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CodeSnippets

CodeSnippets combines a code-snippet library with AI conversations for developers and software teams, using saved snippets and an indexed local codebase as programming context.

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Free
Paid plans from
$7.5
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Free

Basic

Free

Pro

$7.5

Enterprise

$12.5

Official pricing

Official sources

CodeSnippets official websiteCodeSnippets pricing

Verified: September 8, 2026

DevPrice organizes public information and does not sell CodeSnippets subscriptions. Prices and availability are determined by CodeSnippets.

What is CodeSnippets?

CodeSnippets combines a code-snippet library with AI conversations for developers and software teams, using saved snippets and an indexed local codebase as programming context. Developers can ask what code does, investigate bugs, add comments, or discuss new features, then save and share useful snippets with collaborators. The official product page lists Mac and Windows desktop applications alongside VS Code and team-oriented capabilities.

The application supports conversations with open- and closed-source models, using OpenRouter for online models or Ollama for local ones. Codebase indexing and vectorization supply project context to the chat, while member management and usage views support teams maintaining a shared snippet collection. Online context generation requires an OpenRouter or OpenAI API key, and switching models within a conversation depends on the selected model having a context window large enough for the existing chat.

Key features of CodeSnippets

  • Code-context conversations

    It brings saved snippets and an indexed codebase into AI conversations for understanding implementations, discussing features, locating bugs, or adding comments to existing code.

  • Snippet library collaboration

    It centralizes the generation, saving, editing, and sharing of code snippets, with organization-member controls for teams maintaining reusable development material.

  • Multi-model chat sessions

    A conversation can use open- or closed-source models and choose between online services and local Ollama models; model switching remains constrained by the current context length.

  • Local codebase indexing

    It builds project context through indexing, vectorization, and computed embeddings; the documented online workflow requires an OpenRouter or OpenAI API key.