Built something for the LLM builder

community — Versera.

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THE PROBLEM

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Prompts are treated like static strings

when they should be treated like code.

No version history.

No rollback.

No environment separation.

Changing one = full redeploy.

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THE SOLUTION

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Versera is a prompt version control API.

Store prompts:

POST api.versera.dev/v1/prompts

{

"name": "my-prompt",

"environment": "prod",

"template": "Your prompt with

{{variables}} here.",

"message": "What changed and why"

}

Resolve at runtime:

GET api.versera.dev/v1/resolve/my-prompt

?variable=value

Header: x-api-key: vrs_live_...

Works with ANY model — Claude, GPT,

Gemini, Llama, Mistral, whatever

you're running locally or via API.

The endpoint just returns a string.

You pass it to any inference endpoint.

Rollback:

POST /v1/prompts/my-prompt

/versions/N/rollback

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WHAT YOU GET

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→ Full version history with git-style diffs

→ Dev / staging / prod environments

→ Variable injection ({{variable}} syntax)

→ Instant rollback without redeploying

→ Interactive docs with live API tester

→ Model agnostic — works with anything

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LINKS

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→ Product: versera.dev

→ Interactive docs: versera.dev/docs

→ API base URL: api.versera.dev

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Technical questions welcome — happy

to go deep on the architecture,

the schema design, or anything else.

Also curious: what's your current

approach to prompt management when

running local models? Is this a

problem you've felt or solved

differently?