This project empowers you to effortlessly build powerful LLM tools and agents using familiar languages like Bash, JavaScript, and Python.
Forget complex integrations, harness the power of function calling to connect your LLMs directly to custom code and unlock a world of possibilities. Execute system commands, process data, interact with APIs – the only limit is your imagination.
Kickstart your journey with a curated library of pre-built LLM tools and agents ready for immediate use or customization.
Tools Showcase
Agents showcase
Make sure you have the following tools installed:
Getting Started with AIChat
Currently, AIChat is the only CLI tool that supports llm-functions. We look forward to more tools supporting llm-functions.
git clone https://github.com/sigoden/llm-functionsget_current_weather.sh
execute_command.sh
#execute_py_code.py
Where is the web_search tool?
The web_search tool itself doesn't exist directly, Instead, you can choose from a variety of web search tools.
To use one as the web_search tool, follow these steps:
-
Choose a Tool: Available tools include:
web_search_cohere.shweb_search_perplexity.shweb_search_tavily.shweb_search_vertexai.sh
-
Link Your Choice: Use the
argccommand to link your chosen tool asweb_search. For example, to useweb_search_perplexity.sh:$ argc link-web-search web_search_perplexity.sh
This command creates a symbolic link, making
web_search.shpoint to your selectedweb_search_perplexity.shtool.
Now there is a web_search.sh ready to be added to your ./tools.txt.
coder
todo
argc buildSymlink this repo directory to AIChat's functions_dir:
ln -s "$(pwd)" "$(aichat --info | grep -w functions_dir | awk '{print $2}')"
# OR
argc installDone! Now you can use the tools and agents with AIChat.
aichat --role %functions% what is the weather in Paris?
aichat --agent todo list all my todosBuilding tools for our platform is remarkably straightforward. You can leverage your existing programming knowledge, as tools are essentially just functions written in your preferred language.
LLM Functions automatically generates the JSON declarations for the tools based on comments. Refer to ./tools/demo_tool.{sh,js,py} for examples of how to use comments for autogeneration of declarations.
Create a new bashscript in the ./tools/ directory (.e.g. execute_command.sh).
#!/usr/bin/env bash
set -e
# @describe Execute the shell command.
# @option --command! The command to execute.
main() {
eval "$argc_command" >> "$LLM_OUTPUT"
}
eval "$(argc --argc-eval "$0" "$@")"Create a new javascript in the ./tools/ directory (.e.g. execute_js_code.js).
/**
* Execute the javascript code in node.js.
* @typedef {Object} Args
* @property {string} code - Javascript code to execute, such as `console.log("hello world")`
* @param {Args} args
*/
exports.main = function main({ code }) {
return eval(code);
}Create a new python script in the ./tools/ directory (e.g. execute_py_code.py).
def main(code: str):
"""Execute the python code.
Args:
code: Python code to execute, such as `print("hello world")`
"""
return exec(code)Agent = Prompt + Tools (Function Calling) + Documents (RAG), which is equivalent to OpenAI's GPTs.
The agent has the following folder structure:
└── agents
└── myagent
├── functions.json # JSON declarations for functions (Auto-generated)
├── index.yaml # Agent definition
├── tools.txt # Shared tools
└── tools.{sh,js,py} # Agent tools
The agent definition file (index.yaml) defines crucial aspects of your agent:
name: TestAgent
description: This is test agent
version: 0.1.0
instructions: You are a test ai agent to ...
conversation_starters:
- What can you do?
variables:
- name: foo
description: This is a foo
documents:
- local-file.txt
- local-dir/
- https://example.com/remote-file.txtRefer to ./agents/demo for examples of how to implement a agent.
The project is under the MIT License, Refer to the LICENSE file for detailed information.
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