{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-ekbanasolutions-codellm","slug":"codellm-use-ollama-and-openai-to-write-code","name":"Codellm: Use Ollama and OpenAI to write code","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=ekbanasolutions.codellm","page_url":"https://unfragile.ai/codellm-use-ollama-and-openai-to-write-code","categories":["code-editors"],"tags":["ai","assistance","chatgpt","code","codellm","coding assistance","copilot","explain","find bugs","gemma","gpt3","gpt4","history","llama2","llm","offline","offline chat","offline gpt","offline llm","ollama","openai","pin conversation","programming","refactor","save history","upload history"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-ekbanasolutions-codellm__cap_0","uri":"capability://code.generation.editing.dual.backend.code.generation.with.local.first.fallback","name":"dual-backend code generation with local-first fallback","description":"Generates code via configurable backend selection between local OLLAMA models (offline-capable) and cloud OpenAI models (GPT-3/GPT-4/ChatGPT), with temperature and token limits adjustable per query. The extension maintains a unified prompt interface that routes to either backend without requiring code changes, enabling developers to switch between offline and cloud inference within VS Code preferences. Context is passed as selected code blocks or free-form queries through the sidebar input box.","intents":["I want to generate code snippets without sending data to external servers","I need to switch between local and cloud models based on code sensitivity","I want to control inference parameters like temperature and token count per request"],"best_for":["solo developers building LLM agents with privacy constraints","teams working with proprietary codebases unwilling to use cloud APIs","developers prototyping with free OpenAI models before scaling to paid tiers"],"limitations":["Local OLLAMA models typically have lower code quality than GPT-4; no automatic fallback if local model fails","Cloud models require internet connectivity and valid OpenAI API key; token limits are adjustable but not documented with defaults","No built-in model comparison or A/B testing across backends within single query","Temperature and token parameters are global settings, not per-command overrides"],"requires":["VS Code (minimum version unknown)","OLLAMA installed and running locally (if using local models) or OpenAI API key (if using cloud models)","Network connectivity for cloud models; offline capability requires OLLAMA setup"],"input_types":["free-form text query","selected code block from editor","conversation history context"],"output_types":["code snippet (insertable directly into editor)","plain text explanation","structured code with syntax highlighting"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ekbanasolutions-codellm__cap_1","uri":"capability://code.generation.editing.context.aware.code.explanation.with.selection.scoped.analysis","name":"context-aware code explanation with selection-scoped analysis","description":"Analyzes selected code blocks and generates natural-language explanations by sending the selection to the configured LLM backend (local OLLAMA or OpenAI). The explanation capability is triggered via right-click context menu or command palette (`Codellm: Explain selection`) and returns formatted text in the editor panel. The extension preserves code context by passing only the selected block, avoiding full-file overhead while maintaining semantic accuracy.","intents":["I need to understand what a code block does without reading documentation","I want to explain legacy code to new team members quickly","I need to document unfamiliar patterns or library usage in my codebase"],"best_for":["junior developers learning unfamiliar codebases","teams onboarding new engineers to legacy systems","developers debugging third-party library code"],"limitations":["Explanation quality depends entirely on selected LLM backend; local OLLAMA models may produce less detailed explanations than GPT-4","Only analyzes selected code blocks, not cross-file dependencies or broader architectural context","No caching of explanations; repeated queries on same code block trigger new LLM calls","Explanation format is plain text; no structured output (e.g., JSON with parameters, return types, complexity analysis)"],"requires":["VS Code with Codellm extension installed","Active code selection in editor","Configured LLM backend (OLLAMA or OpenAI API key)"],"input_types":["selected code block (any language supported by VS Code syntax highlighting)"],"output_types":["plain text explanation","formatted markdown (if backend supports it)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ekbanasolutions-codellm__cap_10","uri":"capability://tool.use.integration.right.click.context.menu.integration.for.code.specific.commands","name":"right-click context menu integration for code-specific commands","description":"Integrates code-specific LLM commands (Explain, Refactor, Find Problems, Optimize) into VS Code's right-click context menu. When a code block is selected, right-clicking displays menu options for each command, triggering the corresponding LLM action on the selection. This integration eliminates command-palette navigation for frequent tasks and provides a discoverable interface for code-specific operations.","intents":["I want quick access to code analysis commands without using the command palette","I need to apply multiple LLM operations to the same code block sequentially","I want a discoverable interface for code-specific LLM capabilities"],"best_for":["developers preferring mouse-based workflows over keyboard shortcuts","teams with users unfamiliar with VS Code command palette","engineers performing rapid code review and analysis"],"limitations":["Context menu is only available when code is selected; no menu for free-form queries","Menu items are fixed; no customization of which commands appear in context menu","Right-click triggers menu on every selection; no filtering based on file type or language","Menu items are language-agnostic; no language-specific commands (e.g., 'Optimize for Python')"],"requires":["VS Code with Codellm extension","Selected code block in editor"],"input_types":["selected code block"],"output_types":["LLM response in editor panel (varies by command)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ekbanasolutions-codellm__cap_11","uri":"capability://tool.use.integration.freemium.pricing.model.with.free.openai.tier.support","name":"freemium pricing model with free openai tier support","description":"Offers the extension as freemium software with free access to OpenAI's free-tier models (ChatGPT, code-davinci-002) and local OLLAMA models. Paid OpenAI models (GPT-3, GPT-4, text-davinci-003) require an OpenAI API key and incur usage costs. The extension does not charge for its own usage; costs are determined by the underlying LLM provider (OpenAI or OLLAMA). This pricing model enables developers to start using the extension without upfront costs.","intents":["I want to try AI-assisted coding without paying for the extension","I need to use free OpenAI models for cost-sensitive projects","I want to avoid vendor lock-in by using local OLLAMA models"],"best_for":["solo developers and students exploring AI-assisted coding","teams evaluating LLM-based code generation before committing to paid tiers","organizations with budget constraints preferring free or self-hosted models"],"limitations":["Free OpenAI models (ChatGPT, code-davinci-002) have lower code quality and capability than GPT-4","Free tier models may have rate limits or usage quotas (not documented)","OLLAMA models require local hardware and setup; no managed service option","Paid OpenAI models require API key and incur per-token costs; no pricing transparency in extension"],"requires":["VS Code with Codellm extension (free)","OpenAI account (free tier) or OLLAMA installation (free, self-hosted)"],"input_types":["none (pricing is determined by LLM provider usage)"],"output_types":["none (pricing model, not a capability)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ekbanasolutions-codellm__cap_2","uri":"capability://code.generation.editing.refactoring.suggestion.generation.with.custom.prompt.templates","name":"refactoring suggestion generation with custom prompt templates","description":"Generates refactoring suggestions for selected code by routing the selection through a customizable prompt template to the configured LLM backend. The `Codellm: Refactor selection` command applies user-defined prompt customization (configurable via VS Code preferences) to guide the LLM toward specific refactoring goals (e.g., performance, readability, design patterns). Suggestions are returned as text in the editor panel and can be manually applied or copied into the editor.","intents":["I want to improve code readability without manually rewriting it","I need suggestions for applying design patterns to existing code","I want to optimize performance-critical sections with LLM guidance"],"best_for":["developers performing code reviews and seeking improvement suggestions","teams standardizing code style across a codebase","engineers learning design patterns by seeing LLM-suggested refactorings"],"limitations":["Refactoring suggestions are not automatically applied; manual review and copy-paste required","Custom prompt templates require manual configuration; no pre-built templates for common patterns (SOLID, DRY, etc.)","No validation that suggested refactorings maintain semantic equivalence or pass tests","LLM may suggest refactorings that introduce breaking changes or incompatibilities with dependencies"],"requires":["VS Code with Codellm extension","Selected code block in editor","Configured LLM backend","Optional: custom prompt template defined in VS Code preferences"],"input_types":["selected code block","custom prompt template (string, user-defined)"],"output_types":["refactored code snippet","plain text suggestions with explanation"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ekbanasolutions-codellm__cap_3","uri":"capability://code.generation.editing.automated.bug.detection.and.problem.reporting","name":"automated bug detection and problem reporting","description":"Scans selected code blocks for potential bugs, anti-patterns, and code smells by submitting the selection to the configured LLM backend with a problem-detection prompt. The `Codellm: Find problems` command returns a list of identified issues with explanations in the editor panel. The extension does not modify code; it only reports findings for manual review. Problem detection leverages the LLM's training data on common vulnerabilities and code issues.","intents":["I want to catch bugs before code review without running static analysis tools","I need to identify security vulnerabilities in legacy code quickly","I want to find performance bottlenecks or inefficient patterns in my code"],"best_for":["developers performing self-review before submitting pull requests","teams without access to enterprise static analysis tools","security-conscious developers auditing third-party code"],"limitations":["Problem detection is heuristic-based on LLM training; may miss real bugs or report false positives","No integration with test suites or CI/CD pipelines; findings are not actionable without manual verification","Cannot detect runtime errors, type mismatches (in dynamically-typed languages), or issues requiring full codebase context","Local OLLAMA models may have lower bug-detection accuracy than GPT-4; no confidence scores provided"],"requires":["VS Code with Codellm extension","Selected code block","Configured LLM backend"],"input_types":["selected code block (any programming language)"],"output_types":["list of identified problems with descriptions","plain text report"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ekbanasolutions-codellm__cap_4","uri":"capability://code.generation.editing.code.optimization.suggestion.with.performance.focused.prompting","name":"code optimization suggestion with performance-focused prompting","description":"Generates performance and efficiency optimization suggestions for selected code by routing the selection through a performance-focused prompt to the LLM backend. The `Codellm: Optimize selection` command applies customizable optimization prompts (configurable via VS Code preferences) to guide the LLM toward specific optimization goals (e.g., algorithmic complexity, memory usage, I/O efficiency). Suggestions are returned as text and can be manually reviewed and applied.","intents":["I want to reduce algorithmic complexity in a bottleneck function","I need to optimize memory usage in a data-processing pipeline","I want suggestions for caching, parallelization, or lazy-loading patterns"],"best_for":["performance engineers optimizing hot paths in applications","developers learning optimization techniques through LLM-suggested improvements","teams standardizing performance best practices across codebases"],"limitations":["Optimization suggestions are not benchmarked; LLM may suggest changes that don't improve actual performance","No profiling integration; suggestions are based on code inspection, not runtime metrics","Custom prompt templates required for domain-specific optimizations (e.g., GPU acceleration, distributed computing)","Suggestions may introduce subtle bugs or change code semantics; manual testing required"],"requires":["VS Code with Codellm extension","Selected code block","Configured LLM backend","Optional: custom optimization prompt template"],"input_types":["selected code block","custom prompt template (user-defined)"],"output_types":["optimized code snippet","explanation of optimization rationale"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ekbanasolutions-codellm__cap_5","uri":"capability://memory.knowledge.conversation.history.management.with.persistence.and.export","name":"conversation history management with persistence and export","description":"Maintains a local conversation history of all queries and LLM responses within the extension, accessible via the sidebar panel. The extension supports pinning important conversations, saving history as JSON for export/import, and retrieving past context for follow-up queries. Conversation state is stored locally (storage location unknown) and persists across VS Code sessions. The sidebar displays conversation history with pin/save controls, enabling developers to reference past interactions without re-querying the LLM.","intents":["I want to reference previous code explanations or suggestions without re-querying the LLM","I need to export conversation history for documentation or team sharing","I want to pin important code generation results for quick access"],"best_for":["developers building complex features requiring iterative LLM assistance","teams documenting code generation decisions for future reference","engineers sharing LLM-assisted solutions with colleagues"],"limitations":["Conversation history is stored locally; no cloud sync or multi-device access","No built-in search or filtering of conversation history; manual scrolling required","Export format is JSON; no integration with documentation tools or wikis","Storage size limits unknown; no automatic cleanup or archival of old conversations","Pinned conversations are extension-local; not shareable via version control or team collaboration tools"],"requires":["VS Code with Codellm extension","Local file system write access (for history storage)"],"input_types":["conversation history (auto-generated from queries and responses)"],"output_types":["JSON export file","sidebar panel display with pin/save metadata"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ekbanasolutions-codellm__cap_6","uri":"capability://memory.knowledge.codebase.embedding.and.vector.based.context.retrieval","name":"codebase embedding and vector-based context retrieval","description":"Embeds entire codebases into vector storage (Redis locally or OpenAI cloud) to enable context-aware code generation and queries. The extension supports uploading files (PDF, DOCX, JSON, TXT) and indexing them for semantic search. When generating code or explanations, the extension can retrieve relevant code snippets from the vector store to augment prompts, improving LLM accuracy for codebase-specific tasks. Embeddings are generated via local or OpenAI embedding models (configurable).","intents":["I want code generation to be aware of my entire codebase's patterns and conventions","I need to retrieve relevant code examples from a large codebase without manual search","I want to augment LLM prompts with semantically similar code for better suggestions"],"best_for":["teams with large codebases (10k+ lines) requiring context-aware code generation","developers working with unfamiliar codebases who need pattern discovery","organizations building custom code generation workflows with domain-specific knowledge"],"limitations":["Embedding generation is one-time or manual; no automatic re-indexing when codebase changes","Redis setup required for local embeddings; no built-in Redis server, requires external installation","OpenAI embeddings require API calls and incur costs; no pricing information provided","Vector retrieval quality depends on embedding model; no tuning of retrieval parameters (e.g., similarity threshold, top-k results)","Supported file types are limited (PDF, DOCX, JSON, TXT); no support for code files directly (e.g., .py, .js, .go)"],"requires":["VS Code with Codellm extension","Redis server (if using local embeddings) or OpenAI API key (if using cloud embeddings)","Files to embed in supported formats (PDF, DOCX, JSON, TXT)","Sufficient storage for vector database (size depends on codebase size)"],"input_types":["codebase files (PDF, DOCX, JSON, TXT)","query text for semantic search"],"output_types":["vector embeddings (stored in Redis or OpenAI)","retrieved code snippets (augmented in LLM prompts)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ekbanasolutions-codellm__cap_7","uri":"capability://code.generation.editing.inline.code.snippet.insertion.from.llm.responses","name":"inline code snippet insertion from llm responses","description":"Enables one-click insertion of code snippets from LLM responses directly into the active editor. When the LLM generates code, the response is displayed in the editor panel with clickable code blocks. Clicking a snippet inserts it at the current cursor position or replaces the selected text. This capability eliminates manual copy-paste workflows and integrates code generation output directly into the editing flow.","intents":["I want to quickly apply generated code without manual copy-paste","I need to insert multiple code snippets from a single LLM response","I want to preview generated code before committing it to the file"],"best_for":["developers using code generation frequently in their workflow","teams prototyping features rapidly with LLM assistance","engineers iterating on code generation results without context switching"],"limitations":["Insertion is not undoable via LLM; requires manual undo (Ctrl+Z) if insertion is incorrect","No preview or diff view before insertion; code is inserted directly without review","Multiple snippet insertion requires sequential clicks; no batch insertion","Insertion respects cursor position but may not handle indentation or formatting correctly in all languages"],"requires":["VS Code with Codellm extension","Active editor with open file","LLM response containing code snippets"],"input_types":["code snippet from LLM response (any language)"],"output_types":["inserted code in active editor at cursor position"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ekbanasolutions-codellm__cap_8","uri":"capability://text.generation.language.sidebar.based.conversational.query.interface","name":"sidebar-based conversational query interface","description":"Provides a dedicated sidebar panel in VS Code with a text input box for free-form queries to the configured LLM backend. The sidebar maintains conversation context across queries, displays responses in a scrollable panel, and integrates with the editor's selection context (selected code can be included in queries). The `Ask Codellm` command activates the sidebar input, enabling developers to ask general questions, request code generation, or seek explanations without using command palette.","intents":["I want to ask general coding questions without leaving the editor","I need to include selected code in a free-form query to the LLM","I want to maintain a conversation thread with the LLM across multiple queries"],"best_for":["developers preferring chat-based interaction over command-palette commands","teams using LLM assistance for exploratory coding and learning","engineers building complex features requiring iterative LLM guidance"],"limitations":["Sidebar input is single-line; no multi-line query support (unclear if line breaks are supported)","Conversation context is maintained locally; no cross-session context persistence beyond history export","No syntax highlighting or code block formatting in sidebar input; queries are plain text","Response display is text-only; no interactive elements (e.g., buttons, links) in responses"],"requires":["VS Code with Codellm extension","Sidebar panel visible (can be toggled via View menu)"],"input_types":["free-form text query","optional: selected code block from editor"],"output_types":["plain text response from LLM","formatted markdown (if backend supports it)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ekbanasolutions-codellm__cap_9","uri":"capability://tool.use.integration.configurable.model.and.inference.parameters","name":"configurable model and inference parameters","description":"Exposes LLM model selection, temperature, and token count as configurable parameters in VS Code preferences. Developers can switch between OLLAMA local models and OpenAI cloud models (GPT-3, GPT-4, ChatGPT, text-davinci-003, code-davinci-002) without restarting the extension. Temperature and token limits are adjustable globally (specific ranges and defaults unknown). Configuration is persisted in VS Code settings and applied to all subsequent queries.","intents":["I want to switch between local and cloud models based on code sensitivity","I need to adjust inference parameters (temperature, tokens) for different tasks","I want to use free OpenAI models (code-davinci-002, ChatGPT) instead of paid tiers"],"best_for":["developers experimenting with different LLM backends","teams managing costs by switching between free and paid OpenAI models","engineers tuning inference parameters for specific code generation tasks"],"limitations":["Configuration is global; no per-command or per-file model overrides","Temperature and token ranges are not documented; users must discover valid values through trial","No validation of configuration values; invalid settings may cause silent failures","Model switching requires VS Code preferences UI; no in-extension configuration panel","No automatic fallback if configured model is unavailable (e.g., OLLAMA server down)"],"requires":["VS Code with Codellm extension","Access to VS Code Preferences (Ctrl+,)","Configured LLM backend (OLLAMA or OpenAI API key)"],"input_types":["configuration values: model name (string), temperature (float), token count (integer)"],"output_types":["applied configuration (persisted in VS Code settings.json)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["VS Code (minimum version unknown)","OLLAMA installed and running locally (if using local models) or OpenAI API key (if using cloud models)","Network connectivity for cloud models; offline capability requires OLLAMA setup","VS Code with Codellm extension installed","Active code selection in editor","Configured LLM backend (OLLAMA or OpenAI API key)","VS Code with Codellm extension","Selected code block in editor","VS Code with Codellm extension (free)","OpenAI account (free tier) or OLLAMA installation (free, self-hosted)"],"failure_modes":["Local OLLAMA models typically have lower code quality than GPT-4; no automatic fallback if local model fails","Cloud models require internet connectivity and valid OpenAI API key; token limits are adjustable but not documented with defaults","No built-in model comparison or A/B testing across backends within single query","Temperature and token parameters are global settings, not per-command overrides","Explanation quality depends entirely on selected LLM backend; local OLLAMA models may produce less detailed explanations than GPT-4","Only analyzes selected code blocks, not cross-file dependencies or broader architectural context","No caching of explanations; repeated queries on same code block trigger new LLM calls","Explanation format is plain text; no structured output (e.g., JSON with parameters, return types, complexity analysis)","Context menu is only available when code is selected; no menu for free-form queries","Menu items are fixed; no customization of which commands appear in context menu","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.41,"quality":0.49,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.9,"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":"2026-05-24T12:16:34.118Z","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=codellm-use-ollama-and-openai-to-write-code","compare_url":"https://unfragile.ai/compare?artifact=codellm-use-ollama-and-openai-to-write-code"}},"signature":"6y5iBOsetz2PaEgBDlnTISp5Yw+4tDV6Cd3hylZEIFLI06UPUawn7lAt4CXORB3Vi76RQ0OIuIBG1dECVu/dCQ==","signedAt":"2026-06-15T15:20:45.675Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/codellm-use-ollama-and-openai-to-write-code","artifact":"https://unfragile.ai/codellm-use-ollama-and-openai-to-write-code","verify":"https://unfragile.ai/api/v1/verify?slug=codellm-use-ollama-and-openai-to-write-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"}}