AgentIndex icon
AgentIndex
AReaL logo
AReaL
★ 5.9k
vs
AgentRunKit logo
AgentRunKit
★ 35

AReaL vs AgentRunKit

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.; AgentRunKit: AgentRunKit is a lightweight Swift 6 framework for building LLM-powered agents with type-safe tool calling. It features zero dependencies, full Sendable support, async/await concurrency, and compatibility with both cloud and on-device models via MCP.

01

TL;DR

AReaL logo
AReaLLLM Infra · ★ 5.9k

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

Read AReaL full review →
AgentRunKit logo
AgentRunKitLLM Infra · ★ 35

Build AI-powered chatbots with tool integration

Read AgentRunKit full review →
02

Side-by-Side Comparison

Field
AReaL logoAReaL
AgentRunKit logoAgentRunKit
Category
LLM Infra
LLM Infra
Stars
★ 5.9k
★ 35
License
Apache-2.0
MIT
Install Command
git clone https://github.com/areal-project/AReaL && cd AReaL && pip install uv && uv sync --extra cuda
swift package add https://github.com/Tom-Ryder/AgentRunKit.git
Updated
1d ago
3w ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
agent, agent-framework, llm
agent-framework, ai-agents, anthropic
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
AgentRunKit logoAgentRunKit
01Agent loop with configurable iteration limits and token budgets
02Streaming with AsyncThrowingStream and @Observable SwiftUI wrapper
03Type-safe tools with compile-time JSON schema validation
04Sub-agent composition with depth control and streaming propagation
05Multimodal input: images, audio, video, PDF
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
AgentRunKit logoAgentRunKit
↳Build AI-powered chatbots with tool integration
↳Automate complex workflows using LLM agents
↳Create intelligent assistants for on-device or cloud inference
05

Best For

AReaL logoAReaL
EssentialTrending
AgentRunKit logoAgentRunKit
—
FAQ

FAQ

What is the difference between AReaL and AgentRunKit?
Both AReaL and AgentRunKit are in the LLM Infra category. AReaL has 5.9k stars, while AgentRunKit has 35 stars.
Which is better, AReaL or AgentRunKit?
The best choice depends on your use case. Choose AReaL if RL fine-tuning of reasoning models on math and coding tasks, and AgentRunKit if Build AI-powered chatbots with tool integration.
Is AReaL free or open source?
Yes, AReaL is open source on GitHub (Apache-2.0).
Is AgentRunKit free or open source?
Yes, AgentRunKit is open source on GitHub (MIT).
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Related

Alternatives to AReaL →Alternatives to AgentRunKit →AReaL details →AgentRunKit details →