110 lines
4.3 KiB
Markdown
110 lines
4.3 KiB
Markdown
<div align="center">
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<img src="assets/alias.png" alt="Alias-Agent Logo" width="500" height="250" style="vertical-align: middle; margin-right: 20px;">
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<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>
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</div>
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## Project introduction
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*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:
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- **Simple React**: Employs vanilla reasoning-acting loops to iteratively solve problems and execute tool calls.
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- **Planner-Worker**: Uses intelligent planning to decompose complex tasks into manageable subtasks, with dedicated worker agents handling each subtask independently.
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- **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.
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We aim for Alias to serve as an out-of-the-box solution that users can readily deploy for various tasks.
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## Coming soon
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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.
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We are actively developing specialized enhancements and adaptations for:
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- **Business Intelligence (BI)** scenarios
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- **Financial** analysis applications
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- **Question-Answering (QA)** systems
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Stay tuned for upcoming releases!
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## Installation
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Install the Alias package in development mode:
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```bash
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pip install -e .
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# SETUP SANDBOX
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# If you are using colima, then you need to run the following
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# export DOCKER_HOST=unix://$HOME/.colima/default/docker.sock
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# More details can refer to https://runtime.agentscope.io/en/sandbox.html
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# Option 1: Pull from registry
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export RUNTIME_SANDBOX_REGISTRY=agentscope-registry.ap-southeast-1.cr.aliyuncs.com
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docker pull agentscope-registry.ap-southeast-1.cr.aliyuncs.com/agentscope/runtime-sandbox-alias:latest
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# Option 2: pull from docker hub
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docker pull agentscope/runtime-sandbox-alias:latest
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```
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This will install the `alias` command-line tool.
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## Basic Usage
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The `alias` CLI provides a terminal interface to run AI agents for various tasks.
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### Run Command
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First of all, set up API keys
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```bash
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# Model API keys
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export DASHSCOPE_API_KEY=your_dashscope_api_key_here
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# 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:
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#export MODEL=gpt-5
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#export OPENAI_API_KEY=your_openai_api_key_here
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# Search api key (required for deep research)
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export TAVILY_API_KEY=your_tavily_api_key_here
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```
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Execute an agent task:
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```bash
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alias_agent run --task "Your task description here"
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```
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### Examples
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#### Run with all agents (Meta Planner with workers):
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```bash
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alias_agent run --task "Analyze Meta stock performance in Q1 2025"
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```
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[//]: # (#### Run with only browser agent:)
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[//]: # (```bash)
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[//]: # (alias_agent run --mode browser --task "Search five latest research papers about browser-use agent")
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```
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#### Upload files to sandbox workspace:
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```bash
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# Upload a single file
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alias_agent run --task "Analyze this data" --files data.csv
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# Upload multiple files
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alias_agent run --task "Process these files and create a summary report" --files report.txt data.csv notes.md
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# Using short form (-f)
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alias_agent run --task "Review the documents" -f document1.pdf document2.txt
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# Combine with other options
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alias_agent run --mode all --task "Analyze the data and generate insights" --files dataset.csv --verbose
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```
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**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.
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### Obtain agent-generated files
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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.
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