AI Alliance GitHub Organization
Quick Links
AI Alliance Regional Chapters
The AI Alliance is a global organization. However, localization matters and is key to representation. To ensure effectiveness, relevance, and impact across diverse markets and communities, regional chapters of the AI Alliance exist and more are planned:
- Japan
- India - coming soon!
The AI Alliance Projects
Welcome to the GitHub organization for the AI Alliance, a community of technology creators, developers, and adopters collaborating to advance safe, responsible, and effective AI rooted in open innovation. Here, Alliance members collaborate on technical initiatives, AI technology guides, and related projects.
The AI Alliance is focused on accelerating and disseminating open innovation across the AI technology landscape to improve foundational capabilities, safety, security and trust in AI, and to responsibly maximize benefits to people and society everywhere.
See about us for more information about The AI Alliance.
Focus Areas
The Open Agent Lab: Collaborate, Experiment, and Build Domain-specific AI Agents with Open Source
The Open Agent Lab is a collaborative community, open source projects and domain-specific work groups that seek to make AI Agents successful in the real world through fast experimentation and distillation of learning into usable reference architectures and implementations, and build out of new tools to enable development and deployment.
We are especially focused on domain-speciic challenges with work groups in:
Industrial AI: check out SemiKong a foundation model for semiconductor process agents.
Legal: try Bartlebot if you want to work with case law and other legal topics.
Climate and Geospatial: check out projects like GeoBench and TerraTorch.
Chemistry and Materials: take a look at new science foundation models for molecular analysis.
(Coming soon.) Finance, Health, and Time Series domain work groups.
The Open Agent Lab builds with open weight models from our members, including Llama, Granite, AI2, ServiceNow, and more.
Agent Projects
These projects are divided into two tables, one table for projects related to Model Context Protocol (MCP) and a second table for the more general-purpose agent projects.
Model Context Protocol
| Links | Description |
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| MCP Gateway | |
| A Model Context Protocol (MCP) Gateway. Serves as a central management point for tools, resources, and prompts that can be accessed by MCP-compatible LLM applications. Converts REST API endpoints to MCP, composes virtual MCP servers with added security and observability, and converts between protocols (stdio, SSE). (Principal developer: IBM) | |
| Enkrypt AI Secure MCP Gateway | |
| A secure MCP gateway built with authentication, automatic tool discovery, caching, and guardrail enforcement. It sits between your MCP client and MCP servers. So, by its nature, it also acts as an MCP server as well as an MCP client. When your MCP client connects to the gateway, it acts as an MCP server. When the gateway connects to the actual MCP server, it acts as an MCP client. (Principal developer: Enkrypt AI) | |
General Purpose Agent Projects
| Links | Description |
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AllyCat 
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| (Beginner friendly!) Get started with a simple and fun end to end RAG application that scrapes your website so you can ask it questions. | |
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Agent Lab UI
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| AgentLabUI is a web-based interface designed to simplify the creation, management, and deployment of AI agents. It supports integration with Gofannon tools, enabling developers and researchers to rapidly prototype and experiment with sophisticated AI agent architectures. | |
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Gofannon
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| A repository of functions consumable by other agent frameworks. | |
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Proscenium
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| Collaborative, Asynchronous Human/Agent Interactions. | |
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Lapidarist
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| Document enrichment and knowledge structure (eg knowledge graph) extraction and resolution. | |
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Llama Stack Agents
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| A deployable, domain-specific application demonstrating the use of Llama Stack. See also the Llama Stack section below. | |
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The Living Guide to Applying AI
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| Tips from experts on using AI for various applications, including popular design patterns. | |
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OpenDXA (coming soon!)
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| Domain Expert Agents (DXA) for industrial AI. (Principal developer: Aitomatic) | |
Llama Stack
(coming soon to AI Alliance) The Llama Stack project from Meta provides standardized APIs, component abstractions, and integrations with other open source tools and managed services to help develoeprs build and deploy AI applications and agents.
We are organizing several reference implementations of agents on Llama Stack in the Llama Stack Lab part of the Open Agent Lab. See also Llama Stack Agents discussed under General Purpose Agent Projects above.
Evaluation and Safety
How do we know that applications built with AI are trustworthy, that they perform as required, in particular that they are safe, free of harmful outputs? Our Evaluation and Safety projects and initiatives address these concerns.
Trust actually has a broad interpretation. Increasingly, organizations moving from proofs of concept to production are not only concerned about evaluating their chosen models and applications for safety, but also for general alignment; do they actually perform well for the specific use cases implemented? This focus area is now exploring these areas of evaluation, in general terms and for specific domains.
| Links | Description |
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Trust and Safety Evaluations Initiative
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TSEI seeks to define the global taxonomy of evaluations (from safety to performance to efficacy), catalog available implementations of them, and provide a reference stack of industry-standard tools to run the evaluations. Our projects:
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Ranking AI Safety Priorities by Domain
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| What are the most important safety concerns for your specific domain and use cases? This project explores these questions in several industries, healthcare, finance, education, and legal, with more to come. | |
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AI Application Testing for Developers
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| If you are a software developer, you are accustomed to writing deterministic tests. What do you do when generative models aren't deterministic? This project adapts AI evaluation techniques for the "last mile" of evaluation; how do you verify that an AI application correctly implements its specific requirements and use cases, beyond the general concerns of common evaluation? This project also aims to educate developers on how to use these techniques effectively in combination with the traditional tools they already know. | |
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The AI Trust and Safety User Guide
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| Introduction to T&S with guidance from diverse experts. | |
| unitxt | |
| Unitxt is a Python library for enterprise-grade evaluation of AI performance, offering the world's largest catalog of tools and data for end-to-end AI benchmarking. (Principal developer: IBM Research) | |
Open Trusted Data and Models
The Open Trusted Data and Models focus area has projects to build a variety of multilingual, multimodal, and domain-specific models. Examples include models for molecular discovery, geospatial, and time series applications. This focus area is also cataloging, and in some cases building, datasets with clear license for use, backed by unambiguous provenance and governance controls, which are needed for model training, tuning, and other purposes.
| Links | Description |
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The Open, Trusted Data Initiative
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Open data has clear license for use, across a wide range of topic areas, with clear provenance and governance. OTDI seeks to clarify the criteria for openness and catalog the world’s datasets that meet the criteria. Our projects:
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| Open Models | |
Several AI Alliance work groups are collaborating on the development of domain-specific models:
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Parsing PDFs to Build AI Datasets for Science
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| There is a need for domain-specific datasets for tuning special-purpose models and use in data-heavy application patterns like RAG. In many technical domains, much of the expertise is published in the open, but difficult to exploit as AI training data. For example, while text extraction from PDFs is already common, extracting information from the tables and images in technical PDFs, and associating that information with the surrounding text, is not yet widespread. This project aims to solve this problem by applying the recently-developed Docling tool to parse PDF datasets and create new datasets in formats that preserve this rich content, yet are easier to exploit in model training, tuning, etc. The project will start with the Math-PDF dataset of PDFs published recently by PleIAs. See also Docling, discussed next. | |
| Docling | |
| Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem. Docling is a key tool for the project Parsing PDFs to Build AI Datasets for Science, discussed above. (Principal developer: IBM Research) | |
| TerraTorch | |
| TerraTorch is a library based on PyTorch Lightning and the TorchGeo domain library for geospatial data. (Principal developer: IBM Research) | |
| GEO-bench | |
| GEO-Bench is a General Earth Observation benchmark for evaluating the performance of large pre-trained models on geospatial data. (Principal developer: ServiceNow) | |
Deployment and Scaling
Deploying and scaling AI systems, especially to the growing diversity of hardware accelerators for AI, and efficiently scaling from PoCs and single node deployments to large numbers of users and distributed deployments are a key set of challenges.
| Links | Description |
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The AI Accelerator Software Ecosystem Guide
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| A guide to the most common AI accelerators and the software stacks they use to integrate with tools you know, like PyTorch. | |
For More Information
- The AI Alliance GitHub Organization
- Contributing to the AI Alliance community.
- The microsite template: The template used for Alliance projects, including all the websites listed above. See the README-template.md for instructions.
- The AI Alliance website: About the AI Alliance, our goals and initiatives.
- Learn more about getting involved.
Additional Links
- Contributing: We welcome your contributions! Here’s how you can contribute.
- About Us: More about the AI Alliance and this project.
For More Information
- The full list of Alliance-sponsored and third-party AI events.
- The AI Alliance GitHub Organization
- This documentation’s GitHub repo
- The microsite template: The template used for Alliance projects, including all the websites listed above. See the README-template.md for instructions.
- The AI Alliance website: About the AI Alliance, our goals and initiatives.
- Learn more about getting involved.
| Authors | AI Alliance Team |
| Last Update | V0.2.0, 2025-06-14 |