11. Advanced Usage Patterns¶

Multi-Agent Collaboration¶

You can orchestrate multiple agents to work together on complex tasks.

from agenticaiframework import Agent, AgentManager

agent1 = Agent(name="Researcher", role="research", capabilities=["text"])
agent2 = Agent(name="Summarizer", role="summarize", capabilities=["text"])

manager = AgentManager()
manager.register_agent(agent1)
manager.register_agent(agent2)

research_result = agent1.act("Find the latest AI research papers on reinforcement learning.")
summary = agent2.act(f"Summarize this: {research_result}")
print(summary)

Asynchronous Processing¶

For I/O-bound tasks, use asynchronous processes to improve performance.

import asyncio
from agenticaiframework.processes import run_process_async

async def async_task():
    return "Completed async task"

result = asyncio.run(run_process_async(async_task))
print(result)

Integrating External APIs¶

Agents can call external APIs as part of their workflow.

import requests
from agenticaiframework.agents import Agent

class WeatherAgent(Agent):
    def act(self, location):
        response = requests.get(f"https://api.weatherapi.com/v1/current.json?q={location}&key=YOUR_KEY")
        return response.json()

weather_agent = WeatherAgent(name="WeatherBot", role="weather", capabilities=["data"])
print(weather_agent.act("New York"))

12. Deployment Scenarios¶

Local Development¶

Install dependencies with pip install -e .[dev]
Run tests locally before deployment.

Docker Deployment¶

Create a Dockerfile:

FROM python:3.10
WORKDIR /app
COPY . .
RUN pip install .
CMD ["python", "main.py"]

Cloud Deployment¶

Deploy to AWS Lambda, Google Cloud Functions, or Azure Functions by packaging the code and dependencies.

13. Security Considerations¶

Always validate and sanitize inputs to agents.
Use guardrails to prevent unsafe actions.
Store API keys securely using environment variables or secret managers.
Limit network access for agents running in untrusted environments.

14. Performance Optimization¶

Use caching for repeated computations.
Optimize prompt templates for LLMs to reduce token usage.
Use batch processing for large datasets.
Profile and monitor agent performance using monitoring.py.

15. Troubleshooting Complex Workflows¶

Enable debug logging: set_config("log_level", "DEBUG")
Break down workflows into smaller steps for easier debugging.
Use pytest -v for verbose test output.

16. Contributing to AgenticAI¶

We welcome contributions!
1. Fork the repository.
2. Create a feature branch.
3. Implement your changes with tests.
4. Submit a pull request.

17. Additional Resources¶

Using AgenticAI¶

This guide explains how to install, configure, and use the AgenticAI package with practical examples.

1. Installation¶

Install AgenticAI from PyPI:

pip install agenticaiframework

Or install from source:

git clone https://github.com/isathish/AgenticAI.git
cd AgenticAI
pip install .

2. Basic Usage¶

Creating and Running an Agent¶

from agenticaiframework.agents import Agent
from agenticaiframework.hub import register_agent, get_agent

class EchoAgent(Agent):
    def act(self, input_data):
        return f"Echo: {input_data}"

register_agent("echo", EchoAgent)

agent = get_agent("echo")
print(agent.act("Hello World"))

3. Using Built-in Agents¶

AgenticAI comes with prebuilt agents. You can load them via the hub:

from agenticaiframework.hub import get_agent

agent = get_agent("default_agent")
response = agent.act("Summarize this text.")
print(response)

4. Configuring the System¶

Edit configurations.py or pass configuration at runtime:

from agenticaiframework.configurations import set_config

set_config("llm_provider", "openai")
set_config("api_key", "your_api_key_here")

5. Using Tools¶

from agenticaiframework.hub import get_tool

sentiment_tool = get_tool("sentiment_analysis")
result = sentiment_tool("I love this product!")
print(result)

6. Running Processes¶

from agenticaiframework.processes import run_process

result = run_process("data_analysis", {"dataset": "data.csv"})
print(result)

7. Memory Usage¶

from agenticaiframework.memory import Memory

mem = Memory()
mem.store("user_name", "Alice")
print(mem.retrieve("user_name"))

8. Example Workflow¶

from agenticaiframework.hub import get_agent, get_tool

agent = get_agent("default_agent")
tool = get_tool("sentiment_analysis")

text = "The movie was fantastic!"
analysis = tool(text)
response = agent.act(f"Summarize the sentiment: {analysis}")
print(response)

9. Testing¶

Run tests with:

pytest