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KwaiAgents is a series of Agent-related works open-sourced by the [KwaiKEG](https://github.com/KwaiKEG) from [Kuaishou Technology](https://www.kuaishou.com/en). 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.

## News * 2024.4.19 - Qwen1.5-14B-MAT model [[link]](https://huggingface.co/kwaikeg/kagentlms_qwen1.5_14b_mat) released. * 2024.4.9 - Benchmark results have been refreshed. * 2024.1.29 - Qwen-14B-MAT model [[link]](https://huggingface.co/kwaikeg/kagentlms_qwen_14b_mat) released. * 2023.1.5 - Training data [[link]](https://huggingface.co/datasets/kwaikeg/KAgentInstruct) released. * 2023.12.27 - 🔥🔥🔥 KwaiAgents have been reported on many sites. [[机器之心]](https://mp.weixin.qq.com/s/QhZIFL1GHH90z98gnk194g) [[Medium]](https://medium.com/@myscarletpan/can-7b-models-now-master-ai-agents-a-look-at-kwais-recent-llm-open-source-release-8b9e84647412) [[InfoQ]](https://www.infoq.cn/article/xHGJwG3b8hXSdaP4m6r0), etc. * 2023.12.13 - The benchmark and evaluation code [[link]](https://huggingface.co/datasets/kwaikeg/KAgentBench) released. * 2023.12.08 - Technical report [[link]](https://arxiv.org/abs/2312.04889) release. * 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 | 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: ```bash conda create -n kagent python=3.10 conda activate kagent pip install -r requirements.txt ``` ### Using AgentLMs #### Serving by [vLLM](https://github.com/vllm-project/vllm) (GPU) We recommend using [vLLM](https://github.com/vllm-project/vllm) and [FastChat](https://github.com/lm-sys/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 ```bash pip install vllm pip install "fschat[model_worker,webui]" ``` 2. For Baichuan-13B-MAT, install the corresponding packages with the following commands ```bash 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. ```bash python -m fastchat.serve.controller ``` Secondly, you should use the following command in another terminal for single-gpu inference service deployment: ```bash 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. ```bash 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: ```bash 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](https://github.com/ggerganov/llama.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](https://huggingface.co/kwaikeg/kagentlms_qwen_7b_mat_gguf). To install the server package and get started: ```bash 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: ```bash 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 ```bash git clone git@github.com: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](https://www.weatherapi.com/) (Same for local model usage). ```bash 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 ```bash 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](https://chromedriver.chromium.org/getting-started) 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: ```bash 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} } ```