# agentscope-runtime
**Repository Path**: zhang_yi_68/agentscope-runtime
## Basic Information
- **Project Name**: agentscope-runtime
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-02-16
- **Last Updated**: 2026-02-16
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# AgentScope Runtime: A Production-grade Runtime for Agent Applications
[](https://github.com/agentscope-ai/agentscope-runtime)
[](http://webui.runtime.agentscope.io/)
[](https://pypi.org/project/agentscope-runtime/)
[](https://pepy.tech/project/agentscope-runtime)
[](https://python.org)
[](https://github.com/agentscope-ai/agentscope-runtime)
[](LICENSE)
[](https://github.com/psf/black)
[](https://github.com/agentscope-ai/agentscope-runtime/stargazers)
[](https://github.com/agentscope-ai/agentscope-runtime/network)
[](https://github.com/agentscope-ai/agentscope-runtime/actions)
[](https://runtime.agentscope.io)
[](https://deepwiki.com/agentscope-ai/agentscope-runtime)
[](https://a2a-protocol.org/)
[](https://modelcontextprotocol.io/)
[](https://discord.gg/eYMpfnkG8h)
[](https://qr.dingtalk.com/action/joingroup?code=v1,k1,OmDlBXpjW+I2vWjKDsjvI9dhcXjGZi3bQiojOq3dlDw=&_dt_no_comment=1&origin=11)
[[Cookbook]](https://runtime.agentscope.io/)
[[Try WebUI]](http://webui.runtime.agentscope.io/)
[[δΈζREADME]](README_zh.md)
[[Samples]](https://github.com/agentscope-ai/agentscope-samples)
> **Core capabilities:**
>
> **Tool Sandboxing** β tool call runs inside a **hardened sandbox**
>
> **Agent-as-a-Service (AaaS) APIs** β expose agents as **streaming, production-ready APIs**
>
> **Scalable Deployment** β deploy locally, on Kubernetes, or serverless for **elastic scale**
>
>
> Plus
>
>
>
> **Full-stack observability** (logs / traces)
>
> **Framework compatibility** with mainstream agent frameworks
>
>
---
## Table of Contents
> [!NOTE]
>
> **Recommended reading order:**
>
> - **I want to run an agent app in 5 minutes**: Quick Start (Agent App example) β verify with curl (SSE streaming)
> - **I care about secure tool execution / automation**: Quick Start (Sandbox examples) β sandbox image registry/namespace/tag configuration β (optional) production-grade serverless sandbox deployment
> - **I want production deployment / expose APIs**: Quick Start (Agent App example) β Quick Start (Deployment example) β Guides
> - **I want to contribute**: Contributing β Contact
- [News](#-news)
- [Key Features](#-key-features)
- [Quick Start](#-quick-start): From installation to running a minimal Agent API service. Learn the three-stage `AgentApp` development pattern: `init` / `query` / `shutdown`.
- [Prerequisites](#prerequisites): Required runtime environment and dependencies
- [Installation](#installation): Install from PyPI or from source
- [Agent App Example](#agent-app-example): How to build a streaming (SSE) Agent-as-a-Service API
- [Sandbox Example](#sandbox-example): How to safely execute Python/Shell/GUI/Browser/Filesystem/Mobile tools in an isolated sandbox
- [Deployment Example](#deployment-example): Learn to deploy with `DeployManager` locally or in a serverless environment, and access the service via A2A, Response API, or the OpenAI SDK in compatible mode
- [Guides](#-guides): A tutorial site covering AgentScope Runtime concepts, architecture, APIs, and sample projectsβhelping you move from βit runsβ to βscalable and maintainableβ.
- [Contact](#-contact)
- [Contributing](#-contributing)
- [License](#-license)
- [Contributors](#-contributors)
---
## π NEWS
* **[2026-02]** A major architectural refactor of `AgentApp` in **v1.1.0**. By adopting direct inheritance from `FastAPI` and deprecating the previous factory pattern, `AgentApp` now offers seamless integration with the full FastAPI ecosystem, significantly boosting extensibility. Furthermore, we've introduced a **Distributed Interrupt Service**, enabling manual task preemption during agent execution and allowing developers to customize state persistence and recovery logic flexibly. Please refer to the **[CHANGELOG](https://runtime.agentscope.io/en/CHANGELOG.html)** for full update details and migration guide.
* **[2026-01]** Added **asynchronous sandbox** implementations (`BaseSandboxAsync`, `GuiSandboxAsync`, `BrowserSandboxAsync`, `FilesystemSandboxAsync`, `MobileSandboxAsync`) enabling non-blocking, concurrent tool execution in async program. Improved `run_ipython_cell` and `run_shell_command` methods with enhanced **concurrency and parallel execution** capabilities for more efficient sandbox operations.
* **[2025-12]** We have released **AgentScope Runtime v1.0**, introducing a unified βAgent as APIβ white-box development experience, with enhanced multi-agent collaboration, state persistence, and cross-framework integration. This release also streamlines abstractions and modules to ensure consistency between development and production environments. Please refer to the **[CHANGELOG](https://runtime.agentscope.io/en/CHANGELOG.html)** for full update details and migration guide.
---
## β¨ Key Features
- **Deployment Infrastructure**: Built-in services for agent state management, conversation history, long-term memory, and sandbox lifecycle control
- **Framework-Agnostic**: Not tied to any specific agent framework; seamlessly integrates with popular open-source and custom implementations
- **Developer-Friendly**: Offers `AgentApp` for easy deployment with powerful customization options
- **Observability**: Comprehensive tracking and monitoring of runtime operations
- **Sandboxed Tool Execution**: Isolated sandbox ensures safe tool execution without affecting the system
- **Out-of-the-Box Tools & One-Click Adaptation**: Rich set of ready-to-use tools, with adapters enabling quick integration into different frameworks
> [!NOTE]
>
> **About Framework-Agnostic**: Currently, AgentScope Runtime supports the **AgentScope** framework. We plan to extend compatibility to more agent development frameworks in the future. This table shows the current versionβs adapter support for different frameworks. The level of support for each functionality varies across frameworks:
>
> | Framework/Feature | Message/Event | Tool |
> | ------------------------------------------------------------ | ------------- | ---- |
> | [AgentScope](https://runtime.agentscope.io/en/quickstart.html) | β
| β
|
> | [LangGraph](https://runtime.agentscope.io/en/langgraph_guidelines.html) | β
| π§ |
> | [Microsoft Agent Framework](https://runtime.agentscope.io/en/ms_agent_framework_guidelines.html) | β
| β
|
> | [Agno](https://runtime.agentscope.io/en/agno_guidelines.html) | β
| β
|
> | AutoGen | π§ | β
|
---
## π Quick Start
### Prerequisites
- Python 3.10 or higher
- pip or uv package manager
### Installation
From PyPI:
```bash
# Install core dependencies
pip install agentscope-runtime
# Install extension
pip install "agentscope-runtime[ext]"
# Install preview version
pip install --pre agentscope-runtime
```
(Optional) From source:
```bash
# Pull the source code from GitHub
git clone -b main https://github.com/agentscope-ai/agentscope-runtime.git
cd agentscope-runtime
# Install core dependencies
pip install -e .
```
### Agent App Example
This example demonstrates how to create an agent API server using agentscope `ReActAgent` and `AgentApp`. To run a minimal `AgentScope` Agent with AgentScope Runtime, you generally need to implement:
1. **`Define lifespan`** β Use `contextlib.asynccontextmanager` to manage resource initialization (e.g., state services) at startup and cleanup on exit.
2. **`@agent_app.query(framework="agentscope")`** β Core logic for handling requests, **must use** `stream_printing_messages` to `yield msg, last` for streaming output
```python
import os
from contextlib import asynccontextmanager
from fastapi import FastAPI
from agentscope.agent import ReActAgent
from agentscope.model import DashScopeChatModel
from agentscope.formatter import DashScopeChatFormatter
from agentscope.tool import Toolkit, execute_python_code
from agentscope.pipeline import stream_printing_messages
from agentscope.memory import InMemoryMemory
from agentscope.session import RedisSession
from agentscope_runtime.engine import AgentApp
from agentscope_runtime.engine.schemas.agent_schemas import AgentRequest
# 1. Define lifespan manager
@asynccontextmanager
async def lifespan(app: FastAPI):
"""Manage resources during service startup and shutdown"""
# Startup: Initialize Session manager
import fakeredis
fake_redis = fakeredis.aioredis.FakeRedis(decode_responses=True)
# NOTE: This FakeRedis instance is for development/testing only.
# In production, replace it with your own Redis client/connection
# (e.g., aioredis.Redis)
app.state.session = RedisSession(connection_pool=fake_redis.connection_pool)
yield # Service is running
# Shutdown: Add cleanup logic here (e.g., closing database connections)
print("AgentApp is shutting down...")
# 2. Create AgentApp instance
agent_app = AgentApp(
app_name="Friday",
app_description="A helpful assistant",
lifespan=lifespan,
)
# 3. Define request handling logic
@agent_app.query(framework="agentscope")
async def query_func(
self,
msgs,
request: AgentRequest = None,
**kwargs,
):
session_id = request.session_id
user_id = request.user_id
toolkit = Toolkit()
toolkit.register_tool_function(execute_python_code)
agent = ReActAgent(
name="Friday",
model=DashScopeChatModel(
"qwen-turbo",
api_key=os.getenv("DASHSCOPE_API_KEY"),
stream=True,
),
sys_prompt="You're a helpful assistant named Friday.",
toolkit=toolkit,
memory=InMemoryMemory(),
formatter=DashScopeChatFormatter(),
)
agent.set_console_output_enabled(enabled=False)
# Load state
await agent_app.state.session.load_session_state(
session_id=session_id,
user_id=user_id,
agent=agent,
)
async for msg, last in stream_printing_messages(
agents=[agent],
coroutine_task=agent(msgs),
):
yield msg, last
# Save state
await agent_app.state.session.save_session_state(
session_id=session_id,
user_id=user_id,
agent=agent,
)
# 4. Run the application
agent_app.run(host="127.0.0.1", port=8090)
```
The server will start and listen on: `http://localhost:8090/process`. You can send JSON input to the API using `curl`:
```bash
curl -N \
-X POST "http://localhost:8090/process" \
-H "Content-Type: application/json" \
-d '{
"input": [
{
"role": "user",
"content": [
{ "type": "text", "text": "What is the capital of France?" }
]
}
]
}'
```
Youβll see output streamed in **Server-Sent Events (SSE)** format:
```bash
data: {"sequence_number":0,"object":"response","status":"created", ... }
data: {"sequence_number":1,"object":"response","status":"in_progress", ... }
data: {"sequence_number":2,"object":"message","status":"in_progress", ... }
data: {"sequence_number":3,"object":"content","status":"in_progress","text":"The" }
data: {"sequence_number":4,"object":"content","status":"in_progress","text":" capital of France is Paris." }
data: {"sequence_number":5,"object":"message","status":"completed","text":"The capital of France is Paris." }
data: {"sequence_number":6,"object":"response","status":"completed", ... }
```
### Sandbox Example
These examples demonstrate how to create sandboxed environments and execute tools within them, with some examples featuring interactive frontend interfaces accessible via VNC (Virtual Network Computing):
> [!NOTE]
>
> If you want to run the sandbox locally, the current version supports **Docker (optionally with gVisor)** or **[BoxLite](https://github.com/boxlite-ai/boxlite)** as the backend, and you can switch the backend by setting the environment variable `CONTAINER_DEPLOYMENT` (supported values include `docker` / `gvisor` / `boxlite` etc.; default: `docker`).
>
> For large-scale remote/production deployments, we recommend using **Kubernetes (K8s)**, **Function Compute (FC)**, or [**Alibaba Cloud Container Service for Kubernetes (ACK)**](https://computenest.console.aliyun.com/service/instance/create/default?ServiceName=AgentScope%20Runtime%20%E6%B2%99%E7%AE%B1%E7%8E%AF%E5%A2%83) as the backend. Please refer to [this tutorial](https://runtime.agentscope.io/en/sandbox/advanced.html) for more details.
> [!TIP]
> AgentScope Runtime provides **both synchronous** and **asynchronous** versions for each sandbox type
| Synchronous Class | Asynchronous Class |
| ------------------- | ------------------------ |
| `BaseSandbox` | `BaseSandboxAsync` |
| `GuiSandbox` | `GuiSandboxAsync` |
| `FilesystemSandbox` | `FilesystemSandboxAsync` |
| `BrowserSandbox` | `BrowserSandboxAsync` |
| `MobileSandbox` | `MobileSandboxAsync` |
| `TrainingSandbox` | - |
| `AgentbaySandbox` | - |
#### Base Sandbox
Use for running **Python code** or **shell commands** in an isolated environment.
```python
# --- Synchronous version ---
from agentscope_runtime.sandbox import BaseSandbox
with BaseSandbox() as box:
# By default, pulls `agentscope/runtime-sandbox-base:latest` from DockerHub
print(box.list_tools()) # List all available tools
print(box.run_ipython_cell(code="print('hi')")) # Run Python code
print(box.run_shell_command(command="echo hello")) # Run shell command
input("Press Enter to continue...")
# --- Asynchronous version ---
from agentscope_runtime.sandbox import BaseSandboxAsync
async with BaseSandboxAsync() as box:
# Default image is `agentscope/runtime-sandbox-base:latest`
print(await box.list_tools_async()) # List all available tools
print(await box.run_ipython_cell(code="print('hi')")) # Run Python code
print(await box.run_shell_command(command="echo hello")) # Run shell command
input("Press Enter to continue...")
```
#### GUI Sandbox
Provides a **virtual desktop** environment for mouse, keyboard, and screen operations.
```python
# --- Synchronous version ---
from agentscope_runtime.sandbox import GuiSandbox
with GuiSandbox() as box:
# By default, pulls `agentscope/runtime-sandbox-gui:latest` from DockerHub
print(box.list_tools()) # List all available tools
print(box.desktop_url) # Web desktop access URL
print(box.computer_use(action="get_cursor_position")) # Get mouse cursor position
print(box.computer_use(action="get_screenshot")) # Capture screenshot
input("Press Enter to continue...")
# --- Asynchronous version ---
from agentscope_runtime.sandbox import GuiSandboxAsync
async with GuiSandboxAsync() as box:
# Default image is `agentscope/runtime-sandbox-gui:latest`
print(await box.list_tools_async()) # List all available tools
print(box.desktop_url) # Web desktop access URL
print(await box.computer_use(action="get_cursor_position")) # Get mouse cursor position
print(await box.computer_use(action="get_screenshot")) # Capture screenshot
input("Press Enter to continue...")
```
#### Browser Sandbox
A GUI-based sandbox with **browser operations** inside an isolated sandbox.
```python
# --- Synchronous version ---
from agentscope_runtime.sandbox import BrowserSandbox
with BrowserSandbox() as box:
# By default, pulls `agentscope/runtime-sandbox-browser:latest` from DockerHub
print(box.list_tools()) # List all available tools
print(box.desktop_url) # Web desktop access URL
box.browser_navigate("https://www.google.com/") # Open a webpage
input("Press Enter to continue...")
# --- Asynchronous version ---
from agentscope_runtime.sandbox import BrowserSandboxAsync
async with BrowserSandboxAsync() as box:
# Default image is `agentscope/runtime-sandbox-browser:latest`
print(await box.list_tools_async()) # List all available tools
print(box.desktop_url) # Web desktop access URL
await box.browser_navigate("https://www.google.com/") # Open a webpage
input("Press Enter to continue...")
```
#### Filesystem Sandbox
A GUI-based sandbox with **file system operations** such as creating, reading, and deleting files.
```python
# --- Synchronous version ---
from agentscope_runtime.sandbox import FilesystemSandbox
with FilesystemSandbox() as box:
# By default, pulls `agentscope/runtime-sandbox-filesystem:latest` from DockerHub
print(box.list_tools()) # List all available tools
print(box.desktop_url) # Web desktop access URL
box.create_directory("test") # Create a directory
input("Press Enter to continue...")
# --- Asynchronous version ---
from agentscope_runtime.sandbox import FilesystemSandboxAsync
async with FilesystemSandboxAsync() as box:
# Default image is `agentscope/runtime-sandbox-filesystem:latest`
print(await box.list_tools_async()) # List all available tools
print(box.desktop_url) # Web desktop access URL
await box.create_directory("test") # Create a directory
input("Press Enter to continue...")
```
#### Mobile Sandbox
Provides a **sandboxed Android emulator environment** that allows executing various mobile operations, such as tapping, swiping, inputting text, and taking screenshots.
##### Prerequisites
- **Linux Host**:
When running on a Linux host, this sandbox requires the `binder` and `ashmem` kernel modules to be loaded. If they are missing, execute the following commands on your host to install and load the required modules:
```bash
# 1. Install extra kernel modules
sudo apt update && sudo apt install -y linux-modules-extra-`uname -r`
# 2. Load modules and create device nodes
sudo modprobe binder_linux devices="binder,hwbinder,vndbinder"
sudo modprobe ashmem_linux
- **Architecture Compatibility**:
When running on an ARM64/aarch64 architecture (e.g., Apple M-series chips), you may encounter compatibility or performance issues. It is recommended to run on an x86_64 host.
```python
# --- Synchronous version ---
from agentscope_runtime.sandbox import MobileSandbox
with MobileSandbox() as box:
# By default, pulls 'agentscope/runtime-sandbox-mobile:latest' from DockerHub
print(box.list_tools()) # List all available tools
print(box.mobile_get_screen_resolution()) # Get the screen resolution
print(box.mobile_tap([500, 1000])) # Tap at coordinate (500, 1000)
print(box.mobile_input_text("Hello from AgentScope!")) # Input text
print(box.mobile_key_event(3)) # HOME key event
screenshot_result = box.mobile_get_screenshot() # Get screenshot
print(screenshot_result)
input("Press Enter to continue...")
# --- Asynchronous version ---
from agentscope_runtime.sandbox import MobileSandboxAsync
async with MobileSandboxAsync() as box:
# Default image is 'agentscope/runtime-sandbox-mobile:latest'
print(await box.list_tools_async()) # List all available tools
print(await box.mobile_get_screen_resolution()) # Get the screen resolution
print(await box.mobile_tap([500, 1000])) # Tap at coordinate (500, 1000)
print(await box.mobile_input_text("Hello from AgentScope!")) # Input text
print(await box.mobile_key_event(3)) # HOME key event
screenshot_result = await box.mobile_get_screenshot() # Get screenshot
print(screenshot_result)
input("Press Enter to continue...")
```
> [!NOTE]
>
> To add tools to the AgentScope `Toolkit`:
>
> 1. Wrap sandbox tool with `sandbox_tool_adapter`, so the AgentScope agent can call them:
>
> ```python
> from agentscope_runtime.adapters.agentscope.tool import sandbox_tool_adapter
>
> wrapped_tool = sandbox_tool_adapter(sandbox.browser_navigate)
> ```
>
> 2. Register the tool with `register_tool_function`:
>
> ```python
> toolkit = Toolkit()
> Toolkit.register_tool_function(wrapped_tool)
> ```
#### Configuring Sandbox Image Registry, Namespace, and Tag
##### 1. Registry
If pulling images from DockerHub fails (for example, due to network restrictions), you can switch the image source to Alibaba Cloud Container Registry for faster access:
```bash
export RUNTIME_SANDBOX_REGISTRY="agentscope-registry.ap-southeast-1.cr.aliyuncs.com"
```
##### 2. Namespace
A namespace is used to distinguish images of different teams or projects. You can customize the namespace via an environment variable:
```bash
export RUNTIME_SANDBOX_IMAGE_NAMESPACE="agentscope"
```
For example, here `agentscope` will be used as part of the image path.
##### 3. Tag
An image tag specifies the version of the image, for example:
```bash
export RUNTIME_SANDBOX_IMAGE_TAG="preview"
```
Details:
- Default is `latest`, which means the image version matches the PyPI latest release.
- `preview` means the latest preview version built in sync with the **GitHub main branch**.
- You can also use a specified version number such as `20250909`. You can check all available image versions at [DockerHub](https://hub.docker.com/repositories/agentscope).
##### 4. Complete Image Path
The sandbox SDK will build the full image path based on the above environment variables:
```bash
//runtime-sandbox-base:
```
Example:
```bash
agentscope-registry.ap-southeast-1.cr.aliyuncs.com/agentscope/runtime-sandbox-base:preview
```
#### Serverless Sandbox Deployment
AgentScope Runtime also supports serverless deployment, which is suitable for running sandboxes in a serverless environment, e.g. [Alibaba Cloud Function Compute (FC)](https://help.aliyun.com/zh/functioncompute/fc/).
First, please refer to the [documentation](https://runtime.agentscope.io/en/sandbox/advanced.html#optional-function-compute-fc-settings) to configure the serverless environment variables. Make `CONTAINER_DEPLOYMENT` to `fc` to enable serverless deployment.
Then, start a sandbox server, use the `--config` option to specify a serverless environment setup:
```bash
# This command will load the settings defined in the `custom.env` file
runtime-sandbox-server --config fc.env
```
After the server starts, you can access the sandbox server at baseurl `http://localhost:8000` and invoke sandbox tools described above.
### Deployment Example
The `AgentApp` exposes a `deploy` method that takes a `DeployManager` instance and deploys the agent.
* The service port is set as the parameter `port` when creating the `LocalDeployManager`.
* The service endpoint path is set as the parameter `endpoint_path` to `/process` when deploying the agent.
* The deployer will automatically add common agent protocols, such as **A2A**, **Response API**.
After deployment, users can access the service at http://localhost:8090/process:
```python
from agentscope_runtime.engine.deployers import LocalDeployManager
# Create deployment manager
deployer = LocalDeployManager(
host="0.0.0.0",
port=8090,
)
# Deploy the app as a streaming service
deploy_result = await app.deploy(
deployer=deployer,
endpoint_path="/process"
)
```
After deployment, users can also access this service using the Response API of the OpenAI SDK:
```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8090/compatible-mode/v1")
response = client.responses.create(
model="any_name",
input="What is the weather in Beijing?"
)
print(response)
```
Besides, `DeployManager` also supports serverless deployments, such as deploying your agent app to [ModelStudio](https://bailian.console.aliyun.com/?admin=1&tab=doc#/doc/?type=app&url=2983030).
```python
import os
from agentscope_runtime.engine.deployers.modelstudio_deployer import (
ModelstudioDeployManager,
OSSConfig,
ModelstudioConfig,
)
# Create deployment manager
deployer = ModelstudioDeployManager(
oss_config=OSSConfig(
access_key_id=os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_ID"),
access_key_secret=os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_SECRET"),
),
modelstudio_config=ModelstudioConfig(
workspace_id=os.environ.get("MODELSTUDIO_WORKSPACE_ID"),
access_key_id=os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_ID"),
access_key_secret=os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_SECRET"),
dashscope_api_key=os.environ.get("DASHSCOPE_API_KEY"),
),
)
# Deploy to ModelStudio
result = await app.deploy(
deployer,
deploy_name="agent-app-example",
telemetry_enabled=True,
requirements=["agentscope", "fastapi", "uvicorn"],
environment={
"PYTHONPATH": "/app",
"DASHSCOPE_API_KEY": os.environ.get("DASHSCOPE_API_KEY"),
},
)
```
For more advanced serverless deployment guides, please refer to the [documentation](https://runtime.agentscope.io/en/advanced_deployment.html#method-4-modelstudio-deployment).
---
## π Guides
For a more detailed tutorial, please refer to: [](https://runtime.agentscope.io)
---
## π¬ Contact
Welcome to join our community on
| [Discord](https://discord.gg/eYMpfnkG8h) | DingTalk |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| 
| 
|
---
## π€ Contributing
We welcome contributions from the community! Here's how you can help:
### π Bug Reports
- Use GitHub Issues to report bugs
- Include detailed reproduction steps
- Provide system information and logs
### π‘ Feature Requests
- Discuss new ideas in GitHub Discussions
- Follow the feature request template
- Consider implementation feasibility
### π§ Code Contributions
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
For detailed contributing guidelines, please see [CONTRIBUTE](cookbook/en/contribute.md).
---
## π License
AgentScope Runtime is released under the [Apache License 2.0](LICENSE).
```
Copyright 2025 Tongyi Lab
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
```
## β¨ Contributors
[](#contributors-)
Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/emoji-key/)):
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! 
