Alias-Agent (short for Alias) is an LLM-empowered agent built on AgentScope and AgentScope-runtime, designed to solve diverse real-world problems. It provides three operational modes for flexible task execution:

Simple React: Employs vanilla reasoning-acting loops to iteratively solve problems and execute tool calls.
Planner-Worker: Uses intelligent planning to decompose complex tasks into manageable subtasks, with dedicated worker agents handling each subtask independently.
Built-in Agents: Leverages specialized agents tailored for specific domains, including Deep Research Agent for comprehensive analysis and Browser-use Agent for web-based interactions. More details can refer to the following "Basic Usage" section.

We aim for Alias to serve as an out-of-the-box solution that users can readily deploy for various tasks.

Coming soon

Beyond being a ready-to-use agent, we envision Alias as a foundational template that can be adapted to different scenarios. Developers can extend and customize Alias at the tool, prompt, and agent levels to meet their specific requirements.

We are actively developing specialized enhancements and adaptations for:

Business Intelligence (BI) scenarios
Financial analysis applications
Question-Answering (QA) systems

Stay tuned for upcoming releases!

Installation

Install the Alias package in development mode:

pip install -e .

# SETUP SANDBOX
# If you are using colima, then you need to run the following
# export DOCKER_HOST=unix://$HOME/.colima/default/docker.sock
# More details can refer to https://runtime.agentscope.io/en/sandbox.html

# Option 1: Pull from registry
export RUNTIME_SANDBOX_REGISTRY=agentscope-registry.ap-southeast-1.cr.aliyuncs.com
docker pull agentscope-registry.ap-southeast-1.cr.aliyuncs.com/agentscope/runtime-sandbox-alias:latest

# Option 2: pull from docker hub
docker pull agentscope/runtime-sandbox-alias:latest

This will install the alias command-line tool.

Basic Usage

The alias CLI provides a terminal interface to run AI agents for various tasks.

Run Command

First of all, set up API keys

# Model API keys
export DASHSCOPE_API_KEY=your_dashscope_api_key_here

# Using other models: go to src/alias/agent/run.py and add your model to MODEL_FORMATTER_MAPPING, then run the bash to set your model and api key. For example:
#export MODEL=gpt-5
#export OPENAI_API_KEY=your_openai_api_key_here

# Search api key (required for deep research)
export TAVILY_API_KEY=your_tavily_api_key_here

Execute an agent task:

alias_agent run --task "Your task description here"

Examples

Run with all agents (Meta Planner with workers):

alias_agent run --task "Analyze Meta stock performance in Q1 2025"


#### Upload files to sandbox workspace:
```bash
# Upload a single file
alias_agent run --task "Analyze this data" --files data.csv

# Upload multiple files
alias_agent run --task "Process these files and create a summary report" --files report.txt data.csv notes.md

# Using short form (-f)
alias_agent run --task "Review the documents" -f document1.pdf document2.txt

# Combine with other options
alias_agent run --mode all --task "Analyze the data and generate insights" --files dataset.csv --verbose

Note: Files uploaded with --files are automatically copied to the /workspace directory in the sandbox with their original filenames, making them immediately accessible to the agent.

Obtain agent-generated files

In the directory where you ran alias_agent, you should find a sessions_mount_dir directory with subdirectories, each containing the content from /workspace of the sandboxes' mounted file systems. All generated files should be located there.