Loom AI Labs

Independent open-source engineering

About Loom AI Labs

Loom AI Labs is the project and brand home for open-source products, runnable engineering proof and implementation-linked research into dependable AI-enabled software.

Maintained by Mahmoud Ashraf Algammal with open-source contributors

Mission

Make AI behavior legible inside real applications.

The work focuses on the space between a model API and a dependable product: trusted application context, current data, retrieval evidence, governed actions, visible limitations and reusable user experience.

Loom AI Labs does not treat prompts as a substitute for identity, authorization, source-of-truth data or business execution.

Engineering principles

A small set of rules shapes every layer.

Application-owned

Identity, policy, domain data and execution stay with the host application.

Explicit contracts

Context, evidence, clarification and actions cross typed, inspectable boundaries.

Proof before claims

Live demos and public implementation support claims; limitations remain visible.

Composable adoption

Teams can adopt one capability without relocating their whole application.

Source in view

Products and runnable proof link back to the implementation they describe.

Bounded language

Prototype, evidence and maturity terms match what public artifacts establish.

One connected body of work

Products, experiments and research have different jobs.

  1. 01 Engineering question
  2. 02 Open-source product
  3. 03 Runnable experiment
  4. 04 Documented observation
Products

Reusable open-source software with a supported public integration surface.

Experiments

Bounded applications that make one technical capability inspectable.

Research

Engineering investigations linking questions, implementations and limitations.

Maintainer identity

Mahmoud Ashraf Algammal

Loom AI Labs is independently maintained. Contributions are reviewed against the same application-boundary, evidence and source-linking principles used across the public work.

Open-source model

Public foundations, inspectable change.

AI Fabric Framework and AI Fabric Chat UI are released under Apache 2.0. Issues and pull requests belong in the relevant public repository, close to code and verification.

Contribution path

Start with a reproducible boundary.

Bring an issue, an integration gap or a bounded experiment with enough context for another engineer to inspect it.