flowtic 1.6.1

A modern Python library for building multi-agent communication systems with LLM integration

Flowtic

Build agent workflows that actually talk to each other. No complex orchestration, just simple communication patterns that work.

What it does

• Agent sessions: Each agent keeps its own conversation history (text + images)
• Tool system: Write functions once, agents use them automatically
• Communication graph: Tell agents who can talk to whom with simple syntax
• Callbacks: Hook into conversations for logging, user input, whatever you need

Install

pip install git+https://github.com/PrAsAnNaRePo/Flowtic.git

Quick start

Single agent with tools

from flowtic import Agent, Tool, Tools

def calculate(expression: str):
    return str(eval(expression)), None

calc_tool = Tool(
    tool_definition={
        "type": "function",
        "function": {
            "name": "calculate", 
            "description": "Calculate math expressions",
            "parameters": {
                "type": "object",
                "properties": {
                    "expression": {"type": "string", "description": "Math expression"}
                },
                "required": ["expression"]
            }
        }
    },
    tool_execution=calculate
)

agent = Agent(
    agent_name="calculator",
    model_name="gpt-4o",  # any litellm model
    instructions="You help with math problems.",
    tools=Tools([calc_tool])
)

result = agent("What's 15 * 23?")

Multi-agent workflow

from flowtic import Agent, CommunicationProtocol

# Create agents
analyst = Agent(
    agent_name="analyst",
    model_name="gpt-4o",
    instructions="Analyze requirements and ask clarifying questions.",
    allow_user_input=True  # can talk to user
)

coder = Agent(
    agent_name="coder", 
    model_name="gpt-4o",
    instructions="Write code based on requirements.",
    allow_user_input=False  # only talks to other agents
)

# Set up who talks to whom
protocol = CommunicationProtocol(
    "analyst<->coder",  # bidirectional
    [analyst, coder]
)

# Start the workflow
protocol.execute("Build a simple todo app")

Communication patterns

The syntax is dead simple:

• A->B means A can send messages to B
• A<->B means they can talk both ways
• A->B, B->C chains them together
• A<->B, A->C means A talks to both B and C

When agents communicate, they automatically get tools to message each other. No setup needed.

Images and multimodal

# Agent automatically handles images
agent("Here's a screenshot", images=["path/to/image.png"])

# Works with URLs and base64 too
agent("Analyze this", images=["https://example.com/chart.png"])

Custom callbacks

from flowtic import Callback

class MyCallbacks(Callback):
    def on_user_loop(self, agent_name, message):
        return input(f"{agent_name}: {message}\n> ")
    
    def on_tool_call(self, agent_name, tool_name, args):
        print(f"{agent_name} is using {tool_name}")

agent = Agent(
    agent_name="helper",
    model_name="gpt-4o", 
    callbacks=MyCallbacks()
)

Session management

Each agent keeps its own conversation buffer. Even if multiple agents reuse the same SessionManager, their histories stay isolated by agent name:

from flowtic import SessionManager

session_store = SessionManager()

agent1 = Agent(
    agent_name="researcher",
    model_name="gpt-4o",
    session=session_store
)

agent2 = Agent(
    agent_name="writer", 
    model_name="gpt-4o",
    session=session_store
)

# The writer still has its own memory
agent1("Find info about climate change")
agent2("Write a summary based on what the researcher found")

If you want agents to share context, use agent-to-agent communication. CommunicationProtocol injects _spin_into, so one agent can explicitly hand context to another instead of silently sharing history.

Sessions handle images automatically - no extra work needed:

session = SessionManager()

# Add context manually if needed
session.add_user_context("my_agent", 
    text="Here's the data", 
    images=["chart1.png", "chart2.png"]
)

# Get the full conversation
history = session.get_buffer_memory("my_agent")

That's it

Three main pieces: agents that remember conversations, tools they can use, and simple rules for who talks to whom. Everything else just works.