{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-voideditor--void","slug":"voideditor--void","name":"void","type":"repo","url":"https://voideditor.com","page_url":"https://unfragile.ai/voideditor--void","categories":["chatbots-assistants"],"tags":["chatgpt","claude","copilot","cursor","developer-tools","editor","llm","open-source","openai","visual-studio-code","vscode","vscode-extension"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-voideditor--void__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.message.dispatch.with.provider.abstraction.layer","name":"multi-provider llm message dispatch with provider abstraction layer","description":"Void implements a provider-agnostic LLM message pipeline that abstracts OpenAI, Anthropic, Gemini, Ollama, Mistral, and Groq behind a unified interface. Messages flow through a dispatch system that handles provider-specific formatting, token counting, and response parsing without exposing provider details to UI components. The LLM Message Service converts between Void's internal message format and each provider's API contract, enabling seamless provider switching at runtime via settings.","intents":["I want to support multiple LLM providers without rewriting chat/edit logic for each one","I need to switch between OpenAI, Claude, and local Ollama models without restarting the editor","I want to add a new LLM provider without touching the chat UI code"],"best_for":["teams building multi-provider AI coding assistants","developers wanting provider flexibility without vendor lock-in","organizations needing to swap between cloud and self-hosted models"],"limitations":["Provider-specific features (e.g., Claude's extended thinking, OpenAI's vision) require conditional logic in message formatting","Token counting differs per provider; Void uses provider-specific tokenizers which adds ~50ms overhead per message","Streaming response handling varies by provider; some providers have slower streaming latency than others"],"requires":["API keys for at least one provider (OpenAI, Anthropic, Gemini, Mistral, Groq) OR local Ollama instance","TypeScript runtime (Node.js 18+)","VS Code 1.80+ or Void fork of VS Code"],"input_types":["chat messages (text with optional code context)","code snippets for editing","system prompts with model capabilities metadata"],"output_types":["streamed text responses","structured tool calls (function definitions + arguments)","diff hunks for code edits"],"categories":["tool-use-integration","llm-provider-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-voideditor--void__cap_1","uri":"capability://text.generation.language.sidebar.chat.with.persistent.thread.management.and.context.accumulation","name":"sidebar chat with persistent thread management and context accumulation","description":"Void provides a sidebar chat interface that maintains conversation threads with full message history, allowing users to build context across multiple turns. Each thread is persisted in the settings service and can be resumed later. The Chat Thread Service orchestrates message history, context window management, and thread lifecycle (create, append, delete, resume). Context from the current file, selection, or entire workspace can be injected into messages via a context injection system that prepares code snippets for LLM consumption.","intents":["I want to have a multi-turn conversation with the AI about my code without losing context","I need to reference previous messages in the same conversation thread","I want to save a conversation thread and resume it later with full history intact"],"best_for":["developers iterating on code with the AI over multiple turns","teams using AI for code review and architectural discussions","solo developers building features with AI pair programming"],"limitations":["Thread persistence is local to the Void installation; threads are not synced across machines","Context window is bounded by the selected LLM's max tokens; large codebases may require selective file inclusion","No built-in thread search or filtering; users must manually navigate thread list","Thread history is stored in VS Code settings; no database backend for large-scale thread management"],"requires":["Void editor or VS Code with Void extension","Active LLM provider configuration (API key or local Ollama)","File system access for workspace context injection"],"input_types":["user text messages","code selections from editor","file paths for context injection","workspace directory for codebase context"],"output_types":["streamed chat responses","message history (array of user/assistant turns)","thread metadata (creation time, model used, token count)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-voideditor--void__cap_10","uri":"capability://memory.knowledge.codebase.indexing.and.workspace.context.extraction.for.llm.consumption","name":"codebase indexing and workspace context extraction for llm consumption","description":"Void extracts workspace context (file structure, code snippets, dependencies) and prepares it for LLM consumption. The context extraction system analyzes the current file, selected code, and workspace structure, then formats relevant code snippets for inclusion in LLM messages. This enables the LLM to understand the broader codebase context without requiring users to manually copy-paste code. The system respects .gitignore and other exclusion rules to avoid indexing irrelevant files.","intents":["I want the AI to understand my entire codebase, not just the current file","I need the AI to reference related files when suggesting code changes","I want to exclude certain directories (node_modules, build artifacts) from context"],"best_for":["developers working on large codebases with multiple files","teams using AI for cross-file refactoring and architectural changes","organizations with large monorepos requiring selective context inclusion"],"limitations":["Full codebase indexing is expensive; Void uses heuristics to select relevant files rather than indexing everything","Context window is bounded by the LLM's max tokens; large codebases may exceed the token limit","No persistent index; context is extracted on-demand, adding latency to each request",".gitignore rules are respected, but custom exclusion patterns require manual configuration"],"requires":["Void editor or VS Code with Void extension","File system access to the workspace","LLM provider with sufficient context window (8k+ tokens recommended)"],"input_types":["workspace directory path","current file path","selected code range",".gitignore rules"],"output_types":["formatted code snippets for LLM","file structure metadata","dependency information"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-voideditor--void__cap_11","uri":"capability://automation.workflow.remote.development.support.with.ssh.and.wsl.integration","name":"remote development support with ssh and wsl integration","description":"Void extends VS Code's remote development capabilities with dedicated extensions for SSH and WSL (Windows Subsystem for Linux). The open-remote-ssh and open-remote-wsl extensions enable users to run Void on remote machines or WSL environments, with the LLM integration working seamlessly across the remote connection. The server setup process (serverSetup.ts) configures the remote environment and establishes the connection, allowing users to develop on remote machines while using local LLM providers or cloud-based APIs.","intents":["I want to use Void on a remote machine via SSH","I need to develop in WSL while using Void's AI features","I want to run Void on a server and access it from my local machine"],"best_for":["developers working on remote servers or cloud instances","Windows developers using WSL for Linux development","teams with centralized development environments"],"limitations":["Remote connection latency may impact LLM response times if the provider is local to the remote machine","SSH key management is required; password-based authentication is not supported","WSL integration is Windows-only; no equivalent for macOS","Remote server must have Node.js and other dependencies installed for Void to run"],"requires":["Void editor or VS Code with Void extension","SSH access to remote machine (with public key authentication) OR WSL environment","Node.js 18+ on the remote machine","Network connectivity to the remote machine"],"input_types":["SSH connection details (host, port, username)","WSL distribution name","remote file paths"],"output_types":["remote editor session","LLM responses from remote context","file changes on remote machine"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-voideditor--void__cap_12","uri":"capability://automation.workflow.update.service.and.release.management.with.automatic.version.checking","name":"update service and release management with automatic version checking","description":"Void's Update Service manages version checking and release updates. The service periodically checks for new releases on GitHub and notifies users when updates are available. Updates can be installed manually or automatically (if configured). The service tracks the current version and compares it against the latest release, providing users with release notes and changelog information. This enables Void to stay current with bug fixes and new features without requiring manual GitHub monitoring.","intents":["I want to be notified when new versions of Void are available","I need to update Void to get bug fixes and new features","I want to see what changed in the latest release before updating"],"best_for":["users who want to stay current with Void releases","teams managing Void deployments across multiple machines","developers who want automatic update notifications"],"limitations":["Update checking requires internet connectivity; offline users will not receive notifications","Automatic updates may interrupt work if not configured carefully","Release notes are fetched from GitHub; no built-in changelog viewer in the UI","Version checking is periodic (not real-time); users may not see updates immediately after release"],"requires":["Void editor or VS Code with Void extension","Internet connectivity for GitHub API access","Write permissions to the Void installation directory (for updates)"],"input_types":["current version number","GitHub repository URL"],"output_types":["available version information","release notes (from GitHub)","update status (up-to-date, update available, updating)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-voideditor--void__cap_13","uri":"capability://data.processing.analysis.message.format.conversion.with.provider.specific.api.contract.handling","name":"message format conversion with provider-specific api contract handling","description":"Void's message format conversion layer translates between Void's internal message representation and each provider's wire protocol. This includes converting Void's tool call format to OpenAI's function_call, Anthropic's tool_use, or Ollama's raw JSON; handling different message role conventions (user/assistant vs user/model); and formatting system prompts according to provider requirements. The conversion is bidirectional—outgoing messages are converted to provider format, and incoming responses are converted back to Void's internal format. This abstraction enables seamless provider switching without UI changes.","intents":["I want to use different LLM providers without rewriting message formatting logic","I need to handle provider-specific message formats (OpenAI vs Anthropic vs Ollama) transparently","I want to convert between tool call formats without manual mapping"],"best_for":["developers building multi-provider LLM applications","teams that need to support multiple LLM APIs","organizations switching between providers without code changes"],"limitations":["Message format conversion adds ~50-100ms latency per message","Provider-specific features (e.g., Claude's vision, OpenAI's function calling) require conditional logic in the conversion layer","Some message formats are not directly translatable; lossy conversions may occur","New providers require manual implementation of the conversion logic"],"requires":["Void editor or VS Code with Void extension","LLM provider with documented API contract","TypeScript runtime for message format conversion"],"input_types":["Void internal message format (user/assistant messages, tool calls)","provider API specification"],"output_types":["provider-specific message format (OpenAI, Anthropic, Ollama, etc.)","converted responses back to Void format"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-voideditor--void__cap_14","uri":"capability://safety.moderation.error.handling.and.ui.patterns.with.graceful.degradation","name":"error handling and ui patterns with graceful degradation","description":"Void implements comprehensive error handling across the service layer and UI, with graceful degradation when LLM providers are unavailable or misconfigured. Errors are caught at the service level, logged, and displayed to users via toast notifications or modal dialogs. The UI remains responsive even when LLM requests fail, allowing users to continue editing or switch providers. Common error scenarios (invalid API key, rate limiting, network timeout) are handled with specific error messages and recovery suggestions.","intents":["I want clear error messages when my LLM provider is misconfigured","I need the editor to remain responsive even if the LLM is unavailable","I want to know how to fix errors (e.g., invalid API key) without leaving the editor"],"best_for":["developers debugging LLM provider issues","teams managing multiple provider configurations","users who want a robust editing experience even with LLM failures"],"limitations":["Error messages may be technical and not user-friendly for non-developers","Some errors (e.g., network timeouts) are difficult to recover from without manual intervention","Error logging is local; no centralized error tracking or analytics"],"requires":["Void editor or VS Code with Void extension","Error handling middleware in the service layer"],"input_types":["LLM request/response","provider configuration","user actions"],"output_types":["error messages (toast, modal, or inline)","error logs (local file or console)","recovery suggestions"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-voideditor--void__cap_2","uri":"capability://code.generation.editing.quick.edit.with.diff.based.code.transformation.and.apply.system","name":"quick edit with diff-based code transformation and apply system","description":"Void's Quick Edit feature (Ctrl+K) enables inline code editing by generating diffs and applying them atomically. The Edit Code Service manages the diff generation pipeline: it sends the selected code + user instruction to the LLM, receives a modified version, computes a unified diff, displays it in a command palette UI, and applies the changes to the editor on user confirmation. The apply system ensures atomic updates—either the entire diff applies or nothing does, preventing partial edits from corrupting code.","intents":["I want to refactor a code block without leaving the editor or opening a chat","I need to see exactly what changes the AI will make before applying them","I want to quickly fix a bug or improve code style with a single keystroke"],"best_for":["developers doing rapid code iteration with AI assistance","teams using AI for code refactoring and style normalization","solo developers who prefer inline editing over chat-based workflows"],"limitations":["Quick Edit works only on selected code; full-file transformations require manual selection or multiple edits","Diff computation adds ~200-500ms latency depending on code size and LLM response time","No automatic undo tracking; users must manually undo if they reject the diff","Large diffs (>50 lines) may be difficult to review in the command palette UI"],"requires":["Void editor or VS Code with Void extension","Active LLM provider with code generation capability","Code selection in the editor (cannot edit without selection)"],"input_types":["selected code text","user instruction (e.g., 'refactor to use async/await')","file language/syntax context"],"output_types":["unified diff (text format)","modified code (applied to editor on confirmation)","edit metadata (lines changed, tokens used)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-voideditor--void__cap_3","uri":"capability://code.generation.editing.context.aware.autocomplete.with.inline.suggestions.and.streaming","name":"context-aware autocomplete with inline suggestions and streaming","description":"Void's Autocomplete Service provides real-time code completion suggestions by analyzing the current cursor position, surrounding code context, and file syntax. It sends partial code context to the LLM and streams completions back, rendering them as inline suggestions in the editor. The service uses debouncing to avoid excessive LLM calls and integrates with VS Code's IntelliSense API to display suggestions alongside built-in completions. Completions are filtered by relevance and deduplicated to avoid redundant suggestions.","intents":["I want AI-powered code completions that understand my codebase context","I need completions to appear as I type without manual triggering","I want to see multiple completion options and choose the best one"],"best_for":["developers seeking faster code writing with AI assistance","teams using Void as a primary IDE with integrated completions","solo developers who prefer inline suggestions over explicit chat"],"limitations":["Autocomplete latency is ~300-800ms depending on LLM provider and network; slower than local completion engines","Debouncing (typically 500ms) means suggestions appear with a slight delay after typing stops","Context window is limited; only surrounding code (typically 50-100 lines) is sent to avoid token limits","Completions may be irrelevant if the LLM misunderstands the code context; no ranking by relevance score"],"requires":["Void editor or VS Code with Void extension","Active LLM provider with low-latency inference (cloud or local)","File syntax detection (language ID from VS Code)"],"input_types":["partial code before cursor","surrounding code context (lines before/after)","file language and syntax","cursor position"],"output_types":["streamed completion text","inline suggestion UI (rendered in editor)","completion metadata (confidence, token count)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-voideditor--void__cap_4","uri":"capability://tool.use.integration.model.context.protocol.mcp.service.with.tool.integration.and.terminal.execution","name":"model context protocol (mcp) service with tool integration and terminal execution","description":"Void implements an MCP Service that enables LLMs to call external tools and execute terminal commands. The service exposes a tool registry where tools (shell commands, file operations, API calls) are registered with JSON schemas. When the LLM generates a tool call, the MCP Service validates the call against the schema, executes the tool (e.g., running a shell command), and returns the result to the LLM for further processing. This enables agentic workflows where the AI can autonomously execute code, run tests, or query external systems.","intents":["I want the AI to run tests and fix code based on test failures","I need the AI to execute shell commands and use the output to inform code changes","I want to enable the AI to call external APIs or tools as part of code generation"],"best_for":["teams building agentic AI coding workflows","developers using AI for test-driven development","organizations needing AI to autonomously execute build/deploy tasks"],"limitations":["Tool execution is synchronous; long-running commands (>30s) may block the UI","No sandboxing; tools execute with the same permissions as the Void process, creating security risks if untrusted LLMs are used","Tool registry is static; adding new tools requires code changes or configuration updates","Error handling is basic; tool failures may not be gracefully communicated back to the LLM"],"requires":["Void editor or VS Code with Void extension","LLM provider that supports tool calling (OpenAI, Anthropic, Ollama with tool support)","Shell access (bash/zsh on Unix, PowerShell on Windows) for command execution","File system permissions for file operations"],"input_types":["tool call from LLM (function name + arguments)","JSON schema definitions for tools","shell commands or file paths"],"output_types":["tool execution results (stdout, stderr, exit code)","structured tool responses (JSON)","file system changes (created/modified files)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-voideditor--void__cap_5","uri":"capability://planning.reasoning.reasoning.and.extended.thinking.with.streaming.token.consumption","name":"reasoning and extended thinking with streaming token consumption","description":"Void supports LLM reasoning modes (e.g., OpenAI's o1, Claude's extended thinking) that enable the model to spend more tokens on internal reasoning before generating a response. The LLM Message Service detects when a model supports reasoning, configures the appropriate parameters (e.g., budget_tokens for Claude), and streams both thinking tokens and response tokens back to the UI. Thinking tokens are displayed separately from the final response, allowing users to see the AI's reasoning process. This capability is model-specific and requires explicit configuration per provider.","intents":["I want the AI to think deeply about complex code problems before suggesting solutions","I need to see the AI's reasoning process to understand why it made a particular suggestion","I want to use reasoning models for architectural decisions and code reviews"],"best_for":["teams solving complex algorithmic or architectural problems with AI","developers using AI for code review and design validation","organizations willing to pay for higher token consumption for better reasoning"],"limitations":["Reasoning models consume significantly more tokens (2-10x) than standard models, increasing API costs","Reasoning latency is high (10-60s for complex problems); unsuitable for real-time autocomplete or quick edits","Not all providers support reasoning; only OpenAI (o1) and Anthropic (extended thinking) currently supported","Thinking tokens are not always interpretable; the AI's internal reasoning may not be human-readable"],"requires":["LLM provider that supports reasoning (OpenAI o1 or Anthropic Claude with extended thinking)","API key with sufficient quota for high token consumption","Explicit model selection in Void settings to enable reasoning mode"],"input_types":["code problem or question","context (file, codebase, error message)","reasoning budget (token limit for thinking)"],"output_types":["thinking tokens (internal reasoning, may be partial or opaque)","response tokens (final answer or suggestion)","token usage metadata (thinking tokens vs response tokens)"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-voideditor--void__cap_6","uri":"capability://tool.use.integration.settings.and.model.configuration.with.runtime.provider.switching","name":"settings and model configuration with runtime provider switching","description":"Void's Settings Service manages LLM provider configuration, model selection, API keys, and user preferences. Settings are persisted in VS Code's settings store and can be modified via the UI or configuration files. The service supports runtime provider switching—users can change the active provider or model without restarting the editor. Model capabilities (e.g., supports tool calling, supports vision) are defined in a model capabilities registry that the LLM Message Service consults to determine which features are available for the selected model.","intents":["I want to configure multiple LLM providers and switch between them without restarting","I need to store API keys securely and manage them per provider","I want to see which features (tool calling, vision, reasoning) are available for my selected model"],"best_for":["developers experimenting with different LLM providers","teams managing multiple API keys and provider accounts","organizations with provider-specific compliance or cost requirements"],"limitations":["API keys are stored in VS Code settings; security depends on VS Code's credential storage (not encrypted by default on all platforms)","Model capabilities registry is static; new models require manual updates to the registry","No built-in cost tracking or quota management; users must monitor API usage externally","Provider switching is immediate but may cause in-flight requests to fail if the provider changes mid-request"],"requires":["Void editor or VS Code with Void extension","API keys for at least one provider (stored in VS Code settings)","Model capabilities metadata (built into Void or manually configured)"],"input_types":["provider name (openai, anthropic, gemini, ollama, etc.)","API key or endpoint URL","model name (gpt-4, claude-3, etc.)","user preferences (temperature, max tokens, etc.)"],"output_types":["active provider configuration","model capabilities (tool calling, vision, reasoning support)","settings metadata (last updated, validation status)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-voideditor--void__cap_7","uri":"capability://code.generation.editing.command.bar.and.diff.navigation.with.unified.diff.display","name":"command bar and diff navigation with unified diff display","description":"Void provides a command bar UI that displays diffs from Quick Edit operations and other code transformations. The command bar renders unified diffs in a searchable, navigable format, allowing users to review changes line-by-line before applying them. The Diff Navigation system enables users to jump between changed hunks, see before/after code side-by-side, and accept or reject individual changes. This UI is built with React and integrates with VS Code's command palette for discoverability.","intents":["I want to review code changes before applying them to my editor","I need to navigate between multiple changed sections in a large diff","I want to accept or reject individual changes rather than all-or-nothing"],"best_for":["developers who want explicit control over code changes","teams using AI for code review and validation","solo developers who prefer reviewing diffs before applying changes"],"limitations":["Diff display is text-based; large diffs (>100 lines) are difficult to navigate in the command palette UI","No side-by-side diff view; users must mentally compare before/after code","Individual change rejection is not supported; users can only accept or reject the entire diff","Diff navigation is linear; no ability to jump to specific line numbers or search within diffs"],"requires":["Void editor or VS Code with Void extension","Quick Edit or other code transformation that generates a diff","React runtime for command bar UI"],"input_types":["unified diff (text format)","original code","transformed code"],"output_types":["diff display (rendered in command palette)","user decision (accept/reject)","applied changes (if accepted)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-voideditor--void__cap_8","uri":"capability://automation.workflow.sidebar.actions.and.keybindings.with.customizable.shortcuts","name":"sidebar actions and keybindings with customizable shortcuts","description":"Void's sidebar provides action buttons and customizable keybindings for common operations: Quick Edit (Ctrl+K), Chat (Ctrl+L), Clear Thread, Delete Thread, and others. Keybindings are configurable via VS Code's keybindings.json, allowing users to customize shortcuts to their preferences. The sidebar UI is built with React and integrates with VS Code's command palette and keybinding system. Actions are dispatched through the service layer, enabling consistent behavior across UI entry points.","intents":["I want quick keyboard shortcuts for common AI operations (chat, edit, clear)","I need to customize keybindings to match my workflow","I want to access AI features from the sidebar without using the keyboard"],"best_for":["power users who prefer keyboard shortcuts","developers with custom keybinding preferences","teams standardizing on specific shortcuts across the organization"],"limitations":["Keybindings are stored in VS Code's keybindings.json; conflicts with other extensions may cause unexpected behavior","No built-in keybinding conflict detection; users must manually resolve conflicts","Sidebar actions are limited to predefined operations; custom actions require code changes"],"requires":["Void editor or VS Code with Void extension","Access to VS Code's keybindings.json for customization"],"input_types":["keybinding definition (key combination + command)","sidebar action (button click)"],"output_types":["command execution (chat, edit, clear, etc.)","keybinding metadata (active keybindings)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-voideditor--void__cap_9","uri":"capability://text.generation.language.editor.watermark.and.welcome.screen.with.onboarding.flow","name":"editor watermark and welcome screen with onboarding flow","description":"Void displays a welcome screen and editor watermark on first launch, guiding users through onboarding. The VoidOnboarding component provides step-by-step instructions for configuring an LLM provider, selecting a model, and testing the chat feature. The watermark appears in the editor background when no file is open, encouraging users to start a chat or create a file. The onboarding flow is skippable and can be re-triggered via settings, allowing users to revisit setup instructions.","intents":["I'm new to Void and need guidance on setting up an LLM provider","I want to test the chat feature to ensure my provider is configured correctly","I need to re-run the onboarding flow if I change providers or reset settings"],"best_for":["new users installing Void for the first time","teams onboarding developers to Void","users switching between LLM providers"],"limitations":["Onboarding is linear; users cannot skip steps or customize the flow","No provider validation during onboarding; users may complete setup with invalid API keys","Watermark is always visible when no file is open; no option to disable it"],"requires":["Void editor or VS Code with Void extension","First-time launch or manual trigger of onboarding"],"input_types":["user interactions (button clicks, text input)","provider configuration (API key, model selection)"],"output_types":["onboarding UI (React component)","settings updates (provider configuration saved)","test chat message (to validate provider)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["API keys for at least one provider (OpenAI, Anthropic, Gemini, Mistral, Groq) OR local Ollama instance","TypeScript runtime (Node.js 18+)","VS Code 1.80+ or Void fork of VS Code","Void editor or VS Code with Void extension","Active LLM provider configuration (API key or local Ollama)","File system access for workspace context injection","File system access to the workspace","LLM provider with sufficient context window (8k+ tokens recommended)","SSH access to remote machine (with public key authentication) OR WSL environment","Node.js 18+ on the remote machine"],"failure_modes":["Provider-specific features (e.g., Claude's extended thinking, OpenAI's vision) require conditional logic in message formatting","Token counting differs per provider; Void uses provider-specific tokenizers which adds ~50ms overhead per message","Streaming response handling varies by provider; some providers have slower streaming latency than others","Thread persistence is local to the Void installation; threads are not synced across machines","Context window is bounded by the selected LLM's max tokens; large codebases may require selective file inclusion","No built-in thread search or filtering; users must manually navigate thread list","Thread history is stored in VS Code settings; no database backend for large-scale thread management","Full codebase indexing is expensive; Void uses heuristics to select relevant files rather than indexing everything","Context window is bounded by the LLM's max tokens; large codebases may exceed the token limit","No persistent index; context is extracted on-demand, adding latency to each request","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7648526488763441,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:39.623Z","last_commit":"2026-01-12T09:57:57Z"},"community":{"stars":28703,"forks":2473,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=voideditor--void","compare_url":"https://unfragile.ai/compare?artifact=voideditor--void"}},"signature":"JXEIxcj+zEpzbBJsJ0iXueM3IY2QP1fa2YwqV3m8yJ+fDcp0jm3B5gjrjcobdNS75KuECgAOZ0PvJMrLkq8gCw==","signedAt":"2026-06-19T13:07:22.356Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/voideditor--void","artifact":"https://unfragile.ai/voideditor--void","verify":"https://unfragile.ai/api/v1/verify?slug=voideditor--void","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}