Argentic Framework Development Skill

You are an expert in building AI agents using the Argentic framework. This skill provides comprehensive knowledge about Argentic's architecture, patterns, and best practices.

Framework Overview

Argentic is a Python 3.11+ microframework for building local AI agents with async MQTT messaging.

Core Architecture:

• Fully async/await based
• MQTT-driven messaging between components
• Multi-LLM support (Google Gemini, Ollama, Llama.cpp)
• Dynamic tool registration via messaging protocol
• Single-agent and multi-agent (Supervisor) patterns

Key Components:

• Agent

  - Main AI agent with LLM integration

• Messager

  - MQTT messaging layer

• ToolManager

  - Tool registry and execution

• Supervisor

  - Multi-agent coordinator

• BaseTool

  - Base class for custom tools

Installation

# From PyPI
pip install argentic

# From source
git clone https://github.com/angkira/argentic.git
cd argentic
pip install -e .

Prerequisites:

• Python 3.11+
• MQTT broker (mosquitto):

  docker run -d -p 1883:1883 eclipse-mosquitto:2.0

• API keys for LLM providers (e.g.,

  GOOGLE_GEMINI_API_KEY

  in

  .env

  )

Pattern 1: Single Agent Application

import asyncio
from argentic import Agent, Messager, LLMFactory
from argentic.core.tools import ToolManager
import yaml

async def main():
    # Load configuration
    with open("config.yaml") as f:
        config = yaml.safe_load(f)
    
    # Initialize LLM
    llm = LLMFactory.create_from_config(config["llm"])
    
    # Setup MQTT messaging
    messager = Messager(
        broker_address=config["messaging"]["broker_address"],
        port=config["messaging"]["port"]
    )
    await messager.connect()
    
    # Initialize ToolManager
    tool_manager = ToolManager(messager)
    await tool_manager.async_init()
    
    # Create Agent
    agent = Agent(
        llm=llm,
        messager=messager,
        tool_manager=tool_manager,
        role="assistant",
        system_prompt="You are a helpful AI assistant.",
        enable_dialogue_logging=True  # For debugging
    )
    await agent.async_init()
    
    # Use the agent
    response = await agent.query("What is the capital of France?")
    print(response)
    
    # Cleanup
    await messager.disconnect()

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

Key Points:

• Always

  await messager.connect()

  before using components
• Initialize

  ToolManager

  before creating agents
• Always

  await agent.async_init()

  after creation
• Clean up with

  await messager.disconnect()

  in finally block

Pattern 2: Custom Tool Development

import json
from typing import Any
from pydantic import BaseModel, Field
from argentic.core.tools.tool_base import BaseTool
from argentic.core.messager.messager import Messager

# Step 1: Define input schema with Pydantic
class MyToolInput(BaseModel):
    param1: str = Field(description="First parameter description")
    param2: int = Field(default=10, ge=1, le=100, description="Integer between 1-100")
    optional_param: str = Field(default="", description="Optional parameter")

# Step 2: Implement tool class
class MyCustomTool(BaseTool):
    def __init__(self, messager: Messager):
        super().__init__(
            name="my_custom_tool",
            manual="Tool description that LLM will see. Explain what it does and how to use it.",
            api=json.dumps(MyToolInput.model_json_schema()),
            argument_schema=MyToolInput,
            messager=messager,
        )
    
    async def _execute(self, **kwargs) -> Any:
        """
        Implement tool logic here.
        Arguments are validated by Pydantic before this is called.
        """
        param1 = kwargs["param1"]
        param2 = kwargs.get("param2", 10)
        optional = kwargs.get("optional_param", "")
        
        # Your implementation here
        try:
            result = f"Processed {param1} with {param2}"
            return result
        except Exception as e:
            raise RuntimeError(f"Tool execution failed: {e}")

# Step 3: Register tool
async def setup_tool(messager):
    tool = MyCustomTool(messager)
    await tool.register(
        registration_topic="agent/tools/register",
        status_topic="agent/status/info",
        call_topic_base="agent/tools/call",
        response_topic_base="agent/tools/response",
    )
    return tool

# Step 4: Use with agent
async def main():
    messager = Messager(broker_address="localhost", port=1883)
    await messager.connect()
    
    tool_manager = ToolManager(messager)
    await tool_manager.async_init()
    
    # Register tool BEFORE creating agent
    tool = await setup_tool(messager)
    await asyncio.sleep(1)  # Wait for registration to complete
    
    # Create agent (will auto-discover registered tools)
    agent = Agent(llm, messager, tool_manager, role="assistant")
    await agent.async_init()
    
    # Agent can now use the tool
    response = await agent.query("Use my_custom_tool with param1='test'")
    print(response)
    
    await messager.disconnect()

Tool Development Best Practices:

• Use Pydantic for automatic validation
• Provide clear

  manual

  description for LLM
• Include field descriptions in Pydantic schema
• Handle errors with specific exceptions
• Test tools independently before agent integration

Pattern 3: Multi-Agent System

from argentic.core.graph.supervisor import Supervisor
from argentic import Agent, Messager, LLMFactory
from argentic.core.tools import ToolManager
import asyncio

async def main():
    # Setup (same as single agent)
    llm = LLMFactory.create_from_config(config["llm"])
    messager = Messager(broker_address="localhost", port=1883)
    await messager.connect()
    
    # IMPORTANT: Use ONE shared ToolManager for all agents
    tool_manager = ToolManager(messager)
    await tool_manager.async_init()
    
    # Create specialized agents with separate MQTT topics
    researcher = Agent(
        llm=llm,
        messager=messager,
        tool_manager=tool_manager,  # Shared!
        role="researcher",
        description="Research and information gathering specialist",
        system_prompt="You are a researcher. Find and synthesize information.",
        expected_output_format="text",
        # Agent-specific topics to avoid conflicts
        register_topic="agent/researcher/tools/register",
        tool_call_topic_base="agent/researcher/tools/call",
        tool_response_topic_base="agent/researcher/tools/response",
        status_topic="agent/researcher/status/info",
        graph_id="multi_agent_system",
    )
    await researcher.async_init()
    
    coder = Agent(
        llm=llm,
        messager=messager,
        tool_manager=tool_manager,  # Same shared instance!
        role="coder",
        description="Code writing and debugging specialist",
        system_prompt="You are a coder. Write clean, efficient code.",
        expected_output_format="code",
        register_topic="agent/coder/tools/register",
        tool_call_topic_base="agent/coder/tools/call",
        tool_response_topic_base="agent/coder/tools/response",
        status_topic="agent/coder/status/info",
        graph_id="multi_agent_system",
    )
    await coder.async_init()
    
    # Create supervisor
    supervisor = Supervisor(
        llm=llm,
        messager=messager,
        role="supervisor",
        system_prompt="You are a supervisor. Analyze tasks and route them to appropriate agents.",
        enable_dialogue_logging=True,
    )
    supervisor.add_agent(researcher)
    supervisor.add_agent(coder)
    await supervisor.async_init()
    
    # Execute workflow with callback
    workflow_complete = asyncio.Event()
    result_data = {}
    
    def completion_callback(task_id, success, result="", error=""):
        result_data["success"] = success
        result_data["result"] = result
        result_data["error"] = error
        workflow_complete.set()
    
    task_id = await supervisor.start_task(
        "Research async patterns in Python and write example code",
        completion_callback
    )
    
    # Wait for completion
    await asyncio.wait_for(workflow_complete.wait(), timeout=180)
    
    if result_data["success"]:
        print(f"Success: {result_data['result']}")
    else:
        print(f"Failed: {result_data['error']}")
    
    await messager.disconnect()

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

Multi-Agent Critical Points:

• Use ONE shared

  ToolManager

  instance for all agents
• Give each agent separate MQTT topic namespaces
• Set unique

  role

  for each agent
• Use clear

  description

  for supervisor routing
• Set same

  graph_id

  for all agents in the system
• Enable logging for debugging:

  enable_dialogue_logging=True

Configuration

config.yaml Structure

llm:
  # Provider: google_gemini, ollama, llama_cpp_server, llama_cpp_cli
  provider: google_gemini
  
  # Google Gemini
  google_gemini_model_name: gemini-2.0-flash
  google_gemini_api_key: ${GOOGLE_GEMINI_API_KEY}  # From env
  google_gemini_parameters:
    temperature: 0.7
    top_p: 0.95
    top_k: 40
    max_output_tokens: 2048
  
  # Ollama
  ollama_model_name: llama3
  ollama_base_url: http://localhost:11434
  ollama_use_chat_model: true
  ollama_parameters:
    temperature: 0.7
    num_predict: 128
  
  # Llama.cpp Server
  llama_cpp_server_host: 127.0.0.1
  llama_cpp_server_port: 5000
  llama_cpp_server_auto_start: false
  llama_cpp_server_binary: ~/llama.cpp/build/bin/llama-server

messaging:
  protocol: mqtt
  broker_address: localhost
  port: 1883
  keepalive: 60
  client_id: ""  # Auto-generated if empty

topics:
  tools:
    register: "agent/tools/register"
    call: "agent/tools/call"
    response_base: "agent/tools/response"
    status: "agent/tools/status"
  commands:
    ask_question: "agent/command/ask_question"
  responses:
    answer: "agent/response/answer"
  log: "agent/log"

.env File

# LLM API Keys
GOOGLE_GEMINI_API_KEY=your_api_key_here

# MQTT (optional)
MQTT_USERNAME=username
MQTT_PASSWORD=password

# Logging
LOG_LEVEL=INFO
CONFIG_PATH=config.yaml

Core API Reference

Agent

Agent(
    llm: ModelProvider,                    # Required
    messager: Messager,                    # Required
    tool_manager: ToolManager = None,      # Recommended
    role: str = "agent",
    system_prompt: str = None,
    description: str = "",
    expected_output_format: Literal["json", "text", "code"] = "json",
    enable_dialogue_logging: bool = False,
    register_topic: str = "agent/tools/register",
    tool_call_topic_base: str = "agent/tools/call",
    tool_response_topic_base: str = "agent/tools/response",
    status_topic: str = "agent/status/info",
    graph_id: str = None,
    state_mode: AgentStateMode = STATEFUL,
)

# Key Methods
await agent.async_init()                   # Initialize
response = await agent.query(question)     # Direct query
await agent.process_task(task)             # Process task message
agent.print_dialogue_summary()             # Debug info

Messager

Messager(
    broker_address: str = "localhost",
    port: int = 1883,
    client_id: str = "",
    username: str = None,
    password: str = None,
    keepalive: int = 60,
)

# Key Methods
await messager.connect()
await messager.disconnect()
await messager.subscribe(topic, handler, message_cls)
await messager.publish(topic, message_obj)

ToolManager

ToolManager(
    messager: Messager,
    register_topic: str = "agent/tools/register",
    tool_call_topic_base: str = "agent/tools/call",
    tool_response_topic_base: str = "agent/tools/response",
    status_topic: str = "agent/status/info",
    default_timeout: int = 30,
)

# Key Methods
await tool_manager.async_init()
result = await tool_manager.execute_tool(name, args, timeout=30)
tools_desc = tool_manager.get_tools_description()

Supervisor

Supervisor(
    llm: ModelProvider,
    messager: Messager,
    role: str = "supervisor",
    system_prompt: str = None,
    enable_dialogue_logging: bool = False,
)

# Key Methods
supervisor.add_agent(agent)
await supervisor.async_init()
task_id = await supervisor.start_task(task, callback)

Important Implementation Details

Tool Registration Flow

1. Tool → ToolManager: Tool publishes

   RegisterToolMessage

   to

   agent/tools/register

2. ToolManager: Generates unique

   tool_id

   (UUID), stores metadata
3. ToolManager → Tool: Publishes

   ToolRegisteredMessage

   to

   agent/status/info

4. Tool: Receives confirmation, subscribes to

   agent/tools/call/{tool_id}

Tool Execution Flow

1. Agent calls

   tool_manager.execute_tool(name, args)

2. ToolManager publishes

   TaskMessage

   to

   agent/tools/call/{tool_id}

3. Tool receives message, validates args, executes

   _execute()

4. Tool publishes

   TaskResultMessage

   to

   agent/tools/response/{tool_id}

5. ToolManager returns result to agent

Message Protocol

All messages inherit from

BaseMessage

(Pydantic models):

from argentic.core.protocol.message import (
    AgentTaskMessage,
    AgentTaskResultMessage,
)
from argentic.core.protocol.task import (
    TaskMessage,
    TaskResultMessage,
    TaskErrorMessage,
)
from argentic.core.protocol.tool import (
    RegisterToolMessage,
    ToolRegisteredMessage,
)

Best Practices

1. Always Use Async/Await

# GOOD
await messager.connect()
result = await agent.query(question)
await messager.disconnect()

# BAD - will not work
messager.connect()  # Missing await

2. Shared ToolManager for Multi-Agent

# GOOD - One shared instance
tool_manager = ToolManager(messager)
agent1 = Agent(llm, messager, tool_manager, ...)
agent2 = Agent(llm, messager, tool_manager, ...)

# BAD - Multiple instances cause conflicts
agent1 = Agent(llm, messager, ToolManager(messager), ...)
agent2 = Agent(llm, messager, ToolManager(messager), ...)

3. Proper Resource Cleanup

# GOOD
try:
    await messager.connect()
    # Use agents
finally:
    await messager.disconnect()

4. Wait After Tool Registration

# GOOD
await tool.register(...)
await asyncio.sleep(1)  # Give time for registration
agent = Agent(...)

5. Separate Topics for Multi-Agent

# GOOD - Each agent has own namespace
researcher = Agent(
    ...,
    register_topic="agent/researcher/tools/register",
    tool_call_topic_base="agent/researcher/tools/call",
)
coder = Agent(
    ...,
    register_topic="agent/coder/tools/register",
    tool_call_topic_base="agent/coder/tools/call",
)

6. Clear System Prompts

# GOOD
system_prompt = "You are a researcher. Your job is to find and synthesize information from various sources. Be thorough and cite sources."

# BAD
system_prompt = "You are helpful."

7. Enable Logging for Debug

agent = Agent(
    ...,
    enable_dialogue_logging=True,  # Shows all interactions
)
# Later
agent.print_dialogue_summary()

Common Patterns

Running Argentic Components

# CLI (after installation)
argentic agent --config-path config.yaml --log-level INFO
argentic rag --config-path config.yaml
argentic cli --config-path config.yaml

# Python module
python -m argentic agent --config-path config.yaml

Import Patterns

# Top-level imports
from argentic import Agent, Messager, LLMFactory

# Core modules
from argentic.core.tools import ToolManager, BaseTool
from argentic.core.graph.supervisor import Supervisor
from argentic.core.protocol.message import AgentTaskMessage

# LLM providers
from argentic.core.llm.providers.google_gemini import GoogleGeminiProvider
from argentic.core.llm.providers.ollama import OllamaProvider

Testing Tools Independently

# Test without full agent setup
async def test_tool():
    messager = Messager(broker_address="localhost")
    await messager.connect()
    
    tool = MyTool(messager)
    
    # Direct execution (bypass MQTT)
    result = await tool._execute(param="test")
    print(result)
    
    await messager.disconnect()

asyncio.run(test_tool())

Troubleshooting

MQTT Connection Failed

Error:

Connection refused

or

No connection to MQTT broker

Solution:

# Check if mosquitto is running
docker ps | grep mosquitto
# Or
sudo systemctl status mosquitto

# Start mosquitto
docker run -d -p 1883:1883 --name mosquitto eclipse-mosquitto:2.0

Tool Not Registered

Error: Tool doesn't appear in agent's tool list

Solution:

• Add delay after registration:

  await asyncio.sleep(1)

• Verify topics match between tool and ToolManager
• Enable logging to see registration messages
• Check tool is registered BEFORE agent initialization

LLM API Error

Error:

Invalid API key

or

Authentication failed

Solution:

• Verify

  .env

  file exists with correct key
• Load environment:

  from dotenv import load_dotenv; load_dotenv()

• Check environment variable:

  os.getenv("GOOGLE_GEMINI_API_KEY")

Tool Timeout

Error:

TimeoutError: Tool execution exceeded timeout

Solution:

• Increase timeout:

  await tool_manager.execute_tool(..., timeout=60)

• Check tool implementation for blocking operations
• Ensure tool publishes result message

Multi-Agent Not Routing

Error: Supervisor doesn't route to agents

Solution:

• Verify supervisor

  system_prompt

  includes routing instructions
• Check all agents have unique

  role

  values
• Ensure agent topics don't overlap
• Enable

  enable_dialogue_logging=True

  on supervisor

Advanced Features

Endless Cycle Support

For long-running agents:

agent = Agent(
    ...,
    adaptive_max_iterations=True,
    max_consecutive_tool_calls=3,
    tool_call_window_size=5,
    enable_completion_analysis=True,
)

State Management

from argentic.core.agent.agent import AgentStateMode

# Stateful (default) - maintains conversation
agent = Agent(..., state_mode=AgentStateMode.STATEFUL)

# Stateless - each query independent
agent = Agent(..., state_mode=AgentStateMode.STATELESS)

When to Use This Skill

Use this skill when:

• Building AI agent applications
• Implementing custom tools for agents
• Creating multi-agent systems
• Debugging Argentic applications
• Configuring LLM providers
• Setting up MQTT messaging
• Working with async Python patterns in Argentic

Key Reminders

1. Always async/await - Everything is asynchronous
2. One ToolManager - Share across all agents
3. Separate topics - Each agent needs its own namespace
4. Wait after registration - Tools need time to register
5. Clean up connections - Always disconnect messager
6. Enable logging - Use

   enable_dialogue_logging=True

   for debug
7. Pydantic validation - Use for all tool inputs
8. Clear prompts - Make system prompts specific and detailed

Resources

• Documentation: Check

  docs/

  directory or

  ARGENTIC_QUICKREF.md

• Examples: See

  examples/single_agent_example.py

  ,

  examples/multi_agent_example.py

• Source:

  src/argentic/

  for implementation details