Neo on VS Code
Quick TL;DR
Neo is a local-first AI engineering agent for code, data, and ML workflows.
The VS Code extension lets you run multi-step tasks, debug environments, and manage data locally without uploading your code.
Install the extension, open a project folder, and start interacting with Neo from the sidebar or terminal in under 5 minutes.
What is the Neo Extension?
Local-first execution
All code and data stay on your machine with no external uploads required.
Encrypted vault
API keys and credentials stored locally, encrypted at rest.
Autonomous workflows
Automatically installs dependencies, handles errors, and self corrects code.
Architecture
Source code and structure
Local AI agent orchestrator
Executes scripts locally
AWS S3, Weights and Biases, Hugging Face
Cloud APIs are optional and only used when explicitly configured.
Platform vs VS Code Extension
| Platform | VS Code Extension | |
|---|---|---|
| Setup | No setup required, just upload files | Install extension, open a project folder, start Neo |
| Data | 50MB file limit or cloud storage | Connect to S3 data buckets, GitHub code repositories, and local datasets directly |
| Security | Upload to cloud environment | All operations local, credentials stay encrypted on device |
| Best For | Quick prototyping and experimentation | Iterative development on your codebase for AI, ML, and data science tasks |
AI and ML Tools and Integrations
Neo’s VS Code extension provides one-click integration with various third-party AI, ML, and data tools, enabling developers to build production-grade pipelines.
AWS S3
Load datasets and model checkpoints locally; configure API keys in vault.
Weights & Biases
Track experiments, logs, and artifacts automatically from VS Code.
Hugging Face
Access model hub locally; pull and push models securely.
Kaggle
Download datasets and competition files directly into project workspace.
More integrations coming soon
Additional tools and platforms in development.
Multi-Workspace Support
Isolated contexts
Each VS Code workspace runs a separate Neo instance, preventing interference.
No context leakage
Credentials, secrets, and project state stay workspace specific.
Parallel execution
Run multiple workspaces and tasks simultaneously without collisions.
Use Cases
Data pipelines
Automate fetching from S3, validating CSVs, and loading into databases.
Experiment tracking
Train ML models and log metrics to Weights and Biases automatically.
Environment fixes
Detect and fix dependency, Python version, and CUDA configuration issues in projects.
Model deployment
Package models and push to cloud registries or local containers.
Security
Local-only execution
Code, data, and credentials never leave your machine.
Training opt-out
Codebase is never used for AI model training or analytics.
Full control
Interrupt, review, and audit all automated actions.