{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-nr-codetools-localaipilot","slug":"local-ai-pilot-ollama-deepseek-r1-and-more","name":"Local AI Pilot - Ollama, Deepseek-R1, and more","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=nr-codetools.localaipilot","page_url":"https://unfragile.ai/local-ai-pilot-ollama-deepseek-r1-and-more","categories":["code-editors"],"tags":["ai","autocomplete","chatGPT","co-pilot","gemini","intellicode","keybindings","ollama","openai","refactor","snippets"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-nr-codetools-localaipilot__cap_0","uri":"capability://code.generation.editing.context.aware.inline.code.completion.with.local.llm.inference","name":"context-aware inline code completion with local llm inference","description":"Provides real-time code suggestions triggered via SHIFT+ALT+W by sending the current file buffer plus explicitly configured context files to a local Ollama instance running models like Deepseek-R1. The extension maintains the full file context in memory and streams completion suggestions back into the editor without sending code to remote servers, enabling privacy-preserving autocomplete that understands multi-file project structure through configurable file path injection.","intents":["Get real-time code suggestions while typing without uploading my code to cloud services","Complete code patterns that depend on context from other files in my project","Use specialized reasoning models like Deepseek-R1 for complex code generation tasks locally"],"best_for":["Solo developers and teams with strict data privacy requirements","Developers working on proprietary codebases that cannot leave the network","Engineers optimizing for latency-sensitive workflows where cloud round-trips are unacceptable"],"limitations":["Completion quality depends entirely on local model capability — smaller models (7B-13B parameters) produce lower-quality suggestions than cloud alternatives","Requires explicit file path configuration for multi-file context; no automatic project tree discovery means missing context from files not explicitly listed","Inference latency varies with hardware; typical 2-10 second completion times on consumer GPUs vs <500ms for cloud services","Context window limited by model size — cannot include entire large codebases, only explicitly configured files"],"requires":["Visual Studio Code (minimum version unknown)","Ollama installed and running locally with at least one model pulled (e.g., ollama pull deepseek-r1)","Sufficient GPU VRAM (8GB+ recommended for 13B models, 16GB+ for larger models)","LF line endings in source files (CRLF may cause formatting issues)"],"input_types":["current file buffer (full text)","configured context file paths (relative or absolute)","cursor position (implicit from editor state)"],"output_types":["inline code suggestions (text)","multi-line completions (code blocks)"],"categories":["code-generation-editing","local-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-nr-codetools-localaipilot__cap_1","uri":"capability://text.generation.language.conversational.code.chat.with.persistent.history.container.mode.only","name":"conversational code chat with persistent history (container mode only)","description":"Provides a sidebar chat interface where developers can discuss code, ask questions, and receive explanations through a stateful conversation that persists across sessions. In Container Mode, the extension maintains chat history and caching via an intermediate API service, enabling the LLM to reference previous messages in the conversation thread. Messages are routed through the container API rather than directly to Ollama, allowing for session management and context carryover across multiple interactions.","intents":["Ask follow-up questions about code without losing conversation context","Maintain a persistent record of code discussions and decisions for team reference","Leverage multi-turn reasoning where the model builds on previous responses in the same session"],"best_for":["Development teams using Container Mode who need persistent code discussion records","Developers debugging complex issues that require multi-turn reasoning and context accumulation","Teams wanting to archive code discussions for knowledge management"],"limitations":["Chat history and caching only available in Container Mode — Standalone Mode has no persistence (each message is stateless)","No documented export mechanism for chat history — unclear if conversations can be saved to disk or shared","Context window limitations mean very long conversations may lose early messages when context fills up","No multi-user collaboration — chat is per-extension instance, not shared across team members"],"requires":["Visual Studio Code (minimum version unknown)","Container Mode enabled and configured (requires intermediate API service running)","Ollama or remote model provider (OpenAI, Gemini, Cohere, Anthropic, Codestral) configured","Network connectivity to container API service"],"input_types":["natural language text messages","code snippets (pasted into chat)","implicit file context (current editor file)"],"output_types":["natural language responses (text)","code suggestions (inline in chat)","explanations and analysis"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-nr-codetools-localaipilot__cap_10","uri":"capability://code.generation.editing.syntax.aware.code.formatting.with.lf.line.ending.enforcement","name":"syntax-aware code formatting with lf line ending enforcement","description":"Ensures that code suggestions and repairs are formatted correctly by enforcing LF (Unix-style) line endings throughout the extension. The extension explicitly requires LF line endings in source files and may convert or reject CRLF (Windows-style) line endings to prevent formatting issues in generated code. This constraint is documented as a requirement ('Use LF line endings for proper formatting'), suggesting that CRLF may cause the LLM to generate malformed suggestions or that the extension's parsing logic assumes LF line endings.","intents":["Ensure code suggestions are formatted correctly without line ending artifacts","Prevent cross-platform line ending issues that could break code generation","Maintain consistent formatting across team members using different operating systems"],"best_for":["Teams working across Windows, macOS, and Linux who need consistent line endings","Projects with strict formatting requirements","Developers wanting to avoid subtle bugs caused by line ending mismatches"],"limitations":["Explicit LF requirement may conflict with Windows-native projects that use CRLF","No automatic CRLF-to-LF conversion documented — users must manually configure their editor or git to use LF","Behavior with CRLF files not documented — unclear if extension rejects them, converts them, or produces malformed suggestions","May require .gitattributes or editor configuration to enforce — not handled automatically by the extension"],"requires":["Source files with LF line endings (not CRLF)","VS Code configured to use LF line endings (Settings > Files: End of Line > LF)"],"input_types":["source code with LF line endings"],"output_types":["formatted suggestions with consistent line endings"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-nr-codetools-localaipilot__cap_2","uri":"capability://text.generation.language.code.explanation.and.semantic.analysis.via.llm","name":"code explanation and semantic analysis via llm","description":"Analyzes selected code blocks by sending them to the configured LLM (local Ollama or remote provider) to generate human-readable explanations of functionality, logic flow, and intent. The extension extracts the selected text from the editor, passes it to the model with an implicit 'explain' prompt, and returns the analysis as text that can be displayed in the chat interface or sidebar. Works with any supported model (Deepseek-R1, OpenAI, Gemini, etc.) and respects the user's privacy mode selection (local vs remote).","intents":["Understand what a complex code block does without reading through all the logic","Get explanations of unfamiliar code patterns or library usage","Generate documentation or comments for legacy code"],"best_for":["Developers onboarding to new codebases and needing quick code comprehension","Teams documenting legacy systems where original authors are unavailable","Code reviewers who need to understand unfamiliar patterns quickly"],"limitations":["Explanation quality depends on model capability — smaller models may miss subtle logic or misinterpret intent","No syntax-aware parsing — treats code as plain text, so explanations may not leverage AST-level understanding","Explanations are generated fresh each time (no caching) — repeated explanations of the same code block require re-inference","Limited to selected code blocks — cannot explain entire files or cross-file dependencies without manual selection"],"requires":["Visual Studio Code (minimum version unknown)","Ollama running locally with a model, OR configured remote API key (OpenAI/Gemini/Cohere/Anthropic/Codestral)","Code selection in editor (explicit selection required)"],"input_types":["selected code text (from editor)","implicit language context (file extension)"],"output_types":["natural language explanation (text)","markdown-formatted analysis"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-nr-codetools-localaipilot__cap_3","uri":"capability://code.generation.editing.automated.bug.detection.and.code.repair.suggestions","name":"automated bug detection and code repair suggestions","description":"Analyzes selected code or entire files to identify potential bugs, logic errors, or code quality issues, then generates repair suggestions by prompting the LLM with implicit 'fix' or 'review' instructions. The extension sends the code to the configured model (local Ollama or remote), receives suggested corrections, and presents them as diffs or inline suggestions in the editor. Supports both local and remote models, respecting the user's privacy mode preference.","intents":["Identify bugs in code before running tests or deploying","Get automated suggestions for fixing common errors (null pointer dereferences, off-by-one errors, etc.)","Improve code quality by detecting anti-patterns and suggesting refactors"],"best_for":["Solo developers without access to code review partners","Teams wanting to catch bugs earlier in the development cycle","Developers learning best practices and wanting real-time feedback"],"limitations":["Bug detection relies on LLM reasoning, not static analysis — may miss type errors, undefined variables, or other issues detectable by linters","Suggested fixes may introduce new bugs or change intended behavior — all suggestions require human review before applying","No integration with language-specific linters (ESLint, Pylint, etc.) — operates independently of existing code quality tools","Context-limited — cannot detect bugs that depend on understanding code in other files unless those files are explicitly provided as context"],"requires":["Visual Studio Code (minimum version unknown)","Ollama running locally with a model, OR configured remote API key (OpenAI/Gemini/Cohere/Anthropic/Codestral)","Code selection or file context"],"input_types":["selected code text or full file buffer","implicit language context (file extension)"],"output_types":["suggested code fixes (text/code)","explanations of identified issues","diff-style comparisons (format unknown)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-nr-codetools-localaipilot__cap_4","uri":"capability://memory.knowledge.document.ingestion.and.retrieval.augmented.q.a.container.mode.only","name":"document ingestion and retrieval-augmented q&a (container mode only)","description":"Enables users to upload documents (PDFs, markdown, text files — exact formats unknown) which are indexed using LlamaIndex and stored in a vector database. When users ask questions in the chat interface, the extension retrieves relevant document excerpts using semantic search and passes them as context to the LLM, enabling question-answering grounded in the uploaded documents. This RAG (Retrieval-Augmented Generation) pattern allows the LLM to answer questions about documentation, specifications, or other reference materials without hallucinating. Available only in Container Mode due to the need for persistent document storage and vector indexing.","intents":["Ask questions about project documentation or API specs without manually searching","Get answers grounded in uploaded documents rather than relying on the model's training data","Build a searchable knowledge base from technical documentation"],"best_for":["Teams with large documentation sets who want conversational access to reference materials","Developers onboarding to projects with extensive specs or design documents","Organizations wanting to ground LLM responses in proprietary knowledge without fine-tuning"],"limitations":["Document Q&A only available in Container Mode — requires intermediate API service and persistent storage","Supported document formats unknown — documentation does not specify whether PDFs, Word docs, or only plain text/markdown are supported","Vector indexing quality depends on embedding model — no documentation on which embedding model is used or how to customize it","Retrieval may miss relevant documents if semantic similarity is low — no keyword fallback or hybrid search documented","No document versioning or update mechanism documented — unclear how to refresh indexed documents after changes","Storage capacity and limits unknown — no documentation on maximum document size or number of documents"],"requires":["Visual Studio Code (minimum version unknown)","Container Mode enabled and configured with persistent storage","LlamaIndex and vector database running in the container","Documents in supported format (format list unknown)"],"input_types":["document files (format unknown)","natural language questions (text)"],"output_types":["answers grounded in document excerpts (text)","retrieved document references (metadata unknown)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-nr-codetools-localaipilot__cap_5","uri":"capability://tool.use.integration.multi.model.provider.abstraction.with.local.and.remote.fallback","name":"multi-model provider abstraction with local and remote fallback","description":"Abstracts the underlying LLM provider through a unified interface, allowing users to configure and switch between local Ollama models (Deepseek-R1, etc.) and remote providers (OpenAI, Google Gemini, Cohere, Anthropic, Codestral/Mistral) via settings. The extension routes all inference requests through a provider-agnostic layer that handles authentication, API formatting, and response parsing, enabling users to choose between privacy (local) and capability (remote) without changing workflows. Configuration is managed through VS Code settings (Settings > Extensions > Local AI Pilot > Mode), with support for both Standalone Mode (direct Ollama) and Container Mode (intermediate API service).","intents":["Switch between local and cloud models based on privacy requirements or capability needs","Use the best model for each task (e.g., Deepseek-R1 for reasoning, GPT-4 for general coding)","Maintain code privacy by default while allowing opt-in to cloud services for specific tasks"],"best_for":["Developers wanting flexibility to choose between privacy and capability on a per-task basis","Teams with mixed requirements (some tasks require local processing, others benefit from cloud models)","Organizations evaluating different LLM providers without rewriting tooling"],"limitations":["Model selection mechanism not documented — unclear how users switch between configured providers at runtime","API key management for remote providers not documented — unclear whether keys are stored in VS Code secrets or plaintext config","No documented model parameter customization (temperature, max_tokens, etc.) — unclear if users can tune inference behavior","Provider-specific features may not be exposed — e.g., OpenAI's function calling or Anthropic's extended thinking may not be accessible","Fallback behavior not documented — unclear what happens if primary provider is unavailable"],"requires":["Visual Studio Code (minimum version unknown)","For local models: Ollama installed and running with at least one model pulled","For remote models: API key for chosen provider (OpenAI, Gemini, Cohere, Anthropic, or Codestral)"],"input_types":["provider configuration (settings)","API keys (for remote providers)","model selection (implicit or explicit)"],"output_types":["unified LLM responses (text/code)","provider-agnostic completions"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-nr-codetools-localaipilot__cap_6","uri":"capability://memory.knowledge.configurable.project.context.injection.for.multi.file.awareness","name":"configurable project context injection for multi-file awareness","description":"Allows users to explicitly specify file paths (relative or absolute) that should be included as context when generating completions or analyzing code. The extension reads these configured files into memory and injects their contents into prompts sent to the LLM, enabling the model to understand cross-file dependencies, shared types, and architectural patterns without requiring automatic project tree discovery. Configuration is done via extension settings (documented as 'Provide the paths of files to use as additional context'), and context is applied to all inference operations (completion, chat, explanation, repair).","intents":["Provide the LLM with type definitions or interfaces from other files so completions are type-aware","Include architectural patterns or shared utilities so the model understands project conventions","Manually control context size to avoid exceeding model context windows"],"best_for":["Developers working on codebases with strong architectural patterns that benefit from explicit context","Teams with strict context window budgets who want fine-grained control over what context is included","Projects where automatic context discovery would be too expensive or unreliable"],"limitations":["Requires manual file path configuration — no automatic project tree discovery or intelligent context selection","Scaling limitation — adding many files to context increases prompt size and latency; no documented limit on number of files","No intelligent context prioritization — all configured files are included equally, even if some are more relevant than others","File changes require manual refresh — no automatic detection of file modifications or invalidation of cached context","No relative path resolution documented — unclear how paths are resolved relative to workspace root or extension installation directory"],"requires":["Visual Studio Code (minimum version unknown)","Explicit file paths configured in extension settings","Files must exist and be readable by the extension process"],"input_types":["file paths (configuration)","file contents (read from disk)"],"output_types":["injected context (appended to prompts)","enhanced LLM responses (code/text)"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-nr-codetools-localaipilot__cap_7","uri":"capability://automation.workflow.dual.mode.architecture.with.standalone.and.container.deployment.options","name":"dual-mode architecture with standalone and container deployment options","description":"Provides two operational modes that users can select via settings: Standalone Mode connects directly to a local Ollama instance for minimal latency and maximum privacy, while Container Mode routes requests through an intermediate API service that enables advanced features like chat history, document indexing, and caching. The extension detects the selected mode and adjusts its behavior accordingly — Standalone Mode disables features requiring persistent state (Document Q&A, chat history), while Container Mode enables them. This architecture allows users to choose between simplicity/privacy (Standalone) and capability/persistence (Container) without installing different extensions.","intents":["Use local models with minimal overhead and latency by connecting directly to Ollama","Enable advanced features like persistent chat history and document indexing by deploying a container service","Switch deployment strategies without changing extension configuration"],"best_for":["Solo developers prioritizing privacy and latency who use Standalone Mode","Teams deploying shared infrastructure who use Container Mode for persistence and collaboration","Organizations evaluating different deployment strategies without committing to one"],"limitations":["Container Mode configuration not documented — unclear how to set up the intermediate API service or what it requires","Feature parity between modes not fully documented — Document Q&A and chat history are Container-only, but other feature availability is unclear","Standalone Mode limitations not explicitly stated — unclear what happens if user tries to access Container-only features in Standalone Mode","No automatic mode detection — users must manually select mode in settings; no intelligent fallback if configured mode is unavailable","Container Mode adds architectural complexity and potential latency — no performance comparison between modes documented"],"requires":["Visual Studio Code (minimum version unknown)","For Standalone Mode: Ollama installed and running locally","For Container Mode: Intermediate API service running (setup instructions not documented)"],"input_types":["mode selection (settings)","Ollama connection parameters (Standalone Mode)","container API endpoint (Container Mode)"],"output_types":["mode-specific feature availability","routed inference requests"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-nr-codetools-localaipilot__cap_8","uri":"capability://code.generation.editing.keyboard.driven.code.completion.triggering.with.explicit.invocation","name":"keyboard-driven code completion triggering with explicit invocation","description":"Provides explicit keyboard shortcut (SHIFT+ALT+W) to trigger code completion on demand, rather than using always-on completion like traditional IDE autocomplete. When invoked, the extension sends the current file buffer plus configured context files to the LLM and streams suggestions back into the editor. This explicit triggering model reduces resource overhead and allows users to control when inference happens, making it suitable for resource-constrained machines or workflows where constant background inference is undesirable. The shortcut is customizable via VS Code keybindings.","intents":["Get code suggestions without the overhead of always-on completion","Reduce resource consumption on machines with limited GPU/CPU","Maintain focus by controlling when suggestions appear"],"best_for":["Developers on resource-constrained machines (laptops, older hardware)","Users who find always-on completion distracting","Workflows where explicit invocation is preferred over implicit suggestions"],"limitations":["Requires explicit invocation — users must remember to press SHIFT+ALT+W, unlike always-on completion that suggests automatically","Latency between invocation and suggestion display may be noticeable (2-10 seconds on typical hardware), breaking flow","No partial/incremental completion — each invocation generates fresh suggestions rather than refining previous ones","Keybinding conflicts possible — SHIFT+ALT+W may conflict with other extensions or OS-level shortcuts (not documented)"],"requires":["Visual Studio Code (minimum version unknown)","Ollama running locally or remote model configured","Keyboard shortcut available and not bound to other commands"],"input_types":["keyboard input (SHIFT+ALT+W)","current file buffer","configured context files"],"output_types":["inline code suggestions (text/code blocks)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-nr-codetools-localaipilot__cap_9","uri":"capability://automation.workflow.freemium.pricing.model.with.free.local.inference.and.optional.premium.features","name":"freemium pricing model with free local inference and optional premium features","description":"Offers the extension free of charge with full support for local Ollama inference, enabling users to use local models (Deepseek-R1, etc.) without paying. Premium features (if any exist) are not documented, but the freemium model suggests that some advanced capabilities may require payment or subscription. The free tier includes all core features: code completion, chat, explanation, bug fixing, and code review with local models. Remote model providers (OpenAI, Gemini, etc.) require their own API keys and billing, but the extension itself does not charge for using them.","intents":["Use AI-powered code assistance without paying for cloud services","Evaluate the extension with local models before committing to premium features","Access core coding features (completion, chat, explanation) without subscription"],"best_for":["Solo developers and small teams with limited budgets","Developers wanting to avoid cloud service costs by using local models","Users evaluating the extension before committing to premium features"],"limitations":["Premium features not documented — unclear what (if anything) requires payment beyond the free tier","Local model quality varies — free tier is limited to whatever models users can run locally, which may be lower quality than premium cloud models","No documented free tier limits — unclear if there are rate limits, usage quotas, or other restrictions on free local inference","Remote model costs not included — using OpenAI, Gemini, or other providers requires separate paid subscriptions"],"requires":["Visual Studio Code (minimum version unknown)","For free tier: Ollama and at least one local model"],"input_types":["none (pricing is transparent)"],"output_types":["free access to core features"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Visual Studio Code (minimum version unknown)","Ollama installed and running locally with at least one model pulled (e.g., ollama pull deepseek-r1)","Sufficient GPU VRAM (8GB+ recommended for 13B models, 16GB+ for larger models)","LF line endings in source files (CRLF may cause formatting issues)","Container Mode enabled and configured (requires intermediate API service running)","Ollama or remote model provider (OpenAI, Gemini, Cohere, Anthropic, Codestral) configured","Network connectivity to container API service","Source files with LF line endings (not CRLF)","VS Code configured to use LF line endings (Settings > Files: End of Line > LF)","Ollama running locally with a model, OR configured remote API key (OpenAI/Gemini/Cohere/Anthropic/Codestral)"],"failure_modes":["Completion quality depends entirely on local model capability — smaller models (7B-13B parameters) produce lower-quality suggestions than cloud alternatives","Requires explicit file path configuration for multi-file context; no automatic project tree discovery means missing context from files not explicitly listed","Inference latency varies with hardware; typical 2-10 second completion times on consumer GPUs vs <500ms for cloud services","Context window limited by model size — cannot include entire large codebases, only explicitly configured files","Chat history and caching only available in Container Mode — Standalone Mode has no persistence (each message is stateless)","No documented export mechanism for chat history — unclear if conversations can be saved to disk or shared","Context window limitations mean very long conversations may lose early messages when context fills up","No multi-user collaboration — chat is per-extension instance, not shared across team members","Explicit LF requirement may conflict with Windows-native projects that use CRLF","No automatic CRLF-to-LF conversion documented — users must manually configure their editor or git to use LF","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.44,"quality":0.47,"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=local-ai-pilot-ollama-deepseek-r1-and-more","compare_url":"https://unfragile.ai/compare?artifact=local-ai-pilot-ollama-deepseek-r1-and-more"}},"signature":"SXimB8xuS1duhJtUjc7e8lQDatVnEqyR+SnbaMx+eZtM9vfDYx2gBo0WHse/J5ebkM5dCDvaIi+FoGB3Ky85Dg==","signedAt":"2026-06-20T07:02:38.635Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/local-ai-pilot-ollama-deepseek-r1-and-more","artifact":"https://unfragile.ai/local-ai-pilot-ollama-deepseek-r1-and-more","verify":"https://unfragile.ai/api/v1/verify?slug=local-ai-pilot-ollama-deepseek-r1-and-more","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"}}