{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_codecomplete-ai","slug":"codecomplete-ai","name":"Codecomplete.ai","type":"product","url":"https://codecomplete.ai","page_url":"https://unfragile.ai/codecomplete-ai","categories":["code-editors"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_codecomplete-ai__cap_0","uri":"capability://code.generation.editing.context.aware.code.completion.with.enterprise.model.fine.tuning","name":"context-aware code completion with enterprise model fine-tuning","description":"Generates multi-line code suggestions by analyzing local codebase context and applying fine-tuned language models trained on organization-specific code patterns. Unlike generic models, CodeComplete supports custom model training on internal repositories, enabling suggestions that align with proprietary coding standards, architectural patterns, and domain-specific libraries. The system maintains codebase indexing locally or on-premise to avoid transmitting proprietary code to external servers.","intents":["I want code completions that understand my team's coding conventions and internal libraries without sending code to the cloud","I need to fine-tune the AI model on our proprietary codebase to improve suggestion accuracy for our tech stack","I want multi-line code suggestions that respect our architectural patterns and security practices"],"best_for":["Enterprise development teams with strict IP protection requirements","Organizations in regulated industries (healthcare, finance, government) with data residency mandates","Teams with proprietary frameworks or domain-specific languages requiring custom model training"],"limitations":["Fine-tuning requires significant computational resources and training data preparation; typical fine-tuning cycles take hours to days","Smaller base model size compared to GitHub Copilot results in lower suggestion accuracy across diverse, unfamiliar codebases","On-premise deployment adds infrastructure overhead — requires dedicated GPU resources and model serving infrastructure","Limited language coverage compared to Copilot; may have reduced accuracy for emerging languages or frameworks"],"requires":["IDE plugin compatible with VS Code, JetBrains IDEs, or Vim (version-specific support varies)","On-premise: Docker, Kubernetes, or VM infrastructure with GPU support (NVIDIA CUDA 11.8+)","Cloud deployment: API key and network connectivity to CodeComplete cloud endpoints","For fine-tuning: minimum 1000-5000 code samples from internal repositories in supported languages"],"input_types":["source code (Python, JavaScript, TypeScript, Java, C++, Go, Rust, etc.)","code context (surrounding lines, function signatures, imports)","codebase metadata (file structure, dependency graphs)"],"output_types":["code suggestions (single-line and multi-line completions)","ranked suggestion alternatives with confidence scores"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codecomplete-ai__cap_1","uri":"capability://automation.workflow.on.premise.and.self.hosted.deployment.with.air.gapped.support","name":"on-premise and self-hosted deployment with air-gapped support","description":"Enables deployment of CodeComplete inference and fine-tuning infrastructure within customer-controlled environments (on-premise data centers, private clouds, or air-gapped networks) using containerized model serving and optional offline-first architecture. The system packages language models, inference engines, and API servers as Docker containers or Kubernetes deployments, allowing organizations to run CodeComplete without any data egress to external servers. Supports air-gapped deployments where the system operates entirely offline with no internet connectivity.","intents":["I need to deploy an AI code assistant in our air-gapped network without any external API calls or data transmission","I want to run CodeComplete on our internal infrastructure to maintain full control over model updates and data retention","I need to comply with data residency regulations that prohibit sending code to cloud providers"],"best_for":["Government and defense contractors with classified code and air-gap requirements","Financial institutions and healthcare organizations with strict data residency mandates","Enterprises in regions with data localization laws (EU GDPR, China, Russia)"],"limitations":["Requires dedicated infrastructure management — customers are responsible for model serving, scaling, and updates","No automatic model updates; organizations must manually download and deploy new model versions","Air-gapped deployments cannot benefit from cloud-based improvements or real-time model updates","Operational overhead: monitoring, logging, and debugging require internal DevOps expertise","Scaling requires manual resource provisioning; no auto-scaling like cloud SaaS offerings"],"requires":["Docker 20.10+ or Kubernetes 1.20+ for container orchestration","GPU infrastructure: NVIDIA GPUs (A100, H100, or equivalent) with 40GB+ VRAM for inference","Network: isolated network segment or air-gapped environment with no external connectivity","Storage: 50GB+ for model artifacts, fine-tuning datasets, and inference caches","System administration expertise for deployment, monitoring, and troubleshooting"],"input_types":["Docker images and Kubernetes manifests","Model weights and configuration files","IDE plugin packages for offline installation"],"output_types":["Deployed inference API endpoints","Monitoring and logging data (Prometheus, ELK stack compatible)","Model serving metrics and performance telemetry"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codecomplete-ai__cap_10","uri":"capability://tool.use.integration.team.collaboration.and.suggestion.sharing","name":"team collaboration and suggestion sharing","description":"Enables teams to share, discuss, and rate code suggestions within the IDE or web interface. Developers can comment on suggestions, mark them as useful or problematic, and share suggestions with teammates for feedback. The system aggregates feedback to improve future suggestions and identify patterns in what the team finds useful. Shared suggestions can be stored in a team knowledge base for reference and reuse.","intents":["I want to discuss AI suggestions with my team before accepting them","I need to share useful suggestions with teammates and build a team knowledge base","I want to provide feedback on suggestions to improve future recommendations"],"best_for":["Collaborative development teams that value peer review and discussion","Organizations building shared coding standards and best practices","Teams with distributed developers who need asynchronous collaboration"],"limitations":["Collaboration features add complexity to the IDE integration and may impact performance","Team knowledge base requires ongoing curation and maintenance","Feedback aggregation is heuristic-based and may not always lead to meaningful improvements","Privacy concerns: sharing suggestions may expose proprietary code patterns to team members"],"requires":["Team collaboration infrastructure (shared database, messaging system)","IDE plugin support for collaboration features (comments, ratings, sharing)"],"input_types":["code suggestions","team feedback (comments, ratings, acceptance/rejection)","suggestion metadata (context, confidence scores)"],"output_types":["shared suggestions and team knowledge base","feedback aggregation and analytics","collaboration notifications and discussions"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codecomplete-ai__cap_2","uri":"capability://memory.knowledge.codebase.aware.context.retrieval.and.indexing","name":"codebase-aware context retrieval and indexing","description":"Builds and maintains a searchable index of the organization's codebase to provide relevant context for code completion and fine-tuning. The system uses semantic and syntactic indexing (AST-based or embedding-based) to retrieve similar code patterns, function definitions, and architectural examples from the codebase, injecting this context into the model's prompt window. This enables suggestions that are consistent with existing code style and patterns without requiring explicit configuration.","intents":["I want code suggestions that match the patterns and conventions already used in my codebase","I need the AI to understand my project structure and suggest code that integrates with existing modules and libraries","I want to reduce boilerplate by having the AI learn from similar code patterns in our repository"],"best_for":["Teams with large, mature codebases (100K+ lines of code) where consistency is critical","Organizations with domain-specific patterns or architectural conventions","Development teams working on monorepos or multi-service architectures"],"limitations":["Indexing large codebases (1M+ lines) can take significant time and storage; incremental indexing may lag behind code changes","Context retrieval adds latency to completion requests — typically 50-200ms per retrieval operation","Semantic indexing requires embedding models; accuracy depends on model quality and may miss domain-specific patterns","Requires periodic re-indexing to stay synchronized with codebase changes; real-time indexing is computationally expensive"],"requires":["Codebase access via Git repository or local file system","Indexing infrastructure: 10GB+ storage for typical enterprise codebase indexes","Supported languages: Python, JavaScript, TypeScript, Java, C++, Go, Rust (language-specific AST parsers required)","On-premise: local or network-accessible storage for index artifacts"],"input_types":["source code files in supported languages","Git repository metadata (commit history, file changes)","codebase structure and dependency graphs"],"output_types":["indexed code embeddings and AST representations","retrieved context snippets for prompt injection","similarity scores and relevance rankings"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codecomplete-ai__cap_3","uri":"capability://tool.use.integration.ide.integration.with.multi.editor.support","name":"ide integration with multi-editor support","description":"Provides native plugins and extensions for popular IDEs (VS Code, JetBrains IDEs, Vim, Neovim) that integrate CodeComplete's inference API into the editor's code completion UI and keybindings. Plugins communicate with local or remote CodeComplete inference servers via HTTP/gRPC APIs, displaying suggestions in the editor's native autocomplete menu and supporting keyboard shortcuts for accepting, rejecting, or cycling through suggestions. The integration handles editor-specific APIs for syntax highlighting, cursor positioning, and multi-cursor editing.","intents":["I want code completions to appear in my IDE's native autocomplete menu without switching tools","I need keyboard shortcuts to accept or reject suggestions without breaking my editing workflow","I want to use CodeComplete across multiple IDEs without learning different interfaces"],"best_for":["Development teams using JetBrains IDEs (IntelliJ, PyCharm, WebStorm) or VS Code","Developers who prefer Vim/Neovim and want AI assistance without leaving the terminal","Organizations standardizing on specific IDEs and needing consistent AI assistant experience"],"limitations":["IDE plugin support is limited compared to GitHub Copilot; not all IDEs are supported (e.g., Sublime Text, Emacs have limited or no support)","Plugin latency varies by IDE and network conditions; slow inference servers can cause UI lag","Multi-cursor editing support is IDE-specific and may not work consistently across all editors","Plugin updates require manual installation; no automatic update mechanism like VS Code Marketplace"],"requires":["VS Code 1.60+ or JetBrains IDE 2021.1+ or Vim 8.0+ / Neovim 0.5+","Network connectivity to CodeComplete inference server (local or remote)","Plugin installation: manual download and installation or IDE marketplace (where available)"],"input_types":["editor context (current file, cursor position, selected text, open files)","editor configuration (language, indentation, formatting rules)"],"output_types":["code suggestions displayed in editor autocomplete menu","suggestion metadata (confidence scores, alternative suggestions)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codecomplete-ai__cap_4","uri":"capability://safety.moderation.enterprise.compliance.and.audit.logging","name":"enterprise compliance and audit logging","description":"Implements comprehensive audit logging and compliance features including detailed logging of all code completion requests, model fine-tuning operations, and user interactions. The system tracks which users requested which completions, what code was suggested, and whether suggestions were accepted or rejected. Logs are stored locally or in customer-controlled storage (S3, on-premise databases) and can be exported in compliance-friendly formats (JSON, CSV). Supports integration with SIEM systems (Splunk, ELK) for centralized security monitoring.","intents":["I need to audit all AI-generated code suggestions for compliance and security purposes","I want to track which developers are using the AI assistant and what code it suggested to them","I need to export audit logs for regulatory compliance (SOC 2, ISO 27001, HIPAA)"],"best_for":["Financial institutions and healthcare organizations with strict audit requirements","Government contractors and defense organizations with compliance mandates","Enterprises undergoing SOC 2, ISO 27001, or HIPAA compliance audits"],"limitations":["Comprehensive logging adds storage overhead — typical enterprise deployments generate 10-100GB+ of logs monthly","Log retention policies must be manually configured; no automatic archival or purging","Audit log analysis requires manual review or custom tooling; no built-in compliance reporting dashboards","SIEM integration requires custom configuration and may not support all SIEM platforms"],"requires":["Storage infrastructure for audit logs: 100GB+ for typical enterprise deployments","Optional: SIEM system (Splunk, ELK, Datadog) for centralized log analysis","Log retention policy: customer-defined retention periods (typically 1-7 years for compliance)"],"input_types":["completion requests (user ID, timestamp, code context)","model fine-tuning operations (dataset, training parameters, results)","user interactions (suggestion acceptance/rejection, editing)"],"output_types":["audit logs (JSON, CSV, syslog formats)","compliance reports (request counts, user activity, suggestion acceptance rates)","SIEM-compatible log streams"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codecomplete-ai__cap_5","uri":"capability://code.generation.editing.custom.model.fine.tuning.on.internal.codebases","name":"custom model fine-tuning on internal codebases","description":"Enables organizations to fine-tune CodeComplete's base language models on their internal code repositories to improve suggestion accuracy for proprietary patterns, frameworks, and conventions. The fine-tuning pipeline accepts code samples from Git repositories, applies supervised fine-tuning (SFT) or reinforcement learning from human feedback (RLHF) techniques, and produces custom model weights that can be deployed in the organization's inference infrastructure. Fine-tuning is performed on-premise or in a customer-controlled cloud environment to avoid exposing proprietary code.","intents":["I want to train a custom AI model on our proprietary codebase to improve suggestion accuracy for our tech stack","I need the AI to understand our internal libraries, frameworks, and architectural patterns","I want to fine-tune the model without sending our proprietary code to external servers"],"best_for":["Large enterprises with proprietary frameworks or domain-specific languages","Organizations with unique architectural patterns or coding conventions","Teams with sufficient data science expertise to manage fine-tuning pipelines"],"limitations":["Fine-tuning requires significant computational resources (GPU clusters) and takes hours to days per training run","Requires 1000-10000+ high-quality code samples for effective fine-tuning; data preparation is labor-intensive","Fine-tuned models may overfit to internal patterns and perform worse on unfamiliar code or external libraries","No automatic retraining; organizations must manually trigger fine-tuning when codebases or patterns change significantly","Requires data science expertise to tune hyperparameters, evaluate model quality, and manage training pipelines"],"requires":["GPU infrastructure: NVIDIA GPUs (A100, H100, or equivalent) with 80GB+ VRAM for training","Training data: 1000-10000+ code samples from internal repositories in supported languages","Storage: 100GB+ for training datasets, model checkpoints, and evaluation artifacts","Data science expertise: knowledge of fine-tuning techniques, hyperparameter tuning, and model evaluation","Optional: distributed training infrastructure (PyTorch Distributed, Hugging Face Accelerate) for large-scale fine-tuning"],"input_types":["code samples from Git repositories (Python, JavaScript, TypeScript, Java, C++, Go, Rust)","training configuration (learning rate, batch size, number of epochs, validation split)","optional: human feedback or preference data for RLHF"],"output_types":["fine-tuned model weights and configuration","training metrics (loss, perplexity, evaluation scores)","model evaluation reports and comparison with base model"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codecomplete-ai__cap_6","uri":"capability://code.generation.editing.code.review.and.suggestion.explanation","name":"code review and suggestion explanation","description":"Analyzes code suggestions and provides explanations of why the AI generated a particular suggestion, including references to similar code patterns in the codebase and reasoning about the suggestion's correctness. The system can highlight potential issues (type mismatches, missing error handling, security vulnerabilities) in suggestions before they are accepted. Explanations are displayed in the IDE or via API responses, helping developers understand and validate AI-generated code.","intents":["I want to understand why the AI suggested a particular piece of code before accepting it","I need to validate that AI suggestions don't introduce security vulnerabilities or type errors","I want to see examples of similar code patterns in our codebase that influenced the suggestion"],"best_for":["Development teams prioritizing code quality and security over speed","Organizations with junior developers who benefit from learning why suggestions are made","Teams with strict code review processes that require understanding of AI-generated code"],"limitations":["Explanation generation adds latency to completion requests — typically 100-500ms per explanation","Explanations are heuristic-based and may not always be accurate or complete","Security vulnerability detection is limited to common patterns; sophisticated vulnerabilities may be missed","Explanation quality depends on the quality of the underlying model and codebase indexing"],"requires":["Codebase indexing enabled for pattern matching and similarity analysis","Optional: static analysis tools (linters, type checkers) for enhanced validation"],"input_types":["code suggestions from the completion engine","codebase context and similar code patterns","optional: static analysis results"],"output_types":["explanation text describing why the suggestion was made","references to similar code patterns in the codebase","validation results (potential issues, warnings)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codecomplete-ai__cap_7","uri":"capability://code.generation.editing.multi.language.support.with.language.specific.models","name":"multi-language support with language-specific models","description":"Supports code completion across multiple programming languages (Python, JavaScript, TypeScript, Java, C++, Go, Rust, C#, PHP, etc.) with optional language-specific fine-tuned models. The system uses language-specific AST parsers and tokenizers to understand code structure and generate contextually appropriate suggestions. Organizations can fine-tune separate models for each language or use a unified multi-language model, depending on their needs and available resources.","intents":["I want code completions for multiple programming languages in a single tool","I need language-specific models that understand the idioms and conventions of each language","I want to fine-tune separate models for languages where we have unique patterns or conventions"],"best_for":["Polyglot development teams working across multiple languages","Organizations with language-specific architectural patterns or conventions","Teams with sufficient resources to maintain and fine-tune language-specific models"],"limitations":["Language support is limited compared to GitHub Copilot; emerging languages may not be supported","Language-specific models require separate fine-tuning and maintenance, increasing operational overhead","Suggestion quality varies significantly across languages; some languages have better training data and models than others","Multi-language model may have lower accuracy than single-language models due to model capacity constraints"],"requires":["Supported languages: Python, JavaScript, TypeScript, Java, C++, Go, Rust, C#, PHP (others may have limited support)","Language-specific AST parsers for each supported language","Optional: language-specific fine-tuning data for improved accuracy"],"input_types":["source code in supported languages","language-specific context (imports, type annotations, function signatures)"],"output_types":["code suggestions in the target language","language-specific formatting and conventions"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codecomplete-ai__cap_8","uri":"capability://tool.use.integration.api.first.architecture.with.rest.and.grpc.endpoints","name":"api-first architecture with rest and grpc endpoints","description":"Exposes CodeComplete's inference and fine-tuning capabilities via REST and gRPC APIs, enabling integration with custom tools, CI/CD pipelines, and third-party applications. The API supports batch processing for fine-tuning and bulk code analysis, streaming responses for real-time completion suggestions, and webhooks for asynchronous operations. API authentication uses API keys or OAuth 2.0, with fine-grained access control for different operations (completion, fine-tuning, audit logs).","intents":["I want to integrate CodeComplete into our custom development tools and workflows","I need to use CodeComplete's completion API in our CI/CD pipeline for automated code generation","I want to build custom applications on top of CodeComplete's inference and fine-tuning APIs"],"best_for":["Development teams building custom tools and integrations on top of CodeComplete","Organizations with existing CI/CD pipelines that need AI-assisted code generation","Teams building internal developer platforms that require AI code assistance"],"limitations":["API latency varies based on network conditions and inference server load; typical latency is 100-500ms per request","Batch processing has throughput limits; very large batch jobs may require queuing or rate limiting","API documentation may be limited compared to mature APIs like OpenAI's; community support is smaller","Custom integrations require development effort and ongoing maintenance"],"requires":["Network connectivity to CodeComplete API endpoints (local or remote)","API key or OAuth 2.0 credentials for authentication","HTTP client library (curl, requests, axios, etc.) or gRPC client library"],"input_types":["code context (file content, cursor position, surrounding lines)","completion parameters (temperature, max tokens, language)","fine-tuning configuration (training data, hyperparameters)"],"output_types":["code suggestions (text, structured JSON with metadata)","fine-tuning job status and results","audit logs and compliance reports"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_codecomplete-ai__cap_9","uri":"capability://safety.moderation.security.scanning.and.vulnerability.detection.in.suggestions","name":"security scanning and vulnerability detection in suggestions","description":"Analyzes code suggestions for common security vulnerabilities (SQL injection, XSS, hardcoded credentials, insecure cryptography, etc.) using static analysis techniques and pattern matching. The system flags potentially unsafe suggestions before they are accepted, providing developers with security warnings and remediation guidance. Integration with popular security scanning tools (SAST, dependency checkers) enables comprehensive security analysis of AI-generated code.","intents":["I want to ensure AI-generated code doesn't introduce security vulnerabilities into our codebase","I need to flag potentially unsafe suggestions before developers accept them","I want to integrate security scanning into our AI-assisted code generation workflow"],"best_for":["Security-conscious development teams in regulated industries (finance, healthcare, government)","Organizations with strict security policies and code review processes","Teams prioritizing security over development speed"],"limitations":["Vulnerability detection is pattern-based and may miss sophisticated or novel vulnerabilities","False positives are common; developers may become desensitized to warnings if too many are generated","Detection accuracy depends on the quality of the underlying static analysis rules","Some vulnerability types (logic errors, business logic flaws) cannot be detected by static analysis"],"requires":["Static analysis rules and patterns for common vulnerabilities","Optional: integration with SAST tools (SonarQube, Checkmarx, Fortify) for enhanced analysis"],"input_types":["code suggestions from the completion engine","codebase context and dependency information"],"output_types":["security warnings and vulnerability flags","remediation guidance and suggested fixes","security scan reports"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["IDE plugin compatible with VS Code, JetBrains IDEs, or Vim (version-specific support varies)","On-premise: Docker, Kubernetes, or VM infrastructure with GPU support (NVIDIA CUDA 11.8+)","Cloud deployment: API key and network connectivity to CodeComplete cloud endpoints","For fine-tuning: minimum 1000-5000 code samples from internal repositories in supported languages","Docker 20.10+ or Kubernetes 1.20+ for container orchestration","GPU infrastructure: NVIDIA GPUs (A100, H100, or equivalent) with 40GB+ VRAM for inference","Network: isolated network segment or air-gapped environment with no external connectivity","Storage: 50GB+ for model artifacts, fine-tuning datasets, and inference caches","System administration expertise for deployment, monitoring, and troubleshooting","Team collaboration infrastructure (shared database, messaging system)"],"failure_modes":["Fine-tuning requires significant computational resources and training data preparation; typical fine-tuning cycles take hours to days","Smaller base model size compared to GitHub Copilot results in lower suggestion accuracy across diverse, unfamiliar codebases","On-premise deployment adds infrastructure overhead — requires dedicated GPU resources and model serving infrastructure","Limited language coverage compared to Copilot; may have reduced accuracy for emerging languages or frameworks","Requires dedicated infrastructure management — customers are responsible for model serving, scaling, and updates","No automatic model updates; organizations must manually download and deploy new model versions","Air-gapped deployments cannot benefit from cloud-based improvements or real-time model updates","Operational overhead: monitoring, logging, and debugging require internal DevOps expertise","Scaling requires manual resource provisioning; no auto-scaling like cloud SaaS offerings","Collaboration features add complexity to the IDE integration and may impact performance","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.717Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=codecomplete-ai","compare_url":"https://unfragile.ai/compare?artifact=codecomplete-ai"}},"signature":"c1d+rLH/G/0QdAJ7qKuhm3DXjTrxwG2A5l0Tx5SHWK1HPSSsgZo2QCP29gDcy9g8b6heRIMU3UaWTjwfm+dHCg==","signedAt":"2026-06-20T00:40:04.509Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/codecomplete-ai","artifact":"https://unfragile.ai/codecomplete-ai","verify":"https://unfragile.ai/api/v1/verify?slug=codecomplete-ai","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"}}