{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-namdang-ollama-copilot-vscode","slug":"ollama-copilot-vs-code","name":"Ollama Copilot VS Code","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=namdang.ollama-copilot-vscode","page_url":"https://unfragile.ai/ollama-copilot-vs-code","categories":["code-editors"],"tags":["ai","autocomplete","chat","code completion","coding","copilot","gpt","llm","ollama","vscode"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-namdang-ollama-copilot-vscode__cap_0","uri":"capability://code.generation.editing.local.context.aware.code.autocomplete.with.configurable.debounce","name":"local-context-aware code autocomplete with configurable debounce","description":"Generates inline ghost-text code suggestions as the user types by reading the current file's content and cursor position, then querying a locally-running Ollama inference engine with configurable debounce delay (default 300ms) to prevent excessive inference calls. The extension integrates with VS Code's IntelliSense system to display suggestions that can be accepted via Tab or dismissed via Esc, with generation parameters (temperature, max tokens) tunable via settings.","intents":["I want AI-powered code completion suggestions without sending my code to a remote server","I need to reduce typing overhead by getting intelligent next-token predictions as I code","I want to use a specific code-trained model (CodeLlama, DeepSeek Coder, StarCoder2) for completions without vendor lock-in","I need to tune autocomplete aggressiveness and response latency for my hardware constraints"],"best_for":["solo developers building locally-run LLM workflows who prioritize privacy","teams with air-gapped or on-premise development environments","developers experimenting with different open-source code models without API costs","users with GPU hardware capable of running 7B-70B parameter models locally"],"limitations":["Debounce mechanism adds 300ms minimum latency before suggestions appear; cannot be disabled entirely","Autocomplete context limited to current file only — no cross-file or project-wide context awareness documented","Max token limit (default 100) may truncate longer logical completions; requires manual tuning per model","No built-in conflict detection with other autocomplete extensions (GitHub Copilot, Codeium); behavior undefined if multiple IntelliSense providers active simultaneously","Silent failure if Ollama service becomes unavailable mid-session; no reconnection logic or user notification","Suggestion quality entirely dependent on locally-installed model capability; no fallback or model switching during inference"],"requires":["Visual Studio Code (minimum version UNKNOWN — not documented)","Ollama runtime installed and running locally on configured baseUrl (default http://localhost:11434)","At least one code-capable model pre-downloaded via 'ollama pull <model>' (e.g., codellama, deepseek-coder, starcoder2)","Sufficient GPU/CPU memory to run selected model (varies by model size: 7B models ~4GB VRAM, 70B models ~40GB+)"],"input_types":["source code (all VS Code-supported languages: Python, JavaScript, Java, C++, Go, Rust, etc.)","current file content (full text buffer)","cursor position (line and column for context window)"],"output_types":["inline ghost-text suggestions (string)","multi-line code completions (string)"],"categories":["code-generation-editing","local-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-namdang-ollama-copilot-vscode__cap_1","uri":"capability://text.generation.language.session.scoped.conversational.code.chat.with.file.context","name":"session-scoped conversational code chat with file context","description":"Provides an interactive chat sidebar panel (accessed via Ollama icon in activity bar or 'Ollama: Open Chat' command) that accepts natural language questions about code and returns explanations or problem-solving responses by sending the current file's content plus user query to a locally-running Ollama model. Conversation history is maintained in memory during the VS Code session but is not persisted across restarts, and the chat model is independently configurable from the autocomplete model via the 'ollama-copilot.chatModel' setting.","intents":["I want to ask questions about code I'm reading without leaving the editor or copying text to a separate tool","I need explanations of unfamiliar code patterns or library usage from an AI assistant","I want to brainstorm solutions or debug issues through conversational back-and-forth with an AI","I need to use a different (potentially larger or more capable) model for chat than for autocomplete"],"best_for":["developers learning unfamiliar codebases or languages who want in-editor AI assistance","teams using local models for compliance reasons and needing interactive debugging support","solo developers who want conversational AI without cloud dependencies or API costs","users experimenting with larger models (e.g., 13B or 70B parameter) for chat while using smaller models for autocomplete"],"limitations":["Chat history is session-scoped only — conversation lost on VS Code restart; no persistence mechanism or export functionality","Context limited to current file; no multi-file or project-wide context awareness","No conversation branching or undo/redo of individual messages","Chat model selection is global; cannot switch models mid-conversation or per-query","No streaming response display — full response generated before display (latency depends on model size and hardware)","No markdown rendering or syntax highlighting in chat responses (plain text only, format UNKNOWN)"],"requires":["Visual Studio Code (minimum version UNKNOWN)","Ollama runtime installed and running on configured baseUrl","At least one code-capable model installed for chat (default 'codellama', but any Ollama model can be selected)","Sufficient GPU/CPU memory for selected chat model (typically larger than autocomplete model)"],"input_types":["natural language text (user question)","current file content (automatically included as context)","conversation history (in-memory, current session only)"],"output_types":["natural language text (assistant response)","code snippets (embedded in text response)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-namdang-ollama-copilot-vscode__cap_2","uri":"capability://tool.use.integration.flexible.multi.model.selection.with.runtime.switching","name":"flexible multi-model selection with runtime switching","description":"Allows users to independently select and switch between any Ollama-compatible model for autocomplete (via 'ollama-copilot.model' setting) and chat (via 'ollama-copilot.chatModel' setting) through VS Code's Settings UI, with no API keys or authentication required. Models must be pre-installed locally via 'ollama pull <model>', and the extension dynamically queries the configured Ollama instance at runtime without requiring extension restart, enabling experimentation with different model sizes and architectures (CodeLlama, DeepSeek Coder, StarCoder2, Phind-CodeLlama, etc.).","intents":["I want to try different open-source code models without committing to a single vendor or API","I need to switch models based on task requirements (fast autocomplete vs high-quality chat) without restarting my editor","I want to use a custom fine-tuned model or a model not available through commercial APIs","I need to optimize for latency vs quality by selecting smaller models for autocomplete and larger models for chat"],"best_for":["researchers and ML engineers evaluating different code models","developers in organizations with model governance policies requiring specific open-source models","teams building custom fine-tuned models and needing to test them immediately","cost-conscious developers who want to avoid per-token API billing"],"limitations":["Model must be manually installed via 'ollama pull <model>' before selection; no in-extension model discovery or installation UI","No model compatibility checking — selecting an incompatible or non-code-trained model will produce poor results without warning","Model switching requires manual setting change; no quick-switch UI or keyboard shortcut","No model performance metrics or recommendations; users must know model capabilities beforehand","Ollama instance must be running and accessible at configured baseUrl; no fallback or automatic startup"],"requires":["Ollama runtime with at least one model installed locally","Access to Ollama HTTP API endpoint (default http://localhost:11434, configurable via 'ollama-copilot.baseUrl')","Knowledge of available Ollama models and their capabilities (external research required)"],"input_types":["model name string (e.g., 'codellama', 'deepseek-coder', 'starcoder2')"],"output_types":["model selection confirmation (implicit via setting change)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-namdang-ollama-copilot-vscode__cap_3","uri":"capability://automation.workflow.tunable.inference.parameters.with.temperature.and.token.limits","name":"tunable inference parameters with temperature and token limits","description":"Exposes inference generation parameters via VS Code settings to control output quality and latency: 'ollama-copilot.temperature' (default 0.2, controls randomness/creativity), 'ollama-copilot.maxTokens' (default 100, limits response length), and 'ollama-copilot.debounceMs' (default 300, delays autocomplete trigger). These settings apply globally to both autocomplete and chat, allowing users to optimize for their hardware constraints and use-case preferences without modifying extension code.","intents":["I want to reduce hallucinations and get more deterministic code suggestions by lowering temperature","I need longer completions for complex code patterns but want to avoid excessive token generation on slower hardware","I want to tune autocomplete latency to match my typing speed and hardware capabilities","I need to experiment with different parameter combinations to find the best balance for my workflow"],"best_for":["developers with limited GPU/CPU resources who need to optimize inference latency","teams with specific code quality requirements (e.g., deterministic suggestions for safety-critical code)","users experimenting with model behavior and wanting to iterate quickly on parameter tuning","developers building custom workflows where suggestion quality vs latency tradeoffs matter"],"limitations":["Temperature and maxTokens settings apply globally to both autocomplete and chat; no per-task tuning","No validation or guidance on parameter ranges; invalid values may cause inference failures without clear error messages","Debounce delay cannot be disabled entirely (minimum 0ms, but practical minimum depends on hardware)","No real-time parameter adjustment UI; changes require editing settings and may require VS Code reload","No A/B testing or comparison tools to evaluate parameter impact","Parameter changes apply immediately but may not take effect for in-flight requests"],"requires":["VS Code Settings UI access (File > Preferences > Settings, search 'Ollama')","Understanding of temperature (0.0 = deterministic, 1.0+ = creative) and token limits (affects response length and latency)"],"input_types":["temperature: float (0.0-2.0, default 0.2)","maxTokens: integer (1-∞, default 100)","debounceMs: integer (0-∞, default 300)"],"output_types":["inference behavior modification (implicit via parameter application)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-namdang-ollama-copilot-vscode__cap_4","uri":"capability://tool.use.integration.local.ollama.http.api.integration.with.configurable.endpoint","name":"local ollama http api integration with configurable endpoint","description":"Integrates with Ollama's HTTP API by making requests to a configurable baseUrl (default http://localhost:11434) to perform inference, with no authentication or API key required. The extension reads the 'ollama-copilot.baseUrl' setting to determine the Ollama endpoint, allowing users to point to local instances, remote Ollama servers on the same network, or custom Ollama-compatible inference servers. All requests are made over HTTP (no TLS/encryption documented), and the extension fails silently if the endpoint is unreachable.","intents":["I want to run Ollama on a separate machine or container and have VS Code connect to it remotely","I need to use a custom Ollama-compatible inference server instead of the standard Ollama distribution","I want to share a single Ollama instance across multiple VS Code instances or team members","I need to integrate with an existing Ollama deployment in my infrastructure"],"best_for":["teams with centralized Ollama inference servers on the same network","developers using containerized Ollama deployments (Docker, Kubernetes)","organizations with custom inference infrastructure compatible with Ollama's API","users with powerful GPU servers running Ollama remotely while using lightweight client machines"],"limitations":["No TLS/HTTPS support documented; communication over HTTP only (security risk for remote endpoints)","No authentication or API key support; endpoint must be network-accessible without credentials","No connection pooling or keep-alive documented; each request may open a new connection","Silent failure if endpoint unreachable; no user notification or fallback mechanism","No load balancing or failover support; single endpoint only","Ollama API compatibility assumed; no version checking or API compatibility validation"],"requires":["Ollama runtime running and accessible at configured baseUrl (default http://localhost:11434)","Network connectivity from VS Code machine to Ollama endpoint (localhost or remote)","Ollama HTTP API compatibility (standard Ollama distribution or compatible fork)"],"input_types":["baseUrl: string (HTTP endpoint, default 'http://localhost:11434')"],"output_types":["HTTP requests to Ollama API (POST /api/generate, /api/chat, etc.)","JSON responses from Ollama (model output, status, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-namdang-ollama-copilot-vscode__cap_5","uri":"capability://automation.workflow.toggle.based.autocomplete.enable.disable.control","name":"toggle-based autocomplete enable/disable control","description":"Provides a boolean 'ollama-copilot.autocompleteEnabled' setting (default true) that allows users to completely disable inline code suggestions without uninstalling the extension or removing the chat functionality. When disabled, the extension stops listening for typing events and generating autocomplete suggestions, but the chat sidebar remains fully functional. This enables users to use chat-only mode or temporarily pause autocomplete without losing other extension features.","intents":["I want to use the chat feature but disable autocomplete suggestions that distract me","I need to temporarily pause autocomplete while working on sensitive code or during pair programming","I want to reduce CPU/GPU load by disabling autocomplete while keeping chat available","I need to test whether autocomplete is interfering with other VS Code features"],"best_for":["developers who prefer chat-only interaction without inline suggestions","users on resource-constrained hardware who want to use chat selectively","teams with policies requiring manual code review before accepting suggestions","developers troubleshooting conflicts with other autocomplete extensions"],"limitations":["Toggle is global; cannot be disabled per-language or per-file","No keyboard shortcut for quick toggling; requires settings UI access","Disabling autocomplete does not free GPU/CPU resources if Ollama is still running","No visual indicator in VS Code UI showing whether autocomplete is enabled/disabled"],"requires":["VS Code Settings UI access"],"input_types":["boolean: true (autocomplete enabled) or false (autocomplete disabled)"],"output_types":["autocomplete behavior modification (implicit via setting change)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-namdang-ollama-copilot-vscode__cap_6","uri":"capability://automation.workflow.command.palette.driven.feature.access","name":"command-palette-driven feature access","description":"Exposes two contributed VS Code commands accessible via the Command Palette (Ctrl+Shift+P / Cmd+Shift+P): 'Ollama: Open Chat' (opens the chat sidebar panel) and 'Ollama: Toggle Autocomplete' (enables/disables autocomplete). These commands provide keyboard-driven access to core features without requiring mouse interaction with the activity bar or settings UI, enabling power users to integrate Ollama features into custom keybindings or macros.","intents":["I want to open the chat panel via keyboard shortcut without clicking the activity bar icon","I want to quickly toggle autocomplete on/off during my workflow without accessing settings","I want to bind custom keybindings to Ollama features for faster access","I want to integrate Ollama commands into VS Code macros or automation workflows"],"best_for":["keyboard-driven developers who prefer command palette over mouse interaction","power users building custom VS Code keybinding configurations","developers using VS Code automation tools or extensions that trigger commands","teams with standardized VS Code configurations and custom keybinding schemes"],"limitations":["Only two commands exposed; no granular control over other features (e.g., model switching, parameter tuning)","No default keybindings assigned; users must manually configure keybindings in keybindings.json","'Toggle Autocomplete' command duplicates 'ollama-copilot.autocompleteEnabled' setting; no additional functionality","Commands are not discoverable without accessing Command Palette; no documentation in VS Code UI"],"requires":["VS Code Command Palette access (Ctrl+Shift+P / Cmd+Shift+P)","Optional: custom keybinding configuration in keybindings.json"],"input_types":["command name string ('Ollama: Open Chat' or 'Ollama: Toggle Autocomplete')"],"output_types":["command execution (chat panel open or autocomplete toggle)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-namdang-ollama-copilot-vscode__cap_7","uri":"capability://text.generation.language.activity.bar.sidebar.panel.for.persistent.chat.interface","name":"activity-bar sidebar panel for persistent chat interface","description":"Provides a dedicated chat interface in the VS Code activity bar sidebar (accessed via Ollama icon) that persists across editor tabs and file switches, maintaining conversation history during the session. The sidebar panel displays chat messages in a scrollable list with user queries and assistant responses, includes a text input field for new messages, and a Send button (or Enter key submission). The panel remains open until explicitly closed, allowing users to reference previous messages while editing code.","intents":["I want a persistent chat interface that stays visible while I work on code","I need to reference previous chat messages while editing without losing conversation context","I want to ask follow-up questions about code without switching between windows or tabs","I need a dedicated space for conversational AI without cluttering the editor"],"best_for":["developers who prefer integrated chat over external tools or browser windows","users working on complex problems requiring iterative back-and-forth with AI","teams using VS Code as their primary development environment and wanting unified tooling","developers who want to maintain chat history during a coding session"],"limitations":["Chat history is session-scoped only; lost on VS Code restart (no persistence)","No message editing or deletion after sending","No conversation branching or undo/redo","Sidebar panel takes up horizontal screen space; no collapsible or floating window option","No markdown rendering or syntax highlighting in responses (plain text only, format UNKNOWN)","No message search or filtering within conversation history","No export or copy-to-clipboard functionality for messages"],"requires":["VS Code with activity bar visible (default configuration)","Ollama runtime running and accessible"],"input_types":["natural language text (user message)","current file context (automatically included)"],"output_types":["chat message display (text)","conversation history (in-memory, session-scoped)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-namdang-ollama-copilot-vscode__cap_8","uri":"capability://safety.moderation.privacy.first.local.only.inference.with.zero.external.api.calls","name":"privacy-first local-only inference with zero external api calls","description":"Explicitly implements a privacy-first architecture where all code and conversation data remains local — no external API calls, no data transmission to remote servers, and no cloud dependencies. The extension communicates only with the locally-running Ollama instance via HTTP on localhost (or configured network endpoint), and all inference, model storage, and conversation history are confined to the local machine. This design eliminates privacy concerns associated with cloud-based copilots and enables use in air-gapped or compliance-restricted environments.","intents":["I need to ensure my proprietary code never leaves my machine or network","I work in a compliance-restricted environment (healthcare, finance, government) where cloud AI is prohibited","I want to avoid vendor lock-in and data collection by cloud AI providers","I need to use AI assistance in an air-gapped or offline environment"],"best_for":["enterprises with strict data governance and compliance requirements (HIPAA, SOC 2, FedRAMP)","teams working on proprietary or classified code","organizations in jurisdictions with data residency requirements","developers prioritizing privacy and data ownership over convenience","teams with air-gapped or offline development environments"],"limitations":["Requires local Ollama infrastructure; no managed cloud option for users without GPU hardware","Users responsible for model updates and security patches (no automatic updates from vendor)","No telemetry or usage analytics; users cannot track productivity metrics","No cloud backup of conversation history; all data lost if local machine fails","Requires network isolation or firewall rules if Ollama is on a separate machine (no built-in encryption for remote endpoints)"],"requires":["Local Ollama runtime with models installed","Hardware capable of running selected models (GPU recommended for latency)","Network isolation or firewall configuration if using remote Ollama endpoint"],"input_types":["source code (all languages)","natural language queries"],"output_types":["code suggestions and explanations (local only, never transmitted externally)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Visual Studio Code (minimum version UNKNOWN — not documented)","Ollama runtime installed and running locally on configured baseUrl (default http://localhost:11434)","At least one code-capable model pre-downloaded via 'ollama pull <model>' (e.g., codellama, deepseek-coder, starcoder2)","Sufficient GPU/CPU memory to run selected model (varies by model size: 7B models ~4GB VRAM, 70B models ~40GB+)","Visual Studio Code (minimum version UNKNOWN)","Ollama runtime installed and running on configured baseUrl","At least one code-capable model installed for chat (default 'codellama', but any Ollama model can be selected)","Sufficient GPU/CPU memory for selected chat model (typically larger than autocomplete model)","Ollama runtime with at least one model installed locally","Access to Ollama HTTP API endpoint (default http://localhost:11434, configurable via 'ollama-copilot.baseUrl')"],"failure_modes":["Debounce mechanism adds 300ms minimum latency before suggestions appear; cannot be disabled entirely","Autocomplete context limited to current file only — no cross-file or project-wide context awareness documented","Max token limit (default 100) may truncate longer logical completions; requires manual tuning per model","No built-in conflict detection with other autocomplete extensions (GitHub Copilot, Codeium); behavior undefined if multiple IntelliSense providers active simultaneously","Silent failure if Ollama service becomes unavailable mid-session; no reconnection logic or user notification","Suggestion quality entirely dependent on locally-installed model capability; no fallback or model switching during inference","Chat history is session-scoped only — conversation lost on VS Code restart; no persistence mechanism or export functionality","Context limited to current file; no multi-file or project-wide context awareness","No conversation branching or undo/redo of individual messages","Chat model selection is global; cannot switch models mid-conversation or per-query","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.38,"quality":0.28,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:33.198Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ollama-copilot-vs-code","compare_url":"https://unfragile.ai/compare?artifact=ollama-copilot-vs-code"}},"signature":"Fr016ZvMJlBBqQKpQrBtmiNzWA5ZsL95ezO7YgnT3+kU0fAgLdzBZEeh1AQb93/MOS11R1ocU/w7sDuuPygrDg==","signedAt":"2026-06-20T18:24:59.591Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ollama-copilot-vs-code","artifact":"https://unfragile.ai/ollama-copilot-vs-code","verify":"https://unfragile.ai/api/v1/verify?slug=ollama-copilot-vs-code","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"}}