AgentIndex icon
AgentIndex
FedML logo
FedML
★ 4.1k
vs
rllm logo
rllm
★ 5.9k

FedML vs rllm

FedML: FedML is a unified and scalable open-source machine learning library powered by TensorOpera AI, enabling training and deployment of AI jobs anywhere at any scale. It offers holistic support for MLOps, scheduling, and high-performance ML libraries, including federated learning, distributed training, and generative AI functionalities.; rllm: rLLM is an open-source framework designed for post-training language agents using reinforcement learning. It allows users to easily build, train, and deploy custom agents and environments for real-world workloads.

01

TL;DR

FedML logo
FedMLLLM Infra · ★ 4.1k

Distributed training and fine-tuning of large models (including LLMs)

Read FedML full review →
rllm logo
rllmLLM Infra · ★ 5.9k

Training powerful coding models for tasks like code generation and bug fixing.

Read rllm full review →
02

Side-by-Side Comparison

Field
FedML logoFedML
rllm logorllm
Category
LLM Infra
LLM Infra
Stars
★ 4.1k
★ 5.9k
License
Apache-2.0
Apache-2.0
Install Command
git clone https://github.com/FedML-AI/FedML.git
uv pip install "rllm[verl] @ git+https://github.com/rllm-org/rllm.git"
Updated
1y ago
1w ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
Federated Learning, MLOps, Distributed Training
Reinforcement Learning, Language Agents, LLM
03

Features

FedML logoFedML
01Unified and scalable ML library
02Support for Generative AI and LLMs (fine-tuning, deployment)
03Federated Learning platform (on-device, cross-cloud)
04Distributed Training for large and foundational models
05Model serving platform for high scalability and low latency
rllm logorllm
01Open-source framework for reinforcement learning-based post-training of language agents.
02Supports building, training, and deploying custom agents and environments.
03Offers multiple training backends including 'verl' and 'tinker'.
04Enables LoRA and VLM training for advanced models.
05Includes AgentWorkflowEngine for training over arbitrary agentic programs.
04

Use Cases

FedML logoFedML
↳Distributed training and fine-tuning of large models (including LLMs)
↳Scalable deployment and serving of AI models
↳Federated learning across various decentralized environments
rllm logorllm
↳Training powerful coding models for tasks like code generation and bug fixing.
↳Developing sophisticated software engineering agents for automated tasks.
↳Building and evaluating multi-agent systems using reinforcement learning techniques.
05

Best For

FedML logoFedML
Essential
rllm logorllm
Trending
FAQ

FAQ

What is the difference between FedML and rllm?
Both FedML and rllm are in the LLM Infra category. FedML has 4.1k stars, while rllm has 5.9k stars.
Which is better, FedML or rllm?
The best choice depends on your use case. Choose FedML if Distributed training and fine-tuning of large models (including LLMs), and rllm if Training powerful coding models for tasks like code generation and bug fixing..
Is FedML free or open source?
Yes, FedML is open source on GitHub (Apache-2.0).
Is rllm free or open source?
Yes, rllm is open source on GitHub (Apache-2.0).
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Related

Alternatives to FedML →Alternatives to rllm →FedML details →rllm details →