AI agent for ML training, LLM fine-tuning, RAG pipelines & production AI systems.
Used by ML engineers & researchers worldwide · Runs on your GPU cloud
NEO helps you offload the engineering work behind building, testing, and improving AI systems.
It can research approaches, write and edit code, run experiments, debug failures, benchmark models, evaluate outputs, and produce reports, all from a single task prompt.
Instead of spending hours stitching together scripts, logs, evals, and manual fixes, you give NEO the goal. Its agent system collaborates with you on planning, execution, result evals, and keeps iterating until the task is done.
And More...
Ask Neo to fix AI model training pipeline
Add new AI features in your brownfield projects
Analyze data leakage in your training pipeline
Neo uses multi-step reasoning with its extensive knowledge base and GPU sandbox to perform iterative ML experimentation — running 100s of experiments and automatically selecting the best model.
Take control via our interactive chat interface. Guide Neo's exploration of models and approaches, providing context and expertise to accelerate tasks.
Unlock Neo's full potential with multi-step reasoning. Neo proactively explores multiple approaches, assesses outcomes, and evaluates risks to find the most effective solution.
34.2%
#1 Score on MLEBench in Aug 2025
75
Competitions entered
#1
vs RD-Agent & AIDE on MLE-bench
10×
Faster ML development
Install the extension in VS Code or Cursor and give NEO your first ML engineering task.
Use cases
NEO helps with the AI engineering work behind modern AI products: model evals, prompt tests, RAG pipelines, dataset prep, experiments, and reports. Share the goal and context, then review, steer, and use the final outputs.
State the outcome in natural language. Fine-tune a model, ship an agent, build a benchmark — no boilerplate prompt engineering.
Point NEO at your repo, data, connectors, and constraints so the plan fits the hardware and conventions you already run.
NEO writes the code, runs long experiments, evaluates, and hands back versioned artifacts for your review.
Replay on real scenarios, ask for sweeps, harden failure modes, and promote the winning run to staging when you are ready.
Evaluate & Benchmark
An async LLM benchmarking platform that evaluates models from OpenAI, Anthropic, Google, and more across 150+ real-world tasks covering coding, reasoning, structured output, and long-context retrieval.
Evaluate & Benchmark
Closed-loop system: an optimizer LLM writes prompts and reads failure summaries, a target LLM runs batches against synthetic data, and a JSON ledger tracks every iteration until scores converge.
Build Agents
10 specialized agents coordinating over async message bus: +4.62% returns across 250 days of S&P 500 data.
Install for Cursor