{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"aider","slug":"aider","name":"aider","type":"agent","url":"https://aider.chat","page_url":"https://unfragile.ai/aider","categories":["code-editors"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"aider__cap_0","uri":"capability://code.generation.editing.interactive.terminal.code.chat.repl","name":"interactive-terminal-code-chat-repl","description":"Launches an interactive chat session in the terminal where developers type natural language prompts and receive code modifications in real-time. Aider maintains conversation context across multiple turns within a session, allowing iterative refinement of code changes through back-and-forth dialogue. The REPL integrates directly with the shell environment, requiring only `aider` command invocation in a git-initialized directory.","intents":["I want to chat with an AI about my code changes in my terminal without leaving my shell","I need to iteratively refine code through multiple conversation turns while staying in my development environment","I want to ask follow-up questions about code changes the AI just made"],"best_for":["terminal-first developers who avoid GUI IDEs","developers working over SSH or remote connections","teams with shell-based development workflows"],"limitations":["Session context does not persist between separate aider invocations — each new `aider` command starts fresh conversation history","No documented batch/scripting mode for non-interactive automation","Terminal rendering may have issues with very large diffs or complex multi-file changes"],"requires":["Python 3.9+","git repository initialized in current directory","API key for at least one supported LLM provider (OpenAI, Anthropic, DeepSeek, etc.)","Internet connection for cloud-based models"],"input_types":["text prompts","code snippets pasted in chat","file paths referenced in prompts"],"output_types":["text responses","code diffs displayed in terminal","git commit messages"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_1","uri":"capability://memory.knowledge.codebase.aware.context.mapping","name":"codebase-aware-context-mapping","description":"Automatically scans and indexes the entire local git repository to build an internal map of the codebase structure, file relationships, and code patterns. This map is used to provide the LLM with relevant context about the project without requiring developers to manually specify which files matter. The mapping mechanism reads git-tracked files and understands 100+ programming languages, enabling language-aware code generation across polyglot projects.","intents":["I want the AI to understand my entire project structure so it makes changes that fit the existing codebase","I need the AI to know about related files when I ask for a change in one file","I want to work in large projects without manually specifying context for every request"],"best_for":["developers working on large codebases (100+ files)","teams with complex multi-module or microservice architectures","polyglot projects mixing multiple programming languages"],"limitations":["Codebase mapping mechanism is undocumented — unclear how it handles very large repos (10k+ files) or performance scaling","No control over which files are included/excluded from the map beyond git's `.gitignore`","Mapping is rebuilt on each aider invocation — no caching between sessions","Cannot map external dependencies or third-party libraries, only project source code"],"requires":["git repository with files committed or staged","Project files in supported languages (100+ languages including Python, JavaScript, Rust, Go, C++, Java, etc.)","Sufficient disk space to read entire codebase into memory"],"input_types":["file system (git-tracked files)","git metadata"],"output_types":["internal codebase map (not directly exposed to user)","context injected into LLM prompts"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_10","uri":"capability://automation.workflow.prompt.caching.for.cost.reduction","name":"prompt-caching-for-cost-reduction","description":"Implements prompt caching at the LLM provider level to reduce token consumption and latency for repeated requests. When the same codebase context or file content is used across multiple requests, aider caches the prompt tokens with the provider (e.g., OpenAI's prompt caching, Anthropic's prompt caching), avoiding re-processing of unchanged context. This reduces both API costs and response latency.","intents":["I want to reduce my API costs when making multiple changes to the same codebase","I need faster response times when iterating on the same files","I want to optimize token usage for large codebases"],"best_for":["developers making multiple requests to the same codebase","teams with large codebases where context is expensive","cost-conscious developers using expensive models like GPT-4o"],"limitations":["Prompt caching mechanism is undocumented — unclear how cache keys are generated or invalidated","Provider support for caching is not documented — unclear which providers support it (OpenAI and Anthropic do, others unclear)","Cache invalidation strategy is undocumented — unclear if cache is invalidated when files change","Cost savings are not quantified — no documentation of typical cache hit rates or cost reduction percentages","Caching may not work across sessions if session context is not persisted"],"requires":["LLM provider with prompt caching support (OpenAI, Anthropic)","Multiple requests to the same codebase in a session"],"input_types":["code context","prompts"],"output_types":["cached prompt tokens","reduced API costs"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_11","uri":"capability://automation.workflow.git.aware.undo.and.rollback","name":"git-aware-undo-and-rollback","description":"Integrates with git to provide undo and rollback capabilities for AI-generated changes. Developers can use standard git commands (`git diff`, `git reset`, `git revert`) to inspect, modify, or undo aider's changes. Each aider request results in a git commit, making it easy to revert specific changes or cherry-pick modifications. This leverages git as the source of truth for change management.","intents":["I want to undo a bad AI change using familiar git commands","I need to review what the AI changed before committing it","I want to selectively apply some of the AI's changes but not others"],"best_for":["developers comfortable with git workflows","teams with strict code review processes","projects where change traceability is important"],"limitations":["Undo is limited to git's capabilities — cannot undo changes that have been force-pushed or garbage collected","No aider-specific rollback UI — requires git command-line knowledge","Rollback granularity is per-commit (per aider request) — cannot undo individual file changes within a request","No integration with git hooks or pre-commit checks that might prevent rollback"],"requires":["git repository with commit history","git command-line tool installed"],"input_types":["git commands"],"output_types":["reverted code","updated git history"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_12","uri":"capability://code.generation.editing.coding.convention.specification","name":"coding-convention-specification","description":"Allows developers to specify project-specific coding conventions, style guides, and architectural patterns that aider should follow when generating code. Conventions can be documented in configuration files or communicated in chat, and aider incorporates them into code generation to ensure consistency with existing code. This enables aider to match project style without explicit instruction for every request.","intents":["I want the AI to follow my project's coding style and conventions automatically","I need the AI to understand our architectural patterns and apply them consistently","I want to avoid having to specify style preferences in every request"],"best_for":["teams with strict style guides and architectural standards","large projects where consistency is critical","organizations with established coding conventions"],"limitations":["Convention specification mechanism is undocumented — unclear how conventions are defined or parsed","No documented format for convention files (YAML, JSON, plain text, etc.)","Unclear how aider prioritizes conventions vs. LLM training data when they conflict","No validation that generated code actually follows specified conventions","Convention enforcement depends on LLM capability — weaker models may ignore conventions"],"requires":["Project conventions documented or specified in configuration"],"input_types":["convention specifications","style guide documentation"],"output_types":["code generated according to conventions"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_13","uri":"capability://code.generation.editing.language.agnostic.code.generation","name":"language-agnostic-code-generation","description":"Supports code generation across 100+ programming languages including Python, JavaScript, TypeScript, Rust, Go, C++, Java, Ruby, PHP, HTML, CSS, and many others. The codebase mapping and code generation logic is language-agnostic, allowing aider to work equally well in polyglot projects. Language detection is automatic based on file extensions and content.","intents":["I want to use aider in my Python backend and JavaScript frontend without switching tools","I need AI assistance for a language I'm less familiar with","I want to generate code in multiple languages within the same project"],"best_for":["polyglot projects with multiple programming languages","full-stack developers working across frontend and backend","teams learning new languages"],"limitations":["Language support list is not exhaustively documented — unclear which of 100+ languages have equal quality support","Code generation quality varies significantly by language — popular languages (Python, JavaScript) likely have better support than niche languages","No documented language-specific optimizations or best practices","Linting and testing integration may not work equally well across all languages"],"requires":["Files in supported programming languages"],"input_types":["code in any supported language","natural language requests"],"output_types":["code in requested language"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_14","uri":"capability://code.generation.editing.web.browser.interface","name":"web-browser-interface","description":"Provides a web-based chat interface as an alternative to the terminal REPL, allowing developers to interact with aider through a browser. The web interface supports the same capabilities as the terminal (code generation, file editing, git integration) but with a GUI. Developers can copy code from the browser and paste it into their editor, or use the web interface for code review before applying changes.","intents":["I want to use aider in a browser without the terminal","I need a GUI interface for code review and change management","I want to share aider sessions with teammates through a web link"],"best_for":["developers who prefer GUI over terminal","teams doing collaborative code review","non-technical stakeholders who want to see AI-generated code"],"limitations":["Web interface implementation is undocumented — unclear if it's a separate service, local web server, or cloud-hosted","Git integration in web mode is undocumented — unclear how changes are committed from the browser","No documented support for real-time collaboration or multi-user sessions","Copy/paste workflow for code is manual — less integrated than terminal mode","Web interface may have different feature set than terminal (unclear which features are supported)"],"requires":["Web browser","Access to aider web interface (local or cloud)"],"input_types":["text prompts in browser","code snippets pasted in browser"],"output_types":["code displayed in browser","copy-paste-able code snippets"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_15","uri":"capability://text.generation.language.help.system.and.context.aware.documentation.with.helpcoder","name":"help system and context-aware documentation with helpcoder","description":"Aider includes a help system (aider/website/docs) with context-aware documentation that can be queried from the CLI. The HelpCoder component assembles relevant documentation based on the user's question and provides targeted help without leaving the CLI. This enables developers to learn Aider's features and troubleshoot issues without switching to external documentation.","intents":["I want to learn how to use a specific Aider feature","I need help troubleshooting an error","I want to see examples of how to use Aider","I need to understand a command without leaving the CLI"],"best_for":["new Aider users learning the tool","developers troubleshooting issues","teams onboarding new members"],"limitations":["Help system is static — doesn't reflect real-time changes to documentation","Context-aware help may not find relevant documentation if keywords don't match","No interactive tutorials or guided walkthroughs","Help content is included in the binary, increasing size"],"requires":["Python 3.9+","Aider documentation (included in package)"],"input_types":["help query (text)"],"output_types":["relevant documentation","examples","command help"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_16","uri":"capability://data.processing.analysis.usage.analytics.and.self.referential.development.metrics","name":"usage analytics and self-referential development metrics","description":"Aider collects anonymous usage analytics (aider/website/assets/sample-analytics.jsonl) to track how the tool is used and identify improvement opportunities. The system logs events like model usage, feature adoption, and error rates. This data is used to guide development priorities and understand user behavior patterns. Analytics are anonymized and can be disabled by users.","intents":["I want to understand how Aider is being used","I need data to prioritize feature development","I want to track adoption of new features","I need to identify common errors and pain points"],"best_for":["Aider maintainers and developers","teams evaluating Aider adoption","researchers studying AI tool usage"],"limitations":["Analytics are anonymized — can't track individual users","Users can disable analytics, creating selection bias","Analytics data is not real-time — may lag behind actual usage","Privacy concerns may limit what data can be collected"],"requires":["Analytics collection enabled (default)","Network access to analytics endpoint","Python 3.9+"],"input_types":["usage events (model selection, feature usage, errors)"],"output_types":["aggregated analytics data","usage patterns","error rates"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_2","uri":"capability://code.generation.editing.multi.file.coordinated.editing","name":"multi-file-coordinated-editing","description":"Enables the LLM to modify multiple files in a single request while maintaining consistency across changes. Aider stages all modifications in git before committing, allowing developers to review the complete changeset. The editing system understands file dependencies and can coordinate changes across related files (e.g., updating imports when moving functions, modifying tests when changing APIs).","intents":["I want to refactor code that spans multiple files and have the AI handle all the cross-file updates automatically","I need to add a new feature that requires changes in multiple places (models, controllers, tests, etc.)","I want to see all changes together before committing them"],"best_for":["developers refactoring large features across multiple files","teams implementing features that touch multiple layers (frontend, backend, database)","projects with strict code review processes that require reviewing all changes together"],"limitations":["File deletion is not documented as supported — unclear if aider can remove files as part of multi-file edits","No documented conflict resolution for simultaneous edits to the same file","Coordination logic is undocumented — unclear how aider determines which files need updating","Large changesets (50+ files) may exceed LLM context windows, requiring manual splitting"],"requires":["git repository with write permissions","All target files must be in the same git repository","LLM with sufficient context window (varies by model, typically 4k-200k tokens)"],"input_types":["natural language requests describing multi-file changes","file paths or patterns"],"output_types":["modified file contents","git staging area with all changes","unified diff view"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_3","uri":"capability://automation.workflow.automatic.git.commit.generation","name":"automatic-git-commit-generation","description":"After each code modification, aider automatically commits changes to git with AI-generated commit messages that summarize the modifications. The commit message generation understands the context of the changes and produces messages following conventional commit patterns. This automation eliminates manual commit message writing while maintaining a clean git history.","intents":["I want meaningful commit messages without having to write them myself","I need to maintain a clean git history that documents what the AI changed and why","I want to use git blame and log to understand the history of AI-assisted changes"],"best_for":["developers who want to maintain clean git history with AI-assisted work","teams using conventional commits or semantic versioning","projects where commit history is used for changelog generation"],"limitations":["Commit message generation quality depends on LLM understanding of changes — may produce generic or inaccurate messages for complex refactors","No option to manually edit commit messages before they're written to git","Commit granularity is fixed (one commit per aider request) — cannot split large changes into multiple logical commits","No integration with commit hooks or pre-commit checks"],"requires":["git repository with user.name and user.email configured","Write permissions to git repository"],"input_types":["code changes staged in git"],"output_types":["git commit with generated message","updated git history"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_4","uri":"capability://text.generation.language.voice.to.code.input","name":"voice-to-code-input","description":"Accepts voice input through the terminal, transcribes it to text using speech-to-text technology, and processes the transcribed prompt as a code request. Developers can speak natural language descriptions of features, bug fixes, or test cases, and aider converts them to code modifications. This capability enables hands-free coding for accessibility and rapid prototyping.","intents":["I want to code without typing, using voice commands to describe changes","I need accessibility features for voice-based development","I want to rapidly prototype features by speaking requirements instead of typing"],"best_for":["developers with accessibility needs (RSI, mobility limitations)","rapid prototyping and brainstorming sessions","developers in noisy environments where typing is impractical"],"limitations":["Speech-to-text accuracy depends on microphone quality and ambient noise — may require multiple attempts for complex technical terms","No documented support for code-specific voice commands or domain-specific speech models","Transcription latency may add delay to the development loop","Voice input mechanism is undocumented — unclear which speech-to-text service is used or if it's local vs cloud-based"],"requires":["Microphone or audio input device","Internet connection (if using cloud-based speech-to-text)","Audio permissions in terminal environment"],"input_types":["audio (voice)"],"output_types":["transcribed text prompts","code modifications"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_5","uri":"capability://image.visual.visual.context.injection","name":"visual-context-injection","description":"Accepts images, screenshots, and web pages as input to provide visual context for code generation requests. Developers can paste images of UI designs, architecture diagrams, or reference documentation into the chat, and aider uses vision capabilities to understand the visual content and generate corresponding code. This bridges the gap between design and implementation.","intents":["I want to show the AI a screenshot of a UI design and have it generate the HTML/CSS/JavaScript to match","I need to reference a diagram or documentation image without manually describing it","I want to implement a feature based on a visual mockup or wireframe"],"best_for":["frontend developers implementing designs from mockups","teams with visual design workflows","developers who want to reference images instead of typing descriptions"],"limitations":["Vision capability depends on LLM provider — not all models support image input (e.g., local Ollama models may not)","Image understanding quality varies by model — complex or ambiguous designs may be misinterpreted","No documented support for video input, only static images","Web page input mechanism is undocumented — unclear if it captures full page or viewport, handles dynamic content, etc.","Large images may consume significant token budget"],"requires":["LLM provider with vision capabilities (e.g., GPT-4o, Claude 3.5 Sonnet, Gemini)","Image files in common formats (PNG, JPG, WebP, etc.) or web URLs"],"input_types":["image files","web page URLs","screenshots"],"output_types":["code generated from visual context","text descriptions of visual content"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_6","uri":"capability://code.generation.editing.linting.and.test.integration","name":"linting-and-test-integration","description":"Automatically runs linters and test suites after each code modification, captures their output, and feeds errors back to the LLM for automatic fixing. Aider detects linting violations (style, type errors, etc.) and test failures, then iteratively modifies code until linters pass and tests succeed. This creates a feedback loop where the AI is responsible for code quality, not just functionality.","intents":["I want the AI to fix linting errors automatically without me having to run linters manually","I need the AI to ensure tests pass before committing changes","I want to enforce code style and quality standards through AI-assisted development"],"best_for":["teams with strict linting and testing requirements","projects using type checkers (TypeScript, mypy, etc.)","developers who want to maintain code quality without manual review"],"limitations":["Linter/test integration mechanism is undocumented — unclear which tools are supported or how they're invoked","No documented support for custom linting rules or project-specific quality gates","Infinite loop risk if linter output is ambiguous or contradictory — no documented timeout or max-iteration limits","Test execution may be slow, adding latency to the development loop","No support for flaky tests or tests requiring external services"],"requires":["Linters and test runners installed in project (pytest, jest, eslint, mypy, etc.)","Linters/tests must be runnable from command line","Project must have passing tests before aider can fix failures"],"input_types":["linter output (error messages, warnings)","test failure output"],"output_types":["modified code that passes linters and tests","linter/test execution logs"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_7","uri":"capability://tool.use.integration.ide.file.watching.mode","name":"ide-file-watching-mode","description":"Monitors files in an IDE or editor for AI-specific comments (e.g., `// aider: add error handling`) and automatically processes those comments as code requests. Developers can annotate code with directives in their editor, and aider responds by modifying the file without requiring a separate terminal session. This bridges the gap between editor-based development and aider's terminal-first approach.","intents":["I want to use my IDE normally but trigger aider changes by adding comments to my code","I need to integrate aider into my existing editor workflow without switching to the terminal","I want to request code changes inline while editing"],"best_for":["developers who prefer their IDE but want aider's capabilities","teams with mixed editor preferences (some use VS Code, others use Vim, etc.)","workflows where code review happens in the editor"],"limitations":["File watching mechanism is undocumented — unclear which editors are supported or how file changes are detected","Comment syntax and parsing rules are not documented — unclear what comment formats trigger aider","No documented conflict resolution if the IDE and aider both modify the same file simultaneously","Latency between comment addition and aider response is undocumented","No integration with IDE-native features like inline diagnostics or code actions"],"requires":["IDE or editor with file system access","Aider running in background (mechanism undocumented)","Project in git repository"],"input_types":["code comments with aider directives"],"output_types":["modified files","git commits"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_8","uri":"capability://planning.reasoning.architect.mode.planning","name":"architect-mode-planning","description":"Provides a specialized mode where the LLM focuses on high-level architectural planning and design decisions rather than immediate code generation. In architect mode, aider helps developers think through system design, propose refactoring strategies, and plan multi-step implementations before writing code. This mode is optimized for planning-heavy tasks where code generation should follow design decisions.","intents":["I want to discuss architecture and design with the AI before implementing","I need help planning a large refactor across multiple files","I want to explore design alternatives and tradeoffs before committing to code"],"best_for":["architects and senior developers planning system design","teams doing major refactors or migrations","developers who want to think through design before implementation"],"limitations":["Architect mode behavior and differences from code mode are not documented","No documented output format for architectural plans (text, diagrams, etc.)","Unclear how architect mode transitions to code generation","No integration with architecture documentation tools or ADR (Architecture Decision Record) formats"],"requires":["LLM with strong reasoning capabilities (Claude, GPT-4o, DeepSeek R1 recommended)"],"input_types":["natural language architectural questions","codebase context"],"output_types":["architectural recommendations","design proposals","implementation plans"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__cap_9","uri":"capability://tool.use.integration.multi.provider.llm.support","name":"multi-provider-llm-support","description":"Supports multiple LLM providers and models through a unified interface, allowing developers to switch between OpenAI, Anthropic, DeepSeek, Google Gemini, local models (Ollama, LM Studio), and 10+ other providers without changing their workflow. API keys are configured via environment variables, YAML config files, or command-line arguments. Developers can specify different providers for different requests or set a default provider.","intents":["I want to use Claude for some tasks and GPT-4o for others without switching tools","I need to run aider with local models to avoid cloud costs and latency","I want to compare different models' outputs on the same code task"],"best_for":["developers who want model flexibility and cost optimization","teams with privacy requirements that mandate local model execution","organizations evaluating different LLM providers"],"limitations":["Provider support list is not exhaustively documented — unclear which providers are officially supported vs community-supported","API key management is undocumented — no guidance on secure storage, rotation, or multi-account scenarios","Model-specific capabilities (vision, function calling, etc.) vary by provider — no documented compatibility matrix","Prompt caching support is mentioned but mechanism is undocumented — unclear which providers support it","Cost tracking across providers is not implemented — no per-provider token counting or billing integration"],"requires":["API key for at least one supported provider","For local models: Ollama or LM Studio installed and running","Internet connection for cloud providers"],"input_types":["provider name and API key","model name"],"output_types":["code modifications from selected provider"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aider__headline","uri":"capability://code.generation.editing.ai.pair.programming.tool.for.terminal","name":"ai pair programming tool for terminal","description":"Aider is an AI pair programming assistant that integrates with your terminal and local git repositories, enabling developers to edit code using natural language commands while automatically managing git commits and providing context-aware suggestions.","intents":["best AI pair programming tool","AI coding assistant for terminal","AI code editor with git integration","AI assistant for collaborative coding","top coding agent for developers"],"best_for":["developers looking for AI assistance in coding"],"limitations":[],"requires":["local git repository"],"input_types":["natural language commands"],"output_types":["code modifications","git commits"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":72,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","git repository initialized in current directory","API key for at least one supported LLM provider (OpenAI, Anthropic, DeepSeek, etc.)","Internet connection for cloud-based models","git repository with files committed or staged","Project files in supported languages (100+ languages including Python, JavaScript, Rust, Go, C++, Java, etc.)","Sufficient disk space to read entire codebase into memory","LLM provider with prompt caching support (OpenAI, Anthropic)","Multiple requests to the same codebase in a session","git repository with commit history"],"failure_modes":["Session context does not persist between separate aider invocations — each new `aider` command starts fresh conversation history","No documented batch/scripting mode for non-interactive automation","Terminal rendering may have issues with very large diffs or complex multi-file changes","Codebase mapping mechanism is undocumented — unclear how it handles very large repos (10k+ files) or performance scaling","No control over which files are included/excluded from the map beyond git's `.gitignore`","Mapping is rebuilt on each aider invocation — no caching between sessions","Cannot map external dependencies or third-party libraries, only project source code","Prompt caching mechanism is undocumented — unclear how cache keys are generated or invalidated","Provider support for caching is not documented — unclear which providers support it (OpenAI and Anthropic do, others unclear)","Cache invalidation strategy is undocumented — unclear if cache is invalidated when files change","builder identity is not verified yet","no observed match outcomes 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