped threads. Prototyping has changed shape because of SOLO mode. I get a lot of requests, internal and client, that boil down to "can you stand up a quick version of this so we can see what we're talking about." Before, that meant a half-day of scaffolding before the interesting work could start. Now I describe the prototype, let SOLO run, and spend that half-day shaping what came out instead of building it from zero. The cost-per-prototype has dropped to the point where I will spin one up to test an idea I would previously have left as a paragraph in a doc. The result is more concrete conversations earlier in a project, which is a better way to work. Agent workflows are no longer a side project. Six months ago, building a custom agent for a specific task in my pipeline meant choosing between writing it from scratch against an API, configuring a third-party agent framework, or living with whatever defaults a closed tool gave me. TRAE's custom agents make the third option roughly as flexible as the first, without the maintenance overhead. I now have a small library of agents tuned for the tasks I do most: one for code review on pull requests, one for writing migration files from a description, one for spinning up internal tooling, and one for triaging error logs. The marginal cost of adding a new one is low enough that I add them when I notice a repeated pattern in my work, rather than when I have an afternoon free. The model question has effectively gone away. Choosing between Claude, GPT-4o, and Gemini used to mean choosing a subscription. Inside TRAE it is a dropdown. The benefit is not only cost. It is being able to use the right model for the task without thinking about it, picking Claude for a careful refactor, GPT-4o for a fast scaffold, Gemini when I want a second opinion on a tricky design choice. The friction of swapping models used to push me to standardize on one. Now I match the model to the work, which is the way it should have been all along. Debugging through an agent is the unexpected benefit. I expected TRAE to be a coding assistant. What I did not expect was to use it as a runtime debugger as often as I do. Auto-run mode plus the in-app preview means the agent can reproduce the issue, read the logs, and propose a fix in a single loop, with me reviewing rather than driving. The before-state was a developer chasing a bug through console.log calls and stack traces. The after-state is the developer reviewing what the agent already found. This is not a substitute for understanding the bug, but it is a faster way of getting to the point where understanding the bug is possible. MCP has turned external tools into first-class members of my agent's toolkit, rather than things I tab over to manually. Pulling a record from the database, checking a deployment status, reading a Slack thread for a spec, these all happen inside the same agent loop. The benefit is keeping context where the work is. Every tab switch I do not have to make is a chance to stay in the problem I am solving, and TRAE removes more of those than any other tool I have used. The cost ladder solves a budget problem too. Running multiple AI subscriptions for a freelance practice gets expensive fast, and the constant calculus of which one to keep this month is a tax on focus. TRAE at ten dollars covers what used to take three separate tools, which means I can hand a license to a junior collaborator on a client engagement without having a conversation about who pays for which model. That kind of simplicity is undervalued in tooling decisions, and it has saved me real time at the start of new projects. ### 2. La interfaz de usuario de primera clase de Trae y sus características útiles siguen mejorando. **Rating:** 4.5/5.0 stars **Reviewed by:** Himanshu J. | Founder, Tecnología de la información y servicios, Pequeña Empresa (50 o menos empleados) **Reviewed Date:** April 03, 2026 **¿Qué es lo que más le gusta de Trae AI?** Trae es el mejor IDE en términos de interfaz de usuario. Incluye varias características útiles, como una vista previa en la aplicación y agentes de uso de computadora integrados, y la reciente actualización Solo 3.0 lo ha mejorado aún más. **¿Qué es lo que no le gusta de Trae AI?** Los precios son demasiado impredecibles y no tiene modelos de Claude, que son las principales desventajas de esto. La comunidad parece odiar los precios también. **¿Qué problemas resuelve Trae AI y cómo le beneficia eso?** No mucho que resuelva y que otras herramientas no puedan, pero de todos modos lo uso por el nivel gratuito que tiene. ### 3. Acelera el desarrollo actualizando el código directamente en nuestra base de código. **Rating:** 5.0/5.0 stars **Reviewed by:** Jojo p. | Software Developer, Pequeña Empresa (50 o menos empleados) **Reviewed Date:** January 21, 2026 **¿Qué es lo que más le gusta de Trae AI?** No necesito escribir códigos complicados, como ingeniero de software la gestión del tiempo es muy importante, después de usar esto puedo completar las tareas un 60% más rápido, solo di nuestros requisitos y ellos darán la solución y directamente actualizará el código en nuestra base de código. **¿Qué es lo que no le gusta de Trae AI?** a veces da códigos complicados en lugar de una solución simple, especialmente cuando usamos Gemini 2.5 pro **¿Qué problemas resuelve Trae AI y cómo le beneficia eso?** No necesito escribir código ahora, si digo el requisito exacto Trae hará la codificación. ### 4. Alternativa asequible a Cursor con buen rendimiento **Rating:** 4.0/5.0 stars **Reviewed by:** Ram M. | Founder, CEO, Tecnología de la información y servicios, Pequeña Empresa (50 o menos empleados) **Reviewed Date:** December 04, 2025 **¿Qué es lo que más le gusta de Trae AI?** Más barato que cursor y windsurf. Calidad y rendimiento de modelo comparable. **¿Qué es lo que no le gusta de Trae AI?** A veces, los cambios en el código se descartan inexplicablemente. **¿Qué problemas resuelve Trae AI y cómo le beneficia eso?** Es un buen editor de código basado en VS Code. El Constructor con funcionalidad MCP es bueno. Aún no he usado Solo, pero también suena bien. ### 5. La peor experiencia de atención al cliente **Rating:** 0.0/5.0 stars **Reviewed by:** prateek b. | Freelancer Software Developer, Pequeña Empresa (50 o menos empleados) **Reviewed Date:** January 26, 2026 **¿Qué es lo que más le gusta de Trae AI?** Trae es como cursor y copiloto, pero su equipo de soporte es muy patético. **¿Qué es lo que no le gusta de Trae AI?** Falta de apoyo profesional, no responden a tiempo, tardan semanas, y mientras tanto envían correos automáticos en chino. **¿Qué problemas resuelve Trae AI y cómo le beneficia eso?** Programación en pareja, IA - [View Trae AI pricing details and edition comparison](https://www.g2.com/es/products/trae-ai/reviews?section=pricing&secure%5Bexpires_at%5D=2026-05-29+12%3A59%3A44+-0500&secure%5Bsession_id%5D=91fb7e0b-44cb-41d9-85d9-d3264c347ef3&secure%5Btoken%5D=13826788aec48ca952a236f055468c50786f91a49b36818c03bdb244a9fe46d8&format=llm_user) ## Trae AI Integrations - [Supabase](https://www.g2.com/es/products/supabase-supabase/reviews) ## Trae AI Features **Funcionalidad** - Precisión - Procesamiento de entradas - Interfaz - Calidad del código **Apoyo** - Comunidad - Programa de actualización - Documentación **Generación de código AI - AI Agente** - Integración entre sistemas - Aprendizaje Adaptativo - Interacción en Lenguaje Natural - Asistencia proactiva - Toma de decisiones ## Top Trae AI Alternatives - [ChatGPT](https://www.g2.com/es/products/chatgpt/reviews) - 4.6/5.0 (2,489 reviews) - [GitLab](https://www.g2.com/es/products/gitlab/reviews) - 4.5/5.0 (875 reviews) - [TESS AI](https://www.g2.com/es/products/tess-ai/reviews) - 4.7/5.0 (384 reviews)