8af2dc6477
* Revise README with enhanced Alias-Agent details Updated project introduction and operational modes for Alias-Agent. * update readme --------- Co-authored-by: xieyxclack <yuexiang.xyx@alibaba-inc.com>
119 lines
5.2 KiB
Markdown
119 lines
5.2 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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<p align="center">
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<a href="https://pypi.org/project/alias-agent/">
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<img
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src="https://img.shields.io/badge/python-3.10+-blue?logo=python"
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alt="python"
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/>
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</a>
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<a href="./LICENSE">
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<img
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src="https://img.shields.io/badge/license-Apache--2.0-black"
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alt="license"
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/>
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</a>
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<a href="https://github.com/agentscope-ai/agentscope">
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<img
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src="https://img.shields.io/badge/built--on-AgentScope-blue?logo=github"
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alt="agentscope"
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/>
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</a>
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</p>
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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 serve as a general-purpose intelligent assistant for responding to user queries. Alias excels at decomposing complicated problems, constructing roadmaps, and applying appropriate strategies to tackle diverse real-world tasks.
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Alias employs a multi-mode operational mechanism for flexible task execution, including `General`, `Browser Use`, `Deep Research`, `Financial Analysis`, and `Data Science`. When switching between different operational modes, Alias is equipped with tailored instructions, specialized tool sets, and the capability to orchestrate various expert agents. This allows Alias to better adapt to the specific requirements of diverse downstream tasks. For example, when handling financial analysis, Alias employs traceable reasoning chains and generates explainable results to increase user trust in its decision-making, along with optimized report visualizations; When resolving data science tasks, Alias can access user-associated databases and is designed to facilitate efficient data analysis, processing, and prediction.
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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, supported by a comprehensive pipeline for agent development, testing, and deployment based on the AgentScope ecosystem. Beyond being a ready-to-use agent, we also envision Alias as a foundational template that can be adapted for diverse scenarios. Developers are encouraged to extend and customize Alias at the tool, prompt, and agent levels to meet specific requirements.
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We welcome more developers to join the community and contribute to ongoing innovation.
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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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