pip install make_agents
MakeAgents is a micro framework for creating LLM-driven agents.
The MakeAgents paradigm is to define an agent's behaviour and capabilities entirely through action functions, and an action graph.
Currently supports OpenAI's GPT chat models out of the box.
- Install
make_agentsusing your package manager, e.g.pip install make_agents - Set the
OPENAI_API_KEYenvironment variable, with your OpenAI api key.- Alternatively,
import openaiand setopenai.api_key = ...
- Alternatively,
- See the examples below, the reference docs, and check out the code.
Video of an example agent in action:
bash_assistant_example.mp4
import json
from IPython.display import display, Markdown
from pydantic import BaseModel, Field
import make_agents as ma# First define the action functions
class MessageUserArg(BaseModel):
message: str = Field(description="Message to send user")
@ma.action
def message_user(arg: MessageUserArg):
"""Send the user a message, and get their response."""
response = ""
while response == "":
response = input(arg.message).strip()
return response
@ma.action
def get_task_instructions():
return "Your task is to get both the user's first and last name."
class LogNameArg(BaseModel):
first_name: str = Field(description="User's first name")
last_name: str = Field(description="User's last name")
@ma.action
def record_first_and_last_name(arg: LogNameArg):
"""Record the users first and last name."""
return {"first_name": arg.first_name, "last_name": arg.last_name}
# Then define action graph
action_graph = {
ma.Start: [get_task_instructions],
get_task_instructions: [message_user],
message_user: [message_user, record_first_and_last_name],
}
display(Markdown("### Action graph"))
display(ma.bonus.draw_graph(action_graph))
# Finally, run the agent
display(Markdown("### Agent execution log"))
for messages in ma.run_agent(action_graph):
ma.bonus.pretty_print(messages[-1]) # print most recent message on stack
print(f"Retrieved user_name: {json.loads(messages[-1]['content'])}")
call `get_task_instructions`: <no arguments>
`get_task_instructions` result: "Your task is to get both the user's first and last name."
call `message_user`: {
"message": "Hello, may I have your first name please?"
}
`message_user` result: "Sure, it's Bill"
call `select_next_func`: {
"thought_process": "I have obtained the user's first name, now I need to get their last name. So, I will call the 'message_user' function again to request for their last name.",
"next_function": "message_user"
}
`select_next_func` result: "message_user"
call `message_user`: {
"message": "Nice to meet you, Bill. Could you please tell me your last name as well?"
}
`message_user` result: "BoBaggins"
call `select_next_func`: {
"thought_process": "Now that I have both the first and last name of the user, the next step is to record both. I will accomplish this task by calling the 'record_first_and_last_name' function.",
"next_function": "record_first_and_last_name"
}
`select_next_func` result: "record_first_and_last_name"
call `record_first_and_last_name`: {
"first_name": "Bill",
"last_name": "BoBaggins"
}
`record_first_and_last_name` result: {"first_name": "Bill", "last_name": "BoBaggins"}
Retrieved user_name: {'first_name': 'Bill', 'last_name': 'BoBaggins'}
This agent is allowed to run bash commands on your system, and read the results... Validation is implemented, so please make sure you understand the commands before allowing them to run.
import subprocess
import shlex@ma.action
def get_task_instructions():
return "Your task is help the user with their computer system, until they ask to end the chat. Please give the user only the relevant information."
class RunBashCommandArg(BaseModel):
plan: str = Field(description="Plan what to run")
command: str = Field(description="Command to run")
@ma.action
def run_bash_command(arg: RunBashCommandArg):
"""Record the users first and last name."""
result = subprocess.run(shlex.split(arg.command), capture_output=True, text=True)
return {"stout": result.stdout, "stderr": result.stderr}
# Define action graph
action_graph = {
ma.Start: [get_task_instructions],
get_task_instructions: [message_user],
message_user: [message_user, run_bash_command, ma.End],
run_bash_command: [message_user, run_bash_command],
}
display(Markdown("### Action graph"))
display(ma.bonus.draw_graph(action_graph))
# Run the agent
display(Markdown("### Agent execution log"))
for messages in ma.run_agent(action_graph):
ma.bonus.pretty_print(messages[-1])
if messages[-1].get("function_call", {}).get("name", "") == run_bash_command.__name__:
command = json.loads(messages[-1]["function_call"]["arguments"])["command"]
input(
f"Please validate the bash command, before pressing enter to continue. Command: `{command}`"
)
call `get_task_instructions`: <no arguments>
`get_task_instructions` result: "Your task is help the user with their computer system, until they ask to end the chat. Please give the user only the relevant information."
call `message_user`: {
"message": "Hello! How may I assist you with your computer system today?"
}
`message_user` result: "Hi, how much space do I have left on my hard drive?"
call `select_next_func`: {
"thought_process": "To check the available space on the user's hard drive, I can utilize the bash command 'df -h'. Therefore, the 'run_bash_command' function should be the next one to use.",
"next_function": "run_bash_command"
}
`select_next_func` result: "run_bash_command"
call `run_bash_command`: {
"plan": "To check the available space on the hard drive",
"command": "df -h"
}
`run_bash_command` result: {"stout": "Filesystem Size Used Avail Use% Mounted on\nudev 7.8G 0 7.8G 0% /dev\ntmpfs 1.6G 2.0M 1.6G 1% /run\n/dev/sda5 909G 790G 82G 91% /\ntmpfs 7.8G 0 7.8G 0% /dev/shm\ntmpfs 5.0M 4.0K 5.0M 1% /run/lock\ntmpfs 7.8G 0 7.8G 0% /sys/fs/cgroup\n/dev/loop0 128K 128K 0 100% /snap/bare/5\n/dev/loop1 304M 304M 0 100% /snap/code/141\n/dev/loop2 304M 304M 0 100% /snap/code/142\n/dev/loop3 106M 106M 0 100% /snap/core/16091\n/dev/loop4 106M 106M 0 100% /snap/core/16202\n/dev/loop5 56M 56M 0 100% /snap/core18/2785\n/dev/loop6 56M 56M 0 100% /snap/core18/2790\n/dev/loop7 64M 64M 0 100% /snap/core20/1974\n/dev/loop8 64M 64M 0 100% /snap/core20/2015\n/dev/loop9 74M 74M 0 100% /snap/core22/858\n/dev/loop10 74M 74M 0 100% /snap/core22/864\n/dev/loop11 219M 219M 0 100% /snap/gnome-3-34-1804/90\n/dev/loop12 219M 219M 0 100% /snap/gnome-3-34-1804/93\n/dev/loop14 350M 350M 0 100% /snap/gnome-3-38-2004/143\n/dev/loop13 350M 350M 0 100% /snap/gnome-3-38-2004/140\n/dev/loop16 497M 497M 0 100% /snap/gnome-42-2204/132\n/dev/loop15 497M 497M 0 100% /snap/gnome-42-2204/141\n/dev/loop18 92M 92M 0 100% /snap/gtk-common-themes/1535\n/dev/loop17 82M 82M 0 100% /snap/gtk-common-themes/1534\n/dev/loop19 46M 46M 0 100% /snap/snap-store/638\n/dev/loop20 13M 13M 0 100% /snap/snap-store/959\n/dev/loop21 41M 41M 0 100% /snap/snapd/19993\n/dev/loop22 41M 41M 0 100% /snap/snapd/20092\n/dev/loop23 2.3M 2.3M 0 100% /snap/starship/1766\n/dev/loop24 3.0M 3.0M 0 100% /snap/starship/2049\n/dev/loop25 296M 296M 0 100% /snap/vlc/2344\n/dev/loop26 321M 321M 0 100% /snap/vlc/3078\ntmpfs 1.6G 16K 1.6G 1% /run/user/125\ntmpfs 1.6G 4.0K 1.6G 1% /run/user/1000\n", "stderr": ""}
call `select_next_func`: {
"thought_process": "The bash command has been successfully executed and returned the status of the user's hard drive space. The relevant information here for the user is that on the root directory ('/') which is of size 909G, 790G is used and 82G is still available. Currently, 91% of the storage space is used. Therefore, the 'message_user' function should be the next one to use, to provide this relevant information to the user.",
"next_function": "message_user"
}
`select_next_func` result: "message_user"
call `message_user`: {
"message": "You have a total of 909GB on your root directory. Currently, about 790GB is used, leaving you with approximately 82GB remaining. This means you've used about 91% of your total space."
}
`message_user` result: "thanks, that's all"
call `select_next_func`: {
"thought_process": "The user has indicated that they are finished and needs no further assistance. Therefore, the 'End' function should be the next one to use.",
"next_function": "End"
}
`select_next_func` result: "End"
@ma.action
def get_task_instructions():
return "Get the users email address, and validate it."
class SendValidationEmailArg(BaseModel):
users_email_address: str = Field(description="The users email address")
@ma.action
def send_validation_email(arg: SendValidationEmailArg):
"""Send the user a validation email."""
if not arg.users_email_address.endswith(".com"):
return {"status": "error", "description": "Email address must end with `.com`"}
else:
return {"status": "success", "description": "Validation code sent"}
class CheckValidationCodeArg(BaseModel):
validation_code: str = Field(description="The validation code (6 digits)")
@ma.action
def check_validation_code(arg: CheckValidationCodeArg):
"""Send the user a validation email."""
if len(arg.validation_code) != 6:
return {"status": "error", "description": "Validation code must be 6 digits"}
elif arg.validation_code == "123456":
return {"status": "success", "description": "Validation code correct"}
else:
return {"status": "error", "description": "Validation code incorrect"}
def action_graph(current_action: callable, current_action_result: dict) -> list[callable]:
"""Return the next action(s) to run, given the current action and its result."""
if current_action == ma.Start:
return [get_task_instructions]
elif current_action == get_task_instructions:
return [message_user]
elif current_action == message_user:
return [message_user, send_validation_email, check_validation_code]
elif current_action == send_validation_email:
if current_action_result["status"] == "success":
return [message_user]
else:
return [message_user, send_validation_email]
elif current_action == check_validation_code:
if current_action_result["status"] == "success":
return [ma.End]
else:
return [message_user, check_validation_code]
else:
raise ValueError(f"Unknown action: {current_action}")
# We lose the ability to plot the graph when using a function to define the action graph.
# Run the agent
display(Markdown("### Agent execution log"))
for messages in ma.run_agent(action_graph):
ma.bonus.pretty_print(messages[-1])call `get_task_instructions`: <no arguments>
`get_task_instructions` result: "Get the users email address, and validate it."
call `message_user`: {
"message": "Could you please share your email address?"
}
`message_user` result: "sure, it's bob@bill.io"
call `select_next_func`: {
"thought_process": "Now that I have the users email, I can proceed to validate it. The best function to use for this is 'send_validation_email'.",
"next_function": "send_validation_email"
}
`select_next_func` result: "send_validation_email"
call `send_validation_email`: {
"users_email_address": "bob@bill.io"
}
`send_validation_email` result: {"status": "error", "description": "Email address must end with `.com`"}
call `select_next_func`: {
"thought_process": "The validation email could not be sent because the users email address does not end with '.com'. I will ask the user to provide a valid email address",
"next_function": "message_user"
}
`select_next_func` result: "message_user"
call `message_user`: {
"message": "I'm sorry, but your email address must end with '.com'. Could you please provide a valid email address?"
}
`message_user` result: "Ah, no prob, it's bob@bill.com"
call `select_next_func`: {
"thought_process": "Now that I have received a correct email ending with '.com', I should now send the validation email. The best function to use is 'send_validation_email'.",
"next_function": "send_validation_email"
}
`select_next_func` result: "send_validation_email"
call `send_validation_email`: {
"users_email_address": "bob@bill.com"
}
`send_validation_email` result: {"status": "success", "description": "Validation code sent"}
call `message_user`: {
"message": "A validation code has been sent to your email. Could you please provide the code to confirm your email address?"
}
`message_user` result: "uh, I think 1234"
call `select_next_func`: {
"thought_process": "The user has provided a validation code. I should now check if the code is correct using the 'check_validation_code' function.",
"next_function": "check_validation_code"
}
`select_next_func` result: "check_validation_code"
call `check_validation_code`: {
"validation_code": "1234"
}
`check_validation_code` result: {"status": "error", "description": "Validation code must be 6 digits"}
call `select_next_func`: {
"thought_process": "The validation code provided by the user is invalid as it is not 6 digits. I should ask the user again to provide the correct validation code",
"next_function": "message_user"
}
`select_next_func` result: "message_user"
call `message_user`: {
"message": "I'm sorry, but the validation code should be 6 digits. Could you please check your email and provide the correct validation code?"
}
`message_user` result: "123456"
call `select_next_func`: {
"thought_process": "The user has provided a 6 digits validation code. I will now validate it using the 'check_validation_code' function.",
"next_function": "check_validation_code"
}
`select_next_func` result: "check_validation_code"
call `check_validation_code`: {
"validation_code": "123456"
}
`check_validation_code` result: {"status": "success", "description": "Validation code correct"}
- Prompting has a big impact on the performance of the agent. Action function names, Pydantic models and docstrings can all be considered part of the prompting strategy.
- The current preferred way to deal with exceptions due to the model not providing correct function args is to modify the prompts / action graph, to reduce the error rate.
- "gpt-4" is used by default, and performs better than "gpt-3.5-turbo", (at least with the current set up and prompts).
- For any ideas/comments/suggestions, create a GitHub issue, or comment in a relevant issue.
- For the development of the framework itself, the aspiration is take an "example driven" development approach. I.e. find compelling examples where a feature / change would be helpful before adding it.
- Consider sharing anything cool you've made!
- Clone the repo and
cdinto it - Run
poetry install - Run
poetry run pre-commit install