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📚 Dataset | 📚 Benchmark | 🤗 Models | 📑 Paper

KwaiAgents is a series of Agent-related works open-sourced by the KwaiKEG from Kuaishou Technology. The open-sourced content includes:

1. KAgentSys-Lite: a lite version of the KAgentSys in the paper. While retaining some of the original system's functionality, KAgentSys-Lite has certain differences and limitations when compared to its full-featured counterpart, such as: (1) a more limited set of tools; (2) a lack of memory mechanisms; (3) slightly reduced performance capabilities; and (4) a different codebase, as it evolves from open-source projects like BabyAGI and Auto-GPT. Despite these modifications, KAgentSys-Lite still delivers comparable performance among numerous open-source Agent systems available.
2. KAgentLMs: a series of large language models with agent capabilities such as planning, reflection, and tool-use, acquired through the Meta-agent tuning proposed in the paper.
3. KAgentInstruct: over 200k Agent-related instructions finetuning data (partially human-edited) proposed in the paper.
4. KAgentBench: over 3,000 human-edited, automated evaluation data for testing Agent capabilities, with evaluation dimensions including planning, tool-use, reflection, concluding, and profiling.

Models	Training Data	Benchmark Data
Qwen-7B-MAT	KAgentInstruct	KAgentBench
Baichuan2-13B-MAT

News

• 2024.4.19 - Qwen1.5-14B-MAT model [link] released.
• 2024.4.9 - Benchmark results have been refreshed, with the Qwen1.5-7B-MAT [link] outcomes now published.
• 2024.1.29 - Qwen-14B-MAT model [link] released.
• 2023.1.5 - Training data [link] released.
• 2023.12.27 - 🔥 KwaiAgents have been reported on many sites. [机器之心] [Medium] [InfoQ], etc.
• 2023.12.13 - The benchmark and evaluation code [link] released
• 2023.12.08 - Technical report [link] released
• 2023.11.17 - Initial release

Evaluation

1. Benchmark Results

	Scale	Planning	Tool-use	Reflection	Concluding	Profile	Overall Score
GPT-3.5-turbo	-	18.55	26.26	8.06	37.26	35.42	25.63
Llama2	13B	0.15	0.44	0.14	16.60	17.73	5.30
ChatGLM3	6B	7.87	11.84	7.52	30.01	30.14	15.88
Qwen	7B	13.34	18.00	7.91	36.24	34.99	21.17
Baichuan2	13B	6.70	16.10	6.76	24.97	19.08	14.89
ToolLlama	7B	0.20	4.83	1.06	15.62	10.66	6.04
AgentLM	13B	0.17	0.15	0.05	16.30	15.22	4.88
Qwen-MAT	7B	31.64	43.30	33.34	44.85	44.78	39.85
Baichuan2-MAT	13B	37.27	52.97	37.00	48.01	41.83	45.34
Qwen-MAT	14B	43.17	63.78	32.14	45.47	45.22	49.94
Qwen1.5-MAT	7B	40.39	61.91	27.90	44.78	44.06	47.92
Qwen1.5-MAT	14B	42.42	64.62	30.58	46.51	45.95	50.18

2. Human evaluation. Each result cell shows the pass rate (%) and the average score (in parentheses)

	Scale	NoAgent	ReACT	Auto-GPT	KAgentSys
GPT-4	-	57.21% (3.42)	68.66% (3.88)	79.60% (4.27)	83.58% (4.47)
GPT-3.5-turbo	-	47.26% (3.08)	54.23% (3.33)	61.74% (3.53)	64.18% (3.69)
Qwen	7B	52.74% (3.23)	51.74% (3.20)	50.25% (3.11)	54.23% (3.27)
Baichuan2	13B	54.23% (3.31)	55.72% (3.36)	57.21% (3.37)	58.71% (3.54)
Qwen-MAT	7B	-	58.71% (3.53)	65.67% (3.77)	67.66% (3.87)
Baichuan2-MAT	13B	-	61.19% (3.60)	66.67% (3.86)	74.13% (4.11)

User Guide

Prebuild environment

Install miniconda for build environment first. Then create build env first:

conda create -n kagent python=3.10
conda activate kagent
pip install -r requirements.txt

Using AgentLMs

Serving by vLLM (GPU)

We recommend using vLLM and FastChat to deploy the model inference service. First, you need to install the corresponding packages (for detailed usage, please refer to the documentation of the two projects):

1. For Qwen-7B-MAT, install the corresponding packages with the following commands

pip install vllm
pip install "fschat[model_worker,webui]"

2. For Baichuan-13B-MAT, install the corresponding packages with the following commands

pip install "fschat[model_worker,webui]"
pip install vllm==0.2.0
pip install transformers==4.33.2

To deploy KAgentLMs, you first need to start the controller in one terminal.

python -m fastchat.serve.controller

Secondly, you should use the following command in another terminal for single-gpu inference service deployment:

python -m fastchat.serve.vllm_worker --model-path $model_path --trust-remote-code

Where $model_path is the local path of the model downloaded. If the GPU does not support Bfloat16, you can add --dtype half to the command line.

Thirdly, start the REST API server in the third terminal.

python -m fastchat.serve.openai_api_server --host localhost --port 8888

Finally, you can use the curl command to invoke the model same as the OpenAI calling format. Here's an example:

curl http://localhost:8888/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model": "kagentlms_qwen_7b_mat", "messages": [{"role": "user", "content": "Who is Andy Lau"}]}'

Here, change kagentlms_qwen_7b_mat to the model you deployed.

Serving by Lamma.cpp (CPU)

llama-cpp-python offers a web server which aims to act as a drop-in replacement for the OpenAI API. This allows you to use llama.cpp compatible models with any OpenAI compatible client (language libraries, services, etc). The converted model can be found in kwaikeg/kagentlms_qwen_7b_mat_gguf.

To install the server package and get started:

pip install "llama-cpp-python[server]"
python3 -m llama_cpp.server --model kagentlms_qwen_7b_mat_gguf/ggml-model-q4_0.gguf --chat_format chatml --port 8888

Finally, you can use the curl command to invoke the model same as the OpenAI calling format. Here's an example:

curl http://localhost:8888/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"messages": [{"role": "user", "content": "Who is Andy Lau"}]}'

Using KAgentSys-Lite

Download and install the KwaiAgents, recommended Python>=3.10

git clone [email protected]:KwaiKEG/KwaiAgents.git
cd KwaiAgents
python setup.py develop

1. ChatGPT usage Declare some environment variables

export OPENAI_API_KEY=sk-xxxxx
export WEATHER_API_KEY=xxxxxx

The WEATHER_API_KEY is not mandatory, but you need to configure it when asking weather-related questions. You can obtain the API key from this website (Same for local model usage).

kagentsys --query="Who is Andy Lau's wife?" --llm_name="gpt-3.5-turbo" --lang="en"

2. Local model usage

To use a local model, you need to deploy the corresponding model service as described in the previous chapter

kagentsys --query="Who is Andy Lau's wife?" --llm_name="kagentlms_qwen_7b_mat" \
--use_local_llm --local_llm_host="localhost" --local_llm_port=8888 --lang="en"

Full command arguments:

options:
  -h, --help            show this help message and exit
  --id ID               ID of this conversation
  --query QUERY         User query
  --history HISTORY     History of conversation
  --llm_name LLM_NAME   the name of llm
  --use_local_llm       Whether to use local llm
  --local_llm_host LOCAL_LLM_HOST
                        The host of local llm service
  --local_llm_port LOCAL_LLM_PORT
                        The port of local llm service
  --tool_names TOOL_NAMES
                        the name of llm
  --max_iter_num MAX_ITER_NUM
                        the number of iteration of agents
  --agent_name AGENT_NAME
                        The agent name
  --agent_bio AGENT_BIO
                        The agent bio, a short description
  --agent_instructions AGENT_INSTRUCTIONS
                        The instructions of how agent thinking, acting, or talking
  --external_knowledge EXTERNAL_KNOWLEDGE
                        The link of external knowledge
  --lang {en,zh}        The language of the overall system
  --max_tokens_num      Maximum length of model input

Note:

1. If you need to use the browse_website tool, you need to configure the chromedriver on your server.
2. If the search fails multiple times, it may be because the network cannot access duckduckgo_search. You can solve this by setting the http_proxy.

Using Custom tools

Custom tools usage can be found in examples/custom_tool_example.py

Using KAgentBench Evaluation

We only need two lines to evaluate the agent capabilities like:

cd benchmark
python infer_qwen.py qwen_benchmark_res.jsonl
python benchmark_eval.py ./benchmark_eval.jsonl ./qwen_benchmark_res.jsonl

The above command will give the results like

plan : 31.64, tooluse : 43.30, reflextion : 33.34, conclusion : 44.85, profile : 44.78, overall : 39.85

Please refer to benchmark/ for more details.

Citation

@article{pan2023kwaiagents,
  author    = {Haojie Pan and
               Zepeng Zhai and
               Hao Yuan and
               Yaojia Lv and
               Ruiji Fu and
               Ming Liu and
               Zhongyuan Wang and
               Bing Qin
               },
  title     = {KwaiAgents: Generalized Information-seeking Agent System with Large Language Models},
  journal   = {CoRR},
  volume    = {abs/2312.04889},
  year      = {2023}
}