Show HN: Enfiy Code – Universal AI coding assistant with multi-provider support

2 haya21_8 2 7/26/2025, 9:51:08 AM github.com ↗

Hi HN! I built Enfiy Code, a command-line AI coding assistant that works with multiple AI providers (Anthropic Claude, OpenAI GPT, Google Gemini, Ollama for local models, etc.) from a single interface.

Key features:

• Switch between AI providers seamlessly - ompare responses from different models

• Works with large codebases using extended context support

• Supports both cloud AI (powerful) and local AI (private) via Ollama • Integrates external tools through MCP (Model Context Protocol)

• Generate apps from PDFs/sketches using multimodal AI

• Auto-handles complex tasks like PR reviews and git operations

The CLI is built with TypeScript/Node.js and is fully open source (Apache 2.0). You can try it without installing: `npx @enfiy/enfiy-code`

What makes it different from other AI coding tools is the provider flexibility - you're not locked into one AI service, and you can run everything locally if privacy is a concern.

Would love feedback from the HN community, especially on the multi-provider approach and MCP integrations!

GitHub: https://github.com/enfiy/enfiy-code

Comments (2)

narmiouh · 17h ago

Looks nice - How does the extended context support work?

haya21_8 · 17h ago

Thanks for asking! Here's how the extended context support works:

Context Management Features:

• Dynamic File Selection - Instead of loading entire files, we intelligently select relevant code sections based on the query

• Automatic Conversation Summarization - When approaching context limits, conversations are automatically summarized to preserve essential information while staying within bounds

• Provider-Specific Optimization - Automatically adjusts to each model's context window: Claude (200K), GPT-4 (128K), Gemini (2M tokens)

• ENFIY.md Memory Files - Hierarchical context files (global, project, component-level) that provide persistent context across sessions

How it works in practice:

# Even with massive codebases

enfiy "find performance issues in this 1000-file React poject"

# → Analyzes only relevant files, dynamically loading what's needed

The system monitors token usage and triggers summarization when approaching 95% of the model's limit. For models like Gemini with 2M context, you can process entire large projects. The key is leveraging each provider's strengths while working within their constraints.

More technical details in our GitHub README!