{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-47823501","slug":"openclawdex-open-source-orchestrator-ui-for-claude","name":"OpenClawdex – Open-Source Orchestrator UI for Claude Code and Codex","type":"repo","url":"https://github.com/alekseyrozh/openclawdex","page_url":"https://unfragile.ai/openclawdex-open-source-orchestrator-ui-for-claude","categories":["automation"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-47823501__cap_0","uri":"capability://code.generation.editing.multi.model.code.generation.orchestration.with.claude.and.codex","name":"multi-model code generation orchestration with claude and codex","description":"Provides a unified UI orchestrator that routes code generation requests to both Claude (via Anthropic API) and OpenAI Codex, allowing developers to compare outputs, switch between models, and chain multiple generation steps. The orchestrator manages API credentials, request formatting, and response handling for both proprietary APIs within a single interface, enabling A/B testing of model outputs without switching tools.","intents":["Compare code generation quality between Claude and Codex for the same prompt","Route code generation tasks to the best-performing model for specific use cases","Build multi-step code generation workflows that leverage both models sequentially","Evaluate model performance on internal codebases without exposing code to multiple services"],"best_for":["Teams evaluating Claude vs Codex for code generation workflows","Developers building hybrid AI-assisted coding systems","Research teams benchmarking code generation models"],"limitations":["Requires valid API keys for both Anthropic and OpenAI — no fallback if one service is unavailable","No built-in rate limiting or quota management — relies on upstream API limits","Latency depends on both APIs responding — slowest model blocks comparison view","No caching of identical requests across model calls — each request incurs full API cost"],"requires":["Anthropic API key with Claude access","OpenAI API key with Codex model access","Node.js 14+ or Python 3.8+ (depending on deployment)","Network access to api.anthropic.com and api.openai.com"],"input_types":["code snippets (any language)","natural language prompts","partial code with context","file paths or code selections"],"output_types":["generated code (Python, JavaScript, Java, Go, etc.)","side-by-side comparison views","structured metadata (tokens used, latency, model version)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47823501__cap_1","uri":"capability://code.generation.editing.prompt.templating.and.context.injection.for.code.generation","name":"prompt templating and context injection for code generation","description":"Supports parameterized prompt templates with variable substitution and context injection, allowing developers to define reusable generation patterns that automatically populate with codebase context, file paths, or custom variables. Templates are stored locally and can include system prompts, few-shot examples, and conditional logic to adapt generation behavior based on input type or project structure.","intents":["Create reusable code generation templates for common patterns (unit tests, API handlers, documentation)","Inject project-specific context (naming conventions, architecture patterns) into every generation request","Build parameterized workflows that adapt prompts based on file type or codebase metadata","Share standardized generation templates across team members"],"best_for":["Teams with consistent coding standards wanting to enforce them via AI generation","Developers building domain-specific code generation workflows","Organizations standardizing on particular architectural patterns"],"limitations":["Template variables are simple string substitution — no complex conditional logic or loops","No built-in validation that injected context is syntactically correct or relevant","Template versioning not included — no audit trail of template changes over time","Context injection is manual — no automatic detection of project structure or conventions"],"requires":["Local file system access to store templates","Understanding of target model's prompt format and capabilities","Manual definition of template variables and injection points"],"input_types":["template definition (text with {{variable}} placeholders)","context data (code snippets, file metadata, custom strings)","user input (values for template variables)"],"output_types":["instantiated prompts (ready to send to Claude or Codex)","rendered code generation requests"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47823501__cap_2","uri":"capability://memory.knowledge.code.generation.request.history.and.result.caching","name":"code generation request history and result caching","description":"Maintains a local history of all code generation requests and responses, indexed by prompt hash and model, enabling quick retrieval of previous results without re-querying APIs. The cache stores full request/response metadata including tokens used, latency, and model version, allowing developers to audit generation decisions and avoid duplicate API calls for identical prompts.","intents":["Retrieve previous code generation results without re-running expensive API calls","Audit which model generated which code snippet and under what conditions","Identify patterns in model performance across similar prompts","Reduce API costs by avoiding duplicate requests during iterative development"],"best_for":["Teams with high API usage wanting to optimize costs","Developers iterating on code generation prompts and needing quick feedback","Organizations requiring audit trails of AI-assisted code generation"],"limitations":["Cache is local only — not shared across team members or machines without manual export","No automatic cache invalidation — stale results may be served if models are updated","Hash-based matching is exact — minor prompt variations create separate cache entries","No built-in cleanup or storage limits — cache can grow unbounded over time"],"requires":["Local persistent storage (SQLite, JSON file, or similar)","Sufficient disk space for storing code snippets and metadata"],"input_types":["code generation requests (prompt + model selection)"],"output_types":["cached responses (code + metadata)","cache statistics (hit rate, storage size, age of entries)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47823501__cap_3","uri":"capability://code.generation.editing.side.by.side.code.generation.comparison.and.diff.visualization","name":"side-by-side code generation comparison and diff visualization","description":"Displays Claude and Codex outputs in a split-pane interface with syntax-aware diff highlighting, allowing developers to visually compare generated code quality, style, and correctness. The comparison view shows token counts, generation latency, and model metadata for each output, enabling quick assessment of which model performed better for the specific task.","intents":["Visually compare code quality between Claude and Codex outputs for the same prompt","Identify stylistic differences in how each model approaches the same coding task","Evaluate token efficiency and latency trade-offs between models","Make informed decisions about which model to use for future similar tasks"],"best_for":["Developers evaluating models for production code generation pipelines","Teams benchmarking code generation quality across models","Individual developers optimizing prompt engineering for specific use cases"],"limitations":["Diff visualization requires both models to complete — no streaming comparison","Syntax highlighting is language-specific — unsupported languages display as plain text","No automated quality scoring — comparison is visual and subjective","Large code outputs (>10KB) may render slowly in browser-based UI"],"requires":["Both Claude and Codex API calls to complete successfully","Browser with JavaScript support for interactive diff rendering","Sufficient screen real estate for side-by-side pane layout"],"input_types":["code generation requests (same prompt sent to both models)"],"output_types":["side-by-side code comparison with syntax highlighting","diff visualization (additions, deletions, modifications)","metadata comparison (tokens, latency, model version)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47823501__cap_4","uri":"capability://planning.reasoning.workflow.composition.for.multi.step.code.generation.chains","name":"workflow composition for multi-step code generation chains","description":"Enables definition of sequential code generation workflows where output from one model feeds as context into the next step, supporting both same-model chains (e.g., Claude → Claude refinement) and cross-model chains (e.g., Codex generation → Claude review). Workflows are stored as configuration and can include conditional branching based on output quality or type.","intents":["Generate code with one model, then refine or review with another in a single workflow","Build multi-step pipelines like: generate unit tests → generate implementation → generate documentation","Create conditional workflows that branch based on code quality or type detection","Automate repetitive code generation sequences without manual copy-paste between tools"],"best_for":["Teams building AI-assisted code generation pipelines","Developers automating multi-step coding tasks (generate → test → document)","Organizations standardizing on specific generation workflows"],"limitations":["No built-in quality gates — workflows proceed regardless of output quality","Conditional branching logic is limited — no complex decision trees or loops","Workflow execution is sequential — no parallel model calls to reduce latency","No built-in error handling — workflow stops on API failure without recovery options"],"requires":["Workflow definition format (JSON, YAML, or UI-based builder)","Valid API keys for all models used in the workflow","Understanding of how to structure prompts for multi-step chains"],"input_types":["workflow definition (steps, models, prompts, context passing rules)","initial input (code snippet, requirements, or context)"],"output_types":["final generated code (output of last workflow step)","intermediate results (outputs from each step)","execution log (which models ran, latency, tokens used)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47823501__cap_5","uri":"capability://code.generation.editing.local.codebase.context.extraction.and.injection","name":"local codebase context extraction and injection","description":"Scans local project files to extract relevant context (imports, function signatures, class definitions, architectural patterns) and automatically injects this context into generation prompts. The extractor uses language-specific parsers (AST-based for supported languages, regex-based fallback) to identify semantically relevant code snippets that inform generation without overwhelming the prompt with irrelevant code.","intents":["Automatically include relevant codebase context in generation prompts without manual copy-paste","Generate code that follows existing project conventions and architectural patterns","Reduce prompt engineering effort by letting the tool identify relevant context","Ensure generated code integrates seamlessly with existing codebase"],"best_for":["Developers working on existing codebases wanting AI-assisted code generation","Teams with consistent architectural patterns that should be reflected in generated code","Projects where context relevance matters more than generic code generation"],"limitations":["AST parsing is language-specific — only supported languages get semantic extraction (others use regex fallback)","Context extraction is heuristic-based — may include irrelevant code or miss important context","Large codebases may extract too much context, exceeding token limits","No understanding of project structure — cannot distinguish between core and utility code"],"requires":["Local file system access to project directory","Language-specific parsers for AST extraction (tree-sitter or similar)","Configuration specifying which files/directories to scan"],"input_types":["local project directory path","file type filters (e.g., only .py files)","context relevance hints (e.g., 'find similar functions')"],"output_types":["extracted code snippets (imports, function signatures, class definitions)","context metadata (file paths, line numbers, relevance scores)","injected prompts (original prompt + extracted context)"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47823501__cap_6","uri":"capability://safety.moderation.api.credential.management.and.secure.storage","name":"api credential management and secure storage","description":"Provides secure local storage for Anthropic and OpenAI API keys with encryption at rest, supporting both environment variable injection and UI-based credential entry. The credential manager handles key rotation, validates API keys before use, and prevents accidental exposure of credentials in logs or exported results.","intents":["Securely store API credentials without exposing them in configuration files or logs","Support multiple API key sets for different environments (dev, staging, production)","Validate API keys before attempting requests to provide early error feedback","Prevent accidental credential leakage when sharing results or exporting data"],"best_for":["Teams deploying OpenClawdex in shared or CI/CD environments","Developers wanting to avoid hardcoding API keys in configuration","Organizations with security policies requiring encrypted credential storage"],"limitations":["Encryption is local-only — credentials are only protected at rest, not in transit (relies on HTTPS)","No built-in key rotation — manual credential updates required","No audit logging of credential access — cannot track which users accessed which keys","Encryption key is typically stored locally — compromise of local machine exposes all credentials"],"requires":["Local persistent storage with encryption support (OS keychain, encrypted file, or similar)","Valid API keys from Anthropic and OpenAI"],"input_types":["API keys (text)","environment variable names (for injection)","credential labels (for organization)"],"output_types":["encrypted credential storage","validation status (key is valid/invalid)","credential metadata (creation date, last used)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47823501__cap_7","uri":"capability://code.generation.editing.generation.result.export.and.integration.with.ides","name":"generation result export and integration with ides","description":"Exports generated code directly to files, clipboard, or IDE plugins (VS Code, JetBrains), with options to apply formatting, linting, and syntax validation before export. The export pipeline supports multiple formats (raw code, diff patches, code review comments) and can integrate with version control to create branches or commits for generated code.","intents":["Export generated code directly into project files without manual copy-paste","Apply project-specific formatting and linting rules to generated code before integration","Create version control branches or commits for generated code with metadata","Review generated code in IDE context before committing to main branch"],"best_for":["Developers integrating AI-generated code into existing projects","Teams wanting to maintain code quality standards for AI-generated code","Organizations using version control to track AI-assisted changes"],"limitations":["IDE integration requires plugin installation — not all IDEs supported","Formatting and linting depend on project configuration — may fail if tools are misconfigured","Version control integration requires local git repository — no support for other VCS","No built-in code review workflow — generated code must be manually reviewed before merge"],"requires":["Local file system write access","IDE plugin (for IDE integration)","Git repository (for version control integration)","Formatter and linter configuration (for code quality checks)"],"input_types":["generated code (from Claude or Codex)","target file path or IDE selection","export format (raw, diff, comments)"],"output_types":["exported code files","diff patches","version control commits or branches","code review comments"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Anthropic API key with Claude access","OpenAI API key with Codex model access","Node.js 14+ or Python 3.8+ (depending on deployment)","Network access to api.anthropic.com and api.openai.com","Local file system access to store templates","Understanding of target model's prompt format and capabilities","Manual definition of template variables and injection points","Local persistent storage (SQLite, JSON file, or similar)","Sufficient disk space for storing code snippets and metadata","Both Claude and Codex API calls to complete successfully"],"failure_modes":["Requires valid API keys for both Anthropic and OpenAI — no fallback if one service is unavailable","No built-in rate limiting or quota management — relies on upstream API limits","Latency depends on both APIs responding — slowest model blocks comparison view","No caching of identical requests across model calls — each request incurs full API cost","Template variables are simple string substitution — no complex conditional logic or loops","No built-in validation that injected context is syntactically correct or relevant","Template versioning not included — no audit trail of template changes over time","Context injection is manual — no automatic detection of project structure or conventions","Cache is local only — not shared across team members or machines without manual export","No automatic cache invalidation — stale results may be served if models are updated","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.28,"quality":0.26,"ecosystem":0.46,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","last_scraped_at":"2026-05-04T08:10:04.759Z","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=openclawdex-open-source-orchestrator-ui-for-claude","compare_url":"https://unfragile.ai/compare?artifact=openclawdex-open-source-orchestrator-ui-for-claude"}},"signature":"DNtvomZmDKUIsHdz8tC4ZV8S9WYKEj2I0Ibl/AL+52EzHQRLnmGIASmTNBETPm2SIm3mAENR68OTOpF0sMnhCQ==","signedAt":"2026-06-20T05:43:25.050Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openclawdex-open-source-orchestrator-ui-for-claude","artifact":"https://unfragile.ai/openclawdex-open-source-orchestrator-ui-for-claude","verify":"https://unfragile.ai/api/v1/verify?slug=openclawdex-open-source-orchestrator-ui-for-claude","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"}}