AutoBE
Turns natural-language requirements into staged, buildable TypeScript backends.
- Source repo
- wrtnlabs/autobe
- Stars
- ★ 1.4k
- Last updated
- 3mo ago
- License
- AGPL-3.0
- Primary language
- TypeScript
- FA score
- 35/100 · Major gaps
At a glance
- Works with
- Universal · cross-platform
- You'll need
- Typical use
- A non-programmer founder wants a first todo-app backend prototype, beginning with a requirements report, schema, and API specification.
- Main limitation
- The repository explicitly says buildability does not guarantee runtime success; database, endpoint, and business-logic failures can still occur.
- Source review
- 35/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
AutoBE is a locally run AI backend builder for moving from a prototype toward a production backend. Through its playground chat interface, users guide a waterfall flow covering requirements analysis, database design, API design, E2E tests, and implementation. A Facade Controller coordinates more than 40 specialized functional agents, which construct and validate language-neutral ASTs before code generation. Its intended outputs include a TypeScript, Prisma, and NestJS backend; database and API documentation; test code; and a type-safe client SDK. The repository presents a 100% buildability goal, while also documenting that runtime behavior, design interpretation, and ongoing maintenance still require human work.
After pnpm run playground starts the local chat application, a user submits backend requirements in stages. The Facade Controller routes work through Analyze, Database, Interface, Test, and Realize; AutoBeDatabase validates the database phase, AutoBeOpenApi produces OpenAPI-related output for the interface phase, AutoBeTest analyzes tests, and Realize uses a Hybrid Compiler. Agents create language-neutral ASTs expressed through predefined schemas, validate them against type rules, and then generate TypeScript and Prisma code. The resulting backend includes API controllers, DTO structures, implementation logic, E2E test functions, and a type-safe SDK for frontend calls and testing.
- A non-programmer founder wants a first todo-app backend prototype, beginning with a requirements report, schema, and API specification.
- A backend team needs reviewable ERDs, controllers, DTOs, and an initial implementation for a new NestJS and Prisma service.
- A test engineer wants generated E2E test functions that exercise endpoints through a type-safe client SDK.
- An engineering lead wants to inspect benchmark results across models for Todo, Reddit, shopping, and ERP backend-generation tasks.
- A development team wants an initial generated backend to extend with coding assistants while retaining specifications and tests as maintenance inputs.
How do you install or deploy this agent?
Prerequisites: Git and pnpm. Run:
git clone https://github.com/wrtnlabs/autobe --depth=1
cd autobe
pnpm install
pnpm run playgroundThe playground is served at http://localhost:5173. The supplied material says AutoBE can use multiple LLM providers and local models, but does not document provider credentials, environment variables, or setup steps.
How do you use this agent?
Open http://localhost:5173 and describe the backend you want in the chat interface. You can request, in order, requirements analysis, a database schema, an API interface specification, E2E test functions, and API implementation, or stop after any stage. Test and benchmark chat replays are available at http://localhost:5173/replay/index.html. Run benchmarks with corepack pnpm estimate, optionally adding --model and --project.
What are this agent's strengths and limitations?
- Produces a staged set of backend artifacts—requirements, database, API, tests, and implementation—rather than only an application code snippet.
- Uses AST validation and database, OpenAPI, and test compilers before code generation as its documented buildability mechanism.
- Generates a type-safe client SDK that is used for both frontend integration and E2E testing.
- Includes an evaluation pipeline across more than 13 models and four project types, with JSON report output paths.
- The repository explicitly says buildability does not guarantee runtime success; database, endpoint, and business-logic failures can still occur.
- Complex projects can consume substantial AI tokens: the documented range is roughly 30M to 250M+, with a simple todo app around 4M.
- Generated database and API designs may differ from the user's intent and should be reviewed before implementation or production use.
- Ongoing maintenance is outside the current scope; teams must handle fixes, features, performance work, and security updates after generation.
- The supplied material lacks provider-specific credential and configuration instructions, so connecting a real model may require additional setup.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|
| AutoBE This agent | 35 · Major gaps | ★ 1.4k | 3mo ago | TypeScript | — |
| OpenConnector | 57 · Major gaps | ★ 5.8k | 3d ago | TypeScript | — |
| Agentica — AI Function Calling Framework | 49 · Major gaps | ★ 1k | 4mo ago | TypeScript | ChatGPT · Claude.ai · OpenAI API · Claude API |
| Solid Skills — Senior-Engineer Coding Skills for AI Agents | 41 · Major gaps | ★ 591 | 20d ago | — | — |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows no documentation or code for permission management, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. All trust criteria scored 0 due to absence of evidence.
Self-consistency: README claims 100% compilation success but no verifiable test results, and runtime errors are acknowledged, so 1. Dependency availability: uses pnpm and multiple packages but no lockfile or integrity checks, so 1. Failure messages: test code has error handling but no user-friendly failure messages, so 1.
Audience and scenarios: README describes multiple use cases and examples, so 2. Capability boundaries: clearly lists current limitations, so 2. Trigger precision: no detailed trigger conditions, so 1. Environment fit: provides installation and run instructions, so 2.
Information architecture: clear documentation structure, so 2. Install notes: provides installation steps, so 2. Naming stability: no naming conventions, so 1. Examples and FAQ: provides examples but no FAQ, so 2. Known limitations: explicitly listed, so 2. License: AGPL-3.0, so 2. Versioning and changelog: has version number but no changelog, so 1. Maintenance responsibility: no clear maintainer, so 1.
Output usability: generated code has type-safe SDK, so 2. Marginal value: provides automated generation but actual effectiveness not verified, so 2. Cost benefit: no cost analysis, so 1.
Claim traceability: claims in README lack verifiable evidence, so 1. Cross-source corroboration: no external verification, so 1. Fact-inference separation: not clearly distinguished, so 1.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- No security-related documentation or code, such as permission management, data encryption, or dependency auditing.
- README claims 100% compilation success but provides no reproducible test results and acknowledges runtime error risks.
- No changelog or clear maintainer, update path unclear.