109 lines
4.3 KiB
Markdown
109 lines
4.3 KiB
Markdown
<div align="center">
|
|
|
|
<img src="assets/alias.png" alt="Alias-Agent Logo" width="500" height="250" style="vertical-align: middle; margin-right: 20px;">
|
|
<h1 style="text-decoration: none; border-bottom: none; display: inline; vertical-align: middle; margin: 0;">Alias-Agent: Start It Now, Extend It Your Way, Deploy All with Ease</h1>
|
|
|
|
</div>
|
|
|
|
## Project introduction
|
|
*Alias-Agent* (short for *Alias*) is an LLM-empowered agent built on [AgentScope](https://github.com/agentscope-ai/agentscope) and [AgentScope-runtime](https://github.com/agentscope-ai/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:
|
|
|
|
```bash
|
|
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
|
|
```bash
|
|
# 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:
|
|
|
|
```bash
|
|
alias_agent run --task "Your task description here"
|
|
```
|
|
|
|
### Examples
|
|
|
|
#### Run with all agents (Meta Planner with workers):
|
|
```bash
|
|
alias_agent run --task "Analyze Meta stock performance in Q1 2025"
|
|
```
|
|
|
|
#### Run with only browser agent:
|
|
|
|
```bash
|
|
alias_agent run --mode browser --task "Search five latest research papers about browser-use agent"
|
|
```
|
|
|
|
#### 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.
|
|
|