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k82cn merged 2 commits into from
May 9, 2026
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docs: update slogan to 'A distributed engine for AI' #443

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docs: update slogan to 'A distributed engine for AI'
k82cn May 9, 2026
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docs: address review comments
k82cn May 9, 2026
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2 changes: 1 addition & 1 deletion AGENTS.md
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# Flame – A Distributed System for Agentic AI
# Flame: A Distributed Engine for AI

Repo: https://github.com/xflops-io/flame

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6 changes: 3 additions & 3 deletions README.md
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# Flame: A Distributed System for Agentic AI
# Flame: A Distributed Engine for AI

[![license](https://img.shields.io/github/license/xflops/flame)](http://github.com/xflops/flame)
[![RepoSize](https://img.shields.io/github/repo-size/xflops/flame)](http://github.com/xflops/flame)
[![Release](https://img.shields.io/github/release/xflops/flame)](https://github.com/xflops/flame/releases)
[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/7299/badge)](https://bestpractices.coreinfrastructure.org/projects/7299)
[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/xflops/flame)

Flame is a distributed system designed for Agentic AI, providing a comprehensive suite of mechanisms commonly required by various classes of Agentic AI workloads, including tools, Agent, and more. Built upon over a decade and a half of experience running diverse high-performance workloads at scale across multiple systems and platforms, Flame incorporates best-of-breed ideas and practices from the open source community.
Flame is a distributed engine for AI, providing a comprehensive suite of mechanisms commonly required by various classes of AI workloads, including agents, reinforcement learning, and more. Built upon over a decade and a half of experience running diverse high-performance workloads at scale across multiple systems and platforms, Flame incorporates best-of-breed ideas and practices from the open source community.

## Motivation

As Agentic AI become increasingly adopted for innovation, a common workload runtime is essential to accelerate these elastic workloads through the following key aspects:
As AI workloads become increasingly adopted for innovation, a common runtime is essential to accelerate these elastic workloads through the following key aspects:

* **Scale**: Unlike applications running on a single node, Flame scales workloads across multiple nodes to maximize performance acceleration while ensuring fair resource sharing across multiple tenants and sessions.
* **Performance**: Elastic workloads typically involve tens of thousands of short tasks. Flame leverages cutting-edge features to improve roundtrip times and throughput in large-scale environments, while intelligently sharing runtime within sessions to minimize startup time.
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