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twinny

The most no-nonsense, locally or API-hosted AI code completion plugin for Visual Studio Code - like GitHub Copilot but 100% free.

84
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392 views
0 reviews
Listed Mar 2026
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Free
Listed on SEOGANT
+12%
MoM Growth
-
Active Users
-
Churn Rate
8:24
EXPERT REVIEW

Expert Video Review by SEOGANT · March 2026

Distribution Score: 84/100 What is this?

SEO & Organic Traffic
92
Affiliate Program
86
Product-Market Fit
88
Community & Social
74
Retention / Churn
87

What is twinny?

Twinny is an open-source AI code completion and assistance plugin for Visual Studio Code, designed to work with both locally-hosted models via Ollama and API-hosted models, giving developers full control over their AI coding assistant without mandatory cloud subscriptions or data transmission to third-party services.

It provides inline code completion (filling code as you type), conversational code assistance in a side panel, code explanation, and test generation covering the core use cases of commercial AI coding tools within a privacy-preserving architecture.

The plugin connects to any OpenAI-compatible API endpoint, making it compatible with local model servers (Ollama, LM Studio, Jan), self-hosted deployments (vLLM, Xinference), and cloud APIs (OpenAI, Anthropic, Mistral).

Model selection, context window size, temperature, and other generation parameters are configurable per use case, allowing users to balance speed and quality using a fast local model for inline completion and a more capable API model for complex code analysis tasks.

Twinny is open-source under the MIT license and is the most fully-featured free alternative to GitHub Copilot and Cursor in the VS Code extension ecosystem.

It is used by developers who want Copilot-level functionality without the subscription cost, engineers in organizations with strict data policies that prohibit sending code to cloud AI services, and practitioners who want to experiment with different models for code generation without being locked into a single provider's quality and pricing.

Who is twinny for?

Developers who want fully local, privacy-first AI code completion in VS Code without sending code to cloud APIs
Engineers working with sensitive codebases who need AI assistance that never leaves their machine or network
Cost-conscious developers who want GitHub Copilot-style completions using free, local models via Ollama
Privacy advocates and security teams who need AI coding assistance with zero data exposure to external services

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Frequently Asked Questions

What is twinny?
twinny is a no-nonsense AI code completion plugin for Visual Studio Code that works with locally or API-hosted models. It provides GitHub Copilot-style completions using Ollama or any OpenAI-compatible backend — with full privacy since code never leaves your machine.
What models does twinny support?
twinny works with any model via Ollama (CodeLlama, DeepSeek Coder, Qwen2.5-Coder, Starcoder2, etc.) or any OpenAI-compatible API. You choose the model based on your hardware and quality requirements.
How does twinny compare to Continue.dev?
Both are VS Code plugins for local AI coding assistance. twinny focuses on lightweight, fast inline completions. Continue.dev offers a broader feature set including chat, codebase indexing, and command execution. twinny is simpler and more focused.
What hardware do I need?
You need enough RAM and VRAM to run your chosen local model via Ollama. Smaller models (3B-7B) run well on 8-16GB RAM with a modern GPU or Apple Silicon. Larger models need more VRAM.
Is twinny free?
Yes — twinny is completely free and open source. Using local models via Ollama is also free. If you configure an API backend (OpenAI, Anthropic), those API costs apply.

Product Details

Listed on SEOGANTFree
MRR Growth+12% / mo
Active Users-+
Churn Rate-
ListedMar 2026

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twinny Team
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"Twinny is an open-source AI code completion and assistance plugin for Visual Studio Code, designed to work with both locally-hosted models via Ollama and API-hosted models, giving developers full control over their AI coding assistant…"
twinny Score: 84
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