Examples

This section contains framework-specific examples that show how to integrate Aeon with popular AI agent frameworks.

LangChain Example

python
1import asyncio
2from aeon import Aeon
3from langchain.agents import create_agent
4from langchain_core.tools import tool
5
6# Initialize Aeon with your credentials
7aeon = Aeon(api_key="YOUR_API", project_id=123)
8
9# Define your tools
10@tool
11def search_tool(query: str) -> str:
12    """Search for information"""
13    return f"Results for: {query}"
14
15# Setup agent
16tools = [search_tool]
17
18agent = create_agent(
19    model="claude-sonnet-4-5-20250929",
20    tools=tools,
21    system_prompt="You are a helpful assistant",
22)
23
24@aeon.track_agent(name="myAgent")
25async def agent():
26    """Process tasks with LangChain agent"""
27
28    # Run the agent
29    result = agent.invoke({"messages": [{"role": "user", "content": event}]})
30
31    # Return the agent response
32    return {
33        costs=0.08,
34        model="claude-sonnet-4-5-20250929",
35    }

CrewAI Example

python
1import asyncio
2from aeon import Aeon
3from crewai import Agent
4from crewai_tools import SerperDevTool
5
6# Initialize Aeon with your credentials
7aeon = Aeon(api_key="YOUR_API", project_id=123)
8
9agent = Agent(
10    role="Research Analyst",
11    goal="Find and summarize information about specific topics",
12    backstory="You are an experienced researcher with attention to detail",
13    tools=[SerperDevTool()],
14    verbose=True
15)
16
17@aeon.track_agent(name="myAgent")
18async def agent():
19    # Run the agent
20    result = agent.kickoff(event)
21
22    # Return model and cost
23    return {
24        costs=0.08,
25        model="gpt-4",
26    }

LlamaIndex Example

python
1import asyncio
2from aeon import Aeon
3from llama_index.core.agent.workflow import FunctionAgent
4from llama_index.llms.openai import OpenAI
5
6# Initialize Aeon with your credentials
7aeon = Aeon(api_key="YOUR_API", project_id=123)
8
9# Define a simple calculator tool
10def multiply(a: float, b: float) -> float:
11    """Useful for multiplying two numbers."""
12    return a * b
13
14# Create an agent workflow with our calculator tool
15agent = FunctionAgent(
16    tools=[multiply],
17    llm=OpenAI(model="gpt-4o-mini"),
18    system_prompt="You are a helpful assistant that can multiply two numbers.",
19)
20
21@aeon.track_agent(name="myAgent")
22async def agent():
23    # Run the agent
24    result = await agent.run(event)
25
26    # Return model and cost
27    return {
28        costs=0.08,
29        model="gpt-4",
30    }

Notes

• Adjust the costs and model based on your actual usage
• All examples use hardcoded costs for simplicity - in production, calculate costs dynamically based on token usage