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Welcome to AgenticAI Framework Documentation

AgenticAI Framework (agenticaiframework) is a powerful Python SDK for building agentic applications with advanced orchestration, monitoring, multimodal capabilities, and enterprise-grade scalability.

It is designed for developers, researchers, and enterprises who want to create intelligent agents that can reason, interact, and execute tasks across multiple domains with ease.


๐ŸŒŸ Key Highlights

  • Modular Architecture โ€“ Build agents with interchangeable components.
  • Multi-Agent Support โ€“ Orchestrate multiple agents in parallel or sequential workflows.
  • Built-in Security โ€“ Guardrails, compliance checks, and safe execution.
  • Observability โ€“ Integrated monitoring and logging.
  • Multimodal Capabilities โ€“ Handle text, images, audio, and video.
  • Cross-Platform Deployment โ€“ Cloud, on-premise, or edge devices.
  • Extensible โ€“ Add your own tools, prompts, and integrations.

๐Ÿ“ฆ Installation

Install the latest version from PyPI:

pip install agenticaiframework

โšก Quick Start

from agenticaiframework import Agent, AgentManager

# Create an agent
agent = Agent(
    name="ExampleAgent",
    role="assistant",
    capabilities=["text"],
    config={"temperature": 0.7}
)

# Manage agents
manager = AgentManager()
manager.register_agent(agent)

# Start the agent
agent.start()

๐Ÿ“š Core Concepts

1. Agents

Agents are the core building blocks. They have: - Name โ€“ Unique identifier. - Role โ€“ Defines their purpose. - Capabilities โ€“ What they can do (e.g., text generation, image analysis). - Configuration โ€“ Parameters like temperature, max tokens, etc.

2. Agent Manager

The AgentManager handles: - Registration of agents. - Starting and stopping agents. - Coordinating multi-agent workflows.

3. Memory

Agents can store and retrieve information using the Memory module.

from agenticaiframework.memory import Memory

memory = Memory()
memory.store("user_name", "Alice")
print(memory.retrieve("user_name"))  # Output: Alice

4. Processes

Run synchronous or asynchronous processes:

from agenticaiframework.processes import run_process

def greet():
    return "Hello, World!"

print(run_process(greet))

5. Communication

Supports multiple protocols: - HTTP - WebSockets - gRPC - Message Queues (MQ) - Server-Sent Events (SSE) - STDIO

6. Guardrails

Define safety and compliance rules for agents:

from agenticaiframework.guardrails import add_guardrail

def no_sensitive_data(input_text):
    return "password" not in input_text.lower()

add_guardrail(no_sensitive_data)

๐Ÿ›  Configuration

You can configure the framework via: - Code โ€“ Using set_config from agenticaiframework.configurations. - Environment Variables. - Configuration Files.

Example:

from agenticaiframework.configurations import set_config
set_config("max_concurrent_tasks", 5)

๐Ÿ”Œ Integrations

AgenticAI Framework supports: - LLMs โ€“ OpenAI, Anthropic, HuggingFace, etc. - Communication Protocols โ€“ HTTP, WebSockets, gRPC, MQ. - Custom Tools โ€“ Easily add your own. - Knowledge Retrieval โ€“ Integrate with vector databases and search engines. - MCP Tools โ€“ Extend capabilities with Model Context Protocol integrations.


๐Ÿงช Testing

Run tests:

pytest

Run with coverage:

pytest --cov=agenticaiframework --cov-report=term-missing

๐Ÿ“„ Documentation Sections


๐Ÿ“˜ Advanced Topics

Multi-Agent Orchestration

Coordinate multiple agents for complex workflows:

from agenticaiframework import Agent, AgentManager

agent1 = Agent(name="DataCollector", role="collector", capabilities=["data"])
agent2 = Agent(name="DataAnalyzer", role="analyzer", capabilities=["analysis"])

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

# Example orchestration logic
agent1.start()
agent2.start()

Monitoring and Logging

from agenticaiframework.monitoring import log_event

log_event("Agent started", level="INFO")

MCP Tools Integration

from agenticaiframework.mcp_tools import load_tool

tool = load_tool("weather")
result = tool.run({"location": "New York"})
print(result)

Knowledge Base Integration

from agenticaiframework.knowledge import KnowledgeBase

kb = KnowledgeBase()
kb.add_document("doc1", "This is a sample document.")
print(kb.search("sample"))

๐Ÿ“Š Performance Tips

  • Use asynchronous processes for I/O-bound tasks.
  • Limit concurrent agents to avoid resource contention.
  • Cache frequently used data in memory.
  • Use guardrails to prevent invalid or unsafe operations.

๐Ÿค Contributing

We welcome contributions!
1. Fork the repo.
2. Create a feature branch.
3. Submit a pull request.
4. Ensure all tests pass before submission.


๐Ÿ“œ License

ยฉ 2025 AgenticAI Framework. Licensed under the MIT License.