¿Te worried about slow, inaccurate, or privacy-risky AI completions in the editor? This guide delivers a reproducible, step-by-step setup for a free autocomplete plugin in VS Code, plus configuration, performance tuning, customization, and troubleshooting to make completions reliable for real projects.
Key takeaways: what to know in 1 minute
- A free autocomplete plugin can be installed and fully configured in under 15 minutes for VS Code using the marketplace or a VSIX.
- Privacy matters: choose local or privacy-first providers, and disable cloud telemetry if needed.
- Performance gains come from caching, file exclusion, and lightweight models for large repos.
- Customization (snippets, templates, prompt hints) improves relevance more than increasing model size.
- Troubleshooting includes checking extension conflicts, logs, and adjusting language server settings.
Step-by-step install for free autocomplete plugin in VS Code
Step 1: choose a free plugin that matches needs
- Compare options: Codeium, Cursor, Amazon CodeWhisperer (free tier), and local adapters like an LSP tied to a local LLM. Choose based on privacy, offline capability, language support, and editor integration.
Step 2: install from VS Code marketplace
- Open VS Code.
- Press Ctrl+P (Cmd+P on macOS) and type: ext install . or open Extensions view and search by name.
- Example: to install Codeium use the marketplace entry or run: ext install codeium.codeium in the quick open box.
- Reload the editor when prompted.
Example marketplace link: Codeium - VS Code Marketplace
Step 3: verify core functionality
- Open a supported language file (JavaScript, Python, TypeScript, etc.).
- Start typing a function or import line. The plugin should surface inline suggestions or a suggestion box.
- If no suggestions appear, open the extension pane, find the plugin, and check Enabled and Workspace Allowed toggles.
Step 4: install from VSIX (offline or air-gapped)
- Download the VSIX from the vendor site or GitHub release.
- In VS Code, open the Extensions view menu and choose "Install from VSIX...".
- Select the file, then reload. This method avoids marketplace access and preserves a specific version for reproducibility.
Step 5: apply recommended settings (copy-ready)
Add the following to workspace .vscode/settings.json to enable completions, tune trigger behaviors, and reduce noise:
{
"editor.suggest.snippetsPreventQuickSuggestions": false,
"editor.quickSuggestions": { "other": true, "comments": false, "strings": false },
"editor.suggestOnTriggerCharacters": true,
"editor.inlineSuggest.enabled": true,
"editor.suggestSelection": "recentlyUsedByPrefix",
"files.exclude": { "**/.git": true, "**/node_modules": true },
"search.exclude": { "**/node_modules": true }
}
- Save the file and reload the workspace.
- Use editor.inlineSuggest to control inline completions. Set to true to display inline predictions, false to use the suggestion list only.
- Adjust editor.suggestSelection to prefer recent completions or top-ranked suggestions.
- Set editor.quickSuggestions to disable suggestions in comments and strings to reduce false positives.
- Most free providers include telemetry toggles in settings. For Codeium and Cursor, find settings under the extension section and disable data sharing or set it to "local only" if available.
- For AWS CodeWhisperer, follow the provider console to opt out of usage data sharing.
Example toggles (workspace settings):
{
"codeium.disableTelemetry": true,
"cursor.privacyMode": "local",
"aws.codeWhisperer.telemetryEnabled": false
}
Choose between cloud and local inference
- Cloud: lower local CPU usage and usually better accuracy but sends code to provider servers—review terms.
- Local: uses a local LLM (via docker + llama.cpp or similar); preferred for sensitive code. Requires hardware and extra setup but ensures code stays on device.
Recommended link for local inference approaches: llama.cpp on GitHub
Best free autocomplete plugins: comparison and recommendations
| Plugin |
Offline support |
Privacy focus |
Languages |
VS Code extension |
Notes |
| Codeium |
No (cloud) |
High (opt-out) |
Many |
Yes |
Fast, free tier with strong language coverage |
| Cursor |
Partial |
Medium |
Many |
Yes |
Offers local agents in paid tiers; free tier limited |
| Tabnine (free) |
Limited |
Medium |
Many |
Yes |
Offers local model for paid plans; free cloud suggestions |
| Amazon CodeWhisperer |
No (cloud) |
High (AWS controls) |
Many |
Yes |
Integrates with AWS credentialed accounts; free tier available |
| Local LSP + llama.cpp |
Yes |
Very high |
Depends on model |
Via community extension |
Full privacy; requires setup and resources |
- Best pick for most freelancers and creators: Codeium for quick setup and quality.
- Best pick for privacy-focused developers: Local LSP with llama.cpp or a self-hosted model.
- Best pick for those deep in AWS: CodeWhisperer with AWS integration.
Recommendations for the primary audience
- Freelancers and content creators: pick the extension that balances privacy, cost, and language support. Use workspace-level opt-out to avoid uploading client code.
- Entrepreneurs: test multiple plugins on a benchmark repository and measure accuracy and latency before adopting.
Reduce scope with exclusions for step by step setup free autocomplete plugin
- Exclude large folders (node_modules, .venv, dist, build) in workspace settings. This reduces indexing time and suggestion noise.
{
"files.exclude": { "**/node_modules": true, "**/dist": true, "**/.venv": true },
"search.exclude": { "**/node_modules": true }
}
Use lightweight language servers and caching
- Prefer lightweight LSPs for certain languages (e.g., pyright for Python, tsserver for TypeScript).
- Enable extension caching if supported (some plugins have a local cache toggle).
Adjust suggestion triggers and throttling
- Increase the acceptance threshold by setting plugin-specific parameters to avoid excessive model calls.
- Use debounce timers where available to batch requests.
Benchmark latency and accuracy
- Simple test: open the same file, type a 3-line function, and measure time from keystroke to suggestion (milliseconds).
- Track accuracy: accept rate over 100 suggestions. Tools: stopwatch or simple logging in the extension logs.
Customize completions: snippets, templates, and AI prompts
Create workspace snippets to bias suggestions
- Create a snippets file under .vscode/snippets/.json.
- Example Python snippet to standardize docstring templates:
{
"Function with docstring": {
"prefix": "docfunc",
"body": [
"def ${1:func_name}(${2:args}):",
"/"/"/"",
"${3:Short description}",
"/"/"/"",
"${0:pass}"
],
"description": "Function with docstring template"
}
}
- Snippets often outperform generic AI suggestions for repetitive structures.
- Insert short comment hints above functions to help AI produce the intended result. For example:
# Implement a fast O(n) solver for merging sorted lists
- Some plugins accept explicit prompt fields when invoking the model; configure them in the extension settings.
Templates and file scaffolding
- Use project scaffolding tools (Yeoman, Cookiecutter) to predefine common file templates so the plugin suggests within an expected structure.
Troubleshoot common setup issues and compatibility problems
Issue: no inline suggestions after install
- Check the extension is enabled and allowed in the workspace.
- Confirm editor.inlineSuggest is true.
- Look at the extension output logs: open View → Output and choose the extension log from the dropdown.
- Restart VS Code with all extensions disabled except the autocomplete plugin to rule out conflicts.
Issue: suggestions are low quality or irrelevant
- Lower model temperature if configurable or choose a higher-quality inference endpoint.
- Provide more context in the file (function signatures, docstrings) and use workspace snippets.
- Check language detection: ensure the file type is recognized (Status bar displays language).
Issue: extension causes high CPU or memory
- Disable inline suggestions and rely on suggestion list.
- Exclude large folders and limit files watched by the plugin.
- Consider switching to a lighter provider or local model with reduced parameter count.
Compatibility checklist for step by step setup free autocomplete plugin
- VS Code version: keep within the supported range (check extension docs).
- Node/py environment conflicts: ensure language servers run on supported runtimes.
- Firewall/proxy: allow connections if using cloud inference, or use VSIX for air-gapped environments.
Quick setup flow for autocomplete
1️⃣
Install plugin
Marketplace or VSIX
2️⃣
Apply workspace settings
settings.json tweaks
3️⃣
Set privacy
Disable telemetry / choose local
4️⃣
Optimize
Exclude large folders, enable caching
✅
Use confidently
Productive, private, and fast completions
Advantages, risks and common mistakes
✅ Benefits and when to apply
- Faster development for repetitive tasks and boilerplate generation.
- Consistency by using standardized snippets and templates.
- Lower cost: free plugins and local models avoid subscription fees.
⚠️ Errors to avoid and risks
- Uploading sensitive code to cloud providers without review.
- Overreliance on AI suggestions without tests and code review.
- Ignoring performance: failing to exclude heavy folders can degrade both editor responsiveness and suggestion quality.
Frequently asked questions
What is the fastest way to install a free autocomplete plugin in VS Code?
Install from the VS Code marketplace, enable the extension, and add recommended workspace settings. The process takes under 15 minutes for most plugins.
Can a free autocomplete plugin run entirely offline?
Yes, when paired with a local LLM and an LSP adapter, the autocomplete flow can run offline. This requires additional setup and hardware resources.
How to keep code private when using cloud-based suggestions?
Disable telemetry in extension settings, use workspace-level opt-outs, and avoid sending proprietary context to cloud endpoints. For full privacy, prefer local models.
Why are suggestions slow in large repositories?
Because the plugin may index many files or make frequent inference calls. Excluding folders, enabling caching, and reducing trigger frequency fixes latency.
How to make AI suggestions follow project style guidelines?
Use workspace snippets, docstrings, and small prompt hints in comments. Configure style-focused linters and formatters so AI suggestions match the codebase.
What logs help diagnose autocomplete issues?
Open View → Output and select the extension output. Also check VS Code developer tools (Help → Toggle Developer Tools) for errors.
Next steps
- Install the chosen free plugin and apply the workspace settings JSON provided above.
- Run a 10-minute benchmark on a typical project: measure latency and acceptance rate.
- If privacy is required, set up a local LSP with a small local model and test reproducibility.
