ReddRadar
Agent skill
agenticx-agent-builder
Guide for creating and configuring AgenticX agents with roles, goals, tools, LLM providers, and execution strategies. Use when the user wants to create agents, assign tools to agents, configure LLM backends, set up agent execution, or build multi-agent systems.
Install this agent skill to your Project
npx add-skill https://github.com/DemonDamon/AgenticX/tree/main/agenticx/skills/agenticx-agent-builder
Metadata
Additional technical details for this skill
- author
- AgenticX
- version
- 0.3.6
SKILL.md
AgenticX Agent Builder
Guide for creating production-grade agents in AgenticX.
Core Concepts
An Agent in AgenticX consists of:
- Identity: id, name, role, goal
- LLM Provider: the language model backend
- Tools: functions the agent can invoke
- Executor: the runtime that orchestrates agent reasoning
Creating an Agent
Minimal Agent
from agenticx import Agent, Task, AgentExecutor
from agenticx.llms import OpenAIProvider
agent = Agent(
id="assistant",
name="Assistant",
role="General Purpose Assistant",
goal="Help users with tasks",
organization_id="default"
)
Agent with Rich Configuration
agent = Agent(
id="research-analyst",
name="Research Analyst",
role="Senior Research Analyst",
goal="Produce thorough, well-cited research reports",
backstory="10 years experience in data-driven research",
organization_id="research-team",
verbose=True
)
CLI Creation
agx agent create research-analyst --role "Senior Research Analyst"
agx agent list
LLM Providers
AgenticX supports multiple LLM backends through a unified interface:
from agenticx.llms import OpenAIProvider, LiteLLMProvider
# OpenAI
llm = OpenAIProvider(model="gpt-4")
# Any model via LiteLLM (Claude, Gemini, local models, etc.)
llm = LiteLLMProvider(model="anthropic/claude-sonnet-4-20250514")
llm = LiteLLMProvider(model="ollama/llama3")
Adding Tools
Function Decorator Tools
from agenticx.tools import tool
@tool
def search_web(query: str) -> str:
"""Search the web for information."""
return f"Results for: {query}"
@tool
def calculate(expression: str) -> float:
"""Evaluate a math expression safely."""
return eval(expression) # use ast.literal_eval in production
Attaching Tools to Execution
executor = AgentExecutor(
agent=agent,
llm=llm,
tools=[search_web, calculate]
)
result = executor.run(task)
Task Definition
task = Task(
id="research-task",
description="Research the latest trends in AI agents",
expected_output="A structured report with sections and citations",
context={"domain": "artificial-intelligence"}
)
Output Validation
AgenticX validates task outputs using Pydantic:
from pydantic import BaseModel
class ResearchReport(BaseModel):
title: str
summary: str
findings: list[str]
task = Task(
id="validated-task",
description="Research AI trends",
expected_output="Structured research report",
output_model=ResearchReport
)
Execution Strategies
Basic Execution
executor = AgentExecutor(agent=agent, llm=llm)
result = executor.run(task)
With Events & Callbacks
AgenticX emits events during execution (TaskStart, ToolCall, LLMCall, etc.):
from agenticx.core import EventLog
event_log = EventLog()
executor = AgentExecutor(agent=agent, llm=llm, event_log=event_log)
result = executor.run(task)
for event in event_log.events:
print(f"{event.type}: {event.data}")
Multi-Agent Patterns
Agent Handoff
from agenticx.core import HandoffOutput
# Agent A can hand off to Agent B
handoff = HandoffOutput(target_agent="agent-b", context={"data": result})
Communication Interface
from agenticx.core import BroadcastCommunication
comm = BroadcastCommunication()
comm.send(sender="agent-a", message="Task complete", data=result)
GuideRails
Constrain agent behavior with guardrails:
from agenticx.core import GuideRails, GuideRailsConfig
config = GuideRailsConfig(
max_iterations=10,
timeout_seconds=60,
abort_on_failure=True
)
guardrails = GuideRails(config=config)
Best Practices
- Specific roles — narrow roles produce better results than generic ones
- Clear goals — state what success looks like
- Minimal tools — only attach tools the agent actually needs
- Output validation — use Pydantic models for structured outputs
- Event logging — always enable for debugging and monitoring
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