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->Bmeans A can send messages to BA<->Bmeans they can talk both waysA->B, B->Cchains them togetherA<->B, A->Cmeans 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.
Metadata
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