miniagents 0.0.23

An asynchronous framework for building LLM-based multi-agent systems in Python, with a focus on token and message streaming between the agents.

🛰 MiniAgents 🌘

A framework on top of asyncio for building LLM-based multi-agent systems in Python, with immutable, Pydantic-based messages and a focus on asynchronous token and message streaming between the agents.

TODO mention that the reason for immutable messages is to shift the framework to the functional programming paradigm

TODO mention the feature of "message sequence flattening" and refer to an example that you will provide later in the README

TODO mention that exceptions that happen in "callee" agents are propagated to the caller agents even though the callee agents were being processed in completely detached "asyncio" tasks; refer to an example that you will provide later in the README

TODO create a Discord server for the project

💾 Installation

pip install -U miniagents

🚀 Usage

Here's a simple example of how to define an agent:

from miniagents import miniagent, InteractionContext, MiniAgents


@miniagent
async def my_agent(ctx: InteractionContext) -> None:
    async for msg_promise in ctx.message_promises:
        ctx.reply(f"You said: {await msg_promise}")


async def main() -> None:
    async for msg_promise in my_agent.inquire(["Hello", "World"]):
        print(await msg_promise)


if __name__ == "__main__":
    MiniAgents().run(main())

This script will print the following lines to the console:

You said: Hello
You said: World

TODO show how (and when) to create class-based agents; point out that separate instances of such class-based agents are created upon each call to those agents - the purpose of turning agents into classes is not for the sake of maintaining agent state, but rather for the sake of breaking down agent functionality into multiple methods (refer to MarkdownHistoryAgent as an example or give an in-place example here in the README ? probably the latter)

🧠 Work with LLMs

MiniAgents provides built-in support for OpenAI and Anthropic language models with possibility to add other integrations.

⚠️ ATTENTION! Make sure to run pip install -U openai and set your OpenAI API key in the OPENAI_API_KEY environment variable before running the example below. ⚠️

from miniagents import MiniAgents
from miniagents.ext.llms import OpenAIAgent

# NOTE: "Forking" an agent is a convenient way of creating a new agent instance
# with the specified configuration. Alternatively, you could pass the `model`
# parameter to `OpenAIAgent.inquire()` directly everytime you talk to the
# agent.
gpt_4o_agent = OpenAIAgent.fork(model="gpt-4o-2024-05-13")


async def main() -> None:
    reply_sequence = gpt_4o_agent.inquire(
        "Hello, how are you?",
        system="You are a helpful assistant.",
        max_tokens=50,
        temperature=0.7,
    )
    async for msg_promise in reply_sequence:
        async for token in msg_promise:
            print(token, end="", flush=True)
        # MINOR: Let's separate messages with a double newline (even though in
        # this particular case we are actually going to receive only one
        # message).
        print("\n")


if __name__ == "__main__":
    MiniAgents().run(main())

Even though OpenAI models return a single assistant response, the OpenAIAgent.inquire() method is still designed to return a sequence of multiple message promises. This generalizes to arbitrary agents, making agents in the MiniAgents framework easily interchangeable (agents in this framework support sending and receiving zero or more messages).

You can read agent responses token-by-token as shown above regardless of whether the agent is streaming token by token or returning full messages. The complete message content will just be returned as a single "token" in the latter case.

🔄 A dialog loop between a user and an AI assistant

The dialog_loop agent is a pre-packaged agent that implements a dialog loop between a user agent and an assistant agent. Here is how you can use it to set up an interaction between a user and your agent (can be bare LLM agent, like OpenAIAgent or AnthropicAgent, can also be a custom agent that you define yourself - a more complex agent that uses LLM agents under the hood but also introduces more complex behavior, i.e. Retrieval Augmented Generation etc.):

⚠️ ATTENTION! Make sure to run pip install -U anthropic and set your Anthropic API key in the ANTHROPIC_API_KEY environment variable before running the example below (or just replace AnthropicAgent with OpenAIAgent and "claude-3-5-sonnet-20240620" with "gpt-4o-2024-05-13" if you already set up the previous example). ⚠️

from miniagents import MiniAgents
from miniagents.ext import (
    dialog_loop,
    console_user_agent,
    MarkdownHistoryAgent,
)
from miniagents.ext.llms import SystemMessage, AnthropicAgent


async def main() -> None:
    dialog_loop.kick_off(
        SystemMessage(
            "Your job is to improve the styling and grammar of the sentences "
            "that the user throws at you. Leave the sentences unchanged if "
            "they seem fine."
        ),
        user_agent=console_user_agent.fork(
            # Write chat history to a markdown file (`CHAT.md` in the current
            # working directory by default, fork `MarkdownHistoryAgent` if
            # you want to customize the filepath to write to).
            history_agent=MarkdownHistoryAgent
        ),
        assistant_agent=AnthropicAgent.fork(
            model="claude-3-5-sonnet-20240620",
            max_tokens=1000,
        ),
    )


if __name__ == "__main__":
    MiniAgents(
        # Log LLM prompts and responses to `llm_logs/` folder in the current
        # working directory. These logs will have a form of time-stamped
        # markdown files - single file per single prompt-response pair.
        llm_logger_agent=True
    ).run(main())

Here is what the interaction might look like if you run this script:

YOU ARE NOW IN A CHAT WITH AN AI ASSISTANT

Press Enter to send your message.
Press Ctrl+Space to insert a newline.
Press Ctrl+C (or type "exit") to quit the conversation.

USER: hi

ANTHROPIC_AGENT: Hello! The greeting "hi" is a casual and commonly used informal
salutation. It's grammatically correct and doesn't require any changes. If you'd
like to provide a more formal or elaborate greeting, you could consider
alternatives such as "Hello," "Good morning/afternoon/evening," or "Greetings."

USER: got it, thanks!

ANTHROPIC_AGENT: You're welcome! The phrase "Got it, thanks!" is a concise and
informal way to express understanding and appreciation. It's perfectly fine as
is for casual communication. If you wanted a slightly more formal version, you
could say:

"I understand. Thank you!"

TODO replace the output above with a gif showing the interaction in real time

🧸 A "toy" implementation of a dialog loop

Here is how you can implement a dialog loop between an agent and a user from ground up yourself (for simplicity there is no history agent in this example - check out in_memory_history_agent and how it is used if you want to know how to implement your own history agent too):

from miniagents import miniagent, InteractionContext, MiniAgents
from miniagents.ext import agent_loop, AWAIT


@miniagent
async def user_agent(ctx: InteractionContext) -> None:
    async for msg_promise in ctx.message_promises:
        print("ASSISTANT: ", end="", flush=True)
        async for token in msg_promise:
            print(token, end="", flush=True)
        print()
    ctx.reply(input("USER: "))


@miniagent
async def assistant_agent(ctx: InteractionContext) -> None:
    # Turn a sequence of message promises into a single message promise (if
    # there had been multiple messages in the sequence they would have had
    # been separated by double newlines - this is how `as_single_promise()`
    # works by default).
    aggregated_message = await ctx.message_promises.as_single_promise()
    ctx.reply(f'You said "{aggregated_message}"')


async def main() -> None:
    agent_loop.kick_off(agents=[user_agent, AWAIT, assistant_agent])


if __name__ == "__main__":
    MiniAgents().run(main())

Output:

USER: hi
ASSISTANT: You said "hi"
USER: nice!
ASSISTANT: You said "nice!"
USER: bye
ASSISTANT: You said "bye"

TODO explain why the presence of AWAIT sentinel is important in the example above

TODO or even better - show how to implement agent_loop from scratch

TODO also, use this as an opportunity to bring up mutable_state dictionary that can be passed to either the @miniagent decorator or the fork() method (implement a toy chat history agent to demonstrate that)

📦 Some other pre-packaged agents (miniagents.ext)

• console_input_agent: Prompts the user for input via the console.
• console_output_agent: Echoes messages to the console token by token.
• user_agent: A user agent that echoes messages from the agent that called it, then reads the user input and returns the user input as its response. This agent is an aggregation of the previous two.
• agent_loop: TODO explain
• agent_chain: TODO explain

Feel free to explore the source code in the miniagents.ext package to see how various agents are implemented and get inspiration for building your own agents!

🔀 Agent parallelism explained

Let's consider an example that consists of two dummy agents and an aggregator agent that aggregates the responses from the two dummy agents (and also adds some messages of its own):

import asyncio
from miniagents.miniagents import (
    MiniAgents,
    miniagent,
    InteractionContext,
    Message,
)


@miniagent
async def agent1(ctx: InteractionContext) -> None:
    print("Agent 1 started")
    ctx.reply("*** MESSAGE #1 from Agent 1 ***")
    print("Agent 1 still working")
    ctx.reply("*** MESSAGE #2 from Agent 1 ***")
    print("Agent 1 finished")


@miniagent
async def agent2(ctx: InteractionContext) -> None:
    print("Agent 2 started")
    ctx.reply("*** MESSAGE from Agent 2 ***")
    print("Agent 2 finished")


@miniagent
async def aggregator_agent(ctx: InteractionContext) -> None:
    print("Aggregator started")
    ctx.reply(
        [
            "*** AGGREGATOR MESSAGE #1 ***",
            agent1.inquire(),
            agent2.inquire(),
        ]
    )
    print("Aggregator still working")
    ctx.reply("*** AGGREGATOR MESSAGE #2 ***")
    print("Aggregator finished")


async def main() -> None:
    print("INQUIRING ON AGGREGATOR")
    msg_promises = aggregator_agent.inquire()
    print("INQUIRING DONE\n")

    print("SLEEPING FOR ONE SECOND")
    # This is when the agents will actually start processing (in fact, any
    # other kind of task switch would have had the same effect).
    await asyncio.sleep(1)
    print("SLEEPING DONE\n")

    print("PREPARING TO GET MESSAGES FROM AGGREGATOR")
    async for msg_promise in msg_promises:
        # MessagePromises always resolve into Message objects (or subclasses),
        # even if the agent was replying with bare strings
        message: Message = await msg_promise
        print(message)

    # You can safely `await` again. Concrete messages (and tokens, if there was
    # token streaming) are cached inside the promises. Message sequences (as
    # well as token sequences) are "replayable".
    print("TOTAL NUMBER OF MESSAGES FROM AGGREGATOR:", len(await msg_promises))


if __name__ == "__main__":
    MiniAgents().run(main())

This script will print the following lines to the console:

INQUIRING ON AGGREGATOR
INQUIRING DONE

SLEEPING FOR ONE SECOND
Aggregator started
Aggregator still working
Aggregator finished
Agent 1 started
Agent 1 still working
Agent 1 finished
Agent 2 started
Agent 2 finished
SLEEPING DONE

PREPARING TO GET MESSAGES FROM AGGREGATOR
*** AGGREGATOR MESSAGE #1 ***
*** MESSAGE #1 from Agent 1 ***
*** MESSAGE #2 from Agent 1 ***
*** MESSAGE from Agent 2 ***
*** AGGREGATOR MESSAGE #2 ***
TOTAL NUMBER OF MESSAGES FROM AGGREGATOR: 5

None of the agent functions start executing upon any of the calls to the inquire() method. Instead, in all cases the inquire() method immediately returns with promises to "talk to the agent(s)" (promises of sequences of promises of response messages, to be super precise - see MessageSequencePromise and MessagePromise classes for details).

As long as the global start_asap setting is set to True (which is the default - see the source code of Promising, the parent class of MiniAgents context manager for details), the actual agent functions will start processing at the earliest task switch (the behaviour of asyncio.create_task(), which is used under the hood). In this example it is going to be await asyncio.sleep(1) inside the main() function, but if this sleep() wasn't there, it would have happened upon the first iteration of the async for loop which is the next place where a task switch happens.

💪 EXERCISE FOR READER: Add another await asyncio.sleep(1) right before print("Aggregator finished") in the aggregator_agent function and then try to predict how the output will change. After that, run the modified script and check if your prediction was correct.

⚠️ ATTENTION! You can play around with setting start_asap to False for individual agent calls if for some reason you need to: some_agent.inquire(request_messages_if_any, start_asap=False). However, setting it to False for the whole system globally is not recommended because it can lead to deadlocks. ⚠️

📨 An alternative inquiry method

Here's a simple example demonstrating how to use agent_call = some_agent.initiate_inquiry() and then do agent_call.send_message() two times before calling agent_call.reply_sequence() (instead of all-in-one some_agent.inquire()):

from miniagents import miniagent, InteractionContext, MiniAgents


@miniagent
async def output_agent(ctx: InteractionContext) -> None:
    async for msg_promise in ctx.message_promises:
        ctx.reply(f"Echo: {await msg_promise}")


async def main() -> None:
    agent_call = output_agent.initiate_inquiry()
    agent_call.send_message("Hello")
    agent_call.send_message("World")
    reply_sequence = agent_call.reply_sequence()

    async for msg_promise in reply_sequence:
        print(await msg_promise)


if __name__ == "__main__":
    MiniAgents().run(main())

This will output:

Echo: Hello
Echo: World

🛠️ Global MiniAgents() context

There are three ways to use the MiniAgents() context:

1. Calling its run() method with your main function as a parameter (the main() function in this example should be defined as async):

   MiniAgents().run(main())
   

2. Using it as an async context manager:

   async with MiniAgents():
       ...  # your async code that works with agents goes here
   

3. Directly calling its activate() (and, potentially, afinalize() at the end) methods:

   mini_agents = MiniAgents()
   mini_agents.activate()
   try:
       ...  # your async code that works with agents goes here
   finally:
       await mini_agents.afinalize()
   

The third way might be ideal for web applications and other cases when there is no single function that you can encapsulate with the MiniAgents() context manager (or it is unclear what such function would be). You just do mini_agents.activate() somewhere upon the init of the server and forget about it.

💬 Existing Message models

from miniagents.ext.llms import UserMessage, SystemMessage, AssistantMessage

user_message = UserMessage("Hello!")
system_message = SystemMessage("System message")
assistant_message = AssistantMessage("Assistant message")

The difference between these message types is in the default values of the role field of the message:

• UserMessage has role="user" by default
• SystemMessage has role="system" by default
• AssistantMessage has role="assistant" by default

💭 Custom Message models

You can create custom message types by subclassing Message.

from miniagents.messages import Message


class CustomMessage(Message):
    custom_field: str


message = CustomMessage("Hello", custom_field="Custom Value")
print(message.content)  # Output: Hello
print(message.custom_field)  # Output: Custom Value

---

For more advanced usage, check out the examples directory.

💡 Motivation behind this project

There are three main features of MiniAgents the idea of which motivated the creation of this framework:

1. It is built around supporting asynchronous token streaming across chains of interconnected agents, making this the core feature of the framework.
2. It is very easy to throw bare strings, messages, message promises, collections, and sequences of messages and message promises (as well as the promises of the sequences themselves) all together into an agent reply (see MessageType). This entire hierarchical structure will be asynchronously resolved in the background into a flat and uniform sequence of message promises (it will be automatically "flattened" in the background).
3. By default, agents work in so called start_asap mode, which is different from the usual way coroutines work where you need to actively await on them and/or iterate over them (in case of asynchronous generators). In start_asap mode, every agent, after it was invoked, actively seeks every opportunity to proceed its processing in the background when async tasks switch.

The third feature combines this start_asap approach with regular async/await and async generators by using so called streamed promises (see StreamedPromise and Promise classes) which were designed to be "replayable" by nature.

It was chosen for messages to be immutable once they are created (see Message and Frozen classes) in order to make all of the above possible (because this way there are no concerns about the state of the message being changed in the background).

🔒 Message persistence and identification

MiniAgents provides a way to persist messages as they are resolved from promises using the @MiniAgents().on_persist_message decorator. This allows you to implement custom logic for storing or logging messages.

Additionally, messages (as well as any other Pydantic models derived from Frozen) have a hash_key property. This property calculates the sha256 hash of the content of the message and is used as the id of the Messages (or any other Frozen model), much like there are commit hashes in git.

Here's a simple example of how to use the on_persist_message decorator:

from miniagents import MiniAgents, Message

mini_agents = MiniAgents()


@mini_agents.on_persist_message
async def persist_message(_, message: Message) -> None:
    print(f"Persisting message with hash key: {message.hash_key}")
    # Here you could save the message to a database or log it to a file

📂 Modules

TODO the structure of this section is outdated - update it

Here's an overview of the module structure and hierarchy in the MiniAgents framework:

• miniagents: The core package containing the main classes and functions
  • miniagents.py: Defines the MiniAgents context manager, MiniAgent class, and miniagent decorator
  • messages.py: Defines the Message class and related message types
  • miniagent_typing.py: Defines type aliases and protocols used in the framework
  • utils.py: Utility functions used throughout the framework
  • promising: Subpackage for the "promising" functionality (promises, streaming, etc.)
    • promising.py: Defines the Promise and StreamedPromise classes
    • promise_typing.py: Defines type aliases and protocols for promises
    • sequence.py: Defines the FlatSequence class for flattening sequences
    • sentinels.py: Defines sentinel objects used in the framework
    • errors.py: Defines custom exception classes
    • ext: Subpackage for extensions to the promising functionality
      • frozen.py: Defines the Frozen class for immutable Pydantic models
• miniagents.ext: Subpackage for pre-packaged agents and extensions
  • agent_aggregators.py: Agents for aggregating other agents (chains, loops, etc.)
  • history_agents.py: Agents for managing conversation history
  • misc_agents.py: Miscellaneous utility agents
  • llm: Subpackage for LLM integrations
    • llm_common.py: Common classes and functions for LLM agents
    • openai.py: OpenAI LLM agent
    • anthropic.py: Anthropic LLM agent

📚 Core concepts

Here are some of the core concepts in the MiniAgents framework:

• MiniAgent: A wrapper around an async function that defines an agent's behavior. Created using the @miniagent decorator.
• InteractionContext: Passed to each agent function, provides access to incoming messages and allows sending replies.
• Message: Represents a message exchanged between agents. Can contain content, metadata, and nested messages. Immutable once created.
• MessagePromise: A promise of a message that can be streamed token by token.
• MessageSequencePromise: A promise of a sequence of message promises.
• Promise: Represents a value that may not be available yet, but will be resolved in the future.
• StreamedPromise: A promise that can be resolved piece by piece, allowing for streaming.
• Frozen: An immutable Pydantic model with a git-style hash key calculated from its JSON representation.

📜 License

MiniAgents is released under the MIT License.

📝 Note to contributors

• Different Promise and StreamedPromise resolvers, piece-by-piece streamers, appenders, and other Promising components should always catch BaseExceptions and not just Exceptions. This is because many of these components involve communications between async tasks via asyncio.Queue objects. Interrupting these promises with KeyboardInterrupt (which extends from BaseException) instead of letting it go through the queue can lead to hanging promises (a queue waiting for END_OF_QUEUE sentinel forever while the task that should send it is dead).

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Happy coding with MiniAgents! 🚀