NEO
FreemiumAutonomous AI engineering agent for ML, LLM optimization, and AI agent building
By Tanmay Verma, Founder · Last verified 06 Jul 2026
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Added 8d ago
95/100Safe BetIn short
NEO — Autonomous AI engineering agent for ML, LLM optimization, and AI agent building. Best for ML engineers automating model training and evaluation, Researchers running large-scale LLM benchmarks, Data scientists building RAG pipelines and fine-tuning models. Free to start; paid plans from $29/mo.
Is NEO actually worth it?
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Editorial Verdict
Best for
ML engineers automating model training and evaluationResearchers running large-scale LLM benchmarksData scientists building RAG pipelines and fine-tuning modelsTeams developing AI agents and agent swarmsOrganizations standardizing prompt optimization workflows
Not ideal for
Complete beginners without ML backgroundProjects requiring no-code drag-and-drop interfacesTeams needing real-time latency-sensitive deployment without cloud computeOne-off quick scripts where setup overhead outweighs benefitUsers who prefer full manual control over iterative coding
NEO is a serious tool for ML engineers who want to offload repetitive experimentation and benchmarking. Its credit-based pricing and GPU sandbox make it practical for long-running tasks. Beginners will face a steep curve—this is built for pros who already know ML.
Compare with: NEO vs Bito, NEO vs Poolside AI, NEO vs Dash0
Last verified: July 2026
What's new in NEO
Checked 8 days agoAcross the latest 6 updates: 6 feature updates.
FeatureBlog·14 days agoNewest
NEO Evaluated Ornith-1.0-35B: Terminal Safety and Coding Skill Ceiling, Built Autonomously
NEO autonomously evaluated Ornith-1.0-35B: 100/100 terminal safety, Level 6/15 coding skill ceiling.
FeatureBlog·15 days ago
GLM 5.2 Built TrackLab: Browser Computer Vision with NEO BYOK
GLM 5.2 built TrackLab (browser CV studio) end-to-end through NEO BYOK, same agent workflow, different model.
FeatureBlog·18 days ago
GLM 5.2 vs Kimi K2.6: Same Agent Workflow, Same Citation Bug, Opposite Failures
NEO BYOK comparison: Kimi K2.6 won capability, GLM 5.2 craftsmanship; both failed Article 75 differently.
FeatureBlog·19 days ago
Kokoro 82M vs Supertonic 3 vs Inflect-Nano-v1: A Real CPU TTS Benchmark
CPU-only TTS benchmark of 3 models across RTF, latency, throughput, and MOS; built end-to-end with Neo.
FeatureBlog·24 days ago
Cut Your Claude Code Limit Hits in Half: Pair It with Neo over MCP
CPU speech-to-speech pipeline: Claude Code orchestrated, Neo over MCP handled research and bug hunt, halved limit hits.
FeatureBlog·24 days ago
From Synthetic Data Generation to Dataset Engineering: What Changed When We Added Neo MCP
874 real-world agent failure records from governed pipeline: one prompt, adversarial verification, no synthetic padding.
Viability Score
95/100
Safe BetHow likely is NEO to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
100
funding runway
80
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Autonomous ML engineering from single task prompt
- Multi-step reasoning with self-correction and iteration
- LLM evaluation and benchmarking across models
- Dual-LLM automatic prompt optimization loop
- Fine-tuning of LLMs and ML models
- RAG pipeline building and optimization
- Agent swarm creation and coordination
- Integration with VS Code, Cursor, Claude Code, OpenVSX
- Bring your own LLM (BYOK) support
- GPU sandbox for running experiments on your compute
- Versioned artifact management and reporting
- Synthetic data generation and dataset engineering
- Lite mode and Pro mode for different compute profiles
- Credit-based usage metering (per month)
- Can run autonomously for days
About NEO
FreemiumIntermediateNo APIDesktop · Plugin · CLI
NEO is an autonomous AI engineering agent that automates machine learning and AI system development from a single task prompt. It is designed for ML engineers, researchers, and teams building AI agents, RAG pipelines, LLM evaluations, and fine-tuning workflows. NEO runs in VS Code, Cursor, or Claude Code, and can operate for days unsupervised—writing code, running experiments, debugging failures, and producing versioned reports. Key features include multi-step reasoning with self-correction, a dual-LLM prompt optimization loop, agent swarm coordination, and bring-your-own-LLM support. NEO scored 34.2% on MLE-bench (top-ranked in August 2025). Unlike manual scripting or simpler automation tools, NEO handles the full engineering lifecycle and supports long-running experimentation on your own GPU cloud. Recent updates show NEO being used for real-world evaluations (e.g., Ornith-1.0-35B terminal safety) and CPU TTS benchmarking, as well as pairing with Claude Code over MCP to reduce API limits. NEO offers four paid tiers (Starter, Value, Pro, Enterprise) with a free trial, using a per-month credit system. While powerful for experienced practitioners, it has a learning curve and is less suited for non-ML beginners or those needing no-code drag-and-drop interfaces.
Behind the Verdict
NEO is one of the most capable autonomous AI engineering agents we've seen, especially for teams that need to run hundreds of experiments without manual supervision. The multi-step reasoning and self-correction loop genuinely save time on model selection and hyperparameter tuning. We'd reach for this when we have a well-defined ML task—like fine-tuning an LLM or building a RAG pipeline—and want to let an agent iterate while we focus on higher-level design. Where it bites: the credit system can get expensive if you're running heavy jobs daily, and the learning curve is real if you're new to ML engineering. Compared to simpler automation tools or manual scripting, NEO shines on complex, multi-step workflows that benefit from autonomous iteration. That said, if you need real-time latency-sensitive deployment or just want a quick one-off script, the setup overhead isn't worth it. Real-world usage caveat: recent reports note that NEO works well with MCP for pairing with Claude Code, and users have shared workarounds for MacBook cursor lag when recording. Overall, NEO is a strong choice for ML professionals who value automation depth over simplicity.
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Use Cases
- Automate fine-tuning of a large language model on custom dataset with iterative hyperparameter optimization.
- Build and evaluate a multi-agent trading system with async coordination over real market data.
- Benchmark multiple LLMs across coding, reasoning, and structured output tasks using a unified framework.
- Synthesize and govern a high-quality dataset for agent failure analysis with provenance and validation.
- Optimize prompts for a production chatbot using dual-LLM loop and synthetic data batches.
- Deploy an end-to-end speech-to-speech pipeline by delegating research and implementation to NEO over MCP.
Limitations
- Credits can be consumed quickly on complex experiments.
- Lite mode restricts access to certain features.
- Bring your own compute requires setup and may incur additional costs.
- Free tier limited to 29K credits with no dedicated compute.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
$348
Over 12 months
Effective monthly
$29
Billed monthly
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Integrations
Resources & Guides
Frequently Asked Questions
Tools that pair well with NEO
Common stack mates teams adopt alongside NEO, with the specific reason each pairing earns its keep.
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