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NeoAn AI-powered autonomous machine learning engineer capable of developing, deploying, and maintaining machine learning models without human intervention.

4.4 (5)
Daniel NikulshynReviewed by Daniel Nikulshyn·Updated June 2026

Overview

Neo is an AI-powered engineering agent that helps automate the process of developing, deploying, and maintaining machine learning models. It works with popular tooling like Claude Code, Cursor, and VS Code IDE, and can perform tasks such as code generation, experiment management, debugging, and model evaluation. Neo uses multi-step reasoning and a GPU sandbox to perform iterative ML experimentation, running hundreds of experiments and automatically selecting the best model. One of the key features of Neo is its ability to learn and adapt to the user's workflow, and it can handle tasks such as fine-tuning models, building benchmarks, and performing prompt optimization. Neo also comes with a user-friendly interface that allows users to guide its exploration of models and approaches, providing context and expertise to accelerate tasks. The company claims that Neo has proven performance on real benchmarks, and it has won competitions such as MLEBench. Some of the use cases that Neo can handle include model evals, prompt tests, RAG pipelines, dataset prep, experiments, and reports. Neo's capabilities can be broken down into a 4-step workflow: describing the task, adding context, running experiments, and steering or testing the results. One of the advantages of using Neo is that it can automate the engineering loop and keep iterating until the task is done, freeing up ML engineers to focus on higher-level tasks. Neo can also make use of SOTA models and software to perform its tasks.

Key features

  • Code generation
  • Experiment management
  • Debugging
  • Model evaluation
  • Fine-tuning models
  • Prompt optimization

Pricing

Model
Freemium
Category
AI Agents
Rating
4.4 / 5 (5)

Use cases

Automated ML Model Development

Build machine learning models end-to-end without manual coding, letting Neo handle data preparation, model selection, and training autonomously.

Hands-Off Model Deployment

Deploy trained machine learning models into production environments automatically, reducing the need for dedicated ML engineering resources.

Ongoing Model Maintenance

Continuously monitor and maintain deployed ML models so they keep performing reliably over time without human intervention.

Pros & Cons

Pros

  • Automates the engineering loop
  • Can handle complex ML tasks
  • Multi-step reasoning capabilities
  • User-friendly interface
  • Proven performance on real benchmarks

Cons

  • Dependence on GPU resources
  • Potential for errors in automated code generation
  • User may need to provide significant context and expertise
  • Cost may be a barrier for some users

Reviews

4.4

Average from 5 ratings.

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Wei Chen

May 6, 2026

Years in this space

I've evaluated a lot of these over the years. What stands out here is the automation — handled better than most — and support is responsive. A few rough edges remain is my one real gripe. Worth the time if this is your use case.

D

Diego Fernández

Mar 4, 2026

Use it every day

Honestly didn't expect to like it this much. The automation is exactly what I needed, and it is genuinely easy to set up. I do wish a few rough edges remain, but I reach for it almost every day now and it just clicks.

K

Kwame Mensah

Jan 7, 2026

Years in this space

I've evaluated a lot of these over the years. What stands out here is the core workflow — handled better than most — and it saves real time. Pricing gets steep at scale is my one real gripe. Worth the time if this is your use case.

B

Beatriz Costa

Nov 23, 2025

Use it every day

Honestly didn't expect to like it this much. The dashboard is exactly what I needed, and support is responsive. but I reach for it almost every day now and it just clicks.

A

Ahmed Saleh

Jun 5, 2025

Does the job

Pretty happy overall. The onboarding just works and it is genuinely easy to set up. Pricing gets steep at scale can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

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