KwaiAgents
VerifiedOpen-source suite of agent-tuned LLMs, datasets, and benchmarks from Kuaishou.
What is KwaiAgents?
KwaiAgents is an open-source project that supplies everything needed to create and measure AI agents. It bundles a lightweight runtime called KAgentSys-Lite, a family of fine-tuned models known as KAgentLMs, a large instruction dataset, and a dedicated benchmark suite.
The models gain agent skills through meta-agent tuning on the provided instruction data. KAgentSys-Lite handles task execution with a restricted tool set while the benchmark measures performance across planning, tool calling, reflection, and related dimensions.
Researchers and developers who want reproducible agent experiments or who need ready-made evaluation data will find the collection useful. All components are hosted on Hugging Face for easy access.
What you can build with KwaiAgents
Model Fine-Tuning
Leverage the 200k instruction examples to train or adapt new base models for agent tasks.
Capability Benchmarking
Run KAgentBench to compare planning, tool-use, and reflection performance across different LLMs.
Lightweight Agent Deployment
Use KAgentSys-Lite to prototype simple agents without heavy memory or tool requirements.
Install KwaiAgents
conda create -n kagent python=3.10
conda activate kagent
pip install -r requirements.txt- 1Visit the KwaiKEG GitHub repository and review the available components.
- 2Download chosen KAgentLMs and the KAgentInstruct dataset from Hugging Face.
- 3Load a model and the benchmark data locally for evaluation runs.
- 4Configure KAgentSys-Lite with the desired base model and limited tool list.
- 5Execute sample tasks and review scores on the five evaluation dimensions.
KwaiAgents: pros & cons
Pros
- +Provides matched training data, models, and evaluation sets in one release.
- +Offers multiple model sizes across Qwen and Baichuan families.
- +Includes transparent benchmark numbers for direct comparison.
- +Fully open weights and datasets hosted on Hugging Face.
Cons
- –KAgentSys-Lite omits memory and uses fewer tools than the full system.
- –Performance still trails closed frontier models on the same benchmark.
- –Requires separate setup for each component rather than a single package.
Frequently asked questions
Fine-tuned checkpoints are released for Qwen-7B, Qwen-14B, Qwen1.5-14B, and Baichuan2-13B.
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