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:

• argc: A bash command-line framewrok and command runner
• jq: A JSON processor

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-functions

get_current_weather.sh
execute_command.sh
#execute_py_code.py

coder
todo

argc build

Symlink this repo directory to AIChat's functions_dir:

ln -s "$(pwd)" "$(aichat --info | grep -w functions_dir | awk '{print $2}')"
# OR
argc install

Done! 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 todos

Building 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.txt

Refer 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.