garph VS datamodel-code-generator

https://garph.dev is pretty good. I have been using it for two months and love the experience. I had started out with nexus and briefly also evaluted pothos but switched to garph because the dev experience was superior. It takes full advantage of the structural type system of typescript rather than frameworks that lean more towards java style idioms.

Location: EU, Germany

Remote: Yes

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Technologies: HTML, CSS, TailwindCSS, JavaScript, TypeScript, Node.js, React/Next.js, Vue/Nuxt, GraphQL, REST, Postgres, Git, AWS, Docker + K8s

GitHub: https://github.com/mishushakov

LinkedIn: https://linkedin.com/in/mishushakov

Email: hey at mish.co

Most recently, I worked at Step CI a Technical Founder and authored the API-Testing Framework (https://stepci.com) and Garph (https://garph.dev), a full-stack API-Framework, which brings the developer-experience of tRPC to GraphQL.

My passion is in making tools developers love using and make them more productive.

If you want something like tRPC but for GraphQL, you should definitely give Garph a try: https://garph.dev

Amazing findings! Really admire your effort here

Btw. If you're building a GraphQL API using TypeScript, you should take a look at garph (https://garph.dev) which helps you to create type-safe GraphQL APIs without code-gen

The next step is undoubtedly the creation of our GraphQL Schema using Garph to create a totally type-safe API without needing to do codegen.

Like generating pydantic models or dataclasses for an OpenAPI schema? I haven't needed to go in that direction myself, but this[0] looks promising!

Apologies if I've misunderstood your comment

https://koxudaxi.github.io/datamodel-code-generator/

I'm sorry, but you have completely misunderstood the purpose of Open API.

It is not a specification to define your business logic classes and objects -- either client or server side. Its goal is to define the interface of an API, and to provide a single source of truth that requests and responses can be validated against. It contains everything you need to know to make requests to an API; code generation is nice to have (and I use it myself, but mainly on the server side, for routing and validation), but not something required or expected from OpenAPI

For what it's worth, my personal preferred workflow to build an API is as follows:

1. Build the OpenAPI spec first. A smaller spec could easily be done by hand, but I prefer using a design tool like Stoplight [0]; it has the best Web-based OpenAPI (and JSON Schema) editor I have encountered, and integrates with git nearly flawlessly.

2. Use an automated tool to generate the API code implementation. Again, a static generation tool such as datamodel-code-generator [1] (which generates Pydantic models) would suffice, but for Python I prefer the dynamic request routing and validation provided by pyapi-server [2].

3. Finally, I use automated testing tools such as schemathesis [3] to test the implementation against the specification.

[0] https://stoplight.io/

[1] https://koxudaxi.github.io/datamodel-code-generator/

[2] https://pyapi-server.readthedocs.io

[3] https://schemathesis.readthedocs.io

The site also provide a link to the github repo of the underlying program.

Not sure who owns/hosts the site, but its based on this github repo.

That's what datamodel-code-generator propose.

had generally good experience creating typed wrappers for api's with json-schema-to-pydantic[0] converter

[0] https://github.com/koxudaxi/datamodel-code-generator

To save you some work, if you have already an OpenAPI specification at hand, you can use datamodel-code-generator to generate your Pydantic models from the spec.

And there are code generators for it! https://github.com/koxudaxi/datamodel-code-generator/