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Acrux Core

Version prompts, route LLM calls, trace and evaluate โ€” one platform. Ship, change, and measure LLM features without redeploying to move a prompt.

Acrux Core sits between your application and every LLM provider, so you can ship, change, and measure AI features without redeploying code just to move a prompt.

It brings together the pieces teams normally stitch from separate tools:

  • Prompt management โ€” prompts are versioned data, not code. Immutable versions, named aliases (e.g. production, staging), server-side Jinja2-style templating, and a full diff/audit trail.
  • AI gateway โ€” one drop-in-compatible endpoint in front of every model provider. Bring your own provider keys, issue virtual keys with budgets, and get cost and token counts on every response.
  • Tracing โ€” every call is recorded as a trace with spans (model, tokens, latency, cost), grouped into sessions, with human feedback and configurable payload capture.
  • Tool catalog โ€” callable functions versioned like prompts, executed two ways, and attached directly to a prompt so the model can call them.
  • Evaluation โ€” build datasets from real periments to prove a new prompt or modelversion is actually better before you ship it.

It's multi-tenant, team-scoped, open soufor TypeScript/Node (@acruxcoreai/sdk onnpm) and Python (acruxcore on PyPI). Free while in beta.

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Acrux Core

Acrux Core Reviews and Details

This page is designed to help you find out whether Acrux Core is good and if it is the right choice for you.

#LLM #Prompt Management #LLM Gateway #AI Agent Observability #AI Agents #AI Tools #AI

Screenshots and images

  • Acrux Core
    Image date //
    2026-08-05

Features & Specs

  1. Prompts

    Versioned, templated message sets. Move the production alias to a new version and the next call picks it up โ€” no redeploy, no release, no code change.

  2. Gateway

    One OpenAI-compatible endpoint in front of every provider. Bring your own keys, issue scoped virtual keys with spend budgets, and get cost and cache-hit info on every response.

  3. Tracing

    Every gateway call is recorded as a trace with spans for model, tokens, latency, and cost โ€” grouped into sessions, with human feedback attachable to any span.

  4. Tools

    Callable functions versioned and promoted like prompts, attached directly to a prompt so one render call returns both, with every call recorded as an analytics span.

  5. Evaluation

    Build datasets from real traces and feedback, sweep them across prompt versions and models as experiments, and read per-cell results before promoting a prompt to production.

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Questions & Answers

As answered by people managing Acrux Core.

  1. What makes Acrux Core unique?

    Most LLM tools own one link in the chain โ€” a prompt manager, a gateway, a tracer, or an eval tool โ€” and teams stitch three or four of them together with glue code. Acrux Core keeps one lineage instead: a prompt version flows through the gateway as a request, becomes a trace with tool spans, gets priced, and folds into an evaluation dataset โ€” all in one data model, so a regression can be traced back to the exact prompt version and model that caused it without cross-referencing four dashboards.

  2. Why should a person choose Acrux Core over its competitors?

    Because the pieces are already wired together. You don't run a prompt CMS, a separate LiteLLM-style proxy, a separate tracing tool, and a separate eval harness and reconcile them yourself โ€” one gateway call is already versioned, traced, and priced, and that trace can become an evaluation dataset with no export/import step. It's also open source, self-hostable, and ships official SDKs (TypeScript and Python) rather than locking you into a dashboard-only workflow.

  3. How would you describe the primary audience of Acrux Core?

    Engineering teams shipping LLM features in production โ€” usually a backend or platform engineer who owns the AI integration for a small-to-mid-size product team. They need to change a prompt without a redeploy, see what a call actually cost and how long it took, and prove a new prompt version is better before promoting it โ€” without adopting a full MLOps stack to get there

  4. Which are the primary technologies used for building Acrux Core?

    TypeScript end to end โ€” an Express API and a BullMQ-based worker on Node.js, a React dashboard, and PostgreSQL (with JSONB for flexible payloads like prompt content and trace data) via Prisma as the ORM. Prompt templating uses a Jinja2-style engine (nunjucks). The public SDKs are TypeScript/Node (@acruxcoreai/sdk) and async Python (acruxcore). The docs site runs on Docusaurus.

  5. What's the story behind Acrux Core?

    I've spent the last three years building chatbots for more than 30 clients. Across all those projects, I kept running into the same problem with the tools already on the market: they were either bloated with features I never needed, or missing the ones I actually did. I'd end up paying for complexity I didn't want, while still gluing together separate tools for prompt versioning, request routing, tracing, and evaluation myself. After enough of that, I decided to build the platform I actually needed โ€” one place to version prompts, route calls to any model provider, trace every request, and evaluate whether a change actually made things better. Acrux Core is that platform, built from real production experience rather than a feature checklist.

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