Everything you need to build production agent systems.
Production-ready execution engine with retry logic and error handling.
Graph-based workflow with conditional routing and parallel execution.
Long-term memory with Mem0 integration, any LLM backend.
Inter-agent messaging protocol
Model Context Protocol support
Tracing, metrics, monitoring
Desktop automation framework
Function decorators & remote tools
Pydantic output parsing
Build complex systems with minimal code.
from agenticx import Agent, Task, AgentExecutor
from agenticx.llms import OpenAIProvider
agent = Agent(
id="research-agent",
name="Research Assistant",
role="Information gatherer",
goal="Find and synthesize information"
)
task = Task(
description="Research latest AI frameworks",
expected_output="Comprehensive analysis"
)
executor = AgentExecutor(agent=agent, llm=OpenAIProvider())
result = executor.run(task)Get up and running in minutes.