Agent Script is an open-source Python framework that lets developers define AI agent behaviors via simple script files. It integrates large language models with external tools, memory stores, and custom actions to automate complex, multi-step tasks. Ideal for rapid prototyping, it offers built-in logging, debugging, and modular plugins for extensibility.
Agent Script is an open-source Python framework that lets developers define AI agent behaviors via simple script files. It integrates large language models with external tools, memory stores, and custom actions to automate complex, multi-step tasks. Ideal for rapid prototyping, it offers built-in logging, debugging, and modular plugins for extensibility.
Agent Script provides a declarative scripting layer over large language models, enabling you to write YAML or JSON scripts that define agent workflows, tool calls, and memory usage. You can plug in OpenAI, local LLMs, or other providers, connect external APIs as tools, and configure long-term memory backends. The framework handles context management, asynchronous execution, and detailed logging out of the box. With minimal code, you can prototype chatbots, RPA workflows, data extraction agents, or custom control loops, making it easy to build, test, and deploy AI-powered automations.
Who will use Agent Script?
AI developers and engineers
Automation architects
Data scientists exploring agent workflows
SaaS product teams building AI features
Researchers prototyping LLM agents
How to use the Agent Script?
Step1: Install via pip: pip install agent-script
Step2: Obtain an API key for your LLM provider
Step3: Write an agent script in YAML or JSON defining tasks, tools, and memory
Step4: Configure environment and tool plugins in a Python runner
Step5: Execute the agent runner to start the script
Step6: Monitor logs, inspect memory stores, and iterate scripts
Platform
mac
windows
linux
Agent Script's Core Features & Benefits
The Core Features
Script-based agent definition in YAML/JSON
LLM provider integration (OpenAI, local models)
External tool and API connectors
Long-term and short-term memory modules
Built-in logging and debugging
The Benefits
Rapid agent prototyping with no boilerplate code
Modular and extensible plugin architecture
Lightweight and open-source under permissive license