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.
๐ 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.
๐ฆ Installation¶
Install the latest version from PyPI:
pip install agenticaiframework
โก 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()
๐ 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)
๐ 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)
๐ 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.
๐งช Testing¶
Run tests:
pytest
Run with coverage:
pytest --cov=agenticaiframework --cov-report=term-missing
๐ Documentation Sections¶
๐ 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"))
๐ 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.
๐ค 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.
๐ License¶
ยฉ 2025 AgenticAI Framework. Licensed under the MIT License.