on-policy
★ 2.0k
vs
AgentRunKit
★ 24
on-policy vs AgentRunKit
on-policy: This repository implements MAPPO, a multi-agent variant of PPO, widely used in cooperative multi-agent games and research. It provides robust implementations for various multi-agent environments like StarCraft II, Hanabi, and Google Research Football, along with detailed training scripts and hyperparameter guidance.; 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
Choose on-policy if…Research and experimentation in cooperative multi-agent reinforcement learning
Choose AgentRunKit if…Build AI-powered chatbots with tool integration
02
Side-by-Side Comparison
03
Features
on-policy01Implementation of MAPPO (Multi-Agent PPO)
02Support for diverse multi-agent environments (e.g., StarCraft II, Hanabi)
03Ready-to-use training scripts for various scenarios
04Detailed hyperparameter guidance and updated results
05Default support for shared policy among agents
AgentRunKit01Agent 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
on-policy↳Research and experimentation in cooperative multi-agent reinforcement learning
↳Benchmarking and evaluating PPO's effectiveness in MARL scenarios
↳Training AI agents for popular multi-agent games like StarCraft II and Hanabi
AgentRunKit↳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
on-policyTrendingReinforcement LearningMulti-Agent AI
AgentRunKitTrendingLLM Infra
FAQ
FAQ
What is the difference between on-policy and AgentRunKit?
Both on-policy and AgentRunKit are in the LLM Infra category. on-policy has 2.0k stars, while AgentRunKit has 24 stars.
Which is better, on-policy or AgentRunKit?
The best choice depends on your use case. Choose on-policy if Research and experimentation in cooperative multi-agent reinforcement learning, and AgentRunKit if Build AI-powered chatbots with tool integration.
Is on-policy free or open source?
Yes, on-policy is open source on GitHub (MIT).
Is AgentRunKit free or open source?
Yes, AgentRunKit is open source on GitHub (MIT).
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