{"id":28202332,"url":"https://github.com/wrtnlabs/autobe","last_synced_at":"2026-04-02T12:44:29.001Z","repository":{"id":292997239,"uuid":"974133844","full_name":"wrtnlabs/autobe","owner":"wrtnlabs","description":"AI Vibe Coding Agent of TS backend server, enhanced by compiler skills, generating 100% working code","archived":false,"fork":false,"pushed_at":"2026-02-25T15:33:40.000Z","size":2708951,"stargazers_count":765,"open_issues_count":19,"forks_count":93,"subscribers_count":11,"default_branch":"main","last_synced_at":"2026-02-25T17:04:05.206Z","etag":null,"topics":["ai-coding-agent","all-in-one","automation","backend","generated-api","llm","nestia","nestjs","no-coding","no-coding-app","no-coding-tools","prisma","server","vibe-coding"],"latest_commit_sha":null,"homepage":"https://autobe.dev","language":"TypeScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/wrtnlabs.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-04-28T09:57:52.000Z","updated_at":"2026-02-25T08:41:45.000Z","dependencies_parsed_at":"2025-06-04T09:38:36.613Z","dependency_job_id":"15bc69cf-aef8-43f1-8da0-0909c5b4c810","html_url":"https://github.com/wrtnlabs/autobe","commit_stats":null,"previous_names":["wrtnlabs/autobe"],"tags_count":96,"template":false,"template_full_name":null,"purl":"pkg:github/wrtnlabs/autobe","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wrtnlabs%2Fautobe","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wrtnlabs%2Fautobe/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wrtnlabs%2Fautobe/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wrtnlabs%2Fautobe/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/wrtnlabs","download_url":"https://codeload.github.com/wrtnlabs/autobe/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wrtnlabs%2Fautobe/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29942859,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-28T13:49:17.081Z","status":"ssl_error","status_checked_at":"2026-02-28T13:48:50.396Z","response_time":90,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai-coding-agent","all-in-one","automation","backend","generated-api","llm","nestia","nestjs","no-coding","no-coding-app","no-coding-tools","prisma","server","vibe-coding"],"created_at":"2025-05-17T00:12:54.971Z","updated_at":"2026-04-02T12:44:28.988Z","avatar_url":"https://github.com/wrtnlabs.png","language":"TypeScript","funding_links":[],"categories":["Domain-Specific Tools","TypeScript"],"sub_categories":["Language-Specific"],"readme":"# AutoBE - AI backend builder for prototype to production\n\n[![GitHub License](https://img.shields.io/badge/license-AGPL--3.0-blue.svg)](https://github.com/wrtnlabs/autobe/blob/master/LICENSE)\n[![NPM Version](https://img.shields.io/npm/v/@autobe/agent.svg)](https://www.npmjs.com/package/@autobe/agent)\n[![NPM Downloads](https://img.shields.io/npm/dm/@autobe/agent.svg)](https://www.npmjs.com/package/@autobe/agent)\n[![Build Status](https://github.com/wrtnlabs/autobe/workflows/build/badge.svg?branch=main)](https://github.com/wrtnlabs/autobe/actions?query=workflow%3Abuild)\n[![Guide Documents](https://img.shields.io/badge/Guide-Documents-forestgreen)](https://autobe.dev/docs/)\n[![Discord Badge](https://dcbadge.limes.pink/api/server/https://discord.gg/aMhRmzkqCx?style=flat)](https://discord.gg/aMhRmzkqCx)\n\nDescribe your backend requirements in natural language through AutoBE's chat interface. \n\nAutoBE will analyze your requirements and build the backend application for you. The generated backend application is designed to be 100% buildable by AI-friendly compilers and ensures stability through powerful e2e test functions.\n\nWith such AutoBE, build your first backend application quickly, then maintain and extend it with AI code assistants like Claude Code for enhanced productivity and stability.\n\nAutoBE will generate complete specifications, detailed database and API documentation, comprehensive test coverage for stability, and clean implementation logic that serves as a learning foundation for juniors while significantly improving senior developer productivity.\n\nCheck out these complete backend application examples generated by AutoBE:\n\nhttps://github.com/user-attachments/assets/b995dd2a-23bd-43c9-96cb-96d5c805f19f\n\n1. **To Do List**: [`todo`](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/todo)\n2. **Reddit Community**: [`reddit`](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/reddit)\n3. **E-Commerce**: [`shopping`](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/shopping)\n4. **ERP System**: [`erp`](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/erp)\n   - Requirements Analysis: [Report](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/erp/docs/analysis)\n   - Database Design: [Entity Relationship Diagram](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/erp/docs/ERD.md) / [Prisma Schema](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/erp/prisma/schema)\n   - API Design: [API Controllers](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/erp/src/controllers) / [DTO Structures](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/erp/src/api/structures)\n   - E2E Test Functions: [`test/features/api`](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/erp/test/features/api)\n   - API Implementations: [`src/providers`](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/erp/src/providers)\n\n## Getting Started\n\n```bash\ngit clone https://github.com/wrtnlabs/autobe --depth=1\ncd autobe\npnpm install\npnpm run playground\n```\n\nTo use AutoBE, clone the repository and run the playground application locally. This allows you to chat with AutoBE's AI agents, manage multiple sessions, and use various LLM providers including local models like `qwen3.5-397b-a17b`.\n\nAfter installation, the playground will be available at http://localhost:5713. You can interact with AutoBE through a chat interface - simply describe what you want to build, and AutoBE will generate the backend application for you.\n\nHere's an example conversation script that guides AutoBE to create an \"Economic/Political Discussion Board\":\n\n1. **Requirements Analysis**: \"I want to create an economic/political discussion board. Since I'm not familiar with programming, please write a requirements analysis report as you see fit.\"\n2. **Database Design**: \"Design the database schema.\"\n3. **API Specification**: \"Create the API interface specification.\"\n4. **Testing**: \"Make the e2e test functions.\"\n5. **Implementation**: \"Implement API functions.\"\n\n![Compilation Success Dashboard](https://autobe.dev/images/demonstrate/replay-z-ai-glm-5.png)\n\n\u003e The playground includes a replay feature at http://localhost:5713/replay/index.html where you can view chat sessions from the AutoBE development team's testing and benchmarks.\n\n## Documentation Resources\n\nFind comprehensive resources at our [official website](https://autobe.dev).\n\n### 🏠 Home\n\n- 🙋🏻‍♂️ [Introduction](https://autobe.dev/docs)\n- 📦 [Setup](https://autobe.dev/docs/setup)\n- 🔍 Concepts\n  - [Waterfall Model](https://autobe.dev/docs/concepts/waterfall)\n  - [Compiler Strategy](https://autobe.dev/docs/concepts/compiler)\n  - [AI Function Calling](https://autobe.dev/docs/concepts/function-calling)\n\n### 📖 Features\n\n- 🤖 Agent Library\n  - [Facade Controller](https://autobe.dev/docs/agent/facade)\n  - [Configuration](https://autobe.dev/docs/agent/config)\n  - [Event Handling](https://autobe.dev/docs/agent/event)\n  - [Prompt Histories](https://autobe.dev/docs/agent/history)\n- 📡 WebSocket Protocol\n  - [Remote Procedure Call](https://autobe.dev/docs/websocket/rpc)\n  - [NestJS Server](https://autobe.dev/docs/websocket/nestjs)\n  - [NodeJS Server](https://autobe.dev/docs/websocket/nodejs)\n  - [Client Application](https://autobe.dev/docs/websocket/client)\n- 🛠️ Backend Stack\n  - [TypeScript](https://autobe.dev/docs/stack/typescript)\n  - [Prisma ORM](https://autobe.dev/docs/stack/prisma)\n  - [NestJS Framework](https://autobe.dev/docs/stack/nestjs)\n\n### 🔗 Appendix\n\n- 🌐 [No-Code Ecosystem](https://autobe.dev/docs/ecosystem)\n- 📅 Roadmap\n  - [Alpha Release (done)](https://autobe.dev/docs/roadmap/alpha)\n  - [Beta Release (done)](https://autobe.dev/docs/roadmap/beta)\n  - [Gamma Release (done)](https://autobe.dev/docs/roadmap/gamma)\n  - [Delta Release (active)](https://autobe.dev/docs/roadmap/delta)\n- 🔧 [API Documentation](https://autobe.dev/api)\n\n## How AutoBE Works\n\n```mermaid\nflowchart\nsubgraph \"Backend Coding Agent\"\n  coder(\"Facade Controller\")\nend\nsubgraph \"Functional Agents\"\n  coder --\"Requirements Analysis\"--\u003e analyze(\"Analyze\")\n  coder --\"ERD\"--\u003e database(\"Database\")\n  coder --\"API Design\"--\u003e interface(\"Interface\")\n  coder --\"Test Codes\" --\u003e test(\"Test\")\n  coder --\"Main Program\" --\u003e realize(\"Realize\")\nend\nsubgraph \"Compiler Feedback\"\n  database --\"validates\" --\u003e prismaCompiler(\"\u003ca href=\"https://github.com/wrtnlabs/autobe/blob/main/packages/interface/src/database/AutoBeDatabase.ts\" target=\"_blank\"\u003ePrisma Compiler\u003c/a\u003e\")\n  interface --\"generates\" --\u003e openapiCompiler(\"\u003ca href=\"https://github.com/wrtnlabs/autobe/blob/main/packages/interface/src/openapi/AutoBeOpenApi.ts\" target=\"_blank\"\u003eOpenAPI Compiler\u003c/a\u003e\")\n  test --\"analyzes\" --\u003e testCompiler(\"\u003ca href=\"https://github.com/wrtnlabs/autobe/blob/main/packages/interface/src/test/AutoBeTest.ts\" target=\"_blank\"\u003eTest Compiler\u003c/a\u003e\")\n  realize --\"compiles\" --\u003e realizeCompiler(\"TypeScript Compiler\")\nend\n```\n\nAutoBE follows a waterfall methodology to generate backend applications, with 40+ specialized agents handling each phase. The agents work in coordinated teams throughout the development process.\n\nEach waterfall stage includes AI-friendly compilers that guarantee type safety of the generated code. Rather than generating code directly, AutoBE's agents first construct language-neutral Abstract Syntax Trees using predefined schemas. Each AST node undergoes validation against type rules before any code generation occurs, catching structural errors at the conceptual level rather than during compilation.\n\nThis approach is designed to ensure that the final generated TypeScript and Prisma code is 100% buildable. Based on our testing with multiple example projects including e-commerce platforms, discussion boards, and task management systems, AutoBE maintains this compilation guarantee across diverse application types.\n\nTo illustrate this process, here are the phase outputs from our \"Economic/Political Discussion Board\" example:\n\n1. **Requirements Analysis**: [Report](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/reddit/docs/analysis)\n2. **Database Design**: [Entity Relationship Diagram](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/reddit/docs/ERD.md) / [Prisma Schema](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/reddit/prisma/schema)\n3. **API Specification**: [API Controllers](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/reddit/src/controllers) / [DTO Structures](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/reddit/src/api/structures)\n4. **E2E Test Functions**: [`test/features/api`](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/reddit/test/features/api)\n5. **API Implementations**: [`src/providers`](https://github.com/wrtnlabs/autobe-examples/tree/main/z-ai/glm-5/reddit/src/providers)\n\n## Benchmark\n\nAutoBE includes an automated evaluation pipeline that scores generated backends across 13+ LLM models and 4 project types. View the live results at **[autobe.dev/benchmark](https://autobe.dev/benchmark/)**.\n\nThe benchmark evaluates compilation correctness, documentation quality, requirements coverage, test coverage, logic completeness, API completeness, and AI agent analysis (security, hallucination, code quality). Each model is scored 0-100 with grades A-F.\n\n| Model | Todo | Reddit | Shopping | ERP | Avg |\n|-------|------|--------|----------|-----|-----|\n| glm-5 | 88 (B) | 87 (B) | 82 (B) | 87 (B) | **86** |\n| claude-sonnet-4.6 | 87 (B) | 85 (B) | 72 (C) | 85 (B) | 82 |\n| gpt-5.4-mini | 89 (B) | 87 (B) | 74 (C) | 78 (C) | 82 |\n| qwen3-coder-next | 86 (B) | 76 (C) | 75 (C) | 88 (B) | 81 |\n| qwen3.5-27b | 88 (B) | 81 (B) | 77 (C) | 78 (C) | 81 |\n| minimax-m2.7 | 90 (A) | 71 (C) | 77 (C) | 79 (C) | 79 |\n| gpt-5.4 | 79 (C) | 78 (C) | 79 (C) | 80 (B) | 79 |\n\n### Running the Benchmark\n\n```bash\n# All models (default)\ncorepack pnpm estimate\n\n# Single model only\ncorepack pnpm estimate -- --model kimi-k2.5\n\n# Single project only\ncorepack pnpm estimate -- --project todo\n\n# Model + project\ncorepack pnpm estimate -- --model glm-5 --project shopping\n\n# If pnpm is globally installed\npnpm estimate\n```\n\nReports are saved to:\n\n```\npackages/estimate/reports/benchmark/{model}/{project}/estimate-report.json\n```\n\nAlso, you don't need to use all phases - stop at any stage that fits your needs. Whether you want just requirements analysis, database design, API specification, or e2e testing, AutoBE adapts to your workflow.\n\nAdditionally, if you're skipping the full pipeline because of language preference rather than workflow needs, this capability is in development - AutoBE's language-neutral AST structure will soon support additional programming languages beyond TypeScript.\n\n## Type-Safe Client SDK\n\nEvery AutoBE-generated backend automatically includes a type-safe client SDK, making frontend integration seamless and error-free. This SDK provides:\n\n- **Zero Configuration**: SDK is auto-generated alongside your backend - no manual setup required\n- **100% Type Safety**: Full TypeScript support with autocomplete and compile-time validation\n- **Framework Agnostic**: Works with React, Vue, Angular, or any TypeScript/JavaScript project\n- **E2E Test Integration**: Powers AI-generated test suites for comprehensive backend testing\n\n```typescript\nimport api, { IPost } from \"autobe-generated-sdk\";\n\n// Type-safe API calls with full autocomplete\nconst connection: api.IConnection = {\n  host: \"http://localhost:1234\",\n};\nawait api.functional.users.login(connection, {\n  body: {\n    email: \"user@example.com\",\n    password: \"secure-password\",\n  },\n});\n\n// TypeScript catches errors at compile time\nconst post: IPost = await api.functional.posts.create(connection, {\n  body: {\n    title: \"Hello World\",\n    content: \"My first post\",\n    // authorId: \"123\" \u003c- TypeScript error if this field is missing!\n  },\n});\n```\n\nThis SDK eliminates the traditional pain points of API integration - no more manual type definitions, no more runtime surprises, and no more API documentation lookups. Your frontend developers can focus on building features, not wrestling with API contracts.\n\n**Beyond Frontend Integration**: The SDK powers both frontend development and E2E test generation. AutoBE uses the same type-safe SDK internally to generate comprehensive test suites, ensuring every API endpoint is thoroughly tested. This creates a robust feedback loop that enhances backend stability - AI writes tests using the SDK, the SDK ensures type safety, and your backend becomes more reliable with every generated test.\n\n## Roadmap Schedule\n\n```mermaid\ngantt\n  dateFormat YYYY-MM-DD\n  title AutoBE Delta Roadmap Timeline (2026 Q1)\n\n  section Local LLM Benchmark\n  Qwen3 Database Phase             : done, 2026-01-01, 31d\n  Qwen3 Interface Phase            : done, 2026-01-16, 31d\n  Qwen3 Test Phase                 : done, 2026-02-01, 21d\n  Qwen3 Realize Phase              : done, 2026-02-01, 59d\n\n  section Validation Logic Enhancement\n  Dynamic Function Calling Schema  : done, 2026-01-01, 7d\n  Validation Feedback Stringify    : done, 2026-01-08, 12d\n  JSON Schema Validator            : done, 2026-01-08, 12d\n  Schema Review Validation Logic   : done, 2026-01-20, 35d\n  Test Mapping Plan Enhancement    : done, 2026-02-05, 24d\n  Realize Mapping Plan Enhancement : done, 2026-02-15, 45d\n\n  section RAG Optimization\n  Hybrid Search (Vector + BM25)    : done, 2026-01-01, 14d\n  Dynamic K Retrieval              : done, 2026-01-15, 14d\n  RAG Preliminary Prompting        : done, 2026-01-15, 28d\n  RAG Benchmark \u0026 Tuning           : done, 2026-02-01, 28d\n  Analyze Agent Restructuring      : done, 2026-02-01, 28d\n\n  section Design Integrity\n  DB Coverage Agent                : done, 2026-01-15, 28d\n  API Endpoint Coverage Agent      : done, 2026-01-22, 28d\n  Schema Relation Agent            : done, 2026-02-01, 28d\n  Schema Structure Agent           : done, 2026-02-01, 28d\n  Schema Content Agent             : done, 2026-03-01, 28d\n\n  section Multi-lingual Support\n  Java Compiler PoC                : done,   2026-01-01, 30d\n  Java Database                    : done,   2026-01-01, 14d\n  Java Interface                   : done,   2026-01-15, 21d\n  Java Test                        : active, 2026-02-05, 28d\n  Java Realize                     : active, 2026-03-01, 31d\n\n  section Human Modification Support\n  Database Schema Parser           : active,  2026-02-15, 28d\n  Interface Schema Parser          : active,  2026-02-22, 28d\n  Requirements Sync Agent          : planned, 2026-03-08, 24d\n\n  section Miscellaneous\n  System Prompt Simplification     : done,   2026-02-01, 28d\n  Estimation Agent                 : done,   2026-02-01, 28d\n  Playground Service Enhancement   : active, 2026-02-15, 28d\n  PR Articles Writing              : active, 2026-02-15, 30d\n```\n\nAutoBE has successfully completed Alpha, Beta, and Gamma development phases, establishing a solid foundation with **100% compilation success rate**. The current **Delta Release** focuses on transitioning from horizontal expansion to vertical deepening.\n\n**Strategic Shift**: In Gamma, we rapidly implemented features like RAG, Modularization, and Complementation under a \"just ship it\" philosophy. Delta fills the stability gaps that remained by systematically discovering and fixing hidden defects through Local LLM benchmarks.\n\n**Key Focus Areas**:\n\n- **Local LLM Benchmark**: Using open-source models like Qwen3 as a touchstone to discover hidden defects that commercial models mask, ensuring more robust operation across all model types\n- **Validation Logic Enhancement**: Strengthening schemas and validation logic through dynamic function calling schemas, JSON Schema validators, and progressive validation pipelines\n- **RAG Optimization**: Completing the Hybrid Search system (Vector + BM25) with dynamic K retrieval and comprehensive benchmark tuning\n- **Design Integrity**: Building mechanisms to verify and ensure design consistency between Database and Interface phases through coverage and schema review agents\n- **Multi-lingual Support**: Launching Java/Spring code generation alongside TypeScript/NestJS, with language-neutral AST structures enabling future language additions\n- **Human Modification Support**: Enabling maintenance continuity by parsing user-modified code back into AutoBE's internal AST representation, ensuring AutoBE remains useful beyond initial generation\n\nThis roadmap prioritizes stability and depth over feature breadth, informed by real-world production experience from Gamma.\n\n## Current Limitations\n\nWhile AutoBE achieves 100% compilation success, please note these current limitations:\n\n**Runtime Behavior**: Generated applications compile successfully, but runtime behavior may require testing and refinement. Unexpected runtime errors can occur during server execution, such as database connection issues, API endpoint failures, or business logic exceptions that weren't caught during compilation. We strongly recommend thorough testing in development environments before deploying to production. Our v1.0 release targets 100% runtime success to address these issues.\n\n**Design Interpretation**: AutoBE's database and API designs may differ from your expectations. We recommend thoroughly reviewing generated specifications before proceeding with implementation, especially before production deployment.\n\n**Token Consumption**: AutoBE requires significant AI token usage for complex projects. Based on our testing, projects typically consume 30M-250M+ tokens depending on complexity (simple todo apps use ~4M tokens, while complex e-commerce platforms may require 250M+ tokens). We are working on RAG optimization to reduce this overhead in future releases.\n\n**Maintenance**: AutoBE focuses on initial generation and does not provide ongoing maintenance capabilities. Once your backend is generated, you'll need to handle bug fixes, feature additions, performance optimizations, and security updates manually. We recommend establishing a development workflow that combines the generated codebase with AI coding assistants like Claude Code for efficient ongoing development and maintenance tasks.\n\n![Entity Relationship Diagram Sample](https://autobe.dev/images/demonstrate/erd.png)\n\n## License\n\nAutoBE is licensed under the [GNU Affero General Public License v3.0 (AGPL-3.0)](LICENSE). If you modify AutoBE itself or offer it as a network service, you must make your source code available under the same license.\n\nHowever, backend applications generated by AutoBE can be relicensed under any license you choose, such as MIT. This means you can freely use AutoBE-generated code in commercial projects without open source obligations, similar to how other code generation tools work.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwrtnlabs%2Fautobe","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwrtnlabs%2Fautobe","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwrtnlabs%2Fautobe/lists"}