LLM Functions

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.

Tools Showcase

Agents showcase

Prerequisites

Make sure you have the following tools installed:

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

Getting Started with AIChat

Currently, AIChat is the only CLI tool that supports llm-functions. We look forward to more tools supporting llm-functions.

1. Clone the repository

git clone https://github.com/sigoden/llm-functions
cd llm-functions

2. Build tools and agents

I. Create a ./tools.txt file with each tool filename on a new line.

get_current_weather.sh
execute_command.sh
#execute_py_code.py

II. Create a ./agents.txt file with each agent name on a new line.

coder
todo

III. Build bin and functions.json

argc build

IV. Ensure that everything is ready (environment variables, Node/Python dependencies, mcp-bridge server)

argc check

3. Link LLM-functions and AIChat

AIChat expects LLM-functions to be placed in AIChat's functions_dir so that AIChat can use the tools and agents that LLM-functions provides.

You can symlink this repository directory to AIChat's functions_dir with:

ln -s "$(pwd)" "$(aichat --info | sed -n 's/^functions_dir\s\+//p')"
# OR
argc link-to-aichat

Alternatively, you can tell AIChat where the LLM-functions directory is by using an environment variable:

export AICHAT_FUNCTIONS_DIR="$(pwd)"

4. Start using the functions

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

Writing Your Own Tools

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.

Bash

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 "$0" "$@")"

Javascript

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.run = function ({ code }) {
  eval(code);
}

Python

Create a new python script in the ./tools/ directory (e.g. execute_py_code.py).

def run(code: str):
    """Execute the python code.
    Args:
        code: Python code to execute, such as `print("hello world")`
    """
    exec(code)

Writing Your Own Agents

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.

MCP (Model Context Protocol)

• mcp/server: Let LLM-Functions tools/agents be used through the Model Context Protocol.
• mcp/bridge: Let external MCP tools be used by LLM-Functions.

Documents

• Tool Guide
• Agent Guide
• Argc Commands

License

The project is under the MIT License, Refer to the LICENSE file for detailed information.