{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-chaitin--monkeycode","slug":"chaitin--monkeycode","name":"MonkeyCode","type":"product","url":"https://monkeycode-ai.com/","page_url":"https://unfragile.ai/chaitin--monkeycode","categories":["app-builders"],"tags":["ai","ai-coding","ai-security","aicodeassistant","aicoding","coding","vscode-extension"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-chaitin--monkeycode__cap_0","uri":"capability://code.generation.editing.ide.integrated.conversational.code.generation.with.context.aware.chat","name":"ide-integrated conversational code generation with context-aware chat","description":"Provides real-time chat-based code generation within VSCode and JetBrains IDEs through a WebSocket-based LLM proxy architecture that maintains session state, tracks token usage, and routes requests to configurable model providers (OpenAI, Anthropic, local models). The system captures active file context, cursor position, and workspace state to inject into prompts, enabling developers to request code generation without leaving their editor. Requests flow through a layered backend architecture with dependency injection (Wire framework) that handles authentication, model selection, and response streaming.","intents":["Generate code snippets from natural language descriptions without switching context","Ask questions about code behavior and get explanations with examples","Request refactoring suggestions for selected code blocks","Get multi-file code generation suggestions based on workspace structure"],"best_for":["Individual developers using VSCode or JetBrains IDEs","Teams deploying MonkeyCode on-premise with private LLM backends","Organizations requiring offline-capable AI coding assistance"],"limitations":["Context window limited by selected model provider (e.g., 4K-200K tokens depending on model)","Real-time synchronization requires active WebSocket connection; no offline queue persistence","Multi-file context requires explicit workspace indexing via CLI tool; automatic indexing not included","Streaming responses add ~100-300ms latency depending on network and model provider"],"requires":["VSCode 1.80+ or JetBrains IDE 2023.1+","Backend server running (Go-based, deployable on-premise or cloud)","API key for model provider (OpenAI, Anthropic) OR local LLM endpoint configured","User authentication via OAuth or API key","Workspace indexed via MonkeyCode CLI for codebase-aware context"],"input_types":["natural language chat messages","selected code blocks from editor","file paths and workspace structure metadata"],"output_types":["streamed code snippets","explanatory text","multi-file code suggestions"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chaitin--monkeycode__cap_1","uri":"capability://code.generation.editing.intelligent.code.completion.with.codebase.indexing.and.context.injection","name":"intelligent code completion with codebase indexing and context injection","description":"Delivers context-aware autocomplete suggestions by indexing the entire codebase via a CLI tool that builds semantic representations, then injecting relevant code context into completion requests. The system uses a completion flow that captures cursor position, surrounding code, and indexed codebase symbols to generate suggestions matching the developer's coding style and project patterns. Completions are streamed back to the IDE plugin with latency optimization through local model support and request batching.","intents":["Get intelligent autocomplete suggestions that match project coding patterns","Complete function signatures based on codebase usage patterns","Auto-suggest imports and dependencies relevant to current context","Receive multi-line code completions for common patterns in the project"],"best_for":["Development teams with consistent code style and patterns","Projects with large codebases where context-aware completion adds significant value","Organizations deploying on-premise with local model inference"],"limitations":["Requires explicit codebase indexing via CLI; incremental indexing not automated","Index staleness: changes to codebase require re-indexing to reflect in completions","Completion quality degrades for languages with limited training data in selected model","Index storage scales linearly with codebase size; large monorepos (>1GB code) may require optimization"],"requires":["MonkeyCode CLI tool installed and codebase indexed","VSCode 1.80+ or JetBrains IDE 2023.1+","Model provider configured (OpenAI, Anthropic, or local LLM)","Backend server with database for storing index metadata"],"input_types":["cursor position in editor","surrounding code context (lines before/after cursor)","indexed codebase symbols and definitions"],"output_types":["streamed code completion suggestions","ranked list of alternatives","metadata (confidence score, source file)"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chaitin--monkeycode__cap_10","uri":"capability://automation.workflow.layered.backend.architecture.with.dependency.injection.and.error.handling","name":"layered backend architecture with dependency injection and error handling","description":"Implements a clean layered architecture (handlers, services, repositories) using Google Wire for dependency injection, enabling testability and loose coupling between components. The system uses centralized error handling with localization support for multi-language error messages, and structured logging for debugging. The architecture separates concerns: HTTP handlers for request routing, service layer for business logic, repository layer for data access, and provider layer for external integrations (LLM APIs, Git platforms).","intents":["Maintain clean separation of concerns for testability and maintainability","Support multi-language error messages for global deployments","Enable rapid feature development with dependency injection","Provide structured logging for debugging and monitoring"],"best_for":["Development teams building and extending MonkeyCode","Organizations deploying MonkeyCode with custom integrations","Teams requiring high code quality and testability"],"limitations":["Dependency injection adds complexity for new developers; Wire configuration requires understanding of Go interfaces","Layered architecture adds ~50-100ms latency per request due to abstraction layers","Error localization requires manual translation of error messages; no automatic translation","Structured logging requires careful instrumentation; missing logs may impact debugging"],"requires":["Go 1.18+ for building backend","Google Wire for dependency injection","Database for persistence layer","Understanding of Go interfaces and dependency injection patterns"],"input_types":["HTTP requests to backend API endpoints","configuration files (YAML) for dependency setup"],"output_types":["HTTP responses with structured error messages","structured logs with request/response metadata","localized error messages in user's language"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chaitin--monkeycode__cap_11","uri":"capability://data.processing.analysis.database.schema.with.workspace.user.and.audit.tables.for.enterprise.data.management","name":"database schema with workspace, user, and audit tables for enterprise data management","description":"Implements a relational database schema with tables for users, workspaces, files, API keys, sessions, usage records, audit logs, and security scan results. The schema supports multi-tenancy through workspace isolation, enabling multiple teams to use the same MonkeyCode instance with data separation. Foreign key relationships enforce referential integrity, and indexes on frequently-queried columns (user_id, workspace_id, timestamp) optimize query performance. The schema design supports both PostgreSQL and MySQL deployments.","intents":["Store user profiles, API keys, and session tokens securely","Isolate workspace data for multi-tenant deployments","Track usage and billing data for cost allocation","Maintain audit logs for compliance and security purposes","Store security scan results with remediation tracking"],"best_for":["Enterprise deployments with multi-tenant requirements","Organizations requiring detailed audit trails and compliance","Teams needing scalable data storage for large user bases"],"limitations":["Schema migration requires downtime for large tables; no zero-downtime migration strategy documented","Query performance degrades with large audit log tables (>100M rows); archival strategy needed","Foreign key constraints add overhead; may impact bulk operations","Multi-tenant isolation relies on application-level enforcement; no database-level row-level security"],"requires":["PostgreSQL 12+ or MySQL 8.0+","Database migration tool (e.g., Flyway, Liquibase) for schema management","Backup and recovery procedures for data protection","Monitoring and alerting for database performance"],"input_types":["user registration and profile data","workspace and file metadata","usage records with token counts","audit log entries with request/response data"],"output_types":["user and workspace records","usage and billing aggregations","audit log queries with filtering and sorting","security scan results with remediation status"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chaitin--monkeycode__cap_12","uri":"capability://data.processing.analysis.cli.tool.for.codebase.indexing.and.semantic.symbol.extraction","name":"cli tool for codebase indexing and semantic symbol extraction","description":"Provides a command-line tool that scans a codebase, extracts semantic symbols (functions, classes, imports), and builds an index stored in the backend database. The tool uses language-specific parsers (AST-based for supported languages) to extract definitions and relationships, enabling context-aware code completion and search. The index includes symbol metadata (name, type, location, usage frequency) and can be queried by the IDE plugins for context injection. The tool supports incremental indexing for fast updates on code changes.","intents":["Build semantic index of codebase for context-aware completion","Extract function and class definitions for symbol search","Track symbol usage patterns for intelligent suggestions","Enable incremental indexing for fast updates on code changes"],"best_for":["Development teams with large codebases seeking context-aware completion","Organizations deploying MonkeyCode with codebase-aware features","Teams using multiple programming languages in same project"],"limitations":["Indexing speed depends on codebase size; large monorepos (>1GB) may take 10+ minutes","Language support limited to languages with AST parsers; dynamic languages (Python, Ruby) have limited symbol extraction","Incremental indexing requires tracking file modification times; clock skew may cause missed updates","Index storage scales linearly with codebase size; no compression or deduplication"],"requires":["MonkeyCode CLI tool installed (Go binary)","Backend server with database for storing index","Read access to codebase files","Network connectivity to backend for uploading index"],"input_types":["codebase directory path","language configuration (supported languages to index)","incremental flag for fast updates"],"output_types":["semantic symbol index stored in backend database","indexing statistics (files processed, symbols extracted, time elapsed)","error report for files that failed to parse"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chaitin--monkeycode__cap_13","uri":"capability://automation.workflow.configuration.management.with.yaml.based.provider.and.model.setup","name":"configuration management with yaml-based provider and model setup","description":"Implements centralized configuration management using YAML files for defining LLM providers, models, authentication credentials, and deployment settings. The configuration system supports environment variable substitution for secrets (API keys), enabling secure deployment without hardcoding credentials. Configuration is loaded at server startup through a configuration loader that validates schema and applies defaults. The system supports hot-reloading of non-critical settings (model weights, load balancing policies) without server restart.","intents":["Configure LLM providers and models without code changes","Manage API keys and credentials securely using environment variables","Set deployment-specific settings (database URL, server port, log level)","Enable hot-reloading of model weights and load balancing policies"],"best_for":["DevOps teams deploying MonkeyCode across multiple environments","Organizations requiring secure credential management","Teams deploying on-premise with custom model configurations"],"limitations":["YAML configuration requires manual editing; no UI for configuration management","Environment variable substitution limited to string values; no complex data structures","Configuration validation happens at startup; invalid configs cause server startup failure","Hot-reloading not supported for all settings; database and authentication changes require restart"],"requires":["YAML configuration file (config.yaml) in backend directory","Environment variables set for sensitive values (API keys, database passwords)","Understanding of YAML syntax and MonkeyCode configuration schema"],"input_types":["YAML configuration file with provider and model definitions","environment variables for secrets (API_KEY_OPENAI, DATABASE_URL, etc.)"],"output_types":["parsed configuration object loaded into memory","validation errors if configuration is invalid","configuration metadata for debugging"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chaitin--monkeycode__cap_2","uri":"capability://safety.moderation.automated.security.vulnerability.scanning.with.sgp.integration","name":"automated security vulnerability scanning with sgp integration","description":"Scans code for security vulnerabilities during development using a queue-based scanning architecture that integrates with Chaitin's SGP (Security Governance Platform) scanner service. The system processes scan requests asynchronously, storing results in the database and exposing them through the IDE plugin and management dashboard. Scanning can be triggered on-demand or integrated into CI/CD pipelines, with results tracked per file, commit, and user for audit and compliance purposes.","intents":["Detect security vulnerabilities in code before commit","Get remediation suggestions for identified vulnerabilities","Track security scanning history and compliance metrics across team","Integrate security scanning into CI/CD pipelines for automated enforcement"],"best_for":["Security-conscious development teams and enterprises","Organizations with compliance requirements (SOC2, ISO27001)","Teams deploying on-premise with internal security scanning infrastructure"],"limitations":["Scanning latency depends on SGP service availability and queue depth; no SLA guarantees documented","Vulnerability detection limited to patterns known to SGP scanner; zero-day vulnerabilities not covered","Scanning results require manual review; no automatic remediation or patch generation","Integration with CI/CD requires custom webhook configuration; no native GitHub Actions/GitLab CI templates provided"],"requires":["SGP scanner service deployed and accessible from MonkeyCode backend","Backend database for storing scan results and audit logs","IDE plugin version 1.0+ for displaying scan results","Optional: CI/CD integration requires webhook configuration"],"input_types":["source code files (all languages supported by SGP)","file paths and commit metadata","scan trigger events (on-demand, scheduled, CI/CD webhook)"],"output_types":["vulnerability list with severity levels","remediation suggestions","audit logs with timestamp and user attribution","compliance reports (JSON, CSV)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chaitin--monkeycode__cap_3","uri":"capability://tool.use.integration.git.platform.bot.integration.for.ai.driven.pr.review.and.issue.implementation","name":"git platform bot integration for ai-driven pr review and issue implementation","description":"Deploys AI employees as bots on GitHub, GitLab, Gitee, and Gitea that respond to commands (e.g., @monkeycode-ai review) to perform code review, issue breakdown, and feature implementation. The system integrates with Git platform APIs to fetch PR diffs, issue descriptions, and repository context, then uses the LLM proxy to generate reviews or implementation suggestions. Results are posted back as PR comments or issue updates, with full audit trail and user attribution stored in the database.","intents":["Request AI code review on pull requests via @mention command","Break down complex issues into sub-tasks automatically","Generate implementation suggestions from issue descriptions","Track AI employee activity and audit all bot-generated comments"],"best_for":["Development teams using GitHub, GitLab, Gitee, or Gitea","Organizations with high PR volume seeking to reduce review bottlenecks","Teams wanting AI-assisted issue triage and task decomposition"],"limitations":["Bot review quality depends on PR context size; large diffs (>10K lines) may exceed model context window","No automatic PR merge; all bot suggestions require manual approval","Issue breakdown relies on natural language parsing; structured issue templates not supported","Git platform API rate limits may throttle bot responsiveness during high-volume periods"],"requires":["Git platform account (GitHub, GitLab, Gitee, Gitea) with admin access to install bot","OAuth token or personal access token for Git platform","Backend server with outbound HTTPS access to Git platform APIs","Model provider configured for LLM inference"],"input_types":["PR diff and metadata from Git platform API","issue description and labels","@mention commands in PR comments","repository context (README, contributing guidelines)"],"output_types":["PR review comments with line-level suggestions","issue sub-task creation","implementation suggestions as PR comments","audit logs with bot action attribution"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chaitin--monkeycode__cap_4","uri":"capability://tool.use.integration.multi.provider.model.selection.and.load.balancing","name":"multi-provider model selection and load balancing","description":"Manages a configurable registry of LLM providers (OpenAI, Anthropic, local models) with dynamic model selection and load balancing logic. The system stores provider credentials and model configurations in the database, allowing administrators to switch models, adjust pricing, and balance load across providers without code changes. Request routing uses configurable policies (round-robin, cost-optimized, latency-optimized) to distribute completion and chat requests across available models, with fallback to secondary providers on failure.","intents":["Switch between different LLM providers without code deployment","Balance load across multiple model instances to reduce latency","Optimize costs by routing requests to cheaper models for simple tasks","Maintain service availability by falling back to secondary providers on primary failure"],"best_for":["Enterprise teams requiring multi-provider flexibility and cost optimization","Organizations deploying local LLMs alongside cloud providers","Teams needing fine-grained control over model selection policies"],"limitations":["Load balancing policies are static; no dynamic adjustment based on real-time provider performance","Model switching requires manual configuration; no automatic provider health checks","Cost optimization requires manual pricing configuration; no automatic price discovery","Fallback logic is sequential; no parallel request hedging to reduce tail latency"],"requires":["Backend database with provider and model configuration tables","API keys for each configured provider (OpenAI, Anthropic, etc.)","Optional: local LLM endpoint (e.g., Ollama, vLLM) for on-premise models","Admin access to management dashboard for provider configuration"],"input_types":["provider configuration (API key, endpoint, model list)","load balancing policy (round-robin, cost-optimized, latency-optimized)","completion/chat requests with model preference hints"],"output_types":["selected provider and model for request","routing decision metadata (policy applied, fallback status)","provider performance metrics (latency, error rate)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chaitin--monkeycode__cap_5","uri":"capability://automation.workflow.real.time.workspace.file.synchronization.and.indexing","name":"real-time workspace file synchronization and indexing","description":"Maintains synchronized state between IDE workspace and backend through WebSocket connections that track file changes, deletions, and renames. The system uses a file operation API to persist workspace metadata in the database and trigger incremental indexing updates. Changes are broadcast to all connected clients for a given workspace, enabling real-time collaboration awareness and consistent context for AI operations. The CLI indexing tool integrates with this system to build semantic indexes from synchronized file state.","intents":["Keep backend workspace state synchronized with IDE file changes","Enable real-time collaboration awareness across team members","Trigger incremental indexing updates when files change","Maintain consistent context for AI operations across distributed team"],"best_for":["Teams using MonkeyCode for collaborative development","Organizations requiring real-time awareness of code changes","Projects with frequent file modifications and refactoring"],"limitations":["WebSocket synchronization requires persistent connection; network interruptions cause temporary desynchronization","Incremental indexing adds ~500ms-2s latency per file change; large batch operations may cause UI lag","Conflict resolution for simultaneous edits not implemented; last-write-wins semantics","Workspace state stored in database; no built-in backup or recovery mechanism"],"requires":["Backend server with WebSocket support","IDE plugin version 1.0+ with file change listener implementation","Database for storing workspace metadata","Network connectivity between IDE and backend (no offline mode)"],"input_types":["file change events from IDE (create, modify, delete, rename)","workspace metadata (file paths, sizes, modification times)","user identity and workspace ID"],"output_types":["synchronized workspace state in backend database","file change notifications to other connected clients","indexing trigger events for CLI tool"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chaitin--monkeycode__cap_6","uri":"capability://data.processing.analysis.token.usage.tracking.and.billing.analytics.with.per.user.attribution","name":"token usage tracking and billing analytics with per-user attribution","description":"Records all LLM API requests with token counts, model selection, and user attribution in the database, enabling detailed usage analytics and billing reports. The system implements token counting at the LLM proxy layer before and after requests, tracking input/output tokens separately. Billing queries aggregate usage by user, model, provider, and time period, supporting flexible billing models (per-token, per-request, subscription). The management dashboard exposes usage trends, cost breakdowns, and audit logs for compliance and cost optimization.","intents":["Track token usage per user for billing and cost allocation","Generate usage reports by model, provider, and time period","Identify high-usage patterns and optimize model selection","Audit all AI operations for compliance and security purposes"],"best_for":["Enterprise teams with chargeback models and cost allocation requirements","Organizations requiring detailed audit trails for compliance","Teams optimizing LLM costs and seeking usage insights"],"limitations":["Token counting accuracy depends on model provider's tokenizer; discrepancies possible between estimated and actual tokens","Billing queries require manual report generation; no automatic invoice generation","Usage data retention policy not documented; long-term storage costs may accumulate","Real-time billing dashboard has ~5-10 minute latency due to database aggregation"],"requires":["Backend database with usage and billing tables","LLM proxy implementation with token counting logic","Management dashboard for viewing reports","Admin access for configuring billing policies"],"input_types":["LLM API requests with model and provider metadata","token counts from model provider API responses","user identity and workspace ID"],"output_types":["usage records with token counts and timestamps","billing reports (CSV, JSON) aggregated by user/model/period","cost breakdown and trend analysis","audit logs with full request/response metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chaitin--monkeycode__cap_7","uri":"capability://safety.moderation.user.authentication.and.authorization.with.oauth.and.api.key.support","name":"user authentication and authorization with oauth and api key support","description":"Implements multi-factor authentication using OAuth (GitHub, GitLab, Google) and API key-based authentication for programmatic access. The system stores user profiles, API keys, and session tokens in the database with role-based access control (RBAC) for workspace and admin operations. Authentication flows use standard OAuth 2.0 with PKCE for web clients and API key validation for IDE plugins and CLI tools. Session management includes token expiration, refresh token rotation, and logout with session revocation.","intents":["Authenticate users via OAuth for web dashboard access","Generate API keys for IDE plugin and CLI tool authentication","Manage user roles and permissions for workspace access","Revoke sessions and API keys for security purposes"],"best_for":["Enterprise teams requiring centralized authentication and authorization","Organizations using OAuth providers (GitHub, GitLab, Google)","Teams deploying on-premise with custom authentication backends"],"limitations":["OAuth integration limited to GitHub, GitLab, Google; no SAML or LDAP support documented","API key rotation requires manual user action; no automatic rotation policy","Session timeout is fixed; no configurable session duration per user","Multi-factor authentication (MFA) not implemented; OAuth provider MFA only"],"requires":["OAuth provider credentials (GitHub, GitLab, Google)","Backend database for storing user profiles and sessions","HTTPS for secure OAuth callback and API key transmission","Optional: custom authentication backend for on-premise deployments"],"input_types":["OAuth authorization code from provider","API key from user profile","username and password (if custom auth backend)","session token from previous login"],"output_types":["session token and refresh token","user profile with roles and permissions","API key for programmatic access","session metadata (expiration, last activity)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chaitin--monkeycode__cap_8","uri":"capability://data.processing.analysis.management.dashboard.with.usage.analytics.audit.logs.and.model.configuration","name":"management dashboard with usage analytics, audit logs, and model configuration","description":"Provides a web-based interface for administrators to view usage analytics, audit logs, security scan results, and configure AI models and providers. The dashboard implements real-time data visualization using React frontend with backend API endpoints for querying usage, billing, and audit data. Administrators can manage user roles, API keys, workspace settings, and model provider credentials through the UI. The dashboard supports exporting reports in CSV and JSON formats for compliance and cost analysis.","intents":["View usage trends and cost breakdowns by user, model, and time period","Review audit logs of all AI operations for compliance purposes","Configure model providers and load balancing policies","Manage user roles, API keys, and workspace permissions","Export usage and billing reports for finance and compliance teams"],"best_for":["Enterprise administrators managing MonkeyCode deployments","Organizations with compliance and audit requirements","Teams optimizing LLM costs and seeking usage insights"],"limitations":["Dashboard queries have ~5-10 minute latency for large datasets; real-time analytics not supported","No custom dashboard widgets or metric definitions; fixed set of pre-built reports","Export functionality limited to CSV and JSON; no integration with BI tools","User interface requires manual refresh; no automatic data refresh or push notifications"],"requires":["Backend API endpoints for usage, billing, and audit data queries","React frontend deployed with backend HTTPS access","Admin user role with dashboard access permissions","Database with usage, billing, and audit tables"],"input_types":["time range and filter criteria for analytics queries","model provider configuration (API key, endpoint, model list)","user role and permission assignments","export format preference (CSV, JSON)"],"output_types":["usage analytics charts and tables","audit log entries with full request/response metadata","billing reports and cost breakdowns","exported reports in CSV/JSON format"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chaitin--monkeycode__cap_9","uri":"capability://tool.use.integration.vscode.and.jetbrains.ide.plugin.architecture.with.streaming.response.handling","name":"vscode and jetbrains ide plugin architecture with streaming response handling","description":"Implements native IDE plugins for VSCode and JetBrains that integrate with the MonkeyCode backend through WebSocket connections for real-time chat, code completion, and security scanning results. The plugins use IDE-native APIs to capture editor context (cursor position, selected text, file path), display streaming responses with syntax highlighting, and manage local state for conversation history. The architecture supports offline graceful degradation and automatic reconnection on network recovery.","intents":["Provide seamless in-editor chat interface without context switching","Display streaming code completions with syntax highlighting","Show security scan results inline with code navigation","Maintain conversation history and context across sessions"],"best_for":["Developers using VSCode 1.80+ or JetBrains IDEs 2023.1+","Teams deploying MonkeyCode with IDE plugin support","Organizations requiring native IDE integration without external tools"],"limitations":["Plugin performance depends on IDE version and system resources; older IDEs may experience lag","Streaming responses require persistent WebSocket connection; no offline queue for requests","Conversation history stored locally in IDE; no cross-device sync","JetBrains plugin requires separate installation per IDE (IntelliJ, PyCharm, WebStorm, etc.)"],"requires":["VSCode 1.80+ or JetBrains IDE 2023.1+","MonkeyCode backend server with WebSocket support","Network connectivity between IDE and backend","User authentication via API key or OAuth"],"input_types":["chat messages from user","editor context (cursor position, selected text, file path)","workspace metadata (file structure, indexed symbols)"],"output_types":["streamed chat responses with syntax highlighting","code completion suggestions","security scan results with line-level annotations","conversation history"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["VSCode 1.80+ or JetBrains IDE 2023.1+","Backend server running (Go-based, deployable on-premise or cloud)","API key for model provider (OpenAI, Anthropic) OR local LLM endpoint configured","User authentication via OAuth or API key","Workspace indexed via MonkeyCode CLI for codebase-aware context","MonkeyCode CLI tool installed and codebase indexed","Model provider configured (OpenAI, Anthropic, or local LLM)","Backend server with database for storing index metadata","Go 1.18+ for building backend","Google Wire for dependency injection"],"failure_modes":["Context window limited by selected model provider (e.g., 4K-200K tokens depending on model)","Real-time synchronization requires active WebSocket connection; no offline queue persistence","Multi-file context requires explicit workspace indexing via CLI tool; automatic indexing not included","Streaming responses add ~100-300ms latency depending on network and model provider","Requires explicit codebase indexing via CLI; incremental indexing not automated","Index staleness: changes to codebase require re-indexing to reflect in completions","Completion quality degrades for languages with limited training data in selected model","Index storage scales linearly with codebase size; large monorepos (>1GB code) may require optimization","Dependency injection adds complexity for new developers; Wire configuration requires understanding of Go interfaces","Layered architecture adds ~50-100ms latency per request due to abstraction layers","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2820092102965377,"quality":0.35,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:34.540Z","last_commit":"2026-05-03T08:17:27Z"},"community":{"stars":2666,"forks":315,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=chaitin--monkeycode","compare_url":"https://unfragile.ai/compare?artifact=chaitin--monkeycode"}},"signature":"91pDggmqBvRX1opimdtD9toRAJhRKnlIgDvlxfHHsuLU0aK8x2AorszA03HJ9vHuzf8FFljY3QWJXKRB3PLRCQ==","signedAt":"2026-06-20T15:35:09.134Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/chaitin--monkeycode","artifact":"https://unfragile.ai/chaitin--monkeycode","verify":"https://unfragile.ai/api/v1/verify?slug=chaitin--monkeycode","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"}}