{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-johnny-zhao-oai-compatible-copilot","slug":"oai-compatible-provider-for-copilot","name":"OAI Compatible Provider for Copilot","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=johnny-zhao.oai-compatible-copilot","page_url":"https://unfragile.ai/oai-compatible-provider-for-copilot","categories":["code-editors"],"tags":["ai","anthropic","chat","claude","copilot","gemini","git-commit","github-copilot","language-model","language-models","llm","ollama","openai","openai-compatible","openai-responses","thinking"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-johnny-zhao-oai-compatible-copilot__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.model.injection.into.copilot.chat","name":"multi-provider llm model injection into copilot chat","description":"Intercepts GitHub Copilot Chat's native model picker and injects custom OpenAI-compatible provider models (OpenAI, Anthropic, Ollama, Gemini, DeepSeek, etc.) as selectable options. Works by registering models via VS Code extension API and mapping them to configured API endpoints with per-model context windows, temperature, and token limits. Users switch between providers directly in Copilot Chat's UI without leaving the editor.","intents":["Use local Ollama models or cheaper third-party LLMs instead of GitHub's default Copilot model","Switch between multiple AI providers (Claude, GPT, Gemini) from a single chat interface","Route different coding tasks to different models based on capability (e.g., reasoning models for architecture, fast models for completions)","Avoid vendor lock-in by maintaining flexibility to swap providers without changing workflows"],"best_for":["Individual developers using GitHub Copilot (free tier) who want provider flexibility","Teams standardizing on alternative LLM providers (Anthropic, local Ollama) while keeping Copilot Chat UX","Cost-conscious developers seeking cheaper inference endpoints than GitHub's default offering"],"limitations":["NOT available to Copilot Business or Copilot Enterprise users — explicitly incompatible with organizational licenses","Requires manual API key management and storage in VS Code settings (no automatic credential rotation)","Depends on external provider API availability and uptime — no fallback if primary provider is down","Context window limited by per-model configuration; no automatic context optimization across providers with different token limits"],"requires":["VS Code 1.104.0 or higher (released ~January 2025)","GitHub Copilot extension (free or individual subscription, NOT Business/Enterprise)","API key for at least one OpenAI-compatible provider (OpenAI, Anthropic, Ollama, Gemini, etc.)","Network access to configured provider API endpoint"],"input_types":["text (chat queries)","code (from current file via read_file tool)","images (if provider supports vision models)"],"output_types":["text (chat responses)","code (completions, refactoring suggestions)","structured reasoning (if thinking models enabled)"],"categories":["tool-use-integration","llm-provider-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-johnny-zhao-oai-compatible-copilot__cap_1","uri":"capability://automation.workflow.dynamic.model.configuration.ui.with.encrypted.api.key.storage","name":"dynamic model configuration ui with encrypted api key storage","description":"Provides a visual configuration panel (accessible via Command Palette or status bar) for managing multiple AI providers and models without editing JSON. Stores API keys securely in VS Code's encrypted secret storage, displays real-time token usage, and allows per-model customization of context length, max tokens, temperature, and top_p. Supports importing/exporting configurations for team sharing.","intents":["Configure multiple LLM providers and models without touching settings.json","Securely store API keys without exposing them in version control or plaintext config files","Monitor token usage across API calls to track costs and quota consumption","Share model configurations with team members via import/export"],"best_for":["Non-technical users or teams unfamiliar with JSON configuration","Developers managing multiple provider credentials across different projects","Teams needing to standardize model configurations across developers"],"limitations":["API key storage is local to individual VS Code instance — no centralized credential management or team-wide secret rotation","Import/export mechanism not fully documented; format and compatibility with other tools unknown","Token usage display is real-time but mechanism for counting tokens (client-side vs. server-side) not specified","No built-in cost estimation or budget alerts despite token tracking"],"requires":["VS Code 1.104.0 or higher","Access to VS Code's secret storage (standard in all VS Code installations)","Valid API key for each configured provider"],"input_types":["text (model ID, API endpoint URL, API key)","numeric (context_length, max_tokens, temperature, top_p)"],"output_types":["configuration object (stored in VS Code settings)","token usage metrics (displayed in status bar)"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-johnny-zhao-oai-compatible-copilot__cap_10","uri":"capability://automation.workflow.temperature.and.nucleus.sampling.parameter.tuning","name":"temperature and nucleus sampling parameter tuning","description":"Exposes `temperature` and `top_p` parameters for per-model configuration, enabling control over response randomness and diversity. Users adjust these parameters to tune model behavior (e.g., temperature=0 for deterministic code generation, temperature=1.5 for creative writing). Parameters are applied at request time, affecting all responses from that model.","intents":["Generate deterministic code completions by setting temperature to 0","Increase creativity for brainstorming or documentation by raising temperature","Control response diversity with nucleus sampling (top_p) for consistent output","Experiment with different sampling strategies without changing models"],"best_for":["Advanced users fine-tuning model behavior for specific tasks","Teams standardizing on specific sampling parameters for consistency","Developers experimenting with different temperature/top_p combinations"],"limitations":["No guidance on recommended values for different tasks — users must experiment to find optimal settings","Parameter effects vary significantly between models — settings that work for GPT may not work for Claude","No validation that parameters are within valid ranges (0-2 for temperature, 0-1 for top_p) — invalid values fail silently","No A/B testing or comparison tools to evaluate impact of different parameter settings"],"requires":["VS Code 1.104.0 or higher","Understanding of temperature and top_p parameters and their effects"],"input_types":["numeric (temperature: 0-2, top_p: 0-1)"],"output_types":["configuration object (stored in settings)"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-johnny-zhao-oai-compatible-copilot__cap_11","uri":"capability://automation.workflow.multi.model.configuration.with.same.model.variants","name":"multi-model configuration with same-model variants","description":"Allows the same model to be configured multiple times with different settings (e.g., GLM-4.6 with thinking enabled and GLM-4.6 without thinking). Each configuration is treated as a separate selectable option in Copilot Chat's model picker, enabling quick switching between variants without reconfiguring. Useful for comparing model behavior or using different settings for different tasks.","intents":["Compare same model with different temperature settings to evaluate quality vs. speed tradeoff","Use same model with and without thinking/reasoning mode for different task types","Maintain multiple configurations of the same model for A/B testing or experimentation","Switch between variants quickly without manual reconfiguration"],"best_for":["Researchers or advanced users experimenting with model parameter variations","Teams comparing different configurations of the same model","Developers optimizing for specific use cases (e.g., fast completions vs. high-quality analysis)"],"limitations":["No built-in comparison or metrics to evaluate which variant is better","Model picker becomes cluttered with many variants — no grouping or filtering","No automatic naming convention for variants — users must manually distinguish them","Switching between variants requires manual selection each time — no context-aware auto-selection"],"requires":["VS Code 1.104.0 or higher","Same model available from configured provider"],"input_types":["model configuration (ID, parameters)"],"output_types":["selectable model option in Copilot Chat picker"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-johnny-zhao-oai-compatible-copilot__cap_2","uri":"capability://automation.workflow.source.control.aware.commit.message.generation","name":"source control-aware commit message generation","description":"Integrates with VS Code's source control UI to generate commit messages using configured LLM providers. Analyzes staged changes and passes them to the selected model (via OpenAI-compatible API) to produce contextually relevant commit messages. Supports all configured providers and models, allowing users to choose which LLM generates each commit message.","intents":["Generate descriptive commit messages automatically based on code changes","Use different models for different commit types (e.g., fast model for small fixes, reasoning model for refactors)","Maintain consistent commit message style across a team by standardizing on a specific model"],"best_for":["Individual developers seeking to reduce friction in the commit workflow","Teams with strict commit message conventions who want to enforce them via LLM","Projects with high commit frequency where manual message writing is a bottleneck"],"limitations":["Commit message generation quality depends entirely on the selected model — no built-in validation or style enforcement","No ability to customize the prompt used for commit message generation (fixed system prompt)","Requires staged changes to be accessible to the extension — may not work with all Git workflows or submodules","No integration with conventional commits or other commit message standards"],"requires":["VS Code 1.104.0 or higher","Git repository initialized in the workspace","At least one configured LLM provider with valid API key","Staged changes in the Git index"],"input_types":["git diff (staged changes)"],"output_types":["text (commit message)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-johnny-zhao-oai-compatible-copilot__cap_3","uri":"capability://image.visual.vision.model.support.with.image.input.processing","name":"vision model support with image input processing","description":"Enables chat queries that include images by passing image data to vision-capable models (e.g., GPT-4V, Claude 3 Vision, Gemini Vision). Images are processed through the OpenAI-compatible API format, allowing users to ask questions about code screenshots, architecture diagrams, or UI mockups directly in Copilot Chat. Supports any provider that implements vision in their OpenAI-compatible API.","intents":["Analyze screenshots of error messages or logs to get debugging suggestions","Describe architecture diagrams or system design images to get implementation guidance","Review UI mockups or design images and get code generation suggestions","Extract text or structure from images (OCR-like functionality)"],"best_for":["Developers debugging visual issues (UI bugs, error dialogs, logs)","Teams collaborating on architecture or design using visual tools","Developers working with image-heavy documentation or specifications"],"limitations":["Vision model support depends entirely on the selected provider — not all providers support vision (e.g., Ollama may not)","Image encoding and transmission overhead adds latency compared to text-only queries","No built-in image preprocessing (resizing, compression) — large images may hit API token limits","Vision model pricing is typically higher than text-only models, increasing inference costs"],"requires":["VS Code 1.104.0 or higher","Configured provider with vision model support (e.g., OpenAI GPT-4V, Anthropic Claude 3, Google Gemini)","Valid API key for vision-capable provider","Image file accessible in VS Code workspace or clipboard"],"input_types":["image (PNG, JPEG, WebP, GIF, or other formats supported by provider)","text (query about the image)"],"output_types":["text (analysis or description of image)","code (if asking for code generation based on image)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-johnny-zhao-oai-compatible-copilot__cap_4","uri":"capability://planning.reasoning.thinking.reasoning.model.control.with.advanced.configuration","name":"thinking/reasoning model control with advanced configuration","description":"Exposes configuration options for reasoning and thinking models (e.g., OpenAI o1, Claude with extended thinking) through per-model settings. Allows users to enable/disable thinking modes, control reasoning depth, and configure related parameters without modifying API requests manually. Passes these flags to the provider's API, enabling access to advanced reasoning capabilities directly from Copilot Chat.","intents":["Use reasoning models for complex architectural or algorithmic problems","Enable extended thinking for code review or security analysis tasks","Control reasoning depth to balance quality vs. latency for different problem types","Experiment with thinking models without leaving the Copilot Chat interface"],"best_for":["Developers tackling complex algorithmic or architectural problems","Teams using reasoning models (o1, Claude with extended thinking) for code review","Researchers or advanced users experimenting with different reasoning configurations"],"limitations":["Thinking/reasoning control syntax and exact flags not documented — implementation details unclear","Reasoning models have significantly higher latency and cost than standard models","Not all providers support thinking models — configuration may fail silently if provider doesn't support the feature","Thinking model output format may differ from standard completions, potentially breaking downstream tooling"],"requires":["VS Code 1.104.0 or higher","Configured provider with reasoning model support (OpenAI o1, Anthropic Claude with extended thinking, etc.)","Valid API key for reasoning-capable provider","Sufficient API quota/credits for reasoning model inference (typically 10-100x more expensive than standard models)"],"input_types":["text (chat query)","code (from current file)"],"output_types":["text (reasoning output with thinking process)","code (if reasoning leads to code generation)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-johnny-zhao-oai-compatible-copilot__cap_5","uri":"capability://memory.knowledge.optimized.file.reading.for.large.codebase.context","name":"optimized file reading for large codebase context","description":"Implements a `read_file` tool that intelligently handles large files by avoiding small chunk reads and instead loading entire files or large semantic blocks. Optimizes context window usage by reducing overhead from fragmented file reads, enabling more efficient analysis of large codebases. Works transparently within Copilot Chat's tool-calling system.","intents":["Analyze large source files (>10KB) without losing context due to chunking","Get accurate code analysis for files with complex interdependencies","Reduce token overhead from multiple small file reads in large projects"],"best_for":["Developers working with large monolithic files or legacy codebases","Teams analyzing complex systems where file fragmentation breaks semantic understanding","Projects with tight token budgets where context efficiency is critical"],"limitations":["Exact threshold for 'small chunks' not documented — behavior on medium-sized files unclear","Loading entire large files may exceed model context windows, causing silent failures or truncation","No built-in file size limits or warnings — users may accidentally load multi-megabyte files","Optimization is file-level only; no cross-file context optimization or dependency tracking"],"requires":["VS Code 1.104.0 or higher","File accessible in VS Code workspace","Sufficient context window in configured model to accommodate file size"],"input_types":["file path (string)"],"output_types":["file contents (text)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-johnny-zhao-oai-compatible-copilot__cap_6","uri":"capability://automation.workflow.automatic.api.error.handling.with.exponential.backoff.retry","name":"automatic api error handling with exponential backoff retry","description":"Implements automatic retry logic with exponential backoff for transient API failures (HTTP 429 rate limit, 500/502/503/504 server errors). Retries failed requests without user intervention, improving reliability when providers experience temporary outages or rate limiting. Backoff strategy prevents overwhelming providers during recovery.","intents":["Continue working during provider API outages or rate limiting without manual retry","Automatically recover from transient network failures","Avoid losing work due to temporary API unavailability"],"best_for":["Developers relying on external API providers with occasional downtime","Teams using rate-limited or quota-constrained API tiers","Users in regions with unreliable network connectivity"],"limitations":["Retry behavior and backoff parameters not configurable — fixed strategy may not suit all use cases","Maximum retry attempts and timeout not documented — unclear how long users wait before failure","No user-facing feedback during retries — users may not know a request is being retried vs. hanging","Exponential backoff may be too aggressive for some providers or too lenient for others"],"requires":["VS Code 1.104.0 or higher","Network connectivity to provider API","Provider API supporting standard HTTP error codes"],"input_types":["API request (internal)"],"output_types":["API response (on success after retry) or error message (after max retries exceeded)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-johnny-zhao-oai-compatible-copilot__cap_7","uri":"capability://data.processing.analysis.real.time.token.usage.tracking.and.status.bar.display","name":"real-time token usage tracking and status bar display","description":"Tracks token consumption in real-time for each API call and displays cumulative usage in VS Code's status bar. Provides visibility into token usage across multiple providers and models, enabling cost monitoring and quota management. Token counting mechanism (client-side vs. server-side) not fully documented but integrated into the extension's core workflow.","intents":["Monitor API costs in real-time to avoid unexpected billing","Track token usage across different models to identify cost optimization opportunities","Manage API quotas and rate limits by understanding consumption patterns"],"best_for":["Developers on limited API budgets or free tiers with strict quotas","Teams tracking infrastructure costs and optimizing LLM usage","Users experimenting with multiple providers and comparing token efficiency"],"limitations":["Token counting mechanism not documented — unclear if client-side estimation or server-reported; accuracy unknown","No cost estimation or budget alerts despite token tracking — users must manually calculate costs","Token usage display is cumulative per session; no historical tracking or per-query breakdown","No integration with provider billing APIs — cannot enforce hard limits or auto-disable on quota exhaustion"],"requires":["VS Code 1.104.0 or higher","At least one configured provider with valid API key"],"input_types":["API response (token usage metadata)"],"output_types":["numeric (token count)","status bar display (visual indicator)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-johnny-zhao-oai-compatible-copilot__cap_8","uri":"capability://tool.use.integration.openai.compatible.api.abstraction.layer","name":"openai-compatible api abstraction layer","description":"Abstracts away provider-specific API differences by normalizing all requests to the OpenAI-compatible API format. Supports any provider implementing OpenAI's chat completion API (OpenAI, Anthropic, Ollama, Gemini, DeepSeek, SiliconFlow, ModelScope, Minimax, etc.) without provider-specific code. Maps provider-specific model IDs and parameters to a unified interface.","intents":["Switch between providers without changing code or workflows","Use local Ollama models with the same interface as cloud providers","Avoid vendor lock-in by maintaining provider flexibility","Support emerging providers that implement OpenAI-compatible APIs"],"best_for":["Developers seeking provider independence and avoiding vendor lock-in","Teams using multiple providers (cloud + local) with a unified interface","Organizations standardizing on OpenAI-compatible APIs across tools"],"limitations":["Only supports OpenAI-compatible API format — providers with proprietary APIs (e.g., some closed-source models) not supported","Provider-specific features not exposed through the abstraction — advanced parameters may be lost","API compatibility varies by provider — some may not fully implement OpenAI spec, causing silent failures","No automatic provider capability detection — users must manually configure supported features per provider"],"requires":["VS Code 1.104.0 or higher","Provider with OpenAI-compatible API endpoint","Valid API key for the provider","Network access to provider's API endpoint"],"input_types":["chat completion request (text, images, tools)"],"output_types":["chat completion response (text, structured data)"],"categories":["tool-use-integration","abstraction-layer"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-johnny-zhao-oai-compatible-copilot__cap_9","uri":"capability://automation.workflow.per.model.context.window.and.token.limit.configuration","name":"per-model context window and token limit configuration","description":"Allows fine-grained configuration of context window size and maximum output tokens for each model independently. Users specify `context_length` and `max_tokens` per model, enabling optimization for different use cases (e.g., large context for analysis, small context for fast completions). Configuration is applied at request time, controlling how much context the model receives and how long responses can be.","intents":["Use different context windows for different models based on their capabilities","Optimize for speed by limiting context on fast models, optimize for quality on reasoning models","Work within provider-specific token limits without manual calculation","Prevent context overflow errors by pre-configuring safe limits per model"],"best_for":["Developers using multiple models with different context window capabilities","Teams optimizing for cost by using smaller context windows on cheaper models","Advanced users fine-tuning model behavior for specific tasks"],"limitations":["No automatic context window detection — users must manually look up and configure limits per model","Configuration is static — no dynamic adjustment based on actual available context at runtime","No validation that configured limits match provider's actual capabilities — misconfiguration fails silently","Context window limits don't account for system prompts or tool definitions, which consume tokens"],"requires":["VS Code 1.104.0 or higher","Knowledge of each model's actual context window and token limits (from provider documentation)"],"input_types":["numeric (context_length, max_tokens)"],"output_types":["configuration object (stored in settings)"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.104.0 or higher (released ~January 2025)","GitHub Copilot extension (free or individual subscription, NOT Business/Enterprise)","API key for at least one OpenAI-compatible provider (OpenAI, Anthropic, Ollama, Gemini, etc.)","Network access to configured provider API endpoint","VS Code 1.104.0 or higher","Access to VS Code's secret storage (standard in all VS Code installations)","Valid API key for each configured provider","Understanding of temperature and top_p parameters and their effects","Same model available from configured provider","Git repository initialized in the workspace"],"failure_modes":["NOT available to Copilot Business or Copilot Enterprise users — explicitly incompatible with organizational licenses","Requires manual API key management and storage in VS Code settings (no automatic credential rotation)","Depends on external provider API availability and uptime — no fallback if primary provider is down","Context window limited by per-model configuration; no automatic context optimization across providers with different token limits","API key storage is local to individual VS Code instance — no centralized credential management or team-wide secret rotation","Import/export mechanism not fully documented; format and compatibility with other tools unknown","Token usage display is real-time but mechanism for counting tokens (client-side vs. server-side) not specified","No built-in cost estimation or budget alerts despite token tracking","No guidance on recommended values for different tasks — users must experiment to find optimal settings","Parameter effects vary significantly between models — settings that work for GPT may not work for Claude","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.52,"quality":0.34,"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=oai-compatible-provider-for-copilot","compare_url":"https://unfragile.ai/compare?artifact=oai-compatible-provider-for-copilot"}},"signature":"tEhUOS5PzNZZuB8RMFM4bOs8FJYYHGJPEY/mlK0vQ33ljCgoLEuRgKA/J1tlc01yT/Z0q6+MsXywZn4LypdBDQ==","signedAt":"2026-06-20T12:01:59.876Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/oai-compatible-provider-for-copilot","artifact":"https://unfragile.ai/oai-compatible-provider-for-copilot","verify":"https://unfragile.ai/api/v1/verify?slug=oai-compatible-provider-for-copilot","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"}}