Skip to content

PrAsAnNaRePo/Flowtic

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

19 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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.

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Packages

 
 
 

Contributors

Languages