AgentIndex icon
AgentIndex
AReaL logo
AReaL
★ 5.9k
vs
xLAM logo
xLAM
★ 641

AReaL vs xLAM

AReaL: AReaL is a fully asynchronous reinforcement learning system from Tsinghua IIIS and Ant Group for training large reasoning and agentic models. Version 2.0 splits it into training, inference, agent and weight-update microservices, and black-box agent apps can be trained online by pointing their base_url at AReaL.; xLAM: xLAM is a research repository for Large Action Models (LAMs), which aggregates and unifies agent trajectories from diverse environments into a consistent format. It streamlines the creation of a generic data loader optimized for agent training, enabling robust model development across various scenarios.

01

TL;DR

AReaL logo
AReaLLLM Infra · ★ 5.9k

RL fine-tuning of reasoning models on math and coding tasks

Read AReaL full review →
xLAM logo
xLAMLLM Infra · ★ 641

Function calling in LLMs

Read xLAM full review →
02

Side-by-Side Comparison

Field
AReaL logoAReaL
xLAM logoxLAM
Category
LLM Infra
LLM Infra
Stars
★ 5.9k
★ 641
License
Apache-2.0
Apache-2.0
Install Command
git clone https://github.com/areal-project/AReaL && cd AReaL && pip install uv && uv sync --extra cuda
pip install -e .
Updated
1d ago
4mo ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
agent, agent-framework, llm
Large Action Models, Function Calling, Agent Training
03

Features

AReaL logoAReaL
01Fully asynchronous RL training, reported 2.77x faster than synchronous systems in v0.3
02Online RL for black-box agents by replacing base_url and api_key
03Microservice architecture in 2.0 with separate training, inference, agent and weight-update services
04SGLang or vLLM inference backends, Ray clusters and Ascend NPU support
05Examples for math, SWE, search agents, tau2-bench and OpenClaw agents
xLAM logoxLAM
01Aggregates agent trajectories from distinct environments
02Standardizes and unifies trajectories into a consistent format
03Optimized generic data loader for agent training
04Maintains equilibrium across different data sources during training
05Supports efficient inference with Transformers and vLLM
04

Use Cases

AReaL logoAReaL
↳RL fine-tuning of reasoning models on math and coding tasks
↳Training multi-turn agents such as search or customer-service agents
↳Continuously improving an existing agent app via online RL
xLAM logoxLAM
↳Function calling in LLMs
↳Training autonomous agents
↳Multi-turn conversation processing
05

Best For

AReaL logoAReaL
EssentialTrending
xLAM logoxLAM
—
FAQ

FAQ

What is the difference between AReaL and xLAM?
Both AReaL and xLAM are in the LLM Infra category. AReaL has 5.9k stars, while xLAM has 641 stars.
Which is better, AReaL or xLAM?
The best choice depends on your use case. Choose AReaL if RL fine-tuning of reasoning models on math and coding tasks, and xLAM if Function calling in LLMs.
Is AReaL free or open source?
Yes, AReaL is open source on GitHub (Apache-2.0).
Is xLAM free or open source?
Yes, xLAM is open source on GitHub (Apache-2.0).
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Related

Alternatives to AReaL →Alternatives to xLAM →AReaL details →xLAM details →