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Template Documentation
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README.md

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If something doesn't work, please [file an issue](https://github.com/agentops-ai/agentstack/issues/new).<br>
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If you have questions or need help, please ask in our [Discord community](https://discord.gg/JdWkh9tgTQ).
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> 🛠️🚨 AgentStack is in open preview. We're building in public, use at your own risk but have fun :)
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> 🛠️🏃🏼‍♀️ The quickest way to build your powerful agent project
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AgentStack serves as a great tool for starting your agent project and offers many CLI utilities for easy code-gen throughout the development process.
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## Creating an Agent Project
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**You'll need to have Python 3.10+ on your local development machine** (but it's not required on the server). We recommend using the latest version. You can use [pyenv](https://github.com/pyenv/pyenv) to switch Python versions between different projects.
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**You'll need to have Python 3.10+ on your local development machine**. We recommend using the latest version. You can use [pyenv](https://github.com/pyenv/pyenv) to switch Python versions between different projects.
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To create a new agent project, run:
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```sh
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pip install agentstack
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agentstack init
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agentstack init <project_name>
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```
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It will create a directory with your project name inside the current folder.<br>
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Once the initialization is done, you can open your project folder:
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```sh
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cd my-agent-project
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cd <your_agent_project>
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```
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### Templates
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Additionally, pre-built templates are available as a shortcut to building your project. [View the available templates]().
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## Building Agent Functionality
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After generating a project, the next step is to build your agent project by creating Agents and Tasks. You can do this quickly with AgentStack:
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## Roadmap
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### Frameworks
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#### CrewAI
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Development of AgentStack is being done primarily on [CrewAI](https://crewai.com).
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### Framework Agnosticism
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#### AutoGen
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Some work has been done to add Microsoft's [AutoGen](https://microsoft.github.io/autogen/0.2/), although these efforts have been paused. AutoGen is currently in the process of making [large design decisions](https://microsoft.github.io/autogen/dev/) that will effect the integration with AgentStack.
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Development of AgentStack is being done primarily on [CrewAI](https://crewai.com). We will soon be working to make AgentStack fully framework-agnostic, meaning that any supported multi-agent framework can be used for your project.
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### Tools
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- Core Tools built by AgentStack
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- A fast interactive test runner with built-in support for coverage reporting.
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- A live development server that warns about common mistakes.
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- A build script to bundle your project for production.
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- Integration with [AgentOps](https://agentops.ai) for AI agent observability.
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- Hassle-free updates for the above tools with a single dependency.
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## License
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docs/README.md

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# Docs
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## Building
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To build and view docs from this folder, install the Mintlify CLI:
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```bash
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pip install mintlify
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```
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then serve it with
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```bash
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mintlify dev
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```

docs/contributing/adding-tools.mdx

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Adding tools is easy once you understand the project structure. A few things need to be done for a tool to be considered completely supported:
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<Steps>
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<Step title="Modify the tool list data">
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Add your tool and relevant information to `agentstack/tools/tools.json`
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</Step>
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<Step title="Create a config for your tool">
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- As an example, look at `mem0.json`
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- Create a new tool config at `agentstack/tools/<your_tool>.json`
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- As an example, look at our [tool config fixture](https://github.com/AgentOps-AI/AgentStack/blob/main/tests/fixtures/tool_config_max.json)
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- AgentStack uses this to know what code to insert where. Follow the structure to add your tool.
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</Step>
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<Step title="Create your implementation for each framework">
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</Step>
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<Step title="Open a PR to add your tool to the next release">
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</Step>
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</Steps>
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</Steps>
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# Tool Config
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- `name` (str) - Name of your tool
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- `category` (str) - Category your tool belongs in
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- `tools` (List[str]) - The exported functions within your tool file
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- `url` (str) - URL to where developers can learn more about your tool
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- `tools_bundled` (bool) - True if the tool file exports a list of tools
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- `cta` (str) - Call To Action printed in the terminal after install
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- `env` (dict) - Key: Environment variable name; Value: default value
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- `packages` (List[str]) - Python packages to be installed to support your tool
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- `post_install` (str) - A script to be run after install of your tool
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- `post_remove` (str) - A script to be run after removal of your tool

docs/installation.mdx

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brew tap agentstack-ai/tap
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brew install agentstack
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```
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```bash uv
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uv install agentstack
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```
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</CodeGroup>
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<Note>
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Conda is not supported at this time. AgentStack uses Poetry in your project which [does not play well with Conda](https://github.com/AgentOps-AI/AgentStack/issues/40).
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</Note>
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</CodeGroup>
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## After installing
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Run `agentstack --version` to verify that the CLI is installed and accessible.

docs/mint.json

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"tools/core",
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"tools/community"
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]
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},{
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},
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{
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"group": "Templating",
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"pages": [
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"templates/templates",
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"templates/community"
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]
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},
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{
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"group": "Contributing",
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"pages": [
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"contributing/how-to-contribute",

docs/quickstart.mdx

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AgentStack's greatest ability is that of quickly scaffolding agent projects
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### Wizard
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To start, ensure you have AgentStack [installed](/installation) and run:
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```bash
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agentstack init <project_name>
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```
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AgentStack v0.1 supports a friendly setup wizard!
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To start, ensure you have AgentStack [installed](/installation) and run:
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**With the Wizard**
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Optionally, if you're a little overwhelmed with the code generated, you can use our Wizard to help guide you through some core setup!
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```bash
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agentstack init
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agentstack init <project_name> --wizard
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```
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<Note>Using the wizard is recommended in AgentStack version 0.1 (current release)</Note>
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**With a Template**
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By default, your project will contain no agents or tasks.
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### Without Wizard
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If you find it easier to start with a complete project and edit it to fit your use-case, you can use [Templates](/templates/templates)
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Starting a project without the wizard is useful if you are past the ideation stage and want to just start with a default project template:
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agentstack init --template=<template_name/url>
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## Building your project
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By default, your project will contain no agents or tasks.
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AgentStack 0.1 is built on top of [CrewAI](https://docs.crewai.com/introduction). The majority of your development experience will be with Crew.
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AgentStack 0.2 is built on top of [CrewAI](https://docs.crewai.com/introduction). The majority of your development experience will be with Crew.
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When working with your Crew project, AgentStack makes generating new agents and tasks easy.
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docs/templates/community.mdx

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---
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title: 'Community Templates'
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description: 'Extending templating outside what's in the repo'
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---
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The easiest way to create your own templates right now is to host them online.
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```bash
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agentstack init --template=<your_url>
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```
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Much more community template support coming soon!

docs/templates/content_creator.mdx

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---
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title: 'Content Creator'
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description: 'Research a topic and create content on it'
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---
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[View Template](https://github.com/AgentOps-AI/AgentStack/blob/main/agentstack/templates/proj_templates/content_creator.json)

docs/templates/researcher.mdx

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---
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title: 'Researcher'
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description: 'Research and report result from a query'
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---
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[View Template](https://github.com/AgentOps-AI/AgentStack/blob/main/agentstack/templates/proj_templates/research.json)
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```bash
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agentstack init --template=research
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```
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# Purpose
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This agent will accept a query as a string, use Perplexity to research it. Another agent will take the data gathered and perform an analysis focused on answering the query.
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# Inputs
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`query` (str): the query for the agent to research and report on

docs/templates/system_analyzer.mdx

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---
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title: 'System Analyzer'
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description: 'Inspect a project directory and improve it'
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---
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[View Template](https://github.com/AgentOps-AI/AgentStack/blob/main/agentstack/templates/proj_templates/system_analyzer.json)
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```bash
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agentstack init --template=system_analyzer
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```
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# Purpose
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This agent will accept a query as a string, use Perplexity to research it. Another agent will take the data gathered and perform an analysis focused on answering the query.
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# Inputs
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`system_path` (str): the absolute path to

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