AReaL logo
AReaL
★ 5.9k
vs
llama-cpp-agent logo
llama-cpp-agent
★ 661

AReaL vs llama-cpp-agent

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.; llama-cpp-agent: llama-cpp-agent is a Python framework for interacting with LLMs running via llama.cpp. It provides a unified interface for chat, structured function calls, and JSON-formatted output — including models not explicitly fine-tuned for function calling. Developers can define tools and callable functions that the agent invokes directly, making it practical for building local agentic workflows without cloud dependencies.

01

TL;DR

AReaL logo
AReaLLLM Infra · ★ 5.9k

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

llama-cpp-agent logo
llama-cpp-agentLLM Infra · ★ 661

Building local agentic pipelines with open-source LLMs

02

Side-by-Side Comparison

Field
AReaL logoAReaL
llama-cpp-agent logollama-cpp-agent
Category
LLM Infra
LLM Infra
Stars
★ 5.9k
★ 661
License
Apache-2.0
NOASSERTION
Install Command
git clone https://github.com/areal-project/AReaL && cd AReaL && pip install uv && uv sync --extra cuda
pip install llama-cpp-agent
Updated
1d ago
7mo ago
Open Source
Yes
Yes
Tags
agent, agent-framework, llm
agent-framework, Communication
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
llama-cpp-agent logollama-cpp-agent
01Structured function calls for models running via llama.cpp
02JSON-structured output even from non-function-call-finetuned models
03Chat interface with multi-turn conversation support
04Python-native tool/function definition and binding
05Compatible with local LLM deployments — no cloud required
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
llama-cpp-agent logollama-cpp-agent
↳Building local agentic pipelines with open-source LLMs
↳Extracting structured data from LLM responses without fine-tuning
↳Prototyping function-calling workflows on consumer hardware
05

Best For

AReaL logoAReaL
EssentialTrending
llama-cpp-agent logollama-cpp-agent
Hidden Gem
FAQ

FAQ

What is the difference between AReaL and llama-cpp-agent?
Both AReaL and llama-cpp-agent are in the LLM Infra category. AReaL has 5.9k stars, while llama-cpp-agent has 661 stars.
Which is better, AReaL or llama-cpp-agent?
The best choice depends on your use case. Choose AReaL if RL fine-tuning of reasoning models on math and coding tasks, and llama-cpp-agent if Building local agentic pipelines with open-source LLMs.
Is AReaL free or open source?
Yes, AReaL is open source on GitHub (Apache-2.0).
Is llama-cpp-agent free or open source?
Yes, llama-cpp-agent is open source on GitHub (NOASSERTION).
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