Start building LLM-empowered multi-agent applications in an easier way.
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If you find our work helpful, please kindly cite our paper.
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Visit our workstation to build multi-agent applications with dragging-and-dropping.
- Welcome to join our community on
| Discord | DingTalk |
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[2024-09-06] AgentScope version 0.1.0 is released now.
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[2024-06-14] A new prompt tuning module is available in AgentScope to help developers generate and optimize the agents' system prompts! Refer to our tutorial for more details!
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[2024-06-11] The RAG functionality is available for agents in AgentScope now! A quick introduction to RAG in AgentScope can help you equip your agent with external knowledge!
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[2024-06-09] We release AgentScope v0.0.5 now! In this new version, AgentScope Workstation (the online version is running on agentscope.io) is open-sourced with the refactored AgentScope Studio!
Full News
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[2024-05-24] We are pleased to announce that features related to the AgentScope Workstation will soon be open-sourced! The online website services are temporarily offline. The online website service will be upgraded and back online shortly. Stay tuned...
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[2024-05-15] A new Parser Module for formatted response is added in AgentScope! Refer to our tutorial for more details. The
DictDialogAgentand werewolf game example are updated simultaneously.
Werewolf-running-example.mp4
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[2024-05-14] Dear AgentScope users, we are conducting a survey on AgentScope Workstation & Copilot user experience. We currently need your valuable feedback to help us improve the experience of AgentScope's Drag & Drop multi-agent application development and Copilot. Your feedback is valuable and the survey will take about 3~5 minutes. Please click URL to participate in questionnaire surveys. Thank you very much for your support and contribution!
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[2024-05-14] AgentScope supports gpt-4o as well as other OpenAI vision models now! Try gpt-4o with its model configuration and new example Conversation with gpt-4o!
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[2024-04-30] We release AgentScope v0.0.4 now!
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[2024-04-27] AgentScope Workstation is now online! You are welcome to try building your multi-agent application simply with our drag-and-drop platform and ask our copilot questions about AgentScope!
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[2024-04-19] AgentScope supports Llama3 now! We provide scripts and example model configuration for quick set-up. Feel free to try llama3 in our examples!
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[2024-04-06] We release AgentScope v0.0.3 now!
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[2024-04-06] New examples Gomoku, Conversation with ReAct Agent, Conversation with RAG Agent and Distributed Parallel Optimization are available now!
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[2024-03-19] We release AgentScope v0.0.2 now! In this new version, AgentScope supports ollama(A local CPU inference engine), DashScope and Google Gemini APIs.
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[2024-03-19] New examples "Autonomous Conversation with Mentions" and "Basic Conversation with LangChain library" are available now!
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[2024-03-19] The Chinese tutorial of AgentScope is online now!
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[2024-02-27] We release AgentScope v0.0.1 now, which is also available in PyPI!
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[2024-02-14] We release our paper "AgentScope: A Flexible yet Robust Multi-Agent Platform" in arXiv now!
AgentScope is an innovative multi-agent platform designed to empower developers to build multi-agent applications with large-scale models. It features three high-level capabilities:
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🤝 Easy-to-Use: Designed for developers, with fruitful components, comprehensive documentation, and broad compatibility. Besides, AgentScope Workstation provides a drag-and-drop programming platform and a copilot for beginners of AgentScope!
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âś… High Robustness: Supporting customized fault-tolerance controls and retry mechanisms to enhance application stability.
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🚀 Actor-Based Distribution: Building distributed multi-agent applications in a centralized programming manner for streamlined development.
Supported Model Libraries
AgentScope provides a list of ModelWrapper to support both local model
services and third-party model APIs.
| API | Task | Model Wrapper | Configuration | Some Supported Models |
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| OpenAI API | Chat | OpenAIChatWrapper |
guidance template |
gpt-4o, gpt-4, gpt-3.5-turbo, ... |
| Embedding | OpenAIEmbeddingWrapper |
guidance template |
text-embedding-ada-002, ... | |
| DALL·E | OpenAIDALLEWrapper |
guidance template |
dall-e-2, dall-e-3 | |
| DashScope API | Chat | DashScopeChatWrapper |
guidance template |
qwen-plus, qwen-max, ... |
| Image Synthesis | DashScopeImageSynthesisWrapper |
guidance template |
wanx-v1 | |
| Text Embedding | DashScopeTextEmbeddingWrapper |
guidance template |
text-embedding-v1, text-embedding-v2, ... | |
| Multimodal | DashScopeMultiModalWrapper |
guidance template |
qwen-vl-max, qwen-vl-chat-v1, qwen-audio-chat | |
| Gemini API | Chat | GeminiChatWrapper |
guidance template |
gemini-pro, ... |
| Embedding | GeminiEmbeddingWrapper |
guidance template |
models/embedding-001, ... | |
| ZhipuAI API | Chat | ZhipuAIChatWrapper |
guidance template |
glm-4, ... |
| Embedding | ZhipuAIEmbeddingWrapper |
guidance template |
embedding-2, ... | |
| ollama | Chat | OllamaChatWrapper |
guidance template |
llama3, llama2, Mistral, ... |
| Embedding | OllamaEmbeddingWrapper |
guidance template |
llama2, Mistral, ... | |
| Generation | OllamaGenerationWrapper |
guidance template |
llama2, Mistral, ... | |
| LiteLLM API | Chat | LiteLLMChatWrapper |
guidance template |
models supported by litellm... |
| Yi API | Chat | YiChatWrapper |
guidance template |
yi-large, yi-medium, ... |
| Post Request based API | - | PostAPIModelWrapper |
guidance template |
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Supported Local Model Deployment
AgentScope enables developers to rapidly deploy local model services using the following libraries.
Supported Services
- Web Search
- Data Query
- Retrieval
- Code Execution
- File Operation
- Text Processing
- Multi Modality
- Wikipedia Search and Retrieval
- TripAdvisor Search
- Web Browser Control
Example Applications
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Model
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Conversation
- Basic Conversation
- Autonomous Conversation with Mentions
- Self-Organizing Conversation
- Basic Conversation with LangChain library
- Conversation with ReAct Agent
- Conversation in Natural Language to Query SQL
- Conversation with RAG Agent
- Conversation with gpt-4o
- Conversation with Software Engineering Agent
- Conversation with Customized Tools
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Game
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Distribution
More models, services and examples are coming soon!
AgentScope requires Python 3.9 or higher.
Note: This project is currently in active development, it's recommended to install AgentScope from source.
- Install AgentScope in editable mode:
# Pull the source code from GitHub
git clone https://github.com/modelscope/agentscope.git
# Install the package in editable mode
cd agentscope
pip install -e .- Install AgentScope from pip:
pip install agentscopeTo support different deployment scenarios, AgentScope provides several optional dependencies. Full list of optional dependencies refers to tutorial Taking distribution mode as an example, you can install its dependencies as follows:
# From source
pip install -e .[distribute]
# From pypi
pip install agentscope[distribute]# From source
pip install -e .\[distribute\]
# From pypi
pip install agentscope\[distribute\]In AgentScope, the model deployment and invocation are decoupled by
ModelWrapper.
To use these model wrappers, you need to prepare a model config file as follows.
model_config = {
# The identifies of your config and used model wrapper
"config_name": "{your_config_name}", # The name to identify the config
"model_type": "{model_type}", # The type to identify the model wrapper
# Detailed parameters into initialize the model wrapper
# ...
}Taking OpenAI Chat API as an example, the model configuration is as follows:
openai_model_config = {
"config_name": "my_openai_config", # The name to identify the config
"model_type": "openai_chat", # The type to identify the model wrapper
# Detailed parameters into initialize the model wrapper
"model_name": "gpt-4", # The used model in openai API, e.g. gpt-4, gpt-3.5-turbo, etc.
"api_key": "xxx", # The API key for OpenAI API. If not set, env
# variable OPENAI_API_KEY will be used.
"organization": "xxx", # The organization for OpenAI API. If not set, env
# variable OPENAI_ORGANIZATION will be used.
}More details about how to set up local model services and prepare model configurations is in our tutorial.
Create built-in user and assistant agents as follows.
from agentscope.agents import DialogAgent, UserAgent
import agentscope
# Load model configs
agentscope.init(model_configs="./model_configs.json")
# Create a dialog agent and a user agent
dialog_agent = DialogAgent(name="assistant",
model_config_name="my_openai_config")
user_agent = UserAgent()In AgentScope, message is the bridge among agents, which is a
dict that contains two necessary fields name and content and an
optional field url to local files (image, video or audio) or website.
from agentscope.message import Msg
x = Msg(name="Alice", content="Hi!")
x = Msg("Bob", "What about this picture I took?", url="/path/to/picture.jpg")Start a conversation between two agents (e.g. dialog_agent and user_agent) with the following code:
x = None
while True:
x = dialog_agent(x)
x = user_agent(x)
if x.content == "exit": # user input "exit" to exit the conversation_basic
breakAgentScope provides an easy-to-use runtime user interface capable of displaying multimodal output on the front end, including text, images, audio and video.
Refer to our tutorial for more details.
- About AgentScope
- Installation
- Quick Start
- Model
- Prompt Engineering
- Agent
- Memory
- Response Parser
- Tool
- Pipeline and MsgHub
- Distribution
- AgentScope Studio
- Logging
- Monitor
- Example: Werewolf Game
AgentScope is released under Apache License 2.0.
Contributions are always welcomed!
We provide a developer version with additional pre-commit hooks to perform checks compared to the official version:
# For windows
pip install -e .[dev]
# For mac
pip install -e .\[dev\]
# Install pre-commit hooks
pre-commit installPlease refer to our Contribution Guide for more details.
If you find our work helpful for your research or application, please cite our papers.
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AgentScope: A Flexible yet Robust Multi-Agent Platform
@article{agentscope, author = {Dawei Gao and Zitao Li and Xuchen Pan and Weirui Kuang and Zhijian Ma and Bingchen Qian and Fei Wei and Wenhao Zhang and Yuexiang Xie and Daoyuan Chen and Liuyi Yao and Hongyi Peng and Ze Yu Zhang and Lin Zhu and Chen Cheng and Hongzhu Shi and Yaliang Li and Bolin Ding and Jingren Zhou} title = {AgentScope: A Flexible yet Robust Multi-Agent Platform}, journal = {CoRR}, volume = {abs/2402.14034}, year = {2024}, }